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 WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below.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 An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason.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 |
| 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 |
MultiQC: summarize analysis results for multiple tools and samples in a single report
Abstract
object_of_studyconcepts↗concepts↗concepts↗concepts↗concepts↗concepts↗content_tags↗Motivation: Fast and accurate quality control is essential for studies involving next-generation sequencing data. Whilst numerous tools exist to quantify QC metrics, there is no common approach to flexibly integrate these across tools and large sample sets. Assessing analysis results across an entire project can be time consuming and error prone; batch effects and outlier samples can easily be missed in the early stages of analysis.
Results: contributionclaim_typeconcepts↗concepts↗concepts↗concepts↗unhoused_relationsunhoused_relationscontent_tagscontent_tags↗We present MultiQC, a tool to create a single report visualising output from multiple tools across many samples, enabling global trends and biases to be quickly identified. MultiQC can plot data from many common bioinformatics tools and is built to allow easy extension and customization.
Availability and implementation: MultiQC is available with an GNU GPLv3 license on GitHub, the Python Package Index and Bioconda. Documentation and example reports are available at http://multiqc.info
Contact: phil.ewels@scilifelab.se
1 Introduction
limitationsconcepts↗concepts↗concepts↗unhoused_relationsconcept_edgescontent_tags↗content_tags↗Advances in next-generation sequencing are leading to an avalanche of data. Whilst opening doors to new analysis types and experimental designs, expanding sample numbers make studies increasingly vulnerable to confounding batch effects ( Leek et al., 2010 ; Meyer and Liu 2014 ; Taub et al., 2010 ). Such biases are often subtle and difficult to detect and require careful quality control measures.
research_questionlimitationsconcepts↗concepts↗claim_relationscontent_tagsMost bioinformatics programs produce logs detailing their results. Dedicated QC tools such as FastQC ( http://www.bioinformatics.babraham.ac.uk/projects/fastqc ), Qualimap ( Okonechnikov et al. , 2015 ) and RSeQC ( Wang et al. , 2012 ) are excellent at highlighting potential problems in data. However, nearly all of these logs and reports are produced on a per-sample basis, requiring the user to find and compile QC results. This process is time consuming, repetitive and complex, making it prone to errors.
phenomenonmechanisms↗limitationsconcepts↗concepts↗concepts↗unhoused_relationsunhoused_relationssensessensesclaim_relationsclaim_relationscontent_tagscontent_tagscontent_tags↗content_tags↗MultiQC addresses this problem by scanning given analysis directories for log files and QC reports, creating a single summary report visualizing results across all samples. Collecting data within a single report provides a fast way to scan key statistics quickly and easily ( Fig. 1 ). Shared plots allow accurate comparison between samples, allowing detection of subtle differences not noticeable when switching between different files. Data visualization aids batch effect detection and minimizes the risk of confounding factors affecting the results of the study. MultiQC is the first tool of its type within the field; it has the potential to greatly improve quality control and reporting for researchers involved in next-generation sequencing, removing the need for custom comparative scripts.
Top of a typical MultiQC report. The general statistics table can be seen with metrics from a number of different tools gathered for each sample (Color version of this figure is available at Bioinformatics online.)
2 Materials and methods
2.1 Running MultiQC
method↗mechanisms↗mechanisms↗mechanisms↗mechanisms↗limitationsconcepts↗concepts↗concepts↗concepts↗concepts↗unhoused_relationsunhoused_relationssensessensescontent_tagscontent_tagscontent_tags↗content_tagscontent_tags↗MultiQC is written in Python and run on the command line; specified directories are searched recursively for any recognized files. Submodules for each supported tool are run, querying input files using configurable search settings. If any log files are found they are parsed, otherwise the module exits silently. Once all submodules have finished, the specified template is loaded and parsed using the Jinja2 package to render the final report. Parsed data is saved as tab delimited text, YAML or JSON for downstream use. Because submodules only contribute to the report if they find logs, MultiQC is run in the same way for every analysis type. At the time of writing, MultiQC supports 22 common bioinformatics tools including aligners, processing tools and QC programs.
