Literature DB >> 23829653

IDBA-MT: de novo assembler for metatranscriptomic data generated from next-generation sequencing technology.

Henry C M Leung1, Siu-Ming Yiu, John Parkinson, Francis Y L Chin.   

Abstract

High-throughput next-generation sequencing technology provides a great opportunity for analyzing metatranscriptomic data. However, the reads produced by these technologies are short and an assembling step is required to combine the short reads into longer contigs. As there are many repeat patterns in mRNAs from different genomes and the abundance ratio of mRNAs in a sample varies a lot, existing assemblers for genomic data, transcriptomic data, and metagenomic data do not work on metatranscriptomic data and produce chimeric contigs, that is, incorrect contigs formed by merging multiple mRNA sequences. To our best knowledge, there is no assembler designed for metatranscriptomic data. In this article, we introduce an assembler called IDBA-MT, which is designed for assembling reads from metatranscriptomic data. IDBA-MT produces much fewer chimeric contigs (reduce by 50% or more) when compared with existing assemblers such as Oases, IDBA-UD, and Trinity.

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Year:  2013        PMID: 23829653     DOI: 10.1089/cmb.2013.0042

Source DB:  PubMed          Journal:  J Comput Biol        ISSN: 1066-5277            Impact factor:   1.479


  12 in total

Review 1.  Microbial genome-enabled insights into plant-microorganism interactions.

Authors:  David S Guttman; Alice C McHardy; Paul Schulze-Lefert
Journal:  Nat Rev Genet       Date:  2014-09-30       Impact factor: 53.242

2.  rnaSPAdes: a de novo transcriptome assembler and its application to RNA-Seq data.

Authors:  Elena Bushmanova; Dmitry Antipov; Alla Lapidus; Andrey D Prjibelski
Journal:  Gigascience       Date:  2019-09-01       Impact factor: 6.524

Review 3.  Systems-based approaches to unravel multi-species microbial community functioning.

Authors:  Florence Abram
Journal:  Comput Struct Biotechnol J       Date:  2014-12-03       Impact factor: 7.271

4.  Comparison of assembly algorithms for improving rate of metatranscriptomic functional annotation.

Authors:  Albi Celaj; Janet Markle; Jayne Danska; John Parkinson
Journal:  Microbiome       Date:  2014-10-28       Impact factor: 14.650

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

Authors:  Mark Davids; Floor Hugenholtz; Vitor Martins dos Santos; Hauke Smidt; Michiel Kleerebezem; Peter J Schaap
Journal:  PLoS One       Date:  2016-01-12       Impact factor: 3.240

6.  Utilizing de Bruijn graph of metagenome assembly for metatranscriptome analysis.

Authors:  Yuzhen Ye; Haixu Tang
Journal:  Bioinformatics       Date:  2015-08-29       Impact factor: 6.937

7.  IMP: a pipeline for reproducible reference-independent integrated metagenomic and metatranscriptomic analyses.

Authors:  Shaman Narayanasamy; Yohan Jarosz; Emilie E L Muller; Anna Heintz-Buschart; Malte Herold; Anne Kaysen; Cédric C Laczny; Nicolás Pinel; Patrick May; Paul Wilmes
Journal:  Genome Biol       Date:  2016-12-16       Impact factor: 13.583

Review 8.  Experimental design and quantitative analysis of microbial community multiomics.

Authors:  Himel Mallick; Siyuan Ma; Eric A Franzosa; Tommi Vatanen; Xochitl C Morgan; Curtis Huttenhower
Journal:  Genome Biol       Date:  2017-11-30       Impact factor: 13.583

9.  SAMSA2: a standalone metatranscriptome analysis pipeline.

Authors:  Samuel T Westreich; Michelle L Treiber; David A Mills; Ian Korf; Danielle G Lemay
Journal:  BMC Bioinformatics       Date:  2018-05-21       Impact factor: 3.169

10.  K-mer clustering algorithm using a MapReduce framework: application to the parallelization of the Inchworm module of Trinity.

Authors:  Chang Sik Kim; Martyn D Winn; Vipin Sachdeva; Kirk E Jordan
Journal:  BMC Bioinformatics       Date:  2017-11-03       Impact factor: 3.169

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