Koichiro TamurapersName, Glen StecherpersName, Sudhir KumarpersName (2021date). “MEGA11: Molecular Evolutionary Genetics Analysis Version 11.” Molecular Biology and Evolutiontitle·j.
Object of study — the MEGAterm software suite for computational molecular evolution, and specifically the methods and interface added in version 11 for building timetreesterm, testing rate autocorrelation and estimating neutral allele probabilities
Research question — what methodological and technical additions are needed to make MEGAterm a comprehensive tool for relaxed-clockterm timetreeterm building and for analysing very large sequence alignments on a desktop
Contribution — node-calibration probability densities and RTDT tip-datingterm for RelTimeterm, a machine-learning test for rate autocorrelation (CorrTestterm), Bayesian neutral evolutionary probabilitiesterm, a large reduction in maximum-likelihood memory use, and a re-threaded GUI for very large data sets
Cross-disciplinary analysis
content-tags & facets:
phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text| phenomenon | maximum-likelihood and maximum-parsimony memory requirements fall by roughly the ratio of distinct site configurations to alignment length, because identical site configurations carry the same likelihood value — “ML methods are widely used for phylogenetic inference but place high demands on computer memory, becoming increasingly burdensome for bigger sequence alignments analyzed these days. In MEGA11, we have now completed a long-overdue refactoring of ML calculations by adding a step to identify common site configurations, that is, sites where all sequences have the same bases as at some other sites, to utilize computer memory more efficiently. The memory requirements of Maximum Likelihood and Maximum parsimony analysis are reduced (approximately) by the factor of m / L when there are m distinct site configurations in a sequence alignment containing L sites. The memory saving can be substantial for multigene and genome-scale alignments. For example, the memory saving was 660 MB (209 vs. 870 MB) for a sequence alignment of 229 birds with 2,728 sites ( Claramunt and Cracraft 2015 ) and 4.5 GB (2.3 vs. 6.8 GB) for an alignment of 162 mammals with 11,010 sites ( Meredith et al. 2011 ). This memory saving does not have any detrimental impact on phylogenetic estimates and computational times because identical site configurations have the same likelihood value. The total log-likelihood is simply the sum of site-configuration log-likelihoods weighted by their frequencies. However, this upgrade required refactoring many different parts of MEGA’s calculation engine, including functions for phylogeny construction and model selection.” | |
| claim-type | method — “Version 11 of MEGA adds many methods and tools to keep pace with researchers' growing needs. The addition of evolutionary dating methods in MEGA make it easier to estimate species and strain divergence times by using more informative node calibrations and sampling times. The new CorrTest and EP calculations will enable a more robust evaluation of assumptions about biological characteristics of molecular data. The reduction in memory needs of ML-based computations will allow users to analyze much larger data sets than before. The refactoring of distance-based methods’ calculation to run in threads independent of the main graphical interface and other GUI enhancements greatly improve MEGA usability for very large data sets.” | |
| method — evidence | — “For RelTime analyses in MEGA11, ML and distance-based approaches can be used to build a timetree for a given phylogeny and multiple sequence alignment. One may also use only a phylogeny with branch lengths, which extends the usefulness of relaxed-clock methods for phylogenies inferred from nonmolecular data or statistical methodologies not available in MEGA. When a phylogeny with branch lengths is used, the CIs will be narrower because the variance associated with branch length estimation cannot be generated without the original data set used to produce the phylogeny and branch lengths. Nevertheless, these CIs will incorporate variance introduced due to rate variation among lineages and clock calibrations’ uncertainty.” | |
| object of study — evidence | — “The Molecular Evolutionary Genetics Analysis (MEGA) software has matured to contain a large collection of methods and tools of computational molecular evolution. Here, we describe new additions that make MEGA a more comprehensive tool for building timetrees of species, pathogens, and gene families using rapid relaxed-clock methods. Methods for estimating divergence times and confidence intervals are implemented to use probability densities for calibration constraints for node-dating and sequence sampling dates for tip-dating analyses. They are supported by new options for tagging sequences with spatiotemporal sampling information, an expanded interactive Node Calibrations Editor , and an extended Tree Explorer to display timetrees. Also added is a Bayesian method for estimating neutral evolutionary probabilities of alleles in a species using multispecies sequence alignments and a machine learning method to test for the autocorrelation of evolutionary rates in phylogenies. The computer memory requirements for the maximum likelihood analysis are reduced significantly through reprogramming, and the graphical user interface has been made more responsive and interactive for very big data sets. These enhancements will improve the user experience, quality of results, and the pace of biological discovery. Natively compiled graphical user interface and command-line versions of MEGA11 are available for Microsoft Windows, Linux, and macOS from www.megasoftware.net .” | |
| research question — evidence | — “MEGA’s computational core (MEGA-CC) has undergone extensive refactoring, hardening, and expansion over time. It advanced from 16-bit to 32-bit ( Kumar et al. 2001 ), became multithreaded and incorporated multicore parallelization for various calculations ( Tamura et al. 2013 ), and stepped up to 64-bit architecture ( Kumar et al. 2016 , 2018 ). MEGA-CC was released for use as a command-line program to address the growing need for batch processing of many data sets and integration into analysis workflows ( Kumar et al. 2012 ; Stecher et al. 2020 ). With both 32- and 64-bit versions of MEGA currently available for use on the command-line and GUI, MEGA is now a suite of applications that responds to the variety of computing environments currently used by researchers in molecular evolution and phylogenetics. Here, we present key methodological additions and technical improvements in MEGA that comprise version 11.” | |
| contribution — evidence | — “The Molecular Evolutionary Genetics Analysis (MEGA) software has matured to contain a large collection of methods and tools of computational molecular evolution. Here, we describe new additions that make MEGA a more comprehensive tool for building timetrees of species, pathogens, and gene families using rapid relaxed-clock methods. Methods for estimating divergence times and confidence intervals are implemented to use probability densities for calibration constraints for node-dating and sequence sampling dates for tip-dating analyses. They are supported by new options for tagging sequences with spatiotemporal sampling information, an expanded interactive Node Calibrations Editor , and an extended Tree Explorer to display timetrees. Also added is a Bayesian method for estimating neutral evolutionary probabilities of alleles in a species using multispecies sequence alignments and a machine learning method to test for the autocorrelation of evolutionary rates in phylogenies. The computer memory requirements for the maximum likelihood analysis are reduced significantly through reprogramming, and the graphical user interface has been made more responsive and interactive for very big data sets. These enhancements will improve the user experience, quality of results, and the pace of biological discovery. Natively compiled graphical user interface and command-line versions of MEGA11 are available for Microsoft Windows, Linux, and macOS from www.megasoftware.net .” | |
| mechanism | site-configuration collapsing Sites whose columns repeat an earlier column are identified once; because identical site configurations have the same likelihood value, the total log-likelihood is the sum of site-configuration log-likelihoods weighted by their frequencies, so memory falls without changing the estimate. — “ML methods are widely used for phylogenetic inference but place high demands on computer memory, becoming increasingly burdensome for bigger sequence alignments analyzed these days. In MEGA11, we have now completed a long-overdue refactoring of ML calculations by adding a step to identify common site configurations, that is, sites where all sequences have the same bases as at some other sites, to utilize computer memory more efficiently. The memory requirements of Maximum Likelihood and Maximum parsimony analysis are reduced (approximately) by the factor of m / L when there are m distinct site configurations in a sequence alignment containing L sites. The memory saving can be substantial for multigene and genome-scale alignments. For example, the memory saving was 660 MB (209 vs. 870 MB) for a sequence alignment of 229 birds with 2,728 sites ( Claramunt and Cracraft 2015 ) and 4.5 GB (2.3 vs. 6.8 GB) for an alignment of 162 mammals with 11,010 sites ( Meredith et al. 2011 ). This memory saving does not have any detrimental impact on phylogenetic estimates and computational times because identical site configurations have the same likelihood value. The total log-likelihood is simply the sum of site-configuration log-likelihoods weighted by their frequencies. However, this upgrade required refactoring many different parts of MEGA’s calculation engine, including functions for phylogeny construction and model selection.” | mechanisms[0] |
| mechanism | variance-composed confidence intervals RelTime confidence intervals are built by combining, at once, the variance from rate heterogeneity among lineages, the variance from substitution-model divergence estimation, and the uncertainty carried by the node-calibration densities. — “Bayesian relaxed-clock methods have long allowed the use of statistical probability distributions that capture prior knowledge (or belief) about the true divergence times in clock calibration constraints on one or more nodes in the phylogeny. Judicious use of these probability densities can make divergence times more accurate and precise ( Tao, Tamura, Mello, et al. 2020 ). Researchers can now use such probability densities for node calibrations in RelTime estimation of divergence times and confidence intervals (CIs). MEGA implements the Tao, Tamura, Mello, et al. (2020 ) approach that estimates CIs by simultaneously accounting for variance introduced by the heterogeneity of evolutionary rate among lineages, estimation of sequence divergence using substitution models, and probability densities for node-calibration constraints. This method produces CIs that contain correct times with a high probability, making them much more suitable for biological hypothesis testing than other rapid methods ( Tao, Tamura, Kumar, et al. 2020 ; Tao, Tamura, Mello, et al. 2020 ).” | mechanisms[1] |
| mechanism | background-thread offloading Long-running file reading, distance-matrix and phylogeny computations were moved off the main GUI thread so the interface stays responsive on very large alignments. — “Using a large multiple sequence alignment containing 68,000 genomes and 30,000 bases each, we assessed MEGA GUI's responsiveness during input data file reading, execution of functions in the Sequence Data Explorer , estimation of pairwise distances, and building of distance-based phylogenies. We found the GUI to become intermittently unresponsive for such large data sets, which are now common due to resequencing and population sequencing efforts. Consequently, we have moved all potentially long-running operations out of the main GUI thread to background threads in a major overhaul of the source code. Now, large input data files are read rapidly, and calculations of pairwise distance matrices, selection tests, and phylogeny construction for distance-based methods are performed in a background thread. The Sequence Data Explorer has been reprogrammed to enable more efficient highlighting of variable sites, and navigation of the sequence alignment has been improved. Also added are options to automatically label sites based on attributes, which annotates sites by providing a one-character label and then using desired labeled sites to subset data for any molecular phylogenetic analysis desired.” | mechanisms[2] |
| builds-on | the RelTime relaxed-clock timetree method introduced in MEGA6 — “Rapid relaxed-clock methods for estimating divergence times are becoming popular because they are feasible and efficient for large contemporary sequence alignments ( Tao, Tamura, Kumar, et al. 2020 ). MEGA6 first added methods and tools for constructing evolutionary timetrees by implementing the RelTime method, which does not assume a molecular clock ( Tamura et al. 2012 , 2013 ). RelTime is known to perform well and has been used to build timetrees in hundreds of research articles ( Tao, Tamura, Kumar, et al. 2020 ). MEGA11 expands on RelTime dating options by advancing the current implementation and adding new facilities for node-dating and tip-dating needed to build timetrees of pathogens, species, and gene families.” | claim_relations[0] |
| builds-on | the Tao, Tamura, Mello et al. (2020) confidence-interval approach for calibrated divergence times — “Bayesian relaxed-clock methods have long allowed the use of statistical probability distributions that capture prior knowledge (or belief) about the true divergence times in clock calibration constraints on one or more nodes in the phylogeny. Judicious use of these probability densities can make divergence times more accurate and precise ( Tao, Tamura, Mello, et al. 2020 ). Researchers can now use such probability densities for node calibrations in RelTime estimation of divergence times and confidence intervals (CIs). MEGA implements the Tao, Tamura, Mello, et al. (2020 ) approach that estimates CIs by simultaneously accounting for variance introduced by the heterogeneity of evolutionary rate among lineages, estimation of sequence divergence using substitution models, and probability densities for node-calibration constraints. This method produces CIs that contain correct times with a high probability, making them much more suitable for biological hypothesis testing than other rapid methods ( Tao, Tamura, Kumar, et al. 2020 ; Tao, Tamura, Mello, et al. 2020 ).” | claim_relations[1] |
| builds-on | the CorrTest machine-learning test of rate autocorrelation (Tao et al. 2019) — “MEGA now contains a facility for detecting autocorrelation of evolutionary rates among branches, which is important for understanding molecular evolution patterns and useful as a clock rate prior in Bayesian relaxed-clock analyses. MEGA implements the CorrTest method developed using machine learning, which is accurate and computationally efficient ( Tao et al. 2019 ). The CorrTest implementation in MEGA requires a phylogeny with sequence alignment (or branch lengths) and is accessed through an easy-to-use wizard. This test's final output is a CorrScore between 0 and 1 and a P -value, where a high CorrScore and low P -value indicates that branch rates among lineages are likely correlated.” | claim_relations[2] |
