Dataset Information

Functional Profiling of Unfamiliar Microbial Communities Using a Validated De Novo Assembly Metatranscriptome Pipeline.

ABSTRACT: BACKGROUND:Metatranscriptomic landscapes can provide insights in functional relationships within natural microbial communities. Analysis of complex metatranscriptome datasets of these communities poses a considerable bioinformatic challenge since they are non-restricted with a varying number of participating strains and species. For RNA-Seq data a standard approach is to align the generated reads to a set of closely related reference genomes. This only works well for microbial communities for which a near complete catalogue of reference genomes is available at a small evolutionary distance. In this study, we focus on the design of a validated de novo metatranscriptome assembly pipeline for single-end Illumina RNA-Seq data to obtain functional and taxonomic profiles of murine microbial communities. RESULTS:The here developed de novo assembly metatranscriptome pipeline combined rRNA removal, IDBA-UD assembler, functional annotation and taxonomic classification. Different assemblers were tested and validated using RNA-Seq data from an in silico generated mock community and in vivo RNA-Seq data from a restricted microbial community taken from a mouse model colonized with Altered Schaedler Flora (ASF). Precision and recall of resulting gene expression, functional and taxonomic profiles were compared to those obtained with a standard alignment method. The validated pipeline was subsequently used to generate expression profiles from non-restricted cecal communities of four C57BL/6J mice fed on a high-fat high-protein diet spiked with an RNA-Seq data set from a well-characterized human sample. The spike in control was used to estimate precision and recall at assembly, functional and taxonomic level of non-restricted communities. CONCLUSIONS:A generic de novo assembly pipeline for metatranscriptome data analysis was designed for microbial ecosystems, which can be applied for microbial metatranscriptome analysis in any chosen niche.

SUBMITTER: Davids M

PROVIDER: S-EPMC4710500 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Functional Profiling of Unfamiliar Microbial Communities Using a Validated De Novo Assembly Metatranscriptome Pipeline.

Davids Mark M Hugenholtz Floor F Martins dos Santos Vitor V Smidt Hauke H Kleerebezem Michiel M Schaap Peter J PJ

PloS one 20160112 1

<h4>Background</h4>Metatranscriptomic landscapes can provide insights in functional relationships within natural microbial communities. Analysis of complex metatranscriptome datasets of these communities poses a considerable bioinformatic challenge since they are non-restricted with a varying number of participating strains and species. For RNA-Seq data a standard approach is to align the generated reads to a set of closely related reference genomes. This only works well for microbial communitie ...[more]

PMID: 26756338

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Project description:Scleractinian corals are a vital component of coral reef ecosystems, and of significant cultural and economic value worldwide. As anthropogenic and natural stressors are contributing to a global decline of coral reefs, understanding coral health is critical to help preserve these ecosystems. Growth anomaly (GA) is a coral disease that has significant negative impacts on coral biology, yet our understanding of its etiology and pathology is lacking. In this study we used RNA-seq along with de novo metatranscriptome assembly and homology assignment to identify coral genes that are expressed in three distinct coral tissue types: tissue from healthy corals ("healthy"), GA lesion tissue from diseased corals ("GA-affected") and apparently healthy tissue from diseased corals ("GA-unaffected"). We conducted pairwise comparisons of gene expression among these three tissue types to identify genes and pathways that help us to unravel the molecular pathology of this coral disease.The quality-filtered de novo-assembled metatranscriptome contained 76,063 genes, of which 13,643 were identified as putative coral genes. Overall gene expression profiles of coral genes revealed high similarity between healthy tissue samples, in contrast to high variance among diseased samples. This indicates GA has a variety of genetic effects at the colony level, including on seemingly healthy (GA-unaffected) tissue. A total of 105 unique coral genes were found differentially expressed among tissue types. Pairwise comparisons revealed the greatest number of differentially expressed genes between healthy and GA-affected tissue (93 genes), followed by healthy and GA-unaffected tissue (33 genes), and GA-affected and -unaffected tissue (7 genes). The putative function of these genes suggests GA is associated with changes in the activity of genes involved in developmental processes and activation of the immune system.This is one of the first transcriptome-level studies to investigate coral GA, and the first metatranscriptome assembly for the M. capitata holobiont. The gene expression data, metatranscriptome assembly and methodology developed through this study represent a significant addition to the molecular information available to further our understanding of this coral disease.

Project description:BackgroundGenomic data have become major resources to understand complex mechanisms at fine-scale temporal and spatial resolution in functional and evolutionary genetic studies, including human diseases, such as cancers. Recently, a large number of whole genomes of evolving populations of yeast (Saccharomyces cerevisiae W303 strain) were sequenced in a time-dependent manner to identify temporal evolutionary patterns. For this type of study, a chromosome-level sequence assembly of the strain or population at time zero is required to compare with the genomes derived later. However, there is no fully automated computational approach in experimental evolution studies to establish the chromosome-level genome assembly using unique features of sequencing data.Methods and resultsIn this study, we developed a new software pipeline, the integrative meta-assembly pipeline (IMAP), to build chromosome-level genome sequence assemblies by generating and combining multiple initial assemblies using three de novo assemblers from short-read sequencing data. We significantly improved the continuity and accuracy of the genome assembly using a large collection of sequencing data and hybrid assembly approaches. We validated our pipeline by generating chromosome-level assemblies of yeast strains W303 and SK1, and compared our results with assemblies built using long-read sequencing and various assembly evaluation metrics. We also constructed chromosome-level sequence assemblies of S. cerevisiae strain Sigma1278b, and three commonly used fungal strains: Aspergillus nidulans A713, Neurospora crassa 73, and Thielavia terrestris CBS 492.74, for which long-read sequencing data are not yet available. Finally, we examined the effect of IMAP parameters, such as reference and resolution, on the quality of the final assembly of the yeast strains W303 and SK1.ConclusionsWe developed a cost-effective pipeline to generate chromosome-level sequence assemblies using only short-read sequencing data. Our pipeline combines the strengths of reference-guided and meta-assembly approaches. Our pipeline is available online at http://github.com/jkimlab/IMAP including a Docker image, as well as a Perl script, to help users install the IMAP package, including several prerequisite programs. Users can use IMAP to easily build the chromosome-level assembly for the genome of their interest.

Dataset Information

Functional Profiling of Unfamiliar Microbial Communities Using a Validated De Novo Assembly Metatranscriptome Pipeline.

Publications

Functional Profiling of Unfamiliar Microbial Communities Using a Validated De Novo Assembly Metatranscriptome Pipeline.

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