standOff. When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote shown on the same row, so the borrowing can be checked against the thing borrowed from without leaving this page. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.| relation to other work | claim_relations | how this paper stands to another: agrees, contradicts, extends, qualifies |
| claim type | claim_type | the shape of the claim: existence, mechanism, method, critique, prediction, null |
| concept edge | concept_edges | an asserted relation between two concepts, with the sentence asserting it |
| concept | concepts | an idea the paper works with, quoted where the paper uses it |
| content tag | content_tags | a finding, definition, quantity, statistic or variable named in the paper |
| contribution | contribution | what the paper claims to add |
| control | controls | an experiment run to eliminate a rival explanation |
| limitation | limitations | what the paper says its evidence cannot support |
| mechanism | mechanisms | how the reported effect is said to come about |
| method | method | how the paper produced its evidence |
| object of study | object_of_study | what the paper is about — the thing it investigates |
| phenomenon | phenomenon | the effect or regularity under study |
| research question | research_question | the question the paper sets itself |
| sense | senses | a distinct use of a term, quoted where the paper fixes it |
| unrecordable relation | unhoused_relations | a relation the paper asserts that this vocabulary cannot express, with the reason |
The reference list is not part of the text we hold for this paper. Our copy was extracted from the publisher's page and the bibliography did not survive that extraction, so the article below ends before its references. Nothing has been removed by us.
eggNOG-mapper v2: Functional Annotation, Orthology Assignments, and Domain Prediction at the Metagenomic Scale
Abstract
object_of_studycontributionclaim_typelimitationsconcepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗sensesclaim_relationscontent_tagscontent_tags↗Even though automated functional annotation of genes represents a fundamental step in most genomic and metagenomic workflows, it remains challenging at large scales. Here, we describe a major upgrade to eggNOG-mapper, a tool for functional annotation based on precomputed orthology assignments, now optimized for vast (meta)genomic data sets. Improvements in version 2 include a full update of both the genomes and functional databases to those from eggNOG v5, as well as several efficiency enhancements and new features. Most notably, eggNOG-mapper v2 now allows for: 1) de novo gene prediction from raw contigs, 2) built-in pairwise orthology prediction, 3) fast protein domain discovery, and 4) automated GFF decoration. eggNOG-mapper v2 is available as a standalone tool or as an online service at http://eggnog-mapper.embl.de .
Introduction
research_questionphenomenonmechanisms↗limitationsconcepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗unhoused_relationsunhoused_relationssensesconcept_edgesconcept_edgesInference of gene function via orthology, rather than by homology detection, is generally considered the most reliable approach for transferring functional information between molecular sequences, as orthologs are expected to retain function more often than paralogs ( Gabaldón and Koonin 2013 ; Glover et al. 2019 ). However, since delineating orthology is highly demanding (both computationally and algorithmically), most automated methods rely on homology-based annotations ( Götz et al. 2008 ; Seemann 2014 ; Blum et al. 2021 ). EggNOG-mapper relies on the eggNOG database ( Huerta-Cepas et al. 2019 ) of orthologs groups (OGs), covering thousands of bacterial, archaeal, and eukaryotic organisms. For this, it takes advantage of the precomputed phylogenies inferred for each OG to efficiently refine orthology assignments and therefore minimize the transferring of annotations from putative in-paralogs. The method was originally proven to provide more accurate predictions than homology-based approaches ( Huerta-Cepas et al. 2017 ), while preserving computational performance at the genomic and metagenomic scale. Here, we present eggNOG-mapper v2, a major upgrade featuring improvements in annotation coverage, overall performance, and program capabilities ( fig. 1 ).
mechanisms↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗sensesclaim_relationsWorkflow and new features of eggNOG-mapper v2. ( A ) The gene prediction stage uses Prodigal to perform protein prediction from assembled contigs. ( B ) During the search stage, HMMER3, Diamond, or MMseqs2 can be used to align the input proteins to eggNOG v5. ( C ) During the orthology inference stage, a report of orthologs is generated based on the desired taxonomic scope. ( D ) Finally, protein annotations and domains are transferred from orthologs to the queries and reported as tabular and GFF files.
Coverage and Performance Improvements
mechanisms↗controlscontrolsconcepts↗concepts↗concepts↗concepts↗concept_edgesconcept_edgescontent_tagscontent_tagscontent_tagscontent_tagscontent_tagscontent_tagscontent_tagscontent_tags↗content_tags↗The underlying genome database has been updated to be in sync with eggNOG v5, spanning 4.4 million OGs and more than twice the number of organisms than in the previous version. This improvement increases annotation coverage and phylogenetic resolution, particularly noticeable when analyzing large metagenomic data sets. For instance, the reannotation of 1.75 million proteins randomly subsampled from a human-gut metagenomic gene catalog ( Almeida et al. 2021 ) yielded a 3.23% increase in annotation coverage (56,569 newly annotated proteins), compared with eggNOG-mapper version 1. The phylogenetic resolution was also improved, obtaining significantly better alignment scores for the query sequences than previous versions (Wilcoxon test W = 1.2E + 12, P -value < 2.2E − 16). mechanisms↗concepts↗concepts↗concepts↗concepts↗concepts↗unhoused_relationsunhoused_relationssensesconcept_edgescontent_tagscontent_tagscontent_tagscontent_tags↗content_tagscontent_tagscontent_tags↗Moreover, although the underlying databases have doubled in size, eggNOG-mapper v2 improves the annotation rate (annotated queries per second) by 16% on average, compared with previous versions. The most important changes regarding computational enhancements relate to database optimizations, allowing for faster queries and parallelization, and a new memory-based mode that significantly reduces the impact of slow I/O disk operations. Taken together, these changes improve annotation rates by 608% on average, with respect to eggNOG-mapper v1 ( fig. 2 A ). Compared with Prokka ( Seemann 2014 ), one of the fastest annotation tools available for prokaryotic genomes according to recent benchmarks ( Shaffer et al. 2020 ), eggNOG-mapper runs faster, especially on large metagenomic data sets ( fig. 2 B ).