| builds-on | the Bayesian evolutionary-probability method for alleles (Liu et al. 2016; Patel and Kumar 2019) — “According to the neutral theory of molecular evolution, most differences in molecular sequences across species are expected to have little to no impact on fitness ( Kimura 1983 ). Therefore, multispecies sequence alignments have been used to estimate neutral evolutionary probabilities ( EP ) of observing alternative alleles (amino acid residues or nucleotides) in a species, contingent on the given species timetree ( Liu et al. 2016 ). MEGA implements an advanced option for this Bayesian approach in which the species timetree containing relative times is computed automatically by using RelTime ( Patel and Kumar 2019 ). Alleles with EP less than 0.05 are nonneutral, whereas evolutionary permissible (neutral) alleles show much higher EPs. Disease-associated amino acid variants in human populations have EP < 0.05 and are rarely found in the population ( Liu et al. 2016 ). Many human adaptive variants in populations also have low EPs, that is, nonneutral from an evolutionary perspective, but they show high allele frequencies ( Patel et al. 2018 ). Therefore, one may use EP s to diagnose disease mutations and detect candidate adaptive variants. An EP wizard system walks the user through the steps required to set up the analysis. The first sequence in the alignment is used automatically as the focal taxon of interest (one can rearrange sequences in the Sequence Data Explorer ). EP values for all possible bases (4 for nucleotides and 20 for amino acids) at each position in the input sequence alignment are reported in a spreadsheet or text format.” | claim_relations[3] |
| extends | the MEGA release lineage from its 1993 MS-DOS version to a cross-platform GUI and command-line suite — “The Molecular Evolutionary Genetics Analysis (MEGA) software has continuously grown to meet the need for sophisticated evolutionary analysis to discover organismal and genome evolutionary patterns and processes. It was first released in 1993 to offer the statistical methods of molecular evolution through an interactive interface on the Microsoft Disk Operating System (MS-DOS) ( Kumar et al. 1993 ). For more than 25 years, MEGA’s scope and usefulness have grown through the addition of new methods, tools, and interfaces, resulting in modern integrated software for comparative sequence analysis ( Caspermeyer 2018 ). Initially, MEGA contained distance-based and maximum parsimony methods for molecular phylogenetic analysis ( Kumar et al. 1994 ). The data acquisition and integration of major approaches for aligning sequences were introduced to expand MEGA's scope ( Kumar et al. 2004 ). Afterward, the maximum likelihood (ML) methods and Bayesian methods were added for molecular evolutionary analyses ( Tamura et al. 2011 ). MEGA now contains methods for selecting the best-fit substitution model(s), estimating evolutionary distances and divergence times, reconstructing phylogenies, predicting ancestral sequences, testing for selection, and diagnosing disease mutations ( Caspermeyer 2018 ).” | claim_relations[4] |
| sense | group - in MEGA a fixed taxonomic group tag on sequences, and from version 11 any meta tag (year, country, species) used dynamically as the grouping key, so one word names both a fixed label and a user-chosen partition — “In MEGA11, we have made a MEGA-wide change to use any meta tag to define groups. For example, if one selects the “Year” meta tag for use as a group, they could estimate average diversity within and between sequences sampled in different years ( Distance menu). In the Sequence Data Explorer , one can select/unselect sequences of certain years for phylogenetic analyses. Also, the display of years would be automatically enabled in the Tree Explorer , and the feature to collapse sequence clusters will be done by years. Additionally, sequences can be sorted based on years in all the input data and result explorer displays. Therefore, a dynamic designation of groups based on the desired meta tag will enable data exploration and analysis more efficiently.” | senses[0] |
| sense | neutral - neutral in the fitness sense of the neutral theory, versus nonneutral by evolutionary probability; adaptive variants are nonneutral from an evolutionary perspective yet common in the population, so a low EP does not mean a rare allele — “According to the neutral theory of molecular evolution, most differences in molecular sequences across species are expected to have little to no impact on fitness ( Kimura 1983 ). Therefore, multispecies sequence alignments have been used to estimate neutral evolutionary probabilities ( EP ) of observing alternative alleles (amino acid residues or nucleotides) in a species, contingent on the given species timetree ( Liu et al. 2016 ). MEGA implements an advanced option for this Bayesian approach in which the species timetree containing relative times is computed automatically by using RelTime ( Patel and Kumar 2019 ). Alleles with EP less than 0.05 are nonneutral, whereas evolutionary permissible (neutral) alleles show much higher EPs. Disease-associated amino acid variants in human populations have EP < 0.05 and are rarely found in the population ( Liu et al. 2016 ). Many human adaptive variants in populations also have low EPs, that is, nonneutral from an evolutionary perspective, but they show high allele frequencies ( Patel et al. 2018 ). Therefore, one may use EP s to diagnose disease mutations and detect candidate adaptive variants. An EP wizard system walks the user through the steps required to set up the analysis. The first sequence in the alignment is used automatically as the focal taxon of interest (one can rearrange sequences in the Sequence Data Explorer ). EP values for all possible bases (4 for nucleotides and 20 for amino acids) at each position in the input sequence alignment are reported in a spreadsheet or text format.” | senses[1] |
| sense | clock - the molecular clock as an assumption RelTime explicitly does not make, against relaxed-clock and calibrating the clock, where the clock is the object being calibrated rather than assumed — “Rapid relaxed-clock methods for estimating divergence times are becoming popular because they are feasible and efficient for large contemporary sequence alignments ( Tao, Tamura, Kumar, et al. 2020 ). MEGA6 first added methods and tools for constructing evolutionary timetrees by implementing the RelTime method, which does not assume a molecular clock ( Tamura et al. 2012 , 2013 ). RelTime is known to perform well and has been used to build timetrees in hundreds of research articles ( Tao, Tamura, Kumar, et al. 2020 ). MEGA11 expands on RelTime dating options by advancing the current implementation and adding new facilities for node-dating and tip-dating needed to build timetrees of pathogens, species, and gene families.” | senses[2] |
| note | the RelTime relaxed-clock timetree method introduced in MEGA6 — Version 11 extends the RelTime dating implementation already shipped in MEGA6. — “Rapid relaxed-clock methods for estimating divergence times are becoming popular because they are feasible and efficient for large contemporary sequence alignments ( Tao, Tamura, Kumar, et al. 2020 ). MEGA6 first added methods and tools for constructing evolutionary timetrees by implementing the RelTime method, which does not assume a molecular clock ( Tamura et al. 2012 , 2013 ). RelTime is known to perform well and has been used to build timetrees in hundreds of research articles ( Tao, Tamura, Kumar, et al. 2020 ). MEGA11 expands on RelTime dating options by advancing the current implementation and adding new facilities for node-dating and tip-dating needed to build timetrees of pathogens, species, and gene families.” | claim_relations[0] |
| weight | the RelTime relaxed-clock timetree method introduced in MEGA6 — 0.95 — “Rapid relaxed-clock methods for estimating divergence times are becoming popular because they are feasible and efficient for large contemporary sequence alignments ( Tao, Tamura, Kumar, et al. 2020 ). MEGA6 first added methods and tools for constructing evolutionary timetrees by implementing the RelTime method, which does not assume a molecular clock ( Tamura et al. 2012 , 2013 ). RelTime is known to perform well and has been used to build timetrees in hundreds of research articles ( Tao, Tamura, Kumar, et al. 2020 ). MEGA11 expands on RelTime dating options by advancing the current implementation and adding new facilities for node-dating and tip-dating needed to build timetrees of pathogens, species, and gene families.” | claim_relations[0] |
| note | the Tao, Tamura, Mello et al. (2020) confidence-interval approach for calibrated divergence times — The interval estimator used with node-calibration densities is taken from that work. — “Bayesian relaxed-clock methods have long allowed the use of statistical probability distributions that capture prior knowledge (or belief) about the true divergence times in clock calibration constraints on one or more nodes in the phylogeny. Judicious use of these probability densities can make divergence times more accurate and precise ( Tao, Tamura, Mello, et al. 2020 ). Researchers can now use such probability densities for node calibrations in RelTime estimation of divergence times and confidence intervals (CIs). MEGA implements the Tao, Tamura, Mello, et al. (2020 ) approach that estimates CIs by simultaneously accounting for variance introduced by the heterogeneity of evolutionary rate among lineages, estimation of sequence divergence using substitution models, and probability densities for node-calibration constraints. This method produces CIs that contain correct times with a high probability, making them much more suitable for biological hypothesis testing than other rapid methods ( Tao, Tamura, Kumar, et al. 2020 ; Tao, Tamura, Mello, et al. 2020 ).” | claim_relations[1] |
| weight | the Tao, Tamura, Mello et al. (2020) confidence-interval approach for calibrated divergence times — 0.93 — “Bayesian relaxed-clock methods have long allowed the use of statistical probability distributions that capture prior knowledge (or belief) about the true divergence times in clock calibration constraints on one or more nodes in the phylogeny. Judicious use of these probability densities can make divergence times more accurate and precise ( Tao, Tamura, Mello, et al. 2020 ). Researchers can now use such probability densities for node calibrations in RelTime estimation of divergence times and confidence intervals (CIs). MEGA implements the Tao, Tamura, Mello, et al. (2020 ) approach that estimates CIs by simultaneously accounting for variance introduced by the heterogeneity of evolutionary rate among lineages, estimation of sequence divergence using substitution models, and probability densities for node-calibration constraints. This method produces CIs that contain correct times with a high probability, making them much more suitable for biological hypothesis testing than other rapid methods ( Tao, Tamura, Kumar, et al. 2020 ; Tao, Tamura, Mello, et al. 2020 ).” | claim_relations[1] |
| note | the CorrTest machine-learning test of rate autocorrelation (Tao et al. 2019) — CorrTest is implemented in MEGA rather than newly derived here. — “MEGA now contains a facility for detecting autocorrelation of evolutionary rates among branches, which is important for understanding molecular evolution patterns and useful as a clock rate prior in Bayesian relaxed-clock analyses. MEGA implements the CorrTest method developed using machine learning, which is accurate and computationally efficient ( Tao et al. 2019 ). The CorrTest implementation in MEGA requires a phylogeny with sequence alignment (or branch lengths) and is accessed through an easy-to-use wizard. This test's final output is a CorrScore between 0 and 1 and a P -value, where a high CorrScore and low P -value indicates that branch rates among lineages are likely correlated.” | claim_relations[2] |
| weight | the CorrTest machine-learning test of rate autocorrelation (Tao et al. 2019) — 0.92 — “MEGA now contains a facility for detecting autocorrelation of evolutionary rates among branches, which is important for understanding molecular evolution patterns and useful as a clock rate prior in Bayesian relaxed-clock analyses. MEGA implements the CorrTest method developed using machine learning, which is accurate and computationally efficient ( Tao et al. 2019 ). The CorrTest implementation in MEGA requires a phylogeny with sequence alignment (or branch lengths) and is accessed through an easy-to-use wizard. This test's final output is a CorrScore between 0 and 1 and a P -value, where a high CorrScore and low P -value indicates that branch rates among lineages are likely correlated.” | claim_relations[2] |
| note | the Bayesian evolutionary-probability method for alleles (Liu et al. 2016; Patel and Kumar 2019) — MEGA adds an option in which the species timetree is computed automatically by RelTime. — “According to the neutral theory of molecular evolution, most differences in molecular sequences across species are expected to have little to no impact on fitness ( Kimura 1983 ). Therefore, multispecies sequence alignments have been used to estimate neutral evolutionary probabilities ( EP ) of observing alternative alleles (amino acid residues or nucleotides) in a species, contingent on the given species timetree ( Liu et al. 2016 ). MEGA implements an advanced option for this Bayesian approach in which the species timetree containing relative times is computed automatically by using RelTime ( Patel and Kumar 2019 ). Alleles with EP less than 0.05 are nonneutral, whereas evolutionary permissible (neutral) alleles show much higher EPs. Disease-associated amino acid variants in human populations have EP < 0.05 and are rarely found in the population ( Liu et al. 2016 ). Many human adaptive variants in populations also have low EPs, that is, nonneutral from an evolutionary perspective, but they show high allele frequencies ( Patel et al. 2018 ). Therefore, one may use EP s to diagnose disease mutations and detect candidate adaptive variants. An EP wizard system walks the user through the steps required to set up the analysis. The first sequence in the alignment is used automatically as the focal taxon of interest (one can rearrange sequences in the Sequence Data Explorer ). EP values for all possible bases (4 for nucleotides and 20 for amino acids) at each position in the input sequence alignment are reported in a spreadsheet or text format.” | claim_relations[3] |
| weight | the Bayesian evolutionary-probability method for alleles (Liu et al. 2016; Patel and Kumar 2019) — 0.92 — “According to the neutral theory of molecular evolution, most differences in molecular sequences across species are expected to have little to no impact on fitness ( Kimura 1983 ). Therefore, multispecies sequence alignments have been used to estimate neutral evolutionary probabilities ( EP ) of observing alternative alleles (amino acid residues or nucleotides) in a species, contingent on the given species timetree ( Liu et al. 2016 ). MEGA implements an advanced option for this Bayesian approach in which the species timetree containing relative times is computed automatically by using RelTime ( Patel and Kumar 2019 ). Alleles with EP less than 0.05 are nonneutral, whereas evolutionary permissible (neutral) alleles show much higher EPs. Disease-associated amino acid variants in human populations have EP < 0.05 and are rarely found in the population ( Liu et al. 2016 ). Many human adaptive variants in populations also have low EPs, that is, nonneutral from an evolutionary perspective, but they show high allele frequencies ( Patel et al. 2018 ). Therefore, one may use EP s to diagnose disease mutations and detect candidate adaptive variants. An EP wizard system walks the user through the steps required to set up the analysis. The first sequence in the alignment is used automatically as the focal taxon of interest (one can rearrange sequences in the Sequence Data Explorer ). EP values for all possible bases (4 for nucleotides and 20 for amino acids) at each position in the input sequence alignment are reported in a spreadsheet or text format.” | claim_relations[3] |
| note | the MEGA release lineage from its 1993 MS-DOS version to a cross-platform GUI and command-line suite — Version 11 is the latest step in a 25-year sequence of releases. — “The Molecular Evolutionary Genetics Analysis (MEGA) software has continuously grown to meet the need for sophisticated evolutionary analysis to discover organismal and genome evolutionary patterns and processes. It was first released in 1993 to offer the statistical methods of molecular evolution through an interactive interface on the Microsoft Disk Operating System (MS-DOS) ( Kumar et al. 1993 ). For more than 25 years, MEGA’s scope and usefulness have grown through the addition of new methods, tools, and interfaces, resulting in modern integrated software for comparative sequence analysis ( Caspermeyer 2018 ). Initially, MEGA contained distance-based and maximum parsimony methods for molecular phylogenetic analysis ( Kumar et al. 1994 ). The data acquisition and integration of major approaches for aligning sequences were introduced to expand MEGA's scope ( Kumar et al. 2004 ). Afterward, the maximum likelihood (ML) methods and Bayesian methods were added for molecular evolutionary analyses ( Tamura et al. 2011 ). MEGA now contains methods for selecting the best-fit substitution model(s), estimating evolutionary distances and divergence times, reconstructing phylogenies, predicting ancestral sequences, testing for selection, and diagnosing disease mutations ( Caspermeyer 2018 ).” | claim_relations[4] |
