ArticleMicrobiome2017
A fast and robust protocol for metataxonomic analysis using RNAseq data.
Article in Microbiome, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Soybean productivity can be enhanced by understanding rhizosphere microbiota: evidence from metagenomics analysis from diverse agroecosystems.Microbiome · 2025Article
- Benchmarking and optimizing qualitative and quantitative pipelines in environmental metatranscriptomics using mixture controlling experiments.ISME communications · 2025Article
- Resources to Facilitate Use of the Altered Schaedler Flora (ASF) Mouse Model to Study Microbiome Function.mSystems · 2022Article
- Application of a bioinformatic pipeline to RNA-seq data identifies novel virus-like sequence in human blood.G3 (Bethesda, Md.) · 2021Article
- Blood Meals With Active and Heat-Inactivated Serum Modifies the Gene Expression and Microbiome ofFrontiers in microbiology · 2021Article
- Prediction of Antimicrobial Resistance in Gram-Negative Bacteria From Whole-Genome Sequencing Data.Frontiers in microbiology · 2020Article
- Tomato RNA-seq Data Mining Reveals the Taxonomic and Functional Diversity of Root-Associated Microbiota.Microorganisms · 2019Article
- Bacterial diversity obtained by culturable approaches in the gut of Glossina pallidipes population from a non sleeping sickness focus in Tanzania: preliminary results.BMC microbiology · 2018Article
- Microbiome and ecotypic adaption of Holcus lanatus (L.) to extremes of its soil pH range, investigated through transcriptome sequencing.Microbiome · 2018Article
- Overview of Virus Metagenomic Classification Methods and Their Biological Applications.Frontiers in microbiology · 2018Review
- TaxMapper: an analysis tool, reference database and workflow for metatranscriptome analysis of eukaryotic microorganisms.BMC genomics · 2017Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundMetagenomics is a rapidly emerging field aimed to analyze microbial diversity and dynamics by studying the genomic content of the microbiota. Metataxonomics tools analyze high-throughput sequencing data, primarily from 16S rRNA gene sequencing and DNAseq, to identify microorganisms and viruses within a complex mixture. With the growing demand for analysis of the functional microbiome, metatranscriptome studies attract more interest. To make metatranscriptomic data sufficient for metataxonomics, new analytical workflows are needed to deal with sparse and taxonomically less informative sequencing data.
resultsWe present a new protocol, IMSA+A, for accurate taxonomy classification based on metatranscriptome data of any read length that can efficiently and robustly identify bacteria, fungi, and viruses in the same sample. The new protocol improves accuracy by using a conservative reference database, employing a new counting scheme, and by assembling shotgun reads. Assembly also reduces analysis runtime. Simulated data were utilized to evaluate the protocol by permuting common experimental variables. When applied to the real metatranscriptome data for mouse intestines colonized by ASF, the protocol showed superior performance in detection of the microorganisms compared to the existing metataxonomics tools. IMSA+A is available at https://github.com/JeremyCoxBMI/IMSA-A .
conclusionsThe developed protocol addresses the need for taxonomy classification from RNAseq data. Previously not utilized, i.e., unmapped to a reference genome, RNAseq reads can now be used to gather taxonomic information about the microbiota present in a biological sample without conducting additional sequencing. Any metatranscriptome pipeline that includes assembly of reads can add this analysis with minimal additional cost of compute time. The new protocol also creates an opportunity to revisit old metatranscriptome data, where taxonomic content may be important but was not analyzed.
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Registered trials
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.