2.2 MultiQC reports
mechanisms↗mechanisms↗concepts↗concepts↗concepts↗content_tagscontent_tags↗MultiQC generates a single s elf-contained HTML report which can be shared and opened in any modern web browser. Reports render plots using the JavaScript plotting library HighCharts ( http://www.highcharts.com ). Plots are resizeable and interactive, some with click and drag zooming. Samples can be renamed, hidden and highlighted using a report toolbox. Plots can be exported in a range of publication-ready formats.
mechanisms↗limitationslimitationsconcepts↗concepts↗unhoused_relationsunhoused_relationssensescontent_tagscontent_tagsReports with hundreds of samples become too large for use with HighCharts; instead MultiQC switches to rendering plots as images at run-time using the Python plotting library MatPlotLib ( Hunter, 2007) . Images are embedded within the HTML, maintaining a stand-alone file with consistent file size. These static reports are also suitable for conversion to PDF using tools such as Pandoc ( http://pandoc.org ).
Each report contains an interactive walk through of features. Tutorial videos can be found at http://multiqc.info along with tutorials and documentation describing installation, usage and troubleshooting.
2.3 Extending MultiQC
mechanisms↗mechanisms↗limitationsconcepts↗concepts↗concepts↗concepts↗concepts↗unhoused_relationssensescontent_tagscontent_tags↗content_tags↗content_tags↗MultiQC supports a lot of common bioinformatics tools but it is inevitable that research groups may have their own bespoke scripts or require other customization. To accommodate this, MultiQC is built in such a way that custom code can be tied into its functionality easily. Code hooks allow external plugins to access and modify the internal workings of the program. The use of Python setuptools entry points allows modules, templates and plugins to be kept within a separate code base, whilst still executing as part of the main MultiQC program.
limitationscontent_tagscontent_tagscontent_tagscontent_tagscontent_tagsExtensive documentation makes adding to MultiQC simple; four new modules have been contributed by users to date and we are aware of at least three plugins written by different research groups. Adoption by the bioinformatics community has been rapid: MultiQC has been downloaded thousands of times within the past few months and is already integrated as standard within the popular bcbio-nextgen analysis toolkit ( http://bcb.io ).
3 Typical applications
3.1 Single cell data and population studies
concepts↗concepts↗concepts↗concepts↗unhoused_relationssensescontent_tagscontent_tagscontent_tags↗Single cell and population studies are perhaps the perfect examples of large projects where accurate quality control of numerous datasets is critical. MultiQC is able to parse data for thousands of samples within minutes, adapting report output as required. Parsed data saved by MultiQC can be used for post-processing and dataset filtering. Reports reveal overall analysis success and make it easy to identify abnormal samples.
3.2 Sequencing facilities
concepts↗content_tagsMultiQC was originally developed for use in a high throughput sequencing facility. Reports give the overview required to spot failing samples and highlighting helps to identify groups of samples behaving in an irregular manner.
mechanisms↗mechanisms↗concepts↗concepts↗unhoused_relationscontent_tagsPlugins allow integration with in-house systems: we have written the MultiQC_NGI plugin which inserts meta data from our LIMS into reports and stores summary results parsed by MultiQC in our database. This functionality is enormously powerful, facilitating large scale internal data collection that would otherwise require numerous custom scripts. Templates allow report branding and reports are self-contained, making MultiQC an ideal tool for creating delivery reports.
4 Conclusion
concepts↗concepts↗concepts↗unhoused_relationsunhoused_relationssensesclaim_relationscontent_tagscontent_tags↗As the field of next-generation sequencing matures, there are increasing numbers of bioinformatics tools producing ever more verbose descriptions of data. Integrating these statistics across tools with large sample sets is difficult and time-consuming. MultiQC can automate the parsing of this metadata, providing powerful visualizations with a simple interface. Extension and data export allow MultiQC to function as a central collection point at the terminus of analysis pipelines. Routine use can aid quality control steps early on in data processing, reducing risk of batch effects and other downstream analysis problems.
Acknowledgements
The authors would like to thank D. Klevebring, L. Pantano, G. Carrasco and R. Andeer for contributed modules and discussion.
Funding
This work was supported by the Science for Life Laboratory and the National Genomics Infrastructure, NGI.
Conflict of Interest : none declared.
References
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; 1 of 1 relation here carries that mark. The rest are the tagger’s reading of the sentence quoted beside them.