| weight | the MEGA release lineage from its 1993 MS-DOS version to a cross-platform GUI and command-line suite — 0.9 — “The Molecular Evolutionary Genetics Analysis (MEGA) software has continuously grown to meet the need for sophisticated evolutionary analysis to discover organismal and genome evolutionary patterns and processes. It was first released in 1993 to offer the statistical methods of molecular evolution through an interactive interface on the Microsoft Disk Operating System (MS-DOS) ( Kumar et al. 1993 ). For more than 25 years, MEGA’s scope and usefulness have grown through the addition of new methods, tools, and interfaces, resulting in modern integrated software for comparative sequence analysis ( Caspermeyer 2018 ). Initially, MEGA contained distance-based and maximum parsimony methods for molecular phylogenetic analysis ( Kumar et al. 1994 ). The data acquisition and integration of major approaches for aligning sequences were introduced to expand MEGA's scope ( Kumar et al. 2004 ). Afterward, the maximum likelihood (ML) methods and Bayesian methods were added for molecular evolutionary analyses ( Tamura et al. 2011 ). MEGA now contains methods for selecting the best-fit substitution model(s), estimating evolutionary distances and divergence times, reconstructing phylogenies, predicting ancestral sequences, testing for selection, and diagnosing disease mutations ( Caspermeyer 2018 ).” | claim_relations[4] |
| relation | RelTime with Dated Tips → tip-dating — MEGA implements a rapid tip-dating method, RelTime with Dated Tips (RTDT). — “MEGA now implements a method to estimate timetrees using sampling dates for molecular sequences. They are often used to infer the origin and diversification of pathogens that generally evolve fast enough to track the evolutionary change over months and years ( Tao, Tamura, Kumar, et al. 2020 ). Tip-dating methods are also useful for analyzing ancient molecular sequences. MEGA implements a rapid tip-dating method, RelTime with Dated Tips (RTDT), that produces divergence times and CIs ( Miura et al. 2018 ). One may use ML or distance-based approaches for a given phylogeny and multiple sequence alignment for tip-dating, or a phylogeny with branch lengths and tip dates can be given as the input.” | unhoused_relations[0] |
| relation | CorrTest → maximum likelihood — MEGA implements the CorrTest method developed using machine learning. — “MEGA now contains a facility for detecting autocorrelation of evolutionary rates among branches, which is important for understanding molecular evolution patterns and useful as a clock rate prior in Bayesian relaxed-clock analyses. MEGA implements the CorrTest method developed using machine learning, which is accurate and computationally efficient ( Tao et al. 2019 ). The CorrTest implementation in MEGA requires a phylogeny with sequence alignment (or branch lengths) and is accessed through an easy-to-use wizard. This test's final output is a CorrScore between 0 and 1 and a P -value, where a high CorrScore and low P -value indicates that branch rates among lineages are likely correlated.” | unhoused_relations[1] |
| weight | CorrTest → maximum likelihood — 0.9 — “MEGA now contains a facility for detecting autocorrelation of evolutionary rates among branches, which is important for understanding molecular evolution patterns and useful as a clock rate prior in Bayesian relaxed-clock analyses. MEGA implements the CorrTest method developed using machine learning, which is accurate and computationally efficient ( Tao et al. 2019 ). The CorrTest implementation in MEGA requires a phylogeny with sequence alignment (or branch lengths) and is accessed through an easy-to-use wizard. This test's final output is a CorrScore between 0 and 1 and a P -value, where a high CorrScore and low P -value indicates that branch rates among lineages are likely correlated.” | unhoused_relations[1] |
| relation | evolutionary probabilities → neutral theory of molecular evolution — Under the neutral theory, multispecies alignments are used to estimate neutral evolutionary probabilities of alleles. — “According to the neutral theory of molecular evolution, most differences in molecular sequences across species are expected to have little to no impact on fitness ( Kimura 1983 ). Therefore, multispecies sequence alignments have been used to estimate neutral evolutionary probabilities ( EP ) of observing alternative alleles (amino acid residues or nucleotides) in a species, contingent on the given species timetree ( Liu et al. 2016 ). MEGA implements an advanced option for this Bayesian approach in which the species timetree containing relative times is computed automatically by using RelTime ( Patel and Kumar 2019 ). Alleles with EP less than 0.05 are nonneutral, whereas evolutionary permissible (neutral) alleles show much higher EPs. Disease-associated amino acid variants in human populations have EP < 0.05 and are rarely found in the population ( Liu et al. 2016 ). Many human adaptive variants in populations also have low EPs, that is, nonneutral from an evolutionary perspective, but they show high allele frequencies ( Patel et al. 2018 ). Therefore, one may use EP s to diagnose disease mutations and detect candidate adaptive variants. An EP wizard system walks the user through the steps required to set up the analysis. The first sequence in the alignment is used automatically as the focal taxon of interest (one can rearrange sequences in the Sequence Data Explorer ). EP values for all possible bases (4 for nucleotides and 20 for amino acids) at each position in the input sequence alignment are reported in a spreadsheet or text format.” | unhoused_relations[2] |
| weight | evolutionary probabilities → neutral theory of molecular evolution — 0.9 — “According to the neutral theory of molecular evolution, most differences in molecular sequences across species are expected to have little to no impact on fitness ( Kimura 1983 ). Therefore, multispecies sequence alignments have been used to estimate neutral evolutionary probabilities ( EP ) of observing alternative alleles (amino acid residues or nucleotides) in a species, contingent on the given species timetree ( Liu et al. 2016 ). MEGA implements an advanced option for this Bayesian approach in which the species timetree containing relative times is computed automatically by using RelTime ( Patel and Kumar 2019 ). Alleles with EP less than 0.05 are nonneutral, whereas evolutionary permissible (neutral) alleles show much higher EPs. Disease-associated amino acid variants in human populations have EP < 0.05 and are rarely found in the population ( Liu et al. 2016 ). Many human adaptive variants in populations also have low EPs, that is, nonneutral from an evolutionary perspective, but they show high allele frequencies ( Patel et al. 2018 ). Therefore, one may use EP s to diagnose disease mutations and detect candidate adaptive variants. An EP wizard system walks the user through the steps required to set up the analysis. The first sequence in the alignment is used automatically as the focal taxon of interest (one can rearrange sequences in the Sequence Data Explorer ). EP values for all possible bases (4 for nucleotides and 20 for amino acids) at each position in the input sequence alignment are reported in a spreadsheet or text format.” | unhoused_relations[2] |
| relation | site configurations → maximum likelihood — The memory saving does not harm estimates because identical site configurations have the same likelihood value. — “ML methods are widely used for phylogenetic inference but place high demands on computer memory, becoming increasingly burdensome for bigger sequence alignments analyzed these days. In MEGA11, we have now completed a long-overdue refactoring of ML calculations by adding a step to identify common site configurations, that is, sites where all sequences have the same bases as at some other sites, to utilize computer memory more efficiently. The memory requirements of Maximum Likelihood and Maximum parsimony analysis are reduced (approximately) by the factor of m / L when there are m distinct site configurations in a sequence alignment containing L sites. The memory saving can be substantial for multigene and genome-scale alignments. For example, the memory saving was 660 MB (209 vs. 870 MB) for a sequence alignment of 229 birds with 2,728 sites ( Claramunt and Cracraft 2015 ) and 4.5 GB (2.3 vs. 6.8 GB) for an alignment of 162 mammals with 11,010 sites ( Meredith et al. 2011 ). This memory saving does not have any detrimental impact on phylogenetic estimates and computational times because identical site configurations have the same likelihood value. The total log-likelihood is simply the sum of site-configuration log-likelihoods weighted by their frequencies. However, this upgrade required refactoring many different parts of MEGA’s calculation engine, including functions for phylogeny construction and model selection.” | unhoused_relations[3] |
| weight | site configurations → maximum likelihood — 0.94 — “ML methods are widely used for phylogenetic inference but place high demands on computer memory, becoming increasingly burdensome for bigger sequence alignments analyzed these days. In MEGA11, we have now completed a long-overdue refactoring of ML calculations by adding a step to identify common site configurations, that is, sites where all sequences have the same bases as at some other sites, to utilize computer memory more efficiently. The memory requirements of Maximum Likelihood and Maximum parsimony analysis are reduced (approximately) by the factor of m / L when there are m distinct site configurations in a sequence alignment containing L sites. The memory saving can be substantial for multigene and genome-scale alignments. For example, the memory saving was 660 MB (209 vs. 870 MB) for a sequence alignment of 229 birds with 2,728 sites ( Claramunt and Cracraft 2015 ) and 4.5 GB (2.3 vs. 6.8 GB) for an alignment of 162 mammals with 11,010 sites ( Meredith et al. 2011 ). This memory saving does not have any detrimental impact on phylogenetic estimates and computational times because identical site configurations have the same likelihood value. The total log-likelihood is simply the sum of site-configuration log-likelihoods weighted by their frequencies. However, this upgrade required refactoring many different parts of MEGA’s calculation engine, including functions for phylogeny construction and model selection.” | unhoused_relations[3] |
| relation | meta tag → MEGA — A MEGA-wide change lets any meta tag define groups. — “In MEGA11, we have made a MEGA-wide change to use any meta tag to define groups. For example, if one selects the “Year” meta tag for use as a group, they could estimate average diversity within and between sequences sampled in different years ( Distance menu). In the Sequence Data Explorer , one can select/unselect sequences of certain years for phylogenetic analyses. Also, the display of years would be automatically enabled in the Tree Explorer , and the feature to collapse sequence clusters will be done by years. Additionally, sequences can be sorted based on years in all the input data and result explorer displays. Therefore, a dynamic designation of groups based on the desired meta tag will enable data exploration and analysis more efficiently.” | unhoused_relations[4] |
| weight | meta tag → MEGA — 0.9 — “In MEGA11, we have made a MEGA-wide change to use any meta tag to define groups. For example, if one selects the “Year” meta tag for use as a group, they could estimate average diversity within and between sequences sampled in different years ( Distance menu). In the Sequence Data Explorer , one can select/unselect sequences of certain years for phylogenetic analyses. Also, the display of years would be automatically enabled in the Tree Explorer , and the feature to collapse sequence clusters will be done by years. Additionally, sequences can be sorted based on years in all the input data and result explorer displays. Therefore, a dynamic designation of groups based on the desired meta tag will enable data exploration and analysis more efficiently.” | unhoused_relations[4] |
| no type for this | RelTime with Dated Tips → tip-dating — RelTime with Dated Tips is the rapid tip-dating method MEGA implements; the paper names it appositively rather than with a kind cue. — “MEGA now implements a method to estimate timetrees using sampling dates for molecular sequences. They are often used to infer the origin and diversification of pathogens that generally evolve fast enough to track the evolutionary change over months and years ( Tao, Tamura, Kumar, et al. 2020 ). Tip-dating methods are also useful for analyzing ancient molecular sequences. MEGA implements a rapid tip-dating method, RelTime with Dated Tips (RTDT), that produces divergence times and CIs ( Miura et al. 2018 ). One may use ML or distance-based approaches for a given phylogeny and multiple sequence alignment for tip-dating, or a phylogeny with branch lengths and tip dates can be given as the input.” | unhoused_relations[0] |
| no type for this | CorrTest → maximum likelihood — CorrTest was developed using machine learning and is offered as a prior-setting aid for Bayesian relaxed-clock analyses, rather than standing in any taxonomic relation to the estimators it informs. — “MEGA now contains a facility for detecting autocorrelation of evolutionary rates among branches, which is important for understanding molecular evolution patterns and useful as a clock rate prior in Bayesian relaxed-clock analyses. MEGA implements the CorrTest method developed using machine learning, which is accurate and computationally efficient ( Tao et al. 2019 ). The CorrTest implementation in MEGA requires a phylogeny with sequence alignment (or branch lengths) and is accessed through an easy-to-use wizard. This test's final output is a CorrScore between 0 and 1 and a P -value, where a high CorrScore and low P -value indicates that branch rates among lineages are likely correlated.” | unhoused_relations[1] |
| no type for this | evolutionary probabilities → neutral theory of molecular evolution — Evolutionary probabilities turn the neutral theory expectation into a per-allele Bayesian quantity computed on a species timetree. — “According to the neutral theory of molecular evolution, most differences in molecular sequences across species are expected to have little to no impact on fitness ( Kimura 1983 ). Therefore, multispecies sequence alignments have been used to estimate neutral evolutionary probabilities ( EP ) of observing alternative alleles (amino acid residues or nucleotides) in a species, contingent on the given species timetree ( Liu et al. 2016 ). MEGA implements an advanced option for this Bayesian approach in which the species timetree containing relative times is computed automatically by using RelTime ( Patel and Kumar 2019 ). Alleles with EP less than 0.05 are nonneutral, whereas evolutionary permissible (neutral) alleles show much higher EPs. Disease-associated amino acid variants in human populations have EP < 0.05 and are rarely found in the population ( Liu et al. 2016 ). Many human adaptive variants in populations also have low EPs, that is, nonneutral from an evolutionary perspective, but they show high allele frequencies ( Patel et al. 2018 ). Therefore, one may use EP s to diagnose disease mutations and detect candidate adaptive variants. An EP wizard system walks the user through the steps required to set up the analysis. The first sequence in the alignment is used automatically as the focal taxon of interest (one can rearrange sequences in the Sequence Data Explorer ). EP values for all possible bases (4 for nucleotides and 20 for amino acids) at each position in the input sequence alignment are reported in a spreadsheet or text format.” | unhoused_relations[2] |
