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Record W1995962575 · doi:10.1093/nar/gks497

METAGENassist: a comprehensive web server for comparative metagenomics

2012· article· en· W1995962575 on OpenAlexafffund
D. Arndt, Jianguo Xia, Youhua Liu, You Zhou, An Chi Guo, J. A. Cruz, I. Sinelnikov, Karen Budwill, Camilla Nesbø, David S. Wishart

Bibliographic record

VenueNucleic Acids Research · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsAlberta InnovatesNational Institute for NanotechnologyUniversity of Alberta
FundersGenome AlbertaCanadian Institutes of Health ResearchAlberta InnovatesGenome Canada
KeywordsMetagenomicsBiologyUploadMetadataComputer scienceComputational biologyWorld Wide WebGenetics

Abstract

fetched live from OpenAlex

With recent improvements in DNA sequencing and sample extraction techniques, the quantity and quality of metagenomic data are now growing exponentially. This abundance of richly annotated metagenomic data and bacterial census information has spawned a new branch of microbiology called comparative metagenomics. Comparative metagenomics involves the comparison of bacterial populations between different environmental samples, different culture conditions or different microbial hosts. However, in order to do comparative metagenomics, one typically requires a sophisticated knowledge of multivariate statistics and/or advanced software programming skills. To make comparative metagenomics more accessible to microbiologists, we have developed a freely accessible, easy-to-use web server for comparative metagenomic analysis called METAGENassist. Users can upload their bacterial census data from a wide variety of common formats, using either amplified 16S rRNA data or shotgun metagenomic data. Metadata concerning environmental, culture, or host conditions can also be uploaded. During the data upload process, METAGENassist also performs an automated taxonomic-to-phenotypic mapping. Phenotypic information covering nearly 20 functional categories such as GC content, genome size, oxygen requirements, energy sources and preferred temperature range is automatically generated from the taxonomic input data. Using this phenotypically enriched data, users can then perform a variety of multivariate and univariate data analyses including fold change analysis, t-tests, PCA, PLS-DA, clustering and classification. To facilitate data processing, users are guided through a step-by-step analysis workflow using a variety of menus, information hyperlinks and check boxes. METAGENassist also generates colorful, publication quality tables and graphs that can be downloaded and used directly in the preparation of scientific papers. METAGENassist is available at http://www.metagenassist.ca.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Software · Consensus signal: Software
Teacher disagreement score0.045
Threshold uncertainty score0.151

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0060.004
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0080.006
Science and technology studies0.0020.001
Scholarly communication0.0050.006
Open science0.0080.006
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0450.089

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.140
GPT teacher head0.384
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreSoftware

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations414
Published2012
Admission routes2
Has abstractyes

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