Bioinformatics for Whole-Genome Shotgun Sequencing of Microbial Communities

By Kevin C. Chen, Lior S. Pachter. PLoS Comput. Biol., 2005

403 citations recorded by the source corpus.

The application of whole-genome shotgun sequencing to microbial communities represents a major development in metagenomics, the study of uncultured microbes via the tools of modern genomic analysis. In the past year, whole-genome shotgun sequencing projects of prokaryotic communities from an acid mine biofilm, the Sargasso Sea, Minnesota farm soil, three deep-sea whale falls, and deep-sea sediments have been reported, adding to previously published work on viral communities from marine and fecal samples. The interpretation of this new kind of data poses a wide variety of exciting and difficult bioinformatics problems. The aim of this review is to introduce the bioinformatics community to this emerging field by surveying existing techniques and promising new approaches for several of the most interesting of these computational problems.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1f4d6c1d0a7191c677049444e05dc282d46a34e1

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Citations in the corpus

MetaVelvet: an extension of Velvet assembler to de novo metagenome assembly from short sequence reads (2011, neutral): “Third approach (not de novo ) is the comparative genome analysis mapping reads or aligning contigs to the reference genomes [9, 3, 13].”
Chapter 12: Human Microbiome Analysis (2012, background): “The term metagenomics is used with some frequency to describe the entire body of high-throughput studies now possible with microbial communities, although it also refers more specifically to whole-metagenome shotgun (WMS) sequencing of genomic DNA fragments from a community’s metagenome [38,39].”
Experimental and analytical tools for studying the human microbiome (2011, background): “Finally, the accumulation of more reference genomes will result in a faster pace at which we can reconstruct new genomes from short reads by using co-assembly method”
IMG/M: a data management and analysis system for metagenomes (2007, methodology): “For each gene on “ Reference Genome Context Viewer” and “ Protein Recruitment Plot” , locus tag and scaffold coordinates are provided locally (by placing the cursor over the gene), while additional information is available in the “ Gene Details ” page which is linked to each gene.”
MetaGene: prokaryotic gene finding from environmental genome shotgun sequences (2006, background): “Gene-finding is a fundamental goal in virtually all metagenomics projects, but the only realistic alternative is similarity searches of the sequence database or the metagenome itself (9).”
A Bioinformatician's Guide to Metagenomics (2008, background): “Chen and Pachter estimated that 6 Gbp of sequence data would be required to sample half the genes in a simulated soil community (21), whereas a typical metagenome project is on the order of 100 Mbp.”
Ab initio gene identification in metagenomic sequences (2010, background): “A vast majority (99%) of microbial species in a given community are likely to be non-cultivable (1).”
Clustering metagenomic sequences with interpolated Markov models (2010, background): “Because these reads come from a diverse population of microbial strains, assembly produces a large collection of small contigs (contiguous stretches of unambiguously overlapping reads) [13,14].”
Abiotic Stress Responses and Microbe-Mediated Mitigation in Plants: The Omics Strategies (2017, background): “The culture-independent approach for the analysis of microbial communities has been a powerful tool for resolution of yetuncultured, unseen microbial diversity that plays various role in the plant rhizosphere (Chen and Pachter, 2005) (Figure 3).”
DRISA: A DRAM-based Reconfigurable In-Situ Accelerator (2017, methodology): “Emerging applications, such as deep learning S. Li et al. and bioinformatics (like meta-genome data analysis [21]), are both compute and memory intensive, with a challenging demand for both powerful computing and large memory capacity/bandwidth (the upper right corner in Figure 1), which may not be…”
PhyloSift: phylogenetic analysis of genomes and metagenomes (2014, background): “Perhaps the biggest challenges lie in analysis 37 of the data [12].”