Evidence map›Paper›PMID 33422152›Full record

ArticleMicrobiome2021

Re-purposing software for functional characterization of the microbiome.

Laura-Jayne Gardiner, Niina Haiminen, Filippo Utro, Laxmi Parida, Ed Seabolt, Ritesh Krishna, James H Kaufman

Open access · goldAbstract readVideo-Audio Media
In one paragraph

Article in Microbiome, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.8field-weighted citation impact, top 30% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 11 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 2 countries.

Laura-Jayne GardinerIBM Research, The Hartree Centre, Warrington, WA4 4AD, UK. Laura-Jayne.Gardiner@ibm.com.ORCID 0000-0002-9177-4452
Niina HaiminenIBM T. J. Watson Research Center, Yorktown Heights, NY, 10598, USA.
Filippo UtroIBM T. J. Watson Research Center, Yorktown Heights, NY, 10598, USA.
Laxmi ParidaIBM T. J. Watson Research Center, Yorktown Heights, NY, 10598, USA.
Ed SeaboltIBM Almaden Research Center, San Jose, CA, 95120, USA.
Ritesh KrishnaIBM Research, The Hartree Centre, Warrington, WA4 4AD, UK.
James H KaufmanIBM Almaden Research Center, San Jose, CA, 95120, USA.
IBM Research - Almaden · USIBM (United Kingdom) · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWidespread bioinformatic resource development generates a constantly evolving and abundant landscape of workflows and software. For analysis of the microbiome, workflows typically begin with taxonomic classification of the microorganisms that are present in a given environment. Additional investigation is then required to uncover the functionality of the microbial community, in order to characterize its currently or potentially active biological processes. Such functional analysis of metagenomic data can be computationally demanding for high-throughput sequencing experiments. Instead, we can directly compare sequencing reads to a functionally annotated database. However, since reads frequently match multiple sequences equally well, analyses benefit from a hierarchical annotation tree, e.g. for taxonomic classification where reads are assigned to the lowest taxonomic unit.

resultsTo facilitate functional microbiome analysis, we re-purpose well-known taxonomic classification tools to allow us to perform direct functional sequencing read classification with the added benefit of a functional hierarchy. To enable this, we develop and present a tree-shaped functional hierarchy representing the molecular function subset of the Gene Ontology annotation structure. We use this functional hierarchy to replace the standard phylogenetic taxonomy used by the classification tools and assign query sequences accurately to the lowest possible molecular function in the tree. We demonstrate this with simulated and experimental datasets, where we reveal new biological insights.

conclusionsWe demonstrate that improved functional classification of metagenomic sequencing reads is possible by re-purposing a range of taxonomic classification tools that are already well-established, in conjunction with either protein or nucleotide reference databases. We leverage the advances in speed, accuracy and efficiency that have been made for taxonomic classification and translate these benefits for the rapid functional classification of microbiomes. While we focus on a specific set of commonly used methods, the functional annotation approach has broad applicability across other sequence classification tools. We hope that re-purposing becomes a routine consideration during bioinformatic resource development. Video abstract.

Indexed as

High-Throughput Nucleotide SequencingSoftwareClassificationComputational BiologyMetagenomeMetagenomicsMicrobiotaPhylogenyFunctional analysisMicrobiomeSequencing read classificationTaxonomy

Identifiers

PMID33422152
PMCPMC7797099
OpenAlexW3120635567

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.