| no type for this | site configurations → maximum likelihood — Collapsing identical site configurations cuts maximum-likelihood memory without harming estimates, because repeated columns carry the same likelihood value. — “ML methods are widely used for phylogenetic inference but place high demands on computer memory, becoming increasingly burdensome for bigger sequence alignments analyzed these days. In MEGA11, we have now completed a long-overdue refactoring of ML calculations by adding a step to identify common site configurations, that is, sites where all sequences have the same bases as at some other sites, to utilize computer memory more efficiently. The memory requirements of Maximum Likelihood and Maximum parsimony analysis are reduced (approximately) by the factor of m / L when there are m distinct site configurations in a sequence alignment containing L sites. The memory saving can be substantial for multigene and genome-scale alignments. For example, the memory saving was 660 MB (209 vs. 870 MB) for a sequence alignment of 229 birds with 2,728 sites ( Claramunt and Cracraft 2015 ) and 4.5 GB (2.3 vs. 6.8 GB) for an alignment of 162 mammals with 11,010 sites ( Meredith et al. 2011 ). This memory saving does not have any detrimental impact on phylogenetic estimates and computational times because identical site configurations have the same likelihood value. The total log-likelihood is simply the sum of site-configuration log-likelihoods weighted by their frequencies. However, this upgrade required refactoring many different parts of MEGA’s calculation engine, including functions for phylogeny construction and model selection.” | unhoused_relations[3] |
| no type for this | meta tag → MEGA — Any meta tag can now act as the grouping key throughout MEGA, so grouping became a dynamic function of user-chosen metadata rather than a fixed tag. — “In MEGA11, we have made a MEGA-wide change to use any meta tag to define groups. For example, if one selects the “Year” meta tag for use as a group, they could estimate average diversity within and between sequences sampled in different years ( Distance menu). In the Sequence Data Explorer , one can select/unselect sequences of certain years for phylogenetic analyses. Also, the display of years would be automatically enabled in the Tree Explorer , and the feature to collapse sequence clusters will be done by years. Additionally, sequences can be sorted based on years in all the input data and result explorer displays. Therefore, a dynamic designation of groups based on the desired meta tag will enable data exploration and analysis more efficiently.” | unhoused_relations[4] |
| limitation | When only a phylogeny with branch lengths is supplied, the confidence intervals are narrower than they should be, because the variance of branch-length estimation cannot be recovered without the original data set. — “For RelTime analyses in MEGA11, ML and distance-based approaches can be used to build a timetree for a given phylogeny and multiple sequence alignment. One may also use only a phylogeny with branch lengths, which extends the usefulness of relaxed-clock methods for phylogenies inferred from nonmolecular data or statistical methodologies not available in MEGA. When a phylogeny with branch lengths is used, the CIs will be narrower because the variance associated with branch length estimation cannot be generated without the original data set used to produce the phylogeny and branch lengths. Nevertheless, these CIs will incorporate variance introduced due to rate variation among lineages and clock calibrations’ uncertainty.” | limitations[0] |
| limitation | Before the re-threading, the graphical interface became intermittently unresponsive on alignments of the size now produced by resequencing efforts. — “Using a large multiple sequence alignment containing 68,000 genomes and 30,000 bases each, we assessed MEGA GUI's responsiveness during input data file reading, execution of functions in the Sequence Data Explorer , estimation of pairwise distances, and building of distance-based phylogenies. We found the GUI to become intermittently unresponsive for such large data sets, which are now common due to resequencing and population sequencing efforts. Consequently, we have moved all potentially long-running operations out of the main GUI thread to background threads in a major overhaul of the source code. Now, large input data files are read rapidly, and calculations of pairwise distance matrices, selection tests, and phylogeny construction for distance-based methods are performed in a background thread. The Sequence Data Explorer has been reprogrammed to enable more efficient highlighting of variable sites, and navigation of the sequence alignment has been improved. Also added are options to automatically label sites based on attributes, which annotates sites by providing a one-character label and then using desired labeled sites to subset data for any molecular phylogenetic analysis desired.” | limitations[1] |
Content tags
content-observations:
finding · quantity · variable · definition · key-term — grounded in the full text| finding | Maximum-likelihood and maximum-parsimony memory requirements are reduced approximately by the factor m/L, for m distinct site configurations in an alignment of L sites. — “ML methods are widely used for phylogenetic inference but place high demands on computer memory, becoming increasingly burdensome for bigger sequence alignments analyzed these days. In MEGA11, we have now completed a long-overdue refactoring of ML calculations by adding a step to identify common site configurations, that is, sites where all sequences have the same bases as at some other sites, to utilize computer memory more efficiently. The memory requirements of Maximum Likelihood and Maximum parsimony analysis are reduced (approximately) by the factor of m / L when there are m distinct site configurations in a sequence alignment containing L sites. The memory saving can be substantial for multigene and genome-scale alignments. For example, the memory saving was 660 MB (209 vs. 870 MB) for a sequence alignment of 229 birds with 2,728 sites ( Claramunt and Cracraft 2015 ) and 4.5 GB (2.3 vs. 6.8 GB) for an alignment of 162 mammals with 11,010 sites ( Meredith et al. 2011 ). This memory saving does not have any detrimental impact on phylogenetic estimates and computational times because identical site configurations have the same likelihood value. The total log-likelihood is simply the sum of site-configuration log-likelihoods weighted by their frequencies. However, this upgrade required refactoring many different parts of MEGA’s calculation engine, including functions for phylogeny construction and model selection.” | content_tags.findings[0] |
| finding | Memory use fell by 660 MB (870 MB to 209 MB) on an alignment of 229 birds with 2,728 sites. — “ML methods are widely used for phylogenetic inference but place high demands on computer memory, becoming increasingly burdensome for bigger sequence alignments analyzed these days. In MEGA11, we have now completed a long-overdue refactoring of ML calculations by adding a step to identify common site configurations, that is, sites where all sequences have the same bases as at some other sites, to utilize computer memory more efficiently. The memory requirements of Maximum Likelihood and Maximum parsimony analysis are reduced (approximately) by the factor of m / L when there are m distinct site configurations in a sequence alignment containing L sites. The memory saving can be substantial for multigene and genome-scale alignments. For example, the memory saving was 660 MB (209 vs. 870 MB) for a sequence alignment of 229 birds with 2,728 sites ( Claramunt and Cracraft 2015 ) and 4.5 GB (2.3 vs. 6.8 GB) for an alignment of 162 mammals with 11,010 sites ( Meredith et al. 2011 ). This memory saving does not have any detrimental impact on phylogenetic estimates and computational times because identical site configurations have the same likelihood value. The total log-likelihood is simply the sum of site-configuration log-likelihoods weighted by their frequencies. However, this upgrade required refactoring many different parts of MEGA’s calculation engine, including functions for phylogeny construction and model selection.” | content_tags.findings[1] |
| finding | Memory use fell by 4.5 GB (6.8 GB to 2.3 GB) on an alignment of 162 mammals with 11,010 sites. — “ML methods are widely used for phylogenetic inference but place high demands on computer memory, becoming increasingly burdensome for bigger sequence alignments analyzed these days. In MEGA11, we have now completed a long-overdue refactoring of ML calculations by adding a step to identify common site configurations, that is, sites where all sequences have the same bases as at some other sites, to utilize computer memory more efficiently. The memory requirements of Maximum Likelihood and Maximum parsimony analysis are reduced (approximately) by the factor of m / L when there are m distinct site configurations in a sequence alignment containing L sites. The memory saving can be substantial for multigene and genome-scale alignments. For example, the memory saving was 660 MB (209 vs. 870 MB) for a sequence alignment of 229 birds with 2,728 sites ( Claramunt and Cracraft 2015 ) and 4.5 GB (2.3 vs. 6.8 GB) for an alignment of 162 mammals with 11,010 sites ( Meredith et al. 2011 ). This memory saving does not have any detrimental impact on phylogenetic estimates and computational times because identical site configurations have the same likelihood value. The total log-likelihood is simply the sum of site-configuration log-likelihoods weighted by their frequencies. However, this upgrade required refactoring many different parts of MEGA’s calculation engine, including functions for phylogeny construction and model selection.” | content_tags.findings[2] |
| finding | Alleles with an evolutionary probability below 0.05 are nonneutral, while evolutionarily permissible alleles show much higher EPs. — “According to the neutral theory of molecular evolution, most differences in molecular sequences across species are expected to have little to no impact on fitness ( Kimura 1983 ). Therefore, multispecies sequence alignments have been used to estimate neutral evolutionary probabilities ( EP ) of observing alternative alleles (amino acid residues or nucleotides) in a species, contingent on the given species timetree ( Liu et al. 2016 ). MEGA implements an advanced option for this Bayesian approach in which the species timetree containing relative times is computed automatically by using RelTime ( Patel and Kumar 2019 ). Alleles with EP less than 0.05 are nonneutral, whereas evolutionary permissible (neutral) alleles show much higher EPs. Disease-associated amino acid variants in human populations have EP < 0.05 and are rarely found in the population ( Liu et al. 2016 ). Many human adaptive variants in populations also have low EPs, that is, nonneutral from an evolutionary perspective, but they show high allele frequencies ( Patel et al. 2018 ). Therefore, one may use EP s to diagnose disease mutations and detect candidate adaptive variants. An EP wizard system walks the user through the steps required to set up the analysis. The first sequence in the alignment is used automatically as the focal taxon of interest (one can rearrange sequences in the Sequence Data Explorer ). EP values for all possible bases (4 for nucleotides and 20 for amino acids) at each position in the input sequence alignment are reported in a spreadsheet or text format.” | content_tags.findings[3] |
| finding | CorrTest returns a CorrScore between 0 and 1 with a P-value; a high CorrScore and low P-value indicate correlated branch rates among lineages. — “MEGA now contains a facility for detecting autocorrelation of evolutionary rates among branches, which is important for understanding molecular evolution patterns and useful as a clock rate prior in Bayesian relaxed-clock analyses. MEGA implements the CorrTest method developed using machine learning, which is accurate and computationally efficient ( Tao et al. 2019 ). The CorrTest implementation in MEGA requires a phylogeny with sequence alignment (or branch lengths) and is accessed through an easy-to-use wizard. This test's final output is a CorrScore between 0 and 1 and a P -value, where a high CorrScore and low P -value indicates that branch rates among lineages are likely correlated.” | content_tags.findings[4] |
| finding | The implemented interval method produces confidence intervals that contain the correct times with high probability, making them suitable for biological hypothesis testing. — “Bayesian relaxed-clock methods have long allowed the use of statistical probability distributions that capture prior knowledge (or belief) about the true divergence times in clock calibration constraints on one or more nodes in the phylogeny. Judicious use of these probability densities can make divergence times more accurate and precise ( Tao, Tamura, Mello, et al. 2020 ). Researchers can now use such probability densities for node calibrations in RelTime estimation of divergence times and confidence intervals (CIs). MEGA implements the Tao, Tamura, Mello, et al. (2020 ) approach that estimates CIs by simultaneously accounting for variance introduced by the heterogeneity of evolutionary rate among lineages, estimation of sequence divergence using substitution models, and probability densities for node-calibration constraints. This method produces CIs that contain correct times with a high probability, making them much more suitable for biological hypothesis testing than other rapid methods ( Tao, Tamura, Kumar, et al. 2020 ; Tao, Tamura, Mello, et al. 2020 ).” | content_tags.findings[5] |
| finding | Disease-associated human amino acid variants have EP below 0.05 and are rarely found in the population. — “According to the neutral theory of molecular evolution, most differences in molecular sequences across species are expected to have little to no impact on fitness ( Kimura 1983 ). Therefore, multispecies sequence alignments have been used to estimate neutral evolutionary probabilities ( EP ) of observing alternative alleles (amino acid residues or nucleotides) in a species, contingent on the given species timetree ( Liu et al. 2016 ). MEGA implements an advanced option for this Bayesian approach in which the species timetree containing relative times is computed automatically by using RelTime ( Patel and Kumar 2019 ). Alleles with EP less than 0.05 are nonneutral, whereas evolutionary permissible (neutral) alleles show much higher EPs. Disease-associated amino acid variants in human populations have EP < 0.05 and are rarely found in the population ( Liu et al. 2016 ). Many human adaptive variants in populations also have low EPs, that is, nonneutral from an evolutionary perspective, but they show high allele frequencies ( Patel et al. 2018 ). Therefore, one may use EP s to diagnose disease mutations and detect candidate adaptive variants. An EP wizard system walks the user through the steps required to set up the analysis. The first sequence in the alignment is used automatically as the focal taxon of interest (one can rearrange sequences in the Sequence Data Explorer ). EP values for all possible bases (4 for nucleotides and 20 for amino acids) at each position in the input sequence alignment are reported in a spreadsheet or text format.” | content_tags.findings[6] |
| quantity | 660 MB saved (870 MB before, 209 MB after) on 229 birds with 2,728 sites — “ML methods are widely used for phylogenetic inference but place high demands on computer memory, becoming increasingly burdensome for bigger sequence alignments analyzed these days. In MEGA11, we have now completed a long-overdue refactoring of ML calculations by adding a step to identify common site configurations, that is, sites where all sequences have the same bases as at some other sites, to utilize computer memory more efficiently. The memory requirements of Maximum Likelihood and Maximum parsimony analysis are reduced (approximately) by the factor of m / L when there are m distinct site configurations in a sequence alignment containing L sites. The memory saving can be substantial for multigene and genome-scale alignments. For example, the memory saving was 660 MB (209 vs. 870 MB) for a sequence alignment of 229 birds with 2,728 sites ( Claramunt and Cracraft 2015 ) and 4.5 GB (2.3 vs. 6.8 GB) for an alignment of 162 mammals with 11,010 sites ( Meredith et al. 2011 ). This memory saving does not have any detrimental impact on phylogenetic estimates and computational times because identical site configurations have the same likelihood value. The total log-likelihood is simply the sum of site-configuration log-likelihoods weighted by their frequencies. However, this upgrade required refactoring many different parts of MEGA’s calculation engine, including functions for phylogeny construction and model selection.” | content_tags.quantities[0] |