method↗controlscontrolscontent_tags↗content_tags↗content_tags↗Performance of eggNOG-mapper v2. ( A ) average minutes to annotate input proteomes. EggNOG-mapper v2 (blue) against eggNOG-mapper v1 (red). ( B ) average minutes to annotate input genomes. EggNOG-mapper v2 (blue) against Prokka (green). ( C ) average minutes (in log scale) to annotate input proteins. MMseqs2 (-s 2,4,6; black) against Diamond (iterate/sensitive mode; orange). ( D ) Specificity (Sp), recall (Re), and F 1 score, of PFAM domain annotation either from direct transference from orthologs, or after realignment. Full de novo realignment results were used as reference. ( E ) average minutes for PFAM domain annotation, using either PFAM full de novo (brown) or realign to orthologs domains (blue) modes. Benchmark setup: tests in ( A ) and ( B ) were done on 20 sets of 1–100 random proteomes ( A ) or genomes ( B ) from ( Almeida et al. 2021 ), and executed using 10 CPUs and 80 GB of RAM. Tests in ( C ) were done on 35 random sets of 10–10,000,000 proteins from Progenomes v2 ( Mende et al. 2020 ), using 30 CPUs and 240 GB of RAM. Tests in ( Dm ) and ( E ) as in ( C ), only for sets of 10–100,000 proteins.
ORF Prediction
mechanisms↗limitationsconcepts↗concepts↗concepts↗unhoused_relationsAnother major capability added to the new eggNOG-mapper workflow is predicting ORFs directly from assembled contigs ( fig. 1 A ). ORF detection, only available for prokaryotic assemblies, is performed using Prodigal ( Hyatt et al. 2010 ), which provides the protein sequences to be used by eggNOG-mapper for functional annotation. Prodigal modes (“normal,” “anonymous,” and “training”) as well as custom translation tables can be further chosen by the user.
Sequence Mapping Modes
mechanisms↗concepts↗concepts↗concepts↗sensesconcept_edgescontent_tagscontent_tagscontent_tagscontent_tagscontent_tagsAdditionally, we have broadened the options for the initial sequence-mapping step carried out by eggNOG-mapper ( fig. 1 B ). Now, Diamond, MMseqs2, and HMMER3 ( Mistry et al. 2013 ) modes are available, each recommended for different use cases. The default Diamond mode provides the best balance between speed and memory consumption. EggNOG-mapper v2 also implements the new Diamond mode ‘–iterate’, which iteratively applies different levels of sensitivity. Using the new –iterate mode, Diamond is twice as fast as MMseqs2 for query sets of 1,000,000, and three times faster for 10,000,000 sequences. The MMseqs2 mode provides faster results than Diamond ( fig. 2 C ) for query sets up to 100,000 sequences for comparable sensitivity. mechanisms↗limitationslimitationsconcepts↗concepts↗concepts↗concepts↗concepts↗concepts↗When input data are nucleotide sequences, a direct translation is done assuming they represent coding sequences starting in an open reading frame. Alternatively, both Diamond and MMseqs2 can be executed in blastx-like mode, particularly useful when using sequencing reads as input data. For the annotation of long, error-prone sequencing reads, the frameshift option of Diamond can also be enabled. The HMMER3 mode is significantly slower than the other two and requires heavy databases to be downloaded. However, HMM-based searches might aid in the annotation of sequences for which distant homology relationships against the eggNOG v5 OGs cannot be detected by Diamond and MMseqs modes.
Adjusting Taxonomic Scopes
mechanisms↗concepts↗concepts↗concepts↗concepts↗concepts↗unhoused_relationsunhoused_relationssensescontent_tagscontent_tags↗content_tags↗content_tags↗Another new feature now available with eggNOG-mapper v2 is the possibility of creating custom annotation databases constrained to specific taxonomic groups. For instance, users could easily create databases spanning only their domain or phylum of interest, therefore reducing computational times of subsequent annotation jobs. Moreover, the new version provides enhanced options to control the taxonomic scope ( fig. 1 C ) used for transferring functional annotations, which can be adjusted from automatic mode (recommended for mixed metagenomic data sets) to lineage-specific scopes (preventing transferring functional terms from orthologs of unwanted lineages).
