Dataset Information

Compacting and correcting Trinity and Oases RNA-Seq de novo assemblies.

ABSTRACT:

Background

De novo transcriptome assembly of short reads is now a common step in expression analysis of organisms lacking a reference genome sequence. Several software packages are available to perform this task. Even if their results are of good quality it is still possible to improve them in several ways including redundancy reduction or error correction. Trinity and Oases are two commonly used de novo transcriptome assemblers. The contig sets they produce are of good quality. Still, their compaction (number of contigs needed to represent the transcriptome) and their quality (chimera and nucleotide error rates) can be improved.

Results

We built a de novo RNA-Seq Assembly Pipeline (DRAP) which wraps these two assemblers (Trinity and Oases) in order to improve their results regarding the above-mentioned criteria. DRAP reduces from 1.3 to 15 fold the number of resulting contigs of the assemblies depending on the read set and the assembler used. This article presents seven assembly comparisons showing in some cases drastic improvements when using DRAP. DRAP does not significantly impair assembly quality metrics such are read realignment rate or protein reconstruction counts.

Conclusion

Transcriptome assembly is a challenging computational task even if good solutions are already available to end-users, these solutions can still be improved while conserving the overall representation and quality of the assembly. The de novo RNA-Seq Assembly Pipeline (DRAP) is an easy to use software package to produce compact and corrected transcript set. DRAP is free, open-source and available under GPL V3 license at http://www.sigenae.org/drap.

SUBMITTER: Cabau C

PROVIDER: S-EPMC5316280 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

Compacting and correcting Trinity and Oases RNA-Seq <i>de novo</i> assemblies.

Cabau Cédric C Escudié Frédéric F Djari Anis A Guiguen Yann Y Bobe Julien J Klopp Christophe C

PeerJ 20170216

<h4>Background</h4><i>De novo</i> transcriptome assembly of short reads is now a common step in expression analysis of organisms lacking a reference genome sequence. Several software packages are available to perform this task. Even if their results are of good quality it is still possible to improve them in several ways including redundancy reduction or error correction. Trinity and Oases are two commonly used <i>de novo</i> transcriptome assemblers. The contig sets they produce are of good qua ...[more]

PMID: 28224052

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Project description:BackgroundFor plant species with unsequenced genomes, cDNA contigs created by de novo assembly of RNA-Seq reads are used as reference sequences for comparative analysis of RNA-Seq datasets and the detection of differentially expressed genes (DEGs). Redundancies in such contigs are evident in previous RNA-Seq studies, and such redundancies can lead to difficulties in subsequent analysis. Nevertheless, the effects of removing redundancy from contig assemblies on comparative RNA-Seq analysis have not been evaluated.ResultsHere we describe a method for removing redundancy from raw contigs that were primarily created by de novo assembly of Arabidopsis thaliana RNA-Seq reads. Specifically, the contigs with the highest bit scores were selected from raw contigs by a homology search against the gene dataset in the TAIR10 database. The two existing methods for removal of redundancy based on contig length or clustering analysis used to eliminate redundancies from raw contigs. Contig number was reduced most effectively with the method based on homology search. In a comparative analysis of RNA-Seq datasets, DEGs detected in contigs that underwent redundancy removal via the homology search method showed the highest identity to the DEGs detected when the TAIR10 gene dataset was used as an exact reference. Redundancy in raw contigs could also be removed by a homology search against integrated protein datasets from several plant species other than A. thaliana. DEGs detected using contigs that underwent such redundancy-removed also showed high homology to DEGs detected using the TAIR10 gene dataset.ConclusionHere we describe a method for removing redundant contigs within raw contigs; this method involves a homology search against a gene or protein database. In principal, this method can be used with unsequenced plant genomes that lack a well-developed gene database. Redundant contigs were not removed adequately via either of two existing methods, but our method allowed for removal of all redundant contigs. To our knowledge, this is the first reported improvement in accurate detection of DEGs via comparative RNA-Seq analysis that involved preparation of a non-redundant reference sequence. This method could be used to rapidly and cost-effectively detect useful genes in unsequenced plants.