| quantity | 4.5 GB saved (6.8 GB before, 2.3 GB after) on 162 mammals with 11,010 sites — “ML methods are widely used for phylogenetic inference but place high demands on computer memory, becoming increasingly burdensome for bigger sequence alignments analyzed these days. In MEGA11, we have now completed a long-overdue refactoring of ML calculations by adding a step to identify common site configurations, that is, sites where all sequences have the same bases as at some other sites, to utilize computer memory more efficiently. The memory requirements of Maximum Likelihood and Maximum parsimony analysis are reduced (approximately) by the factor of m / L when there are m distinct site configurations in a sequence alignment containing L sites. The memory saving can be substantial for multigene and genome-scale alignments. For example, the memory saving was 660 MB (209 vs. 870 MB) for a sequence alignment of 229 birds with 2,728 sites ( Claramunt and Cracraft 2015 ) and 4.5 GB (2.3 vs. 6.8 GB) for an alignment of 162 mammals with 11,010 sites ( Meredith et al. 2011 ). This memory saving does not have any detrimental impact on phylogenetic estimates and computational times because identical site configurations have the same likelihood value. The total log-likelihood is simply the sum of site-configuration log-likelihoods weighted by their frequencies. However, this upgrade required refactoring many different parts of MEGA’s calculation engine, including functions for phylogeny construction and model selection.” | content_tags.quantities[1] |
| quantity | an evolutionary probability below 0.05 marks an allele nonneutral — “According to the neutral theory of molecular evolution, most differences in molecular sequences across species are expected to have little to no impact on fitness ( Kimura 1983 ). Therefore, multispecies sequence alignments have been used to estimate neutral evolutionary probabilities ( EP ) of observing alternative alleles (amino acid residues or nucleotides) in a species, contingent on the given species timetree ( Liu et al. 2016 ). MEGA implements an advanced option for this Bayesian approach in which the species timetree containing relative times is computed automatically by using RelTime ( Patel and Kumar 2019 ). Alleles with EP less than 0.05 are nonneutral, whereas evolutionary permissible (neutral) alleles show much higher EPs. Disease-associated amino acid variants in human populations have EP < 0.05 and are rarely found in the population ( Liu et al. 2016 ). Many human adaptive variants in populations also have low EPs, that is, nonneutral from an evolutionary perspective, but they show high allele frequencies ( Patel et al. 2018 ). Therefore, one may use EP s to diagnose disease mutations and detect candidate adaptive variants. An EP wizard system walks the user through the steps required to set up the analysis. The first sequence in the alignment is used automatically as the focal taxon of interest (one can rearrange sequences in the Sequence Data Explorer ). EP values for all possible bases (4 for nucleotides and 20 for amino acids) at each position in the input sequence alignment are reported in a spreadsheet or text format.” | content_tags.quantities[2] |
| quantity | a CorrScore between 0 and 1, reported with a P-value — “MEGA now contains a facility for detecting autocorrelation of evolutionary rates among branches, which is important for understanding molecular evolution patterns and useful as a clock rate prior in Bayesian relaxed-clock analyses. MEGA implements the CorrTest method developed using machine learning, which is accurate and computationally efficient ( Tao et al. 2019 ). The CorrTest implementation in MEGA requires a phylogeny with sequence alignment (or branch lengths) and is accessed through an easy-to-use wizard. This test's final output is a CorrScore between 0 and 1 and a P -value, where a high CorrScore and low P -value indicates that branch rates among lineages are likely correlated.” | content_tags.quantities[3] |
| quantity | 68,000 genomes of 30,000 bases each were used to assess GUI responsiveness — “Using a large multiple sequence alignment containing 68,000 genomes and 30,000 bases each, we assessed MEGA GUI's responsiveness during input data file reading, execution of functions in the Sequence Data Explorer , estimation of pairwise distances, and building of distance-based phylogenies. We found the GUI to become intermittently unresponsive for such large data sets, which are now common due to resequencing and population sequencing efforts. Consequently, we have moved all potentially long-running operations out of the main GUI thread to background threads in a major overhaul of the source code. Now, large input data files are read rapidly, and calculations of pairwise distance matrices, selection tests, and phylogeny construction for distance-based methods are performed in a background thread. The Sequence Data Explorer has been reprogrammed to enable more efficient highlighting of variable sites, and navigation of the sequence alignment has been improved. Also added are options to automatically label sites based on attributes, which annotates sites by providing a one-character label and then using desired labeled sites to subset data for any molecular phylogenetic analysis desired.” | content_tags.quantities[4] |
| variable | CorrScore — “MEGA now contains a facility for detecting autocorrelation of evolutionary rates among branches, which is important for understanding molecular evolution patterns and useful as a clock rate prior in Bayesian relaxed-clock analyses. MEGA implements the CorrTest method developed using machine learning, which is accurate and computationally efficient ( Tao et al. 2019 ). The CorrTest implementation in MEGA requires a phylogeny with sequence alignment (or branch lengths) and is accessed through an easy-to-use wizard. This test's final output is a CorrScore between 0 and 1 and a P -value, where a high CorrScore and low P -value indicates that branch rates among lineages are likely correlated.” | content_tags.variables[0] |
| definition | RelTime — “Rapid relaxed-clock methods for estimating divergence times are becoming popular because they are feasible and efficient for large contemporary sequence alignments ( Tao, Tamura, Kumar, et al. 2020 ). MEGA6 first added methods and tools for constructing evolutionary timetrees by implementing the RelTime method, which does not assume a molecular clock ( Tamura et al. 2012 , 2013 ). RelTime is known to perform well and has been used to build timetrees in hundreds of research articles ( Tao, Tamura, Kumar, et al. 2020 ). MEGA11 expands on RelTime dating options by advancing the current implementation and adding new facilities for node-dating and tip-dating needed to build timetrees of pathogens, species, and gene families.” | content_tags.definitions[0] |
| definition | RelTime with Dated Tips — “MEGA now implements a method to estimate timetrees using sampling dates for molecular sequences. They are often used to infer the origin and diversification of pathogens that generally evolve fast enough to track the evolutionary change over months and years ( Tao, Tamura, Kumar, et al. 2020 ). Tip-dating methods are also useful for analyzing ancient molecular sequences. MEGA implements a rapid tip-dating method, RelTime with Dated Tips (RTDT), that produces divergence times and CIs ( Miura et al. 2018 ). One may use ML or distance-based approaches for a given phylogeny and multiple sequence alignment for tip-dating, or a phylogeny with branch lengths and tip dates can be given as the input.” | content_tags.definitions[1] |
| definition | CorrTest — “MEGA now contains a facility for detecting autocorrelation of evolutionary rates among branches, which is important for understanding molecular evolution patterns and useful as a clock rate prior in Bayesian relaxed-clock analyses. MEGA implements the CorrTest method developed using machine learning, which is accurate and computationally efficient ( Tao et al. 2019 ). The CorrTest implementation in MEGA requires a phylogeny with sequence alignment (or branch lengths) and is accessed through an easy-to-use wizard. This test's final output is a CorrScore between 0 and 1 and a P -value, where a high CorrScore and low P -value indicates that branch rates among lineages are likely correlated.” | content_tags.definitions[2] |
| definition | CorrScore — “MEGA now contains a facility for detecting autocorrelation of evolutionary rates among branches, which is important for understanding molecular evolution patterns and useful as a clock rate prior in Bayesian relaxed-clock analyses. MEGA implements the CorrTest method developed using machine learning, which is accurate and computationally efficient ( Tao et al. 2019 ). The CorrTest implementation in MEGA requires a phylogeny with sequence alignment (or branch lengths) and is accessed through an easy-to-use wizard. This test's final output is a CorrScore between 0 and 1 and a P -value, where a high CorrScore and low P -value indicates that branch rates among lineages are likely correlated.” | content_tags.definitions[3] |
| definition | evolutionary probabilities — “According to the neutral theory of molecular evolution, most differences in molecular sequences across species are expected to have little to no impact on fitness ( Kimura 1983 ). Therefore, multispecies sequence alignments have been used to estimate neutral evolutionary probabilities ( EP ) of observing alternative alleles (amino acid residues or nucleotides) in a species, contingent on the given species timetree ( Liu et al. 2016 ). MEGA implements an advanced option for this Bayesian approach in which the species timetree containing relative times is computed automatically by using RelTime ( Patel and Kumar 2019 ). Alleles with EP less than 0.05 are nonneutral, whereas evolutionary permissible (neutral) alleles show much higher EPs. Disease-associated amino acid variants in human populations have EP < 0.05 and are rarely found in the population ( Liu et al. 2016 ). Many human adaptive variants in populations also have low EPs, that is, nonneutral from an evolutionary perspective, but they show high allele frequencies ( Patel et al. 2018 ). Therefore, one may use EP s to diagnose disease mutations and detect candidate adaptive variants. An EP wizard system walks the user through the steps required to set up the analysis. The first sequence in the alignment is used automatically as the focal taxon of interest (one can rearrange sequences in the Sequence Data Explorer ). EP values for all possible bases (4 for nucleotides and 20 for amino acids) at each position in the input sequence alignment are reported in a spreadsheet or text format.” | content_tags.definitions[4] |
| definition | site configurations — “ML methods are widely used for phylogenetic inference but place high demands on computer memory, becoming increasingly burdensome for bigger sequence alignments analyzed these days. In MEGA11, we have now completed a long-overdue refactoring of ML calculations by adding a step to identify common site configurations, that is, sites where all sequences have the same bases as at some other sites, to utilize computer memory more efficiently. The memory requirements of Maximum Likelihood and Maximum parsimony analysis are reduced (approximately) by the factor of m / L when there are m distinct site configurations in a sequence alignment containing L sites. The memory saving can be substantial for multigene and genome-scale alignments. For example, the memory saving was 660 MB (209 vs. 870 MB) for a sequence alignment of 229 birds with 2,728 sites ( Claramunt and Cracraft 2015 ) and 4.5 GB (2.3 vs. 6.8 GB) for an alignment of 162 mammals with 11,010 sites ( Meredith et al. 2011 ). This memory saving does not have any detrimental impact on phylogenetic estimates and computational times because identical site configurations have the same likelihood value. The total log-likelihood is simply the sum of site-configuration log-likelihoods weighted by their frequencies. However, this upgrade required refactoring many different parts of MEGA’s calculation engine, including functions for phylogeny construction and model selection.” | content_tags.definitions[5] |
| definition | MEGA-CC — “MEGA’s computational core (MEGA-CC) has undergone extensive refactoring, hardening, and expansion over time. It advanced from 16-bit to 32-bit ( Kumar et al. 2001 ), became multithreaded and incorporated multicore parallelization for various calculations ( Tamura et al. 2013 ), and stepped up to 64-bit architecture ( Kumar et al. 2016 , 2018 ). MEGA-CC was released for use as a command-line program to address the growing need for batch processing of many data sets and integration into analysis workflows ( Kumar et al. 2012 ; Stecher et al. 2020 ). With both 32- and 64-bit versions of MEGA currently available for use on the command-line and GUI, MEGA is now a suite of applications that responds to the variety of computing environments currently used by researchers in molecular evolution and phylogenetics. Here, we present key methodological additions and technical improvements in MEGA that comprise version 11.” | content_tags.definitions[6] |
| definition | neutral theory of molecular evolution — “According to the neutral theory of molecular evolution, most differences in molecular sequences across species are expected to have little to no impact on fitness ( Kimura 1983 ). Therefore, multispecies sequence alignments have been used to estimate neutral evolutionary probabilities ( EP ) of observing alternative alleles (amino acid residues or nucleotides) in a species, contingent on the given species timetree ( Liu et al. 2016 ). MEGA implements an advanced option for this Bayesian approach in which the species timetree containing relative times is computed automatically by using RelTime ( Patel and Kumar 2019 ). Alleles with EP less than 0.05 are nonneutral, whereas evolutionary permissible (neutral) alleles show much higher EPs. Disease-associated amino acid variants in human populations have EP < 0.05 and are rarely found in the population ( Liu et al. 2016 ). Many human adaptive variants in populations also have low EPs, that is, nonneutral from an evolutionary perspective, but they show high allele frequencies ( Patel et al. 2018 ). Therefore, one may use EP s to diagnose disease mutations and detect candidate adaptive variants. An EP wizard system walks the user through the steps required to set up the analysis. The first sequence in the alignment is used automatically as the focal taxon of interest (one can rearrange sequences in the Sequence Data Explorer ). EP values for all possible bases (4 for nucleotides and 20 for amino acids) at each position in the input sequence alignment are reported in a spreadsheet or text format.” | content_tags.definitions[7] |
| definition | operational taxonomic units — “MEGA has long supported a “group” tag for sequences and other operational taxonomic units (OTUs). Using the sequence “group” tags, MEGA offered a group-wise exploration of input data, selection of data subsets, and computational analyses ( Kumar 2001 ). Support for two new tags (“population” and “species”) was added in MEGA7, with the species tags used to mark duplicate genes in multigene family phylogenies ( Kumar et al. 2016 ). In MEGA11, sequences can now be tagged to provide information on the continent, country, city, year, month, day, and time. This spatiotemporal information can be used in tip-dating analyses.” | content_tags.definitions[8] |
Concepts
terms this paper carries, each with the sentence it was cut from — the layer that joins one paper to another. Terms with no quote yet are on CONCEPTS.html.