Orthology Reports
limitationsconcepts↗concepts↗concepts↗sensescontent_tags↗content_tags↗Taking advantage of the rapid orthology assignments performed by eggNOG-mapper, it is now possible to report pairwise orthology relationships for each query against any of the genomes covered by eggNOG v5 ( fig. 1 C ). Although this feature is not intended to substitute more precise orthology prediction methods, it provides a very quick and simple “first-pass” approach to obtain pairwise relationships between query sequences and all eggNOG v5 organisms. Orthology reports can be further adjusted by specifying the target taxa and the type of orthologs to be reported (i.e., one-to-one, many-to-many).
Annotation Sources
concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗In order to provide an integrated report of functional annotations per query, eggNOG-mapper v2 offers new annotation sources and improved reports ( fig. 1 D ). The functional annotation sources, which provide different levels of coverage ( supplementary fig. S1 , Supplementary Material online), are: predicted protein name; KEGG pathways, modules, and orthologs ( Kanehisa et al. 2017 ); Gene Ontology labels ( Gene Ontology Consortium 2018 ); EC numbers, BiGG reactions ( Norsigian et al. 2020 ); CAZy terms ( Lombard et al. 2014 ); COG functional categories ( Tatusov et al. 2000 ); eggNOG OGs; and free text descriptions at all taxonomic levels. Reports are generated in tab-delimited and/or XLSX file formats. Moreover, when ORF prediction mode is enabled, proteins used to annotate are reported in FASTA format, together with a functionally decorated GFF file. Alternatively, eggNOG-mapper annotation reports can be used to decorate any custom GFF file.
Protein Domain Annotations
mechanisms↗mechanisms↗limitationsconcepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗concepts↗unhoused_relationssensessensesconcept_edgescontent_tagscontent_tagscontent_tagscontent_tagsAlong with the functional terms annotated per query, this new version of eggNOG-mapper provides PFAM ( Mistry et al. 2021 ) and SMART ( Letunic et al. 2021 ) protein domain predictions. PFAM domain annotations are by default transferred from the inferred orthologs with very little impact on computational cost, but also with a small proportion of false-positive and negative predictions (F1 score 89.7%, fig. 2 D ). Optionally, de novo PFAM domain annotation is also available at large scales, both as a refinement phase for the orthology-based predictions (thus keeping the computational cost very low, while eliminating the risk of false positives; F 1 score 98.9%, fig. 2 D ) or by full computation (obtaining native results independent from orthology predictions). When using the de novo approach, HMMER3 searches are executed using in-memory mode for higher efficiency. Moreover, GA-based thresholds and PFAM clan disambiguation are automatically applied. Performance comparisons between the different modes are shown in figure 2 E.
Features Comparison with Other Functional Annotation Tools
Although accuracy and performance are the main goal of eggNOG-mapper v2, other practical differences might motivate its use, or not, compared with alternative software. Those practical issues include the type of input and output data, the functional sources used to annotate query sequences, and the target taxonomic scope of each program.
concepts↗concepts↗EggNOG-mapper is tightly bound to the eggNOG database, which covers a wide range of prokaryotic and eukaryotic organisms, and provides normalized functional annotations from multiple sources (see previous sections). As eggNOG is based on a curated selection of representative species spanning the whole tree of life, it provides a broad annotation coverage while keeping redundancy low. Other tools use smaller reference databases (e.g., SwissProt [ UniProt Consortium 2021 ]) for general functional annotations but incorporate niche-specific functional databases for specific purposes. For instance, Prokka is suited for the annotation for prokaryotic-only genomes and includes the detection of tRNAs, transposases and signal peptides (which are not specifically targeted by eggNOG-mapper). DRAM ( Shaffer et al. 2020 ) and MicrobiomeAnnotator ( Ruiz-Perez et al. 2021 ) use broad annotation sources for microbial data sets such as KOFam/KEGG classification. Both provide optional mappings to large protein reference databases comparable to eggNOG (e.g., trEMBL or Uniref90), but at the cost of more computational resources and time ( Ruiz-Perez et al. 2021 ). In the case of DRAM, additional genomic features can be annotated, including rRNAs, tRNAs, peptidases, and carbohydrate-active enzymes. Mantis ( Queirós et al. 2021 ) and InterProScan allow users to annotate sequences based on multiple HMM-based database sources, including eggNOG, PFAM, or PANTHER ( Mi et al. 2010 ).
limitationsconcepts↗concepts↗unhoused_relationssensesOn the other hand, this new version of eggNOG-mapper is particularly focused on improving the annotation process at the metagenomic scale. For instance, it incorporates the possibility of using Prodigal for the automatic prediction of open reading frames (ORFs) out of raw contigs. Those options are not commonly available in other annotation tools, with only Prokka and DRAM providing similar capabilities. Moreover, eggNOG-mapper v2 allows for the direct annotation of reads via blastx-like searches, which is not a recommended mode in terms of accuracy, but it offers the possibility of obtaining functional profiles of metagenomic samples before assembling.
concepts↗concepts↗content_tagsRegarding outputs, eggNOG-mapper is optimized for large-scale annotation jobs, producing GFF files decorated with functional annotations, tab-delimited files with the predicted functional terms per query, orthology assignment tables and taxonomic placements. Other tools provide similar output files (except for the orthology and taxonomic predictions), with only DRAM and MicrobiomeAnnotator providing further statistics and graphical outputs summarizing the results. Notably, Prokka can be set to produce Genbank and Sequin files compliant with the Genbank/ENA/DDJB format, thus facilitating the uploading of annotated genomes to public databases.