This record holds no controls. A layer is empty when the tagger recorded nothing in it — which is not the same as the paper having nothing to record.
| facet | tag | field | conf. | whose words |
|---|---|---|---|---|
| object_of_study | cross-tool and cross-sample integration and visualization of next-generation-sequencing quality-control results | object_of_study | the paper's words | |
| research_question | how to replace repetitive per-sample QC compilation with an extensible summary that exposes global trends, outliers, biases, and batch effects | research_question | the paper's words | |
| contribution | MultiQC, a directory-scanning modular tool that parses many analysis logs into a single interactive report and reusable data exports | contribution | the paper's words | |
| claim_type | method | claim_type | the paper's words | |
| phenomenon | quality-control visibility, batch-effect detection, outlier detection, reporting effort, extensibility, and scaling across many sequencing samples | phenomenon | the paper's words | |
| method | system-or-tool-building also in: system-or-tool-building | method | the paper's words | |
| mechanisms | Specified directories are recursively scanned for recognized output files. also in: recursive directory search | mechanisms[0] | 0.95 | the tagger's words |
| mechanisms | Each supported-tool parser queries configurable patterns and contributes only when logs exist. also in: tool submodules | mechanisms[1] | 0.95 | the tagger's words |
| mechanisms | A loaded template is parsed to render the final report. also in: Jinja2 | mechanisms[2] | 0.95 | the tagger's words |
| mechanisms | Parsed data are exported for downstream processing. also in: tab delimited text, YAML or JSON | mechanisms[3] | 0.95 | the tagger's words |
| mechanisms | Aligned sample visualizations expose differences hidden across separate files. also in: shared plots | mechanisms[4] | 0.95 | the tagger's words |
| mechanisms | Browser-side rendering provides resizeable, interactive, zoomable plots. also in: HighCharts | mechanisms[5] | 0.95 | the tagger's words |
| mechanisms | Large reports use runtime-rendered static images to keep the standalone file manageable. also in: MatPlotLib | mechanisms[6] | 0.95 | the tagger's words |
| mechanisms | Sample renaming, hiding, and highlighting support focused inspection. also in: report toolbox | mechanisms[7] | 0.95 | the tagger's words |
| mechanisms | External plugins access and modify internal functionality. also in: code hooks | mechanisms[8] | 0.95 | the tagger's words |
| mechanisms | Independent modules and templates execute as part of the main program. also in: setuptools entry points | mechanisms[9] | 0.95 | the tagger's words |
| mechanisms | Facility metadata enter reports and parsed summaries flow into a database. also in: MultiQC_NGI plugin | mechanisms[10] | 0.95 | the tagger's words |
| mechanisms | Branding and self-contained output support delivery reports. also in: templates | mechanisms[11] | 0.95 | the tagger's words |
| limitations | Existing QC logs and reports are predominantly per-sample and require manual project-wide compilation. | limitations[0] | 0.95 | the tagger's words |
| limitations | Expanding sample counts increase vulnerability to subtle confounding batch effects. | limitations[1] | 0.95 | the tagger's words |
| limitations | Per-sample switching can conceal subtle differences, outliers, and batch effects. | limitations[2] | 0.95 | the tagger's words |
| limitations | HighCharts becomes impractical for reports containing hundreds of samples. | limitations[3] | 0.95 | the tagger's words |
| limitations | Static large-sample reports sacrifice browser interactivity for consistent standalone size. | limitations[4] | 0.95 | the tagger's words |
| limitations | Research groups may require bespoke parsers and customization beyond built-in tools. | limitations[5] | 0.95 | the tagger's words |
| limitations | Routine value depends on the recognized logs produced by upstream tools. | limitations[6] | 0.95 | the tagger's words |
| limitations | The paper reports adoption and applications but no controlled comparison of error detection against manual review. | limitations[7] | 0.95 | the tagger's words |
| concepts | quality control also in: quality control | concepts[0] | 0.95 | the paper's words |
| concepts | next-generation sequencing data also in: next-generation sequencing data | concepts[1] | 0.95 | the paper's words |
| concepts | QC metrics also in: QC metrics | concepts[2] | 0.95 | the paper's words |