| concept | MEGA — not defined in this paper's own words; the Molecular Evolutionary Genetics Analysis software suite for comparative sequence analysis — “The Molecular Evolutionary Genetics Analysis (MEGA) software has continuously grown to meet the need for sophisticated evolutionary analysis to discover organismal and genome evolutionary patterns and processes. It was first released in 1993 to offer the statistical methods of molecular evolution through an interactive interface on the Microsoft Disk Operating System (MS-DOS) ( Kumar et al. 1993 ). For more than 25 years, MEGA’s scope and usefulness have grown through the addition of new methods, tools, and interfaces, resulting in modern integrated software for comparative sequence analysis ( Caspermeyer 2018 ). Initially, MEGA contained distance-based and maximum parsimony methods for molecular phylogenetic analysis ( Kumar et al. 1994 ). The data acquisition and integration of major approaches for aligning sequences were introduced to expand MEGA's scope ( Kumar et al. 2004 ). Afterward, the maximum likelihood (ML) methods and Bayesian methods were added for molecular evolutionary analyses ( Tamura et al. 2011 ). MEGA now contains methods for selecting the best-fit substitution model(s), estimating evolutionary distances and divergence times, reconstructing phylogenies, predicting ancestral sequences, testing for selection, and diagnosing disease mutations ( Caspermeyer 2018 ).” | concepts[0] |
| concept | timetree — not defined in this paper's own words; a phylogeny whose branch lengths are calibrated to time — “For RelTime analyses in MEGA11, ML and distance-based approaches can be used to build a timetree for a given phylogeny and multiple sequence alignment. One may also use only a phylogeny with branch lengths, which extends the usefulness of relaxed-clock methods for phylogenies inferred from nonmolecular data or statistical methodologies not available in MEGA. When a phylogeny with branch lengths is used, the CIs will be narrower because the variance associated with branch length estimation cannot be generated without the original data set used to produce the phylogeny and branch lengths. Nevertheless, these CIs will incorporate variance introduced due to rate variation among lineages and clock calibrations’ uncertainty.” | concepts[1] |
| concept | RelTime — not defined in this paper's own words; the relaxed-clock dating method that does not assume a molecular clock — “Rapid relaxed-clock methods for estimating divergence times are becoming popular because they are feasible and efficient for large contemporary sequence alignments ( Tao, Tamura, Kumar, et al. 2020 ). MEGA6 first added methods and tools for constructing evolutionary timetrees by implementing the RelTime method, which does not assume a molecular clock ( Tamura et al. 2012 , 2013 ). RelTime is known to perform well and has been used to build timetrees in hundreds of research articles ( Tao, Tamura, Kumar, et al. 2020 ). MEGA11 expands on RelTime dating options by advancing the current implementation and adding new facilities for node-dating and tip-dating needed to build timetrees of pathogens, species, and gene families.” | concepts[2] |
| concept | molecular clock — not defined in this paper's own words; the assumption of a constant evolutionary rate, which RelTime declines to make — “Rapid relaxed-clock methods for estimating divergence times are becoming popular because they are feasible and efficient for large contemporary sequence alignments ( Tao, Tamura, Kumar, et al. 2020 ). MEGA6 first added methods and tools for constructing evolutionary timetrees by implementing the RelTime method, which does not assume a molecular clock ( Tamura et al. 2012 , 2013 ). RelTime is known to perform well and has been used to build timetrees in hundreds of research articles ( Tao, Tamura, Kumar, et al. 2020 ). MEGA11 expands on RelTime dating options by advancing the current implementation and adding new facilities for node-dating and tip-dating needed to build timetrees of pathogens, species, and gene families.” | concepts[3] |
| concept | relaxed-clock — not defined in this paper's own words; dating methods that allow evolutionary rates to vary among lineages — “Bayesian relaxed-clock methods have long allowed the use of statistical probability distributions that capture prior knowledge (or belief) about the true divergence times in clock calibration constraints on one or more nodes in the phylogeny. Judicious use of these probability densities can make divergence times more accurate and precise ( Tao, Tamura, Mello, et al. 2020 ). Researchers can now use such probability densities for node calibrations in RelTime estimation of divergence times and confidence intervals (CIs). MEGA implements the Tao, Tamura, Mello, et al. (2020 ) approach that estimates CIs by simultaneously accounting for variance introduced by the heterogeneity of evolutionary rate among lineages, estimation of sequence divergence using substitution models, and probability densities for node-calibration constraints. This method produces CIs that contain correct times with a high probability, making them much more suitable for biological hypothesis testing than other rapid methods ( Tao, Tamura, Kumar, et al. 2020 ; Tao, Tamura, Mello, et al. 2020 ).” | concepts[4] |
| concept | tip-dating — not defined in this paper's own words; dating a tree from the sampling times of its sequences — “MEGA now implements a method to estimate timetrees using sampling dates for molecular sequences. They are often used to infer the origin and diversification of pathogens that generally evolve fast enough to track the evolutionary change over months and years ( Tao, Tamura, Kumar, et al. 2020 ). Tip-dating methods are also useful for analyzing ancient molecular sequences. MEGA implements a rapid tip-dating method, RelTime with Dated Tips (RTDT), that produces divergence times and CIs ( Miura et al. 2018 ). One may use ML or distance-based approaches for a given phylogeny and multiple sequence alignment for tip-dating, or a phylogeny with branch lengths and tip dates can be given as the input.” | concepts[5] |
| concept | node-dating — not defined in this paper's own words; dating a tree from calibration constraints placed on internal nodes — “Rapid relaxed-clock methods for estimating divergence times are becoming popular because they are feasible and efficient for large contemporary sequence alignments ( Tao, Tamura, Kumar, et al. 2020 ). MEGA6 first added methods and tools for constructing evolutionary timetrees by implementing the RelTime method, which does not assume a molecular clock ( Tamura et al. 2012 , 2013 ). RelTime is known to perform well and has been used to build timetrees in hundreds of research articles ( Tao, Tamura, Kumar, et al. 2020 ). MEGA11 expands on RelTime dating options by advancing the current implementation and adding new facilities for node-dating and tip-dating needed to build timetrees of pathogens, species, and gene families.” | concepts[6] |
| concept | RelTime with Dated Tips — not defined in this paper's own words; the rapid tip-dating estimator implemented in MEGA — “MEGA now implements a method to estimate timetrees using sampling dates for molecular sequences. They are often used to infer the origin and diversification of pathogens that generally evolve fast enough to track the evolutionary change over months and years ( Tao, Tamura, Kumar, et al. 2020 ). Tip-dating methods are also useful for analyzing ancient molecular sequences. MEGA implements a rapid tip-dating method, RelTime with Dated Tips (RTDT), that produces divergence times and CIs ( Miura et al. 2018 ). One may use ML or distance-based approaches for a given phylogeny and multiple sequence alignment for tip-dating, or a phylogeny with branch lengths and tip dates can be given as the input.” | concepts[7] |
| concept | CorrTest — not defined in this paper's own words; the machine-learning test for autocorrelation of evolutionary rates — “MEGA now contains a facility for detecting autocorrelation of evolutionary rates among branches, which is important for understanding molecular evolution patterns and useful as a clock rate prior in Bayesian relaxed-clock analyses. MEGA implements the CorrTest method developed using machine learning, which is accurate and computationally efficient ( Tao et al. 2019 ). The CorrTest implementation in MEGA requires a phylogeny with sequence alignment (or branch lengths) and is accessed through an easy-to-use wizard. This test's final output is a CorrScore between 0 and 1 and a P -value, where a high CorrScore and low P -value indicates that branch rates among lineages are likely correlated.” | concepts[8] |
| concept | evolutionary probabilities — not defined in this paper's own words; Bayesian per-allele probabilities used to call neutrality — “According to the neutral theory of molecular evolution, most differences in molecular sequences across species are expected to have little to no impact on fitness ( Kimura 1983 ). Therefore, multispecies sequence alignments have been used to estimate neutral evolutionary probabilities ( EP ) of observing alternative alleles (amino acid residues or nucleotides) in a species, contingent on the given species timetree ( Liu et al. 2016 ). MEGA implements an advanced option for this Bayesian approach in which the species timetree containing relative times is computed automatically by using RelTime ( Patel and Kumar 2019 ). Alleles with EP less than 0.05 are nonneutral, whereas evolutionary permissible (neutral) alleles show much higher EPs. Disease-associated amino acid variants in human populations have EP < 0.05 and are rarely found in the population ( Liu et al. 2016 ). Many human adaptive variants in populations also have low EPs, that is, nonneutral from an evolutionary perspective, but they show high allele frequencies ( Patel et al. 2018 ). Therefore, one may use EP s to diagnose disease mutations and detect candidate adaptive variants. An EP wizard system walks the user through the steps required to set up the analysis. The first sequence in the alignment is used automatically as the focal taxon of interest (one can rearrange sequences in the Sequence Data Explorer ). EP values for all possible bases (4 for nucleotides and 20 for amino acids) at each position in the input sequence alignment are reported in a spreadsheet or text format.” | concepts[9] |
| concept | neutral theory of molecular evolution — not defined in this paper's own words; the theory that most sequence differences are near-neutral for fitness — “According to the neutral theory of molecular evolution, most differences in molecular sequences across species are expected to have little to no impact on fitness ( Kimura 1983 ). Therefore, multispecies sequence alignments have been used to estimate neutral evolutionary probabilities ( EP ) of observing alternative alleles (amino acid residues or nucleotides) in a species, contingent on the given species timetree ( Liu et al. 2016 ). MEGA implements an advanced option for this Bayesian approach in which the species timetree containing relative times is computed automatically by using RelTime ( Patel and Kumar 2019 ). Alleles with EP less than 0.05 are nonneutral, whereas evolutionary permissible (neutral) alleles show much higher EPs. Disease-associated amino acid variants in human populations have EP < 0.05 and are rarely found in the population ( Liu et al. 2016 ). Many human adaptive variants in populations also have low EPs, that is, nonneutral from an evolutionary perspective, but they show high allele frequencies ( Patel et al. 2018 ). Therefore, one may use EP s to diagnose disease mutations and detect candidate adaptive variants. An EP wizard system walks the user through the steps required to set up the analysis. The first sequence in the alignment is used automatically as the focal taxon of interest (one can rearrange sequences in the Sequence Data Explorer ). EP values for all possible bases (4 for nucleotides and 20 for amino acids) at each position in the input sequence alignment are reported in a spreadsheet or text format.” | concepts[10] |
| concept | maximum likelihood — not defined in this paper's own words; the likelihood-based estimation framework for phylogenies — “The Molecular Evolutionary Genetics Analysis (MEGA) software has continuously grown to meet the need for sophisticated evolutionary analysis to discover organismal and genome evolutionary patterns and processes. It was first released in 1993 to offer the statistical methods of molecular evolution through an interactive interface on the Microsoft Disk Operating System (MS-DOS) ( Kumar et al. 1993 ). For more than 25 years, MEGA’s scope and usefulness have grown through the addition of new methods, tools, and interfaces, resulting in modern integrated software for comparative sequence analysis ( Caspermeyer 2018 ). Initially, MEGA contained distance-based and maximum parsimony methods for molecular phylogenetic analysis ( Kumar et al. 1994 ). The data acquisition and integration of major approaches for aligning sequences were introduced to expand MEGA's scope ( Kumar et al. 2004 ). Afterward, the maximum likelihood (ML) methods and Bayesian methods were added for molecular evolutionary analyses ( Tamura et al. 2011 ). MEGA now contains methods for selecting the best-fit substitution model(s), estimating evolutionary distances and divergence times, reconstructing phylogenies, predicting ancestral sequences, testing for selection, and diagnosing disease mutations ( Caspermeyer 2018 ).” | concepts[11] |
| concept | phylogeny — not defined in this paper's own words; the branching tree relating the sequences — “MEGA now contains a facility for detecting autocorrelation of evolutionary rates among branches, which is important for understanding molecular evolution patterns and useful as a clock rate prior in Bayesian relaxed-clock analyses. MEGA implements the CorrTest method developed using machine learning, which is accurate and computationally efficient ( Tao et al. 2019 ). The CorrTest implementation in MEGA requires a phylogeny with sequence alignment (or branch lengths) and is accessed through an easy-to-use wizard. This test's final output is a CorrScore between 0 and 1 and a P -value, where a high CorrScore and low P -value indicates that branch rates among lineages are likely correlated.” | concepts[12] |