Conclusions
claim_relationsOverall, eggNOG-mapper v2 provides a more efficient, versatile, and scalable automated functional annotation workflow than its predecessor. Standalone versions are available at GitHub ( https://github.com/eggnogdb/eggnog-mapper ), together with extensive documentation and usage examples ( https://github.com/eggnogdb/eggnog-mapper/wiki ). For convenience, an online service for the annotation of large genomic and metagenomic data sets is also available at http://eggnog-mapper.embl.de .
Supplementary Material
Supplementary data are available at Molecular Biology and Evolution online.
Supplementary Material
Click here for additional data file.
Relations on this page carry a quotation for the claim. Where a relation was separately evidenced — and so tested against the vocabulary for its type — the row says so; 7 of 7 relations here carry that mark. The rest are the tagger’s reading of the sentence quoted beside them.
| facet | tag | field | conf. | whose words |
|---|---|---|---|---|
| object_of_study | the coverage, performance, workflow, and metagenomic-scale capabilities of eggNOG-mapper v2 | object_of_study | the paper's words | |
| research_question | how to make orthology-based automated functional annotation more scalable, versatile, and accurate for large genomic and metagenomic data sets | research_question | the paper's words | |
| contribution | eggNOG-mapper v2, with eggNOG v5 coverage, faster mapping, contig-level gene prediction, pairwise orthology, domain prediction, and GFF decoration | contribution | the paper's words | |
| claim_type | method | claim_type | the paper's words | |
| phenomenon | large-scale transfer of gene functions and protein-domain annotations through taxonomically refined orthology | phenomenon | the paper's words | |
| method | system-or-tool-building also in: system-or-tool-building | method | the paper's words | |
| mechanisms | Precomputed OG phylogenies refine orthology assignments and limit transfer from putative in-paralogs. also in: phylogeny-refined orthology transfer | mechanisms[0] | 0.95 | the tagger's words |
| mechanisms | Prodigal prediction is followed by sequence search, taxonomically scoped orthology inference, and annotation/domain transfer. also in: four-stage workflow | mechanisms[1] | 0.95 | the tagger's words |
| mechanisms | Synchronization with eggNOG v5 expands organism and orthologous-group coverage and phylogenetic resolution. also in: database expansion | mechanisms[2] | 0.95 | the tagger's words |
| mechanisms | A memory-based mode reduces the impact of slow disk I/O on annotation rate. also in: memory-resident querying | mechanisms[3] | 0.95 | the tagger's words |
| mechanisms | Prodigal turns prokaryotic assembled contigs into protein sequences for annotation. also in: Prodigal ORF prediction | mechanisms[4] | 0.95 | the tagger's words |
| mechanisms | Diamond's iterate mode applies successive sensitivity levels to improve search efficiency. also in: iterative Diamond search | mechanisms[5] | 0.95 | the tagger's words |
| mechanisms | Diamond or MMseqs2 maps nucleotide sequencing reads without requiring prior assembly. also in: blastx-like mapping | mechanisms[6] | 0.95 | the tagger's words |
| mechanisms | Automatic or lineage-specific scopes limit the orthologs permitted to transfer annotations. also in: taxonomic-scope filtering | mechanisms[7] | 0.95 | the tagger's words |
| mechanisms | PFAM annotations are transferred from inferred orthologs at little computational cost. also in: ortholog-domain transfer | mechanisms[8] | 0.95 | the tagger's words |
| mechanisms | HMMER3 realignment refines transferred domains and applies GA thresholds and clan disambiguation. also in: PFAM realignment refinement | mechanisms[9] | 0.95 | the tagger's words |
| limitations | Automated gene-function annotation remains challenging at large genomic and metagenomic scales. | limitations[0] | 0.95 | the tagger's words |
| limitations | Orthology delineation is computationally and algorithmically demanding, so many tools rely on homology instead. | limitations[1] | 0.95 | the tagger's words |
| limitations | ORF detection is available only for prokaryotic assemblies. | limitations[2] | 0.95 | the tagger's words |
| limitations | Direct nucleotide translation assumes coding sequences begin in an open reading frame. | limitations[3] | 0.95 | the tagger's words |
| limitations | HMMER3 is substantially slower than Diamond and MMseqs2 and requires large local databases. | limitations[4] | 0.95 | the tagger's words |
| limitations | Pairwise orthology reports are a first-pass result and are not intended to replace more precise methods. | limitations[5] | 0.95 | the tagger's words |
| limitations | Direct ortholog transfer of PFAM domains has residual false-positive and false-negative predictions. | limitations[6] | 0.95 | the tagger's words |
| limitations | Direct blastx-like annotation of reads is available but is not recommended in terms of accuracy. | limitations[7] | 0.95 | the tagger's words |
| controls | reannotation of 1.75 million randomly subsampled human-gut proteins eliminates: coverage gains caused only by a small hand-selected test set · outcome: held | controls[0] | 0.95 | the tagger's words |
| controls | Wilcoxon comparison of query alignment scores eliminates: treating apparent phylogenetic-resolution improvement as an untested visual difference · outcome: held | controls[1] | 0.95 | the tagger's words |