| concepts | analysis results also in: analysis results | concepts[3] | 0.95 | the paper's words |
| concepts | batch effects also in: batch effects | concepts[4] | 0.95 | the paper's words |
| concepts | outlier samples also in: outlier samples | concepts[5] | 0.95 | the paper's words |
| concepts | MultiQC also in: MultiQC | concepts[6] | 0.95 | the paper's words |
| concepts | tool also in: tool | concepts[7] | 0.95 | the paper's words |
| concepts | single report also in: single report | concepts[8] | 0.95 | the paper's words |
| concepts | global trends also in: global trends | concepts[9] | 0.95 | the paper's words |
| concepts | logs also in: logs | concepts[10] | 0.95 | the paper's words |
| concepts | reports also in: reports | concepts[11] | 0.95 | the paper's words |
| concepts | shared plots also in: shared plots | concepts[12] | 0.95 | the paper's words |
| concepts | confounding factors also in: confounding factors | concepts[13] | 0.95 | the paper's words |
| concepts | custom comparative scripts also in: custom comparative scripts | concepts[14] | 0.95 | the paper's words |
| concepts | recognized files also in: recognized files | concepts[15] | 0.95 | the paper's words |
| concepts | Submodules also in: Submodules | concepts[16] | 0.95 | the paper's words |
| concepts | template also in: template | concepts[17] | 0.95 | the paper's words |
| concepts | Jinja2 package also in: Jinja2 package | concepts[18] | 0.95 | the paper's words |
| concepts | parsed data also in: parsed data | concepts[19] | 0.95 | the paper's words |
| concepts | HTML report also in: HTML report | concepts[20] | 0.95 | the paper's words |
| concepts | HighCharts also in: HighCharts | concepts[21] | 0.95 | the paper's words |
| concepts | report toolbox also in: report toolbox | concepts[22] | 0.95 | the paper's words |
| concepts | MatPlotLib also in: MatPlotLib | concepts[23] | 0.95 | the paper's words |
| concepts | static reports also in: static reports | concepts[24] | 0.95 | the paper's words |
| concepts | custom code also in: custom code | concepts[25] | 0.95 | the paper's words |
| concepts | Code hooks also in: Code hooks | concepts[26] | 0.95 | the paper's words |
| concepts | external plugins also in: external plugins | concepts[27] | 0.95 | the paper's words |
| concepts | setuptools entry points also in: setuptools entry points | concepts[28] | 0.95 | the paper's words |
| concepts | modules also in: modules | concepts[29] | 0.95 | the paper's words |
| concepts | single cell also in: single cell | concepts[30] | 0.95 | the paper's words |
| concepts | population studies also in: population studies | concepts[31] | 0.95 | the paper's words |
| concepts | post-processing also in: post-processing | concepts[32] | 0.95 | the paper's words |
| concepts | dataset filtering also in: dataset filtering | concepts[33] | 0.95 | the paper's words |
| concepts | sequencing facility also in: sequencing facility | concepts[34] | 0.95 | the paper's words |
| concepts | MultiQC_NGI plugin also in: MultiQC_NGI plugin | concepts[35] | 0.95 | the paper's words |
| concepts | LIMS also in: LIMS | concepts[36] | 0.95 | the paper's words |
| concepts | central collection point also in: central collection point | concepts[37] | 0.95 | the paper's words |
| concepts | analysis pipelines also in: analysis pipelines | concepts[38] | 0.95 | the paper's words |
| concepts | sample numbers also in: sample numbers | concepts[39] | 0.95 | the paper's words |
| concepts | Extension also in: Extension | concepts[40] | 0.95 | the paper's words |
| concepts | Advances in next-generation sequencing | concepts[41] | 0.95 | the paper's words |
| concepts | avalanche of data also in: avalanche of data | concepts[42] | 0.95 | the paper's words |
| unhoused_relations | MultiQC → tool MultiQC is introduced appositively as a tool for integrated reporting. | unhoused_relations[0] | 0.95 | the tagger's words |
| unhoused_relations | HighCharts → MatPlotLib Large reports switch from HighCharts to MatPlotLib image rendering. | unhoused_relations[1] | 0.95 | the tagger's words |
| unhoused_relations | single report → global trends A single integrated report enables rapid identification of global trends and biases. | unhoused_relations[2] | 0.95 | the tagger's words |
| unhoused_relations | shared plots → batch effects Shared cross-sample plots aid detection of subtle batch effects. | unhoused_relations[3] | 0.95 | the tagger's words |
| unhoused_relations | MultiQC → custom comparative scripts MultiQC removes the need for custom cross-sample comparison scripts. | unhoused_relations[4] | 0.95 | the tagger's words |