| concept | divergence times — not defined in this paper's own words; the estimated times at which lineages split — “Rapid relaxed-clock methods for estimating divergence times are becoming popular because they are feasible and efficient for large contemporary sequence alignments ( Tao, Tamura, Kumar, et al. 2020 ). MEGA6 first added methods and tools for constructing evolutionary timetrees by implementing the RelTime method, which does not assume a molecular clock ( Tamura et al. 2012 , 2013 ). RelTime is known to perform well and has been used to build timetrees in hundreds of research articles ( Tao, Tamura, Kumar, et al. 2020 ). MEGA11 expands on RelTime dating options by advancing the current implementation and adding new facilities for node-dating and tip-dating needed to build timetrees of pathogens, species, and gene families.” | concepts[13] |
| concept | confidence intervals — not defined in this paper's own words; interval estimates accompanying the divergence times — “Bayesian relaxed-clock methods have long allowed the use of statistical probability distributions that capture prior knowledge (or belief) about the true divergence times in clock calibration constraints on one or more nodes in the phylogeny. Judicious use of these probability densities can make divergence times more accurate and precise ( Tao, Tamura, Mello, et al. 2020 ). Researchers can now use such probability densities for node calibrations in RelTime estimation of divergence times and confidence intervals (CIs). MEGA implements the Tao, Tamura, Mello, et al. (2020 ) approach that estimates CIs by simultaneously accounting for variance introduced by the heterogeneity of evolutionary rate among lineages, estimation of sequence divergence using substitution models, and probability densities for node-calibration constraints. This method produces CIs that contain correct times with a high probability, making them much more suitable for biological hypothesis testing than other rapid methods ( Tao, Tamura, Kumar, et al. 2020 ; Tao, Tamura, Mello, et al. 2020 ).” | concepts[14] |
| concept | site configurations — not defined in this paper's own words; repeated alignment columns that share a likelihood value — “ML methods are widely used for phylogenetic inference but place high demands on computer memory, becoming increasingly burdensome for bigger sequence alignments analyzed these days. In MEGA11, we have now completed a long-overdue refactoring of ML calculations by adding a step to identify common site configurations, that is, sites where all sequences have the same bases as at some other sites, to utilize computer memory more efficiently. The memory requirements of Maximum Likelihood and Maximum parsimony analysis are reduced (approximately) by the factor of m / L when there are m distinct site configurations in a sequence alignment containing L sites. The memory saving can be substantial for multigene and genome-scale alignments. For example, the memory saving was 660 MB (209 vs. 870 MB) for a sequence alignment of 229 birds with 2,728 sites ( Claramunt and Cracraft 2015 ) and 4.5 GB (2.3 vs. 6.8 GB) for an alignment of 162 mammals with 11,010 sites ( Meredith et al. 2011 ). This memory saving does not have any detrimental impact on phylogenetic estimates and computational times because identical site configurations have the same likelihood value. The total log-likelihood is simply the sum of site-configuration log-likelihoods weighted by their frequencies. However, this upgrade required refactoring many different parts of MEGA’s calculation engine, including functions for phylogeny construction and model selection.” | concepts[15] |
| concept | meta tag — not defined in this paper's own words; a spatiotemporal or other attribute attached to a sequence, usable as a grouping key — “In MEGA11, we have made a MEGA-wide change to use any meta tag to define groups. For example, if one selects the “Year” meta tag for use as a group, they could estimate average diversity within and between sequences sampled in different years ( Distance menu). In the Sequence Data Explorer , one can select/unselect sequences of certain years for phylogenetic analyses. Also, the display of years would be automatically enabled in the Tree Explorer , and the feature to collapse sequence clusters will be done by years. Additionally, sequences can be sorted based on years in all the input data and result explorer displays. Therefore, a dynamic designation of groups based on the desired meta tag will enable data exploration and analysis more efficiently.” | concepts[16] |
| concept | MEGA-CC — not defined in this paper's own words; the command-line computational core of MEGA — “MEGA’s computational core (MEGA-CC) has undergone extensive refactoring, hardening, and expansion over time. It advanced from 16-bit to 32-bit ( Kumar et al. 2001 ), became multithreaded and incorporated multicore parallelization for various calculations ( Tamura et al. 2013 ), and stepped up to 64-bit architecture ( Kumar et al. 2016 , 2018 ). MEGA-CC was released for use as a command-line program to address the growing need for batch processing of many data sets and integration into analysis workflows ( Kumar et al. 2012 ; Stecher et al. 2020 ). With both 32- and 64-bit versions of MEGA currently available for use on the command-line and GUI, MEGA is now a suite of applications that responds to the variety of computing environments currently used by researchers in molecular evolution and phylogenetics. Here, we present key methodological additions and technical improvements in MEGA that comprise version 11.” | concepts[17] |
| concept | operational taxonomic units — not defined in this paper's own words; the units, besides sequences, that group tags apply to — “MEGA has long supported a “group” tag for sequences and other operational taxonomic units (OTUs). Using the sequence “group” tags, MEGA offered a group-wise exploration of input data, selection of data subsets, and computational analyses ( Kumar 2001 ). Support for two new tags (“population” and “species”) was added in MEGA7, with the species tags used to mark duplicate genes in multigene family phylogenies ( Kumar et al. 2016 ). In MEGA11, sequences can now be tagged to provide information on the continent, country, city, year, month, day, and time. This spatiotemporal information can be used in tip-dating analyses.” | concepts[18] |
| concept | Tree Explorer — not defined in this paper's own words; the interactive viewer for phylogenies and timetrees — “MEGA’s Tree Explorer ( A ) is a feature-rich, versatile viewer of phylogenies that provides many interactive exploration and customization facilities. In MEGA11, the new side toolbar of Tree Explorer makes formatting, rearrangement, and tree exploration tools more accessible and intuitive. Instead of a thin toolbar with nameless buttons, we have opted for a wide toolbar with text labels identifying each tool. The toolbar can be moved to either side of the window, and it can be toggled in and out of view. To organize related tools by groups and accommodate limited vertical space, collapsible panels are used. With the new toolbar, formatting tools previously displayed in external dialogs are readily accessible, and formats are applied instantly instead of after the user closes the external dialog. In addition to the updated toolbar, there are now options for auto-collapsing of nodes containing clusters of taxa belonging to the same group, user-specified cluster size, or by the branch length difference. For very large trees with many similar sequences, this feature can greatly facilitate the visualization of evolutionary events at a glance. An option has been added to export pairwise patristic distances between taxa to a text file for phylogenies and timetrees. For maximum likelihood and maximum parsimony trees where ancestral sequences are present, an option has been added to navigate through sites where a change in the estimated ancestral state differs between the parent and child on the currently selected branch. The tree information box ( B ) has been updated for timetrees to show branch- and node-specific information, such as earliest and latest sample times in the currently selected subtree, days elapsed between the divergence time for a selected node and the latest sample time, the nearest and furthest tip from a selected node, clade size and clade taxa, and spatiotemporal information if available.” | concepts[19] |
Full text — tagged
MEGA11: Molecular Evolutionary Genetics Analysis Version 11 Abstract The Molecular Evolutionary Genetics Analysis (MEGAterm) software has matured to contain a large collection of methods and tools of computational molecular evolution. Here, we describe new additions that make MEGAterm a more comprehensive tool for building timetreesterm of species, pathogens, and gene families using rapid relaxed-clockterm methods. Methods for estimating divergence timesterm and confidence intervalsterm are implemented to use probability densities for calibration constraints for node-datingterm and sequence sampling dates for tip-datingterm analyses. They are supported by new options for tagging sequences with spatiotemporal sampling information, an expanded interactive Node Calibrations Editor , and an extended Tree Explorerterm to display timetreesterm. Also added is a Bayesian method for estimating neutral evolutionary probabilitiesterm of alleles in a species using multispecies sequence alignments and a machine learning method to test for the autocorrelation of evolutionary rates in phylogenies. The computer memory requirements for the maximum likelihoodterm analysis are reduced significantly through reprogramming, and the graphical user interface has been made more responsive and interactive for very big data sets. These enhancements will improve the user experience, quality of results, and the pace of biological discovery. Natively compiled graphical user interface and command-line versions of MEGA11 are available for Microsoft Windows, Linux, and macOS from www.megasoftware.net .
Keywords: software, phylogenetics, timetreesterm, tip datingterm, neutrality Introduction The Molecular Evolutionary Genetics Analysis (MEGAterm) software has continuously grown to meet the need for sophisticated evolutionary analysis to discover organismal and genome evolutionary patterns and processes. It was first released in 1993 to offer the statistical methods of molecular evolution through an interactive interface on the Microsoft Disk Operating System (MS-DOS) ( KumarpersName et al. 1993 ). For more than 25 years, MEGAterm’s scope and usefulness have grown through the addition of new methods, tools, and interfaces, resulting in modern integrated software for comparative sequence analysis ( Caspermeyer 2018 ). Initially, MEGAterm contained distance-based and maximum parsimony methods for molecular phylogenetic analysis ( KumarpersName et al. 1994 ). The data acquisition and integration of major approaches for aligning sequences were introduced to expand MEGAterm's scope ( KumarpersName et al. 2004 ). Afterward, the maximum likelihoodterm (ML) methods and Bayesian methods were added for molecular evolutionary analyses ( TamurapersName et al. 2011 ). MEGAterm now contains methods for selecting the best-fit substitution model(s), estimating evolutionary distances and divergence timesterm, reconstructing phylogenies, predicting ancestral sequences, testing for selection, and diagnosing disease mutations ( Caspermeyer 2018 ).
With every new version, MEGAterm has evolved to harness technological innovations and personal desktops' computational power. MEGAterm’s interface evolved from its initial MS-DOS character-based format ( KumarpersName et al. 1993 ) to a rich graphical user interface (GUI) for Microsoft Windows operating system ( KumarpersName et al. 2001 ). It was then redesigned to become activity-driven ( TamurapersName et al. 2011 ), followed by the incorporation of web technologies to ensure a consistent use-and-feel across Microsoft Windows and Linux operating systems ( KumarpersName et al. 2018 ) and macOS ( StecherpersName et al. 2020 ). MEGAterm GUI is now fully cross-platform running natively on Windows, Linux, and macOS.
MEGAterm’s computational core (MEGA-CCterm) has undergone extensive refactoring, hardening, and expansion over time. It advanced from 16-bit to 32-bit ( KumarpersName et al. 2001 ), became multithreaded and incorporated multicore parallelization for various calculations ( TamurapersName et al. 2013 ), and stepped up to 64-bit architecture ( KumarpersName et al. 2016 , 2018 ). MEGA-CCterm was released for use as a command-line program to address the growing need for batch processing of many data sets and integration into analysis workflows ( KumarpersName et al. 2012 ; StecherpersName et al. 2020 ). With both 32- and 64-bit versions of MEGAterm currently available for use on the command-line and GUI, MEGAterm is now a suite of applications that responds to the variety of computing environments currently used by researchers in molecular evolution and phylogenetics. Here, we present key methodological additions and technical improvements in MEGAterm that comprise version 11.
Methodological Additions Expansion of Relaxed-Clockterm Dating Facilities Rapid relaxed-clockterm methods for estimating divergence timesterm are becoming popular because they are feasible and efficient for large contemporary sequence alignments ( Tao, TamurapersName, KumarpersName, et al. 2020 ). MEGA6 first added methods and tools for constructing evolutionary timetreesterm by implementing the RelTimeterm method, which does not assume a molecular clockterm ( TamurapersName et al. 2012 , 2013 ). RelTimeterm is known to perform well and has been used to build timetreesterm in hundreds of research articles ( Tao, TamurapersName, KumarpersName, et al. 2020 ). MEGA11 expands on RelTimeterm dating options by advancing the current implementation and adding new facilities for node-datingterm and tip-datingterm needed to build timetreesterm of pathogens, species, and gene families.