| controls | full de novo PFAM realignment used as the reference eliminates: evaluating transferred-domain accuracy without an independent computational reference · outcome: held | controls[2] | 0.95 | the tagger's words |
| controls | random proteome, genome, and protein sets with stated CPU and RAM allocations eliminates: unreported scale and compute conditions in runtime comparisons | controls[3] | 0.95 | the tagger's words |
| concepts | automated functional annotation of genes | concepts[0] | 0.95 | the paper's words |
| concepts | genomic and metagenomic workflows also in: genomic and metagenomic workflows | concepts[1] | 0.95 | the paper's words |
| concepts | eggNOG-mapper also in: eggNOG-mapper | concepts[2] | 0.95 | the paper's words |
| concepts | tool also in: tool | concepts[3] | 0.95 | the paper's words |
| concepts | precomputed orthology assignments also in: precomputed orthology assignments | concepts[4] | 0.95 | the paper's words |
| concepts | de novo gene prediction also in: de novo gene prediction | concepts[5] | 0.95 | the paper's words |
| concepts | pairwise orthology prediction also in: pairwise orthology prediction | concepts[6] | 0.95 | the paper's words |
| concepts | protein domain discovery also in: protein domain discovery | concepts[7] | 0.95 | the paper's words |
| concepts | automated GFF decoration also in: automated GFF decoration | concepts[8] | 0.95 | the paper's words |
| concepts | orthology also in: orthology | concepts[9] | 0.95 | the paper's words |
| concepts | homology detection also in: homology detection | concepts[10] | 0.95 | the paper's words |
| concepts | functional information also in: functional information | concepts[11] | 0.95 | the paper's words |
| concepts | orthologs also in: orthologs | concepts[12] | 0.95 | the paper's words |
| concepts | paralogs also in: paralogs | concepts[13] | 0.95 | the paper's words |
| concepts | eggNOG database also in: eggNOG database | concepts[14] | 0.95 | the paper's words |
| concepts | orthologs groups also in: orthologs groups | concepts[15] | 0.95 | the paper's words |
| concepts | precomputed phylogenies also in: precomputed phylogenies | concepts[16] | 0.95 | the paper's words |
| concepts | putative in-paralogs also in: putative in-paralogs | concepts[17] | 0.95 | the paper's words |
| concepts | homology-based approaches also in: homology-based approaches | concepts[18] | 0.95 | the paper's words |
| concepts | gene prediction stage also in: gene prediction stage | concepts[19] | 0.95 | the paper's words |
| concepts | Prodigal also in: Prodigal | concepts[20] | 0.95 | the paper's words |
| concepts | protein prediction also in: protein prediction | concepts[21] | 0.95 | the paper's words |
| concepts | assembled contigs also in: assembled contigs | concepts[22] | 0.95 | the paper's words |
| concepts | HMMER3 also in: HMMER3 | concepts[23] | 0.95 | the paper's words |
| concepts | Diamond also in: Diamond | concepts[24] | 0.95 | the paper's words |
| concepts | MMseqs2 also in: MMseqs2 | concepts[25] | 0.95 | the paper's words |
| concepts | eggNOG v5 also in: eggNOG v5 | concepts[26] | 0.95 | the paper's words |
| concepts | orthology inference stage also in: orthology inference stage | concepts[27] | 0.95 | the paper's words |
| concepts | protein annotations and domains also in: protein annotations and domains | concepts[28] | 0.95 | the paper's words |
| concepts | tabular and GFF files also in: tabular and GFF files | concepts[29] | 0.95 | the paper's words |
| concepts | underlying genome database also in: underlying genome database | concepts[30] | 0.95 | the paper's words |
| concepts | annotation coverage also in: annotation coverage | concepts[31] | 0.95 | the paper's words |
| concepts | phylogenetic resolution also in: phylogenetic resolution | concepts[32] | 0.95 | the paper's words |
| concepts | alignment scores also in: alignment scores | concepts[33] | 0.95 | the paper's words |
| concepts | annotation rate also in: annotation rate | concepts[34] | 0.95 | the paper's words |
| concepts | database optimizations also in: database optimizations | concepts[35] | 0.95 | the paper's words |
| concepts | memory-based mode also in: memory-based mode | concepts[36] | 0.95 | the paper's words |
| concepts | slow I/O disk operations also in: slow I/O disk operations | concepts[37] | 0.95 | the paper's words |
| concepts | Prokka also in: Prokka | concepts[38] | 0.95 | the paper's words |
| concepts | ORF detection also in: ORF detection | concepts[39] | 0.95 | the paper's words |
| concepts | protein sequences also in: protein sequences | concepts[40] | 0.95 | the paper's words |
| concepts | custom translation tables also in: custom translation tables | concepts[41] | 0.95 | the paper's words |
| concepts | sequence-mapping step also in: sequence-mapping step | concepts[42] | 0.95 | the paper's words |
| concepts | Diamond mode also in: Diamond mode | concepts[43] | 0.95 | the paper's words |
| concepts | MMseqs2 mode also in: MMseqs2 mode | concepts[44] | 0.95 | the paper's words |
| concepts | direct translation also in: direct translation | concepts[45] | 0.95 | the paper's words |
| concepts | blastx-like mode also in: blastx-like mode | concepts[46] | 0.95 | the paper's words |