| unhoused_relations | sample numbers → batch effects Growing sample sets increase vulnerability to confounding batch effects. | unhoused_relations[5] | 0.95 | the tagger's words |
| unhoused_relations | Submodules → recognized files Each submodule parses only when recognized logs are found. | unhoused_relations[6] | 0.95 | the tagger's words |
| unhoused_relations | template → HTML report Jinja2 parses a selected template to render the report. | unhoused_relations[7] | 0.95 | the tagger's words |
| unhoused_relations | MatPlotLib → static reports MatPlotLib produces embedded images when interactive rendering becomes too large. | unhoused_relations[8] | 0.95 | the tagger's words |
| unhoused_relations | Code hooks → external plugins Code hooks allow external plugins to modify MultiQC internals. | unhoused_relations[9] | 0.95 | the tagger's words |
| unhoused_relations | parsed data → post-processing Saved parsed data can be used for post-processing and filtering. | unhoused_relations[10] | 0.95 | the tagger's words |
| unhoused_relations | MultiQC_NGI plugin → LIMS The plugin inserts LIMS metadata into reports and stores summaries in a database. | unhoused_relations[11] | 0.95 | the tagger's words |
| unhoused_relations | Extension → central collection point Extension and export let MultiQC function as a pipeline-end collection point. | unhoused_relations[12] | 0.95 | the tagger's words |
| unhoused_relations | MultiQC → batch effects Routine early use can reduce downstream risk from batch effects. | unhoused_relations[13] | 0.95 | the tagger's words |
| senses | quality control as project-wide comparison rather than isolated per-sample inspection | senses[0] | 0.95 | the tagger's words |
| senses | batch effect as a subtle confound exposed through shared visualization | senses[1] | 0.95 | the tagger's words |
| senses | single report as an integrated visualization plus reusable data export | senses[2] | 0.95 | the tagger's words |
| senses | submodule as a conditional parser that is silent when its logs are absent | senses[3] | 0.95 | the tagger's words |
| senses | self-contained report as a portable file with embedded plots | senses[4] | 0.95 | the tagger's words |
| senses | extension as separately maintained code executing inside the main program | senses[5] | 0.95 | the tagger's words |
| senses | large-project QC as rapid detection of abnormal samples across thousands of inputs | senses[6] | 0.95 | the tagger's words |
| senses | central collection point as the reporting terminus of an analysis pipeline | senses[7] | 0.95 | the tagger's words |
| claim_relations | builds-on → per-tool logs and QC reports note: MultiQC consumes outputs of established analysis and QC tools. | claim_relations[0] | 0.95 | the tagger's words |
| claim_relations | contrasts-with → manual per-sample result compilation note: One cross-sample report replaces repeated switching and compilation. | claim_relations[1] | 0.95 | the tagger's words |
| claim_relations | extends → existing bioinformatics analysis pipelines note: MultiQC adds an extensible reporting terminus and data export. | claim_relations[2] | 0.95 | the tagger's words |
| claim_relations | contrasts-with → custom comparative scripts note: The common report removes the need for bespoke comparison code. | claim_relations[3] | 0.95 | the tagger's words |
| concept_edges | Advances in next-generation sequencing —causes→ avalanche of data direction on the measure: increases · the relation itself is separately evidenced · strength of the relation: 0.95 · note: Direct flat effect statement. · the relation stated: Advances in next-generation sequencing are leading to an avalanche of data. | concept_edges[0] | 0.95 | the tagger's words |
| content_tags | Per-sample QC logs make project-wide assessment slow, repetitive, complex, and error prone. | content_tags.findings[0] | 0.95 | the tagger's words |
| content_tags | MultiQC creates one report from multiple tools and many samples so global trends and biases can be identified quickly. | content_tags.findings[1] | 0.95 | the tagger's words |
| content_tags | Shared plots expose subtle sample differences that are difficult to see by switching among files. | content_tags.findings[2] | 0.95 | the tagger's words |
| content_tags | Central visualization supports batch-effect detection and reduces the chance that confounders affect downstream results. | content_tags.findings[3] | 0.95 | the tagger's words |
| content_tags | The same command works across analysis types because submodules contribute only when matching logs are found. | content_tags.findings[4] | 0.95 | the tagger's words |