Calibrating the Clock Using Probability Densities on Node-Constraints Bayesian relaxed-clockterm methods have long allowed the use of statistical probability distributions that capture prior knowledge (or belief) about the true divergence timesterm in clock calibration constraints on one or more nodes in the phylogenyterm. Judicious use of these probability densities can make divergence timesterm more accurate and precise ( Tao, TamurapersName, Mello, et al. 2020 ). Researchers can now use such probability densities for node calibrations in RelTimeterm estimation of divergence timesterm and confidence intervalsterm (CIs). MEGAterm implements the Tao, TamurapersName, Mello, et al. (2020 ) approach that estimates CIs by simultaneously accounting for variance introduced by the heterogeneity of evolutionary rate among lineages, estimation of sequence divergence using substitution models, and probability densities for node-calibration constraints. This method produces CIs that contain correct times with a high probability, making them much more suitable for biological hypothesis testing than other rapid methods ( Tao, TamurapersName, KumarpersName, et al. 2020 ; Tao, TamurapersName, Mello, et al. 2020 ).
For RelTimeterm analyses in MEGA11, ML and distance-based approaches can be used to build a timetreeterm for a given phylogenyterm and multiple sequence alignment. One may also use only a phylogenyterm with branch lengths, which extends the usefulness of relaxed-clockterm methods for phylogenies inferred from nonmolecular data or statistical methodologies not available in MEGAterm. When a phylogenyterm with branch lengths is used, the CIs will be narrower because the variance associated with branch length estimation cannot be generated without the original data set used to produce the phylogenyterm and branch lengths. Nevertheless, these CIs will incorporate variance introduced due to rate variation among lineages and clock calibrations’ uncertainty.
A calibration density selector has been added to the Node Calibration Editor that provides an option to select normal, lognormal, uniform, or exponential density ( fig. 1 ). The user can also specify a minimum or a maximum time bound on a node. The calibration text file format has been extended to specify density information and use calibration densities in MEGA-CCterm. The Node Calibration Editor also includes new functionality to specify a fixed evolutionary rate or a known node time to calibrate the molecular clockterm. Such assumptions are often used by investigators when independent calibration information is unknown ( Hipsley and Müller 2014 ; Tao, TamurapersName, KumarpersName, et al. 2020 ).
Calibration points for MEGAterm ’s RelTimeterm method are chosen in the Node Calibration Editor window ( A ), accessed via the Timetreeterm Wizard system (see fig. 2 A ). The Node Calibration Editor displays the phylogenyterm where individual node calibrations and probability densities can be chosen by clicking the calibration button on the top toolbar for the selected node. A dropdown menu ( B ) with several calibration density types is displayed. The Node Calibration Editor then prompts the user for required distribution parameters, depending on the distribution selected: normal distribution (mean and standard deviation), lognormal (offset, mean and standard deviation), exponential (offset and decay parameter), uniform (min and max) ( C ).
Tip-Datingterm for Sequences with Sampling Times MEGAterm now implements a method to estimate timetreesterm using sampling dates for molecular sequences. They are often used to infer the origin and diversification of pathogens that generally evolve fast enough to track the evolutionary change over months and years ( Tao, TamurapersName, KumarpersName, et al. 2020 ). Tip-datingterm methods are also useful for analyzing ancient molecular sequences. MEGAterm implements a rapid tip-datingterm method, RelTime with Dated Tipsterm (RTDT), that produces divergence timesterm and CIs ( Miura et al. 2018 ). One may use ML or distance-based approaches for a given phylogenyterm and multiple sequence alignment for tip-datingterm, or a phylogenyterm with branch lengths and tip dates can be given as the input.
An enhanced Timetreeterm Wizard system ( fig. 2 ) walks the user through many steps needed to configure tip-datingterm analyses, such as loading sequence and tree files, specifying the outgroups, adding sequence sample times, and selecting the analysis options. Sequence sampling times can be specified in multiple ways. MEGAterm will automatically extract them on-demand when they are included in the sequence name. Spatiotemporal information can also be presented in the input alignment files as meta tagsterm (see description below) or loaded using specially formatted calibration text files. Once computed, the timetreeterm is displayed in the Tree Explorerterm that has been extensively revamped and updated ( fig. 3 ). It now has many more formatting tools, including exporting the timetreeterm, individual divergence timesterm, and CI estimates in a tabular format.
The Tip Datingterm Wizard ( A ) guides the user through the steps required to set up the RTDT analysis. Once a sequence alignment and/or a tree is provided, the user is prompted to specify the outgroup by selecting a node in the Tree Explorerterm or specifying outgroup taxa by name (not shown). Next, sample times are specified using the Tip Dates Editor ( B ) with facilities for parsing tip dates ( C ) encoded in taxa names, importing tip dates from a text file, and manually entering the dates. In the next step, the Analysis Preferences dialog (not shown) is displayed, allowing the user to set analysis options to estimate branch lengths used by RTDT. The estimated timetreeterm is displayed in the Tree Explorerterm (see fig. 3 ).
MEGAterm’s Tree Explorerterm ( A ) is a feature-rich, versatile viewer of phylogenies that provides many interactive exploration and customization facilities. In MEGA11, the new side toolbar of Tree Explorerterm makes formatting, rearrangement, and tree exploration tools more accessible and intuitive. Instead of a thin toolbar with nameless buttons, we have opted for a wide toolbar with text labels identifying each tool. The toolbar can be moved to either side of the window, and it can be toggled in and out of view. To organize related tools by groups and accommodate limited vertical space, collapsible panels are used. With the new toolbar, formatting tools previously displayed in external dialogs are readily accessible, and formats are applied instantly instead of after the user closes the external dialog. In addition to the updated toolbar, there are now options for auto-collapsing of nodes containing clusters of taxa belonging to the same group, user-specified cluster size, or by the branch length difference. For very large trees with many similar sequences, this feature can greatly facilitate the visualization of evolutionary events at a glance. An option has been added to export pairwise patristic distances between taxa to a text file for phylogenies and timetreesterm. For maximum likelihoodterm and maximum parsimony trees where ancestral sequences are present, an option has been added to navigate through sites where a change in the estimated ancestral state differs between the parent and child on the currently selected branch. The tree information box ( B ) has been updated for timetreesterm to show branch- and node-specific information, such as earliest and latest sample times in the currently selected subtree, days elapsed between the divergence timeterm for a selected node and the latest sample time, the nearest and furthest tip from a selected node, clade size and clade taxa, and spatiotemporal information if available.
Detecting Autocorrelation of Evolutionary Rates MEGAterm now contains a facility for detecting autocorrelation of evolutionary rates among branches, which is important for understanding molecular evolution patterns and useful as a clock rate prior in Bayesian relaxed-clockterm analyses. MEGAterm implements the CorrTestterm method developed using machine learning, which is accurate and computationally efficient ( Tao et al. 2019 ). The CorrTestterm implementation in MEGAterm requires a phylogenyterm with sequence alignment (or branch lengths) and is accessed through an easy-to-use wizard. This test's final output is a CorrScoreterm·var between 0 and 1 and a P -value, where a high CorrScoreterm·var and low P -value indicates that branch rates among lineages are likely correlated.
Calculating Neutral Evolutionary Probabilitiesterm According to the neutral theory of molecular evolutionterm, most differences in molecular sequences across species are expected to have little to no impact on fitness ( Kimura 1983 ). Therefore, multispecies sequence alignments have been used to estimate neutral evolutionary probabilitiesterm ( EP ) of observing alternative alleles (amino acid residues or nucleotides) in a species, contingent on the given species timetreeterm ( Liu et al. 2016 ). MEGAterm implements an advanced option for this Bayesian approach in which the species timetreeterm containing relative times is computed automatically by using RelTimeterm ( Patel and KumarpersName 2019 ). Alleles with EP less than 0.05 are nonneutral, whereas evolutionary permissible (neutral) alleles show much higher EPs. Disease-associated amino acid variants in human populations have EP < 0.05 and are rarely found in the population ( Liu et al. 2016 ). Many human adaptive variants in populations also have low EPs, that is, nonneutral from an evolutionary perspective, but they show high allele frequencies ( Patel et al. 2018 ). Therefore, one may use EP s to diagnose disease mutations and detect candidate adaptive variants. An EP wizard system walks the user through the steps required to set up the analysis. The first sequence in the alignment is used automatically as the focal taxon of interest (one can rearrange sequences in the Sequence Data Explorer ). EP values for all possible bases (4 for nucleotides and 20 for amino acids) at each position in the input sequence alignment are reported in a spreadsheet or text format.
Technological Advances Although some new user interface elements have already been mentioned above ( figs. 1–3 ), additional technical advances in MEGA11 are as follows.
Expanded Group Designations MEGAterm has long supported a “group” tag for sequences and other operational taxonomic unitsterm (OTUs). Using the sequence “group” tags, MEGAterm offered a group-wise exploration of input data, selection of data subsets, and computational analyses ( KumarpersName 2001 ). Support for two new tags (“population” and “species”) was added in MEGA7, with the species tags used to mark duplicate genes in multigene family phylogenies ( KumarpersName et al. 2016 ). In MEGA11, sequences can now be tagged to provide information on the continent, country, city, year, month, day, and time. This spatiotemporal information can be used in tip-datingterm analyses.
In MEGA11, we have made a MEGA-wide change to use any meta tagterm to define groups. For example, if one selects the “Year” meta tagterm for use as a group, they could estimate average diversity within and between sequences sampled in different years ( Distance menu). In the Sequence Data Explorer , one can select/unselect sequences of certain years for phylogenetic analyses. Also, the display of years would be automatically enabled in the Tree Explorerterm , and the feature to collapse sequence clusters will be done by years. Additionally, sequences can be sorted based on years in all the input data and result explorer displays. Therefore, a dynamic designation of groups based on the desired meta tagterm will enable data exploration and analysis more efficiently.
Memory Efficient ML Analyses ML methods are widely used for phylogenetic inference but place high demands on computer memory, becoming increasingly burdensome for bigger sequence alignments analyzed these days. In MEGA11, we have now completed a long-overdue refactoring of ML calculations by adding a step to identify common site configurationsterm, that is, sites where all sequences have the same bases as at some other sites, to utilize computer memory more efficiently. The memory requirements of Maximum Likelihoodterm and Maximum parsimony analysis are reduced (approximately) by the factor of m / L when there are m distinct site configurationsterm in a sequence alignment containing L sites. The memory saving can be substantial for multigene and genome-scale alignments. For example, the memory saving was 660 MB (209 vs. 870 MB) for a sequence alignment of 229 birds with 2,728 sites ( Claramunt and Cracraft 2015 ) and 4.5 GB (2.3 vs. 6.8 GB) for an alignment of 162 mammals with 11,010 sites ( Meredith et al. 2011 ). This memory saving does not have any detrimental impact on phylogenetic estimates and computational times because identical site configurationsterm have the same likelihood value. The total log-likelihood is simply the sum of site-configuration log-likelihoods weighted by their frequencies. However, this upgrade required refactoring many different parts of MEGAterm’s calculation engine, including functions for phylogenyterm construction and model selection.
Enhanced GUI for Exploring Large Data Sets Using a large multiple sequence alignment containing 68,000 genomes and 30,000 bases each, we assessed MEGAterm GUI's responsiveness during input data file reading, execution of functions in the Sequence Data Explorer , estimation of pairwise distances, and building of distance-based phylogenies. We found the GUI to become intermittently unresponsive for such large data sets, which are now common due to resequencing and population sequencing efforts. Consequently, we have moved all potentially long-running operations out of the main GUI thread to background threads in a major overhaul of the source code. Now, large input data files are read rapidly, and calculations of pairwise distance matrices, selection tests, and phylogenyterm construction for distance-based methods are performed in a background thread. The Sequence Data Explorer has been reprogrammed to enable more efficient highlighting of variable sites, and navigation of the sequence alignment has been improved. Also added are options to automatically label sites based on attributes, which annotates sites by providing a one-character label and then using desired labeled sites to subset data for any molecular phylogenetic analysis desired.
Conclusions Version 11 of MEGAterm adds many methods and tools to keep pace with researchers' growing needs. The addition of evolutionary dating methods in MEGAterm make it easier to estimate species and strain divergence timesterm by using more informative node calibrations and sampling times. The new CorrTestterm and EP calculations will enable a more robust evaluation of assumptions about biological characteristics of molecular data. The reduction in memory needs of ML-based computations will allow users to analyze much larger data sets than before. The refactoring of distance-based methods’ calculation to run in threads independent of the main graphical interface and other GUI enhancements greatly improve MEGAterm usability for very large data sets.
Acknowledgments We thank our laboratory members and many beta testers for providing invaluable feedback and bug reports. This study was supported in part by research grants from the National Institutes of Health (R35GM139504-01), National Science Foundation (DEB-2034228, DBI-1661218), and Japan Society for the Promotion of Science (JSPS) grants-in-aid for scientific research (DB5) to K.T.
Data Availability The software and its source code are available from www.megasoftware.net .
References Associated Data Data Availability Statement The software and its source code are available from www.megasoftware.net .