| concepts | sequencing reads also in: sequencing reads | concepts[47] | 0.95 | the paper's words |
| concepts | frameshift option also in: frameshift option | concepts[48] | 0.95 | the paper's words |
| concepts | HMM-based searches also in: HMM-based searches | concepts[49] | 0.95 | the paper's words |
| concepts | distant homology relationships also in: distant homology relationships | concepts[50] | 0.95 | the paper's words |
| concepts | custom annotation databases also in: custom annotation databases | concepts[51] | 0.95 | the paper's words |
| concepts | specific taxonomic groups also in: specific taxonomic groups | concepts[52] | 0.95 | the paper's words |
| concepts | taxonomic scope also in: taxonomic scope | concepts[53] | 0.95 | the paper's words |
| concepts | automatic mode also in: automatic mode | concepts[54] | 0.95 | the paper's words |
| concepts | lineage-specific scopes also in: lineage-specific scopes | concepts[55] | 0.95 | the paper's words |
| concepts | pairwise orthology relationships also in: pairwise orthology relationships | concepts[56] | 0.95 | the paper's words |
| concepts | first-pass also in: first-pass | concepts[57] | 0.95 | the paper's words |
| concepts | target taxa also in: target taxa | concepts[58] | 0.95 | the paper's words |
| concepts | functional annotation sources also in: functional annotation sources | concepts[59] | 0.95 | the paper's words |
| concepts | Gene Ontology labels also in: Gene Ontology labels | concepts[60] | 0.95 | the paper's words |
| concepts | EC numbers also in: EC numbers | concepts[61] | 0.95 | the paper's words |
| concepts | BiGG reactions also in: BiGG reactions | concepts[62] | 0.95 | the paper's words |
| concepts | CAZy terms also in: CAZy terms | concepts[63] | 0.95 | the paper's words |
| concepts | COG functional categories also in: COG functional categories | concepts[64] | 0.95 | the paper's words |
| concepts | GFF file also in: GFF file | concepts[65] | 0.95 | the paper's words |
| concepts | PFAM also in: PFAM | concepts[66] | 0.95 | the paper's words |
| concepts | SMART also in: SMART | concepts[67] | 0.95 | the paper's words |
| concepts | PFAM domain annotations also in: PFAM domain annotations | concepts[68] | 0.95 | the paper's words |
| concepts | orthology-based predictions also in: orthology-based predictions | concepts[69] | 0.95 | the paper's words |
| concepts | false-positive and negative predictions | concepts[70] | 0.95 | the paper's words |
| concepts | de novo PFAM domain annotation also in: de novo PFAM domain annotation | concepts[71] | 0.95 | the paper's words |
| concepts | refinement phase also in: refinement phase | concepts[72] | 0.95 | the paper's words |
| concepts | native results also in: native results | concepts[73] | 0.95 | the paper's words |
| concepts | GA-based thresholds also in: GA-based thresholds | concepts[74] | 0.95 | the paper's words |
| concepts | PFAM clan disambiguation also in: PFAM clan disambiguation | concepts[75] | 0.95 | the paper's words |
| concepts | broad annotation coverage also in: broad annotation coverage | concepts[76] | 0.95 | the paper's words |
| concepts | redundancy also in: redundancy | concepts[77] | 0.95 | the paper's words |
| concepts | annotation of reads also in: annotation of reads | concepts[78] | 0.95 | the paper's words |
| concepts | functional profiles of metagenomic samples | concepts[79] | 0.95 | the paper's words |
| concepts | orthology assignment tables also in: orthology assignment tables | concepts[80] | 0.95 | the paper's words |
| concepts | taxonomic placements also in: taxonomic placements | concepts[81] | 0.95 | the paper's words |
| unhoused_relations | ORF detection → Prodigal ORF detection is implemented using Prodigal. | unhoused_relations[0] | 0.95 | the tagger's words |
| unhoused_relations | eggNOG-mapper → eggNOG database eggNOG-mapper relies on the eggNOG database of orthologous groups. | unhoused_relations[1] | 0.95 | the tagger's words |
| unhoused_relations | precomputed phylogenies → putative in-paralogs Precomputed phylogenies help minimize annotation transfer from putative in-paralogs. | unhoused_relations[2] | 0.95 | the tagger's words |
| unhoused_relations | database optimizations → annotation rate Database optimizations, parallelization, and memory-based operation improve annotation rates. | unhoused_relations[3] | 0.95 | the tagger's words |
| unhoused_relations | memory-based mode → slow I/O disk operations The memory-based mode reduces the impact of slow I/O disk operations. | unhoused_relations[4] | 0.95 | the tagger's words |
| unhoused_relations | custom annotation databases → specific taxonomic groups Custom annotation databases are constrained to specific taxonomic groups. | unhoused_relations[5] | 0.95 | the tagger's words |
| unhoused_relations | lineage-specific scopes → orthologs of unwanted lineages Lineage-specific scopes prevent annotation transfer from orthologs of unwanted lineages. | unhoused_relations[6] | 0.95 | the tagger's words |
| unhoused_relations | de novo PFAM domain annotation → false-positive and negative predictions Realignment refinement reduces false-positive and false-negative domain predictions. | unhoused_relations[7] | 0.95 | the tagger's words |
| unhoused_relations | annotation of reads → functional profiles of metagenomic samples Direct read annotation can produce functional profiles before assembly. | unhoused_relations[8] | 0.95 | the tagger's words |