| content_tags | At publication MultiQC supported 22 aligners, processing tools, and QC programs. | content_tags.findings[5] | 0.95 | the tagger's words |
| content_tags | The report is a shareable self-contained HTML file with interactive, exportable plots. | content_tags.findings[6] | 0.95 | the tagger's words |
| content_tags | For hundreds of samples MultiQC switches from browser plotting to embedded MatPlotLib images. | content_tags.findings[7] | 0.95 | the tagger's words |
| content_tags | Modules, templates, and plugins can remain in separate code bases while executing within MultiQC. | content_tags.findings[8] | 0.95 | the tagger's words |
| content_tags | Users had contributed four modules and at least three research groups had written plugins by publication. | content_tags.findings[9] | 0.95 | the tagger's words |
| content_tags | MultiQC had already been downloaded thousands of times and integrated into bcbio-nextgen. | content_tags.findings[10] | 0.95 | the tagger's words |
| content_tags | MultiQC parses thousands of samples within minutes and makes abnormal samples easy to identify. | content_tags.findings[11] | 0.95 | the tagger's words |
| content_tags | Sequencing-facility reports expose failing samples and irregular sample groups. | content_tags.findings[12] | 0.95 | the tagger's words |
| content_tags | The MultiQC_NGI plugin links LIMS metadata and stores parsed summaries in an internal database. | content_tags.findings[13] | 0.95 | the tagger's words |
| content_tags | Extension and data export let MultiQC serve as a central collection point at the end of analysis pipelines. | content_tags.findings[14] | 0.95 | the tagger's words |
| content_tags | a tool that summarizes outputs from multiple tools and samples in one report also in: MultiQC | content_tags.definitions[0] | 0.95 | the tagger's words |
| content_tags | a supported-tool component that searches for and parses recognized logs also in: submodule | content_tags.definitions[1] | 0.95 | the tagger's words |
| content_tags | a self-contained HTML artifact combining statistics and visualizations across samples also in: report | content_tags.definitions[2] | 0.95 | the tagger's words |
| content_tags | an extension point through which external plugins modify program internals also in: code hook | content_tags.definitions[3] | 0.95 | the tagger's words |
| content_tags | a separately distributable presentation component used to render or brand reports also in: template | content_tags.definitions[4] | 0.95 | the tagger's words |
| content_tags | external code integrated with MultiQC functionality through hooks and entry points also in: plugin | content_tags.definitions[5] | 0.95 | the tagger's words |
| content_tags | subtle confounding differences across expanding sample sets also in: batch effects | content_tags.definitions[6] | 0.95 | the tagger's words |
| content_tags | the pipeline terminus where analysis metadata are integrated and exported also in: central collection point | content_tags.definitions[7] | 0.95 | the tagger's words |
| content_tags | 22 — common bioinformatics tools supported at publication | content_tags.quantities[0] | 0.95 | the tagger's words |
| content_tags | hundreds of samples — report scale that triggers static image rendering | content_tags.quantities[1] | 0.95 | the tagger's words |
| content_tags | four — new modules contributed by users | content_tags.quantities[2] | 0.95 | the tagger's words |
| content_tags | at least three — research groups known to have written plugins | content_tags.quantities[3] | 0.95 | the tagger's words |
| content_tags | thousands of times — downloads during the first months | content_tags.quantities[4] | 0.95 | the tagger's words |
| content_tags | thousands of samples within minutes — reported large-project parsing capacity | content_tags.quantities[5] | 0.95 | the tagger's words |
| content_tags | sample numbers also in: sample numbers | content_tags.variables[0] | 0.95 | the tagger's words |
| content_tags | batch effects also in: batch effects | content_tags.variables[1] | 0.95 | the tagger's words |
| content_tags | outlier samples also in: outlier samples | content_tags.variables[2] | 0.95 | the tagger's words |
| content_tags | key statistics also in: key statistics | content_tags.variables[3] | 0.95 | the tagger's words |
| content_tags | analysis type also in: analysis type | content_tags.variables[4] | 0.95 | the tagger's words |
| content_tags | parsed data also in: parsed data | content_tags.variables[6] | 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.