| senses | eggNOG-mapper v2 as an orthology-based automated functional-annotation workflow for genomic and metagenomic scale | senses[0] | 0.95 | the tagger's words |
| senses | orthology-based transfer as using expected function retention in orthologs rather than similarity alone | senses[1] | 0.95 | the tagger's words |
| senses | the eggNOG-mapper workflow as prediction, sequence search, orthology inference, and annotation transfer | senses[2] | 0.95 | the tagger's words |
| senses | annotation rate as annotated queries per second | senses[3] | 0.95 | the tagger's words |
| senses | Diamond mode as the default speed-memory compromise | senses[4] | 0.95 | the tagger's words |
| senses | automatic taxonomic scope as the recommended mode for mixed metagenomes | senses[5] | 0.95 | the tagger's words |
| senses | pairwise orthology reporting as a quick first pass rather than a substitute for precise inference | senses[6] | 0.95 | the tagger's words |
| senses | direct PFAM transfer as low-cost domain prediction with residual false positives and negatives | senses[7] | 0.95 | the tagger's words |
| senses | PFAM refinement as realignment of transferred domains to improve accuracy at low cost | senses[8] | 0.95 | the tagger's words |
| senses | metagenomic-scale annotation as accepting contigs or reads and emitting functional profiles and decorated feature files | senses[9] | 0.95 | the tagger's words |
| claim_relations | extends → eggNOG-mapper version 1 note: Version 2 adds databases, performance enhancements, gene prediction, pairwise orthology, domain discovery, and GFF decoration. | claim_relations[0] | 0.95 | the tagger's words |
| claim_relations | builds-on → eggNOG v5 note: The workflow maps input proteins to and transfers annotations from eggNOG v5. | claim_relations[1] | 0.95 | the tagger's words |
| claim_relations | extends → orthology-based functional annotation note: The paper contributes a more efficient, versatile, and scalable annotation workflow. | claim_relations[2] | 0.95 | the tagger's words |
| concept_edges | orthology —contrasted-with→ homology detection the relation itself is separately evidenced · strength of the relation: 0.95 · note: Direct rather-than contrast; weight reflects the paper's generally-considered qualification. · the relation stated: Function inference via orthology is contrasted with homology detection. also in: orthology · homology detection | concept_edges[0] | 0.95 | the tagger's words |
| concept_edges | orthologs —contrasted-with→ paralogs the relation itself is separately evidenced · strength of the relation: 0.9 · note: Comparative expectation, not a necessary distinction. · the relation stated: Orthologs are expected to retain function more often than paralogs. | concept_edges[1] | 0.95 | the tagger's words |
| concept_edges | underlying genome database —causes→ annotation coverage direction on the measure: increases · the relation itself is separately evidenced · strength of the relation: 0.95 · note: Direct increase supported by a reannotation benchmark. · the relation stated: The updated genome database increases annotation coverage. also in: underlying genome database · annotation coverage | concept_edges[2] | 0.95 | the tagger's words |
| concept_edges | underlying genome database —causes→ phylogenetic resolution direction on the measure: increases · the relation itself is separately evidenced · strength of the relation: 0.95 · note: Direct increase supported by better alignment scores. · the relation stated: The updated genome database increases phylogenetic resolution. | concept_edges[3] | 0.95 | the tagger's words |
| concept_edges | eggNOG-mapper —contrasted-with→ Prokka the relation itself is separately evidenced · strength of the relation: 0.95 · note: Direct measured performance comparison. · the relation stated: eggNOG-mapper is benchmarked against Prokka and runs faster, especially on large metagenomic data sets. also in: eggNOG-mapper · Prokka | concept_edges[4] | 0.95 | the tagger's words |
| concept_edges | Diamond mode —contrasted-with→ MMseqs2 mode the relation itself is separately evidenced · strength of the relation: 1 · note: Direct measured comparison; the numerical size regimes make full strength appropriate. · the relation stated: Diamond and MMseqs2 modes are compared by query-set size, speed, sensitivity, and memory use. also in: Diamond mode · MMseqs2 mode | concept_edges[5] | 0.95 | the tagger's words |
| concept_edges | native results —distinct-from→ orthology-based predictions the relation itself is separately evidenced · strength of the relation: 1 · note: Explicit independence statement. · the relation stated: Full de novo computation obtains native results independent from orthology predictions. also in: native results · orthology-based predictions | concept_edges[6] | 0.95 | the tagger's words |
| content_tags | eggNOG-mapper v2 adds contig-level gene prediction, pairwise orthology reports, domain discovery, and GFF decoration. | content_tags.findings[0] | 0.95 | the tagger's words |
| content_tags | The eggNOG v5 update spans 4.4 million orthologous groups and more than twice as many organisms as the previous version. | content_tags.findings[1] | 0.95 | the tagger's words |
| content_tags | Reannotation of 1.75 million proteins increased annotation coverage by 3.23 percent, adding 56,569 annotations. | content_tags.findings[2] | 0.95 | the tagger's words |
| content_tags | The expanded database produced significantly better query alignment scores than previous versions. | content_tags.findings[3] | 0.95 | the tagger's words |
| content_tags | Despite doubled database size, version 2 improves annotated queries per second by 16 percent on average. | content_tags.findings[4] | 0.95 | the tagger's words |
| content_tags | All computational enhancements together improve annotation rates by 608 percent on average over version 1. | content_tags.findings[5] | 0.95 | the tagger's words |
| content_tags | eggNOG-mapper runs faster than Prokka, especially on large metagenomic data sets. | content_tags.findings[6] | 0.95 | the tagger's words |
| content_tags | Diamond iterate mode is twofold faster than MMseqs2 at one million queries and threefold faster at ten million. | content_tags.findings[7] | 0.95 | the tagger's words |
| content_tags | MMseqs2 is faster than Diamond for query sets up to 100,000 at comparable sensitivity. | content_tags.findings[8] | 0.95 | the tagger's words |
| content_tags | Lineage-specific scopes prevent functional transfer from unwanted lineages. | content_tags.findings[9] | 0.95 | the tagger's words |
| content_tags | Direct PFAM transfer has an F1 score of 89.7 percent with little computational cost. | content_tags.findings[10] | 0.95 | the tagger's words |
| content_tags | Realignment refinement raises PFAM F1 to 98.9 percent while keeping computational cost low. | content_tags.findings[11] | 0.95 | the tagger's words |
| content_tags | The workflow emits decorated GFF, tabular annotations, orthology assignments, and taxonomic placements. | content_tags.findings[12] | 0.95 | the tagger's words |
| content_tags | an orthology-based automated functional-annotation workflow optimized for large genomic and metagenomic data sets also in: eggNOG-mapper v2 | content_tags.definitions[0] | 0.95 | the tagger's words |
| content_tags | annotated queries per second also in: annotation rate | content_tags.definitions[1] | 0.95 | the tagger's words |
| content_tags | the annotation-transfer scope recommended for mixed metagenomic data sets also in: taxonomic scope | content_tags.definitions[2] | 0.95 | the tagger's words |
| content_tags | a scope that prevents transfer from orthologs in unwanted lineages also in: lineage-specific scope | content_tags.definitions[3] | 0.95 | the tagger's words |
| content_tags | a quick first-pass query-to-genome orthology result adjustable by taxa and ortholog type also in: orthology reports | content_tags.definitions[4] | 0.95 | the tagger's words |
| content_tags | 4.4 million OGs — orthologous groups in the synchronized eggNOG v5 genome database | content_tags.quantities[0] | 0.95 | the tagger's words |
| content_tags | 1.75 million proteins — random human-gut catalog subsample used for reannotation | content_tags.quantities[1] | 0.95 | the tagger's words |
| content_tags | 3.23% — increase in annotation coverage over version 1 | content_tags.quantities[2] | 0.95 | the tagger's words |
| content_tags | 56,569 — newly annotated proteins in the coverage benchmark | content_tags.quantities[3] | 0.95 | the tagger's words |
| content_tags | 16% — average annotation-rate gain despite doubled database size | content_tags.quantities[4] | 0.95 | the tagger's words |
| content_tags | 608% — average combined annotation-rate improvement over version 1 | content_tags.quantities[5] | 0.95 | the tagger's words |
| content_tags | 1,000,000 — query-set size where Diamond iterate is twice as fast as MMseqs2 | content_tags.quantities[6] | 0.95 | the tagger's words |
| content_tags | 10,000,000 — query-set size where Diamond iterate is three times faster than MMseqs2 | content_tags.quantities[7] | 0.95 | the tagger's words |
| content_tags | 100,000 — upper query-set size where MMseqs2 is faster at comparable sensitivity | content_tags.quantities[8] | 0.95 | the tagger's words |
| content_tags | 89.7% — F1 score for direct ortholog transfer of PFAM domains | content_tags.quantities[9] | 0.95 | the tagger's words |
| content_tags | 98.9% — F1 score after PFAM realignment refinement | content_tags.quantities[10] | 0.95 | the tagger's words |
| content_tags | annotation coverage also in: annotation coverage | content_tags.variables[0] | 0.95 | the tagger's words |
| content_tags | alignment scores also in: alignment scores | content_tags.variables[1] | 0.95 | the tagger's words |
| content_tags | annotation rate also in: annotation rate | content_tags.variables[2] | 0.95 | the tagger's words |
| content_tags | taxonomic scope also in: taxonomic scope | content_tags.variables[4] | 0.95 | the tagger's words |
| content_tags | target taxa also in: target taxa | content_tags.variables[5] | 0.95 | the tagger's words |
| content_tags | specificity also in: specificity | content_tags.variables[6] | 0.95 | the tagger's words |
| content_tags | recall also in: recall | content_tags.variables[7] | 0.95 | the tagger's words |
| content_tags | F 1 score also in: F 1 score | content_tags.variables[8] | 0.95 | the tagger's words |
Tags that could not be placed in the text:
What was deliberately not tagged, and why, is recorded per paper by the tagger. Where a paper has no such record, this page says so rather than implying that nothing was declined.