Optimizing de novo assembly of short-read RNA-seq data for phylogenomics

Ya Yang,Stephen A Smith

doi:10.1186/1471-2164-14-328

Abstract

BackgroundRNA-seq has shown huge potential for phylogenomic inferences in non-model organisms. However, error, incompleteness, and redundant assembled transcripts for each gene in de novo assembly of short reads cause noise in analyses and a large amount of missing data in the aligned matrix. To address these problems, we compare de novo assemblies of paired end 90 bp RNA-seq reads using Oases, Trinity, Trans-ABySS and SOAPdenovo-Trans to transcripts from genome annotation of the model plant Ricinus communis. By doing so we evaluate strategies for optimizing total gene coverage and minimizing assembly chimeras and redundancy.ResultsWe found that the frequency and structure of chimeras vary dramatically among different software packages. The differences were largely due to the number of trans-self chimeras that contain repeats in the opposite direction. More than half of the total chimeras in Oases and Trinity were trans-self chimeras. Within each package, we found a trade-off between maximizing reference coverage and minimizing redundancy and chimera rate. In order to reduce redundancy, we investigated three methods: 1) using cap3 and CD-HIT-EST to combine highly similar transcripts, 2) only retaining the transcript with the highest read coverage, or removing the transcript with the lowest read coverage for each subcomponent in Trinity, and 3) filtering Oases single k-mer assemblies by number of transcripts per locus and relative transcript length, and then finding the transcript with the highest read coverage. We then utilized results from blastx against model protein sequences to effectively remove trans chimeras. After optimization, seven assembly strategies among all four packages successfully assembled 42.9–47.1% of reference genes to more than 200 bp, with a chimera rate of 0.92–2.21%, and on average 1.8–3.1 transcripts per reference gene assembled.ConclusionsWith rapidly improving sequencing and assembly tools, our study provides a framework to benchmark and optimize performance before choosing tools or parameter combinations for analyzing short-read RNA-seq data. Our study demonstrates that choice of assembly package, k-mer sizes, post-assembly redundancy-reduction and chimera cleanup, and strand-specific RNA-seq library preparation and assembly dramatically improves gene coverage by non-redundant and non-chimeric transcripts that are optimized for downstream phylogenomic analyses.

Highlights

RNA-seq has shown huge potential for phylogenomic inferences in non-model organisms
De novo assembly of short RNA-seq reads recovered up to half of total genes The RNA-seq data set consisted of 11,041,065 read pairs
This number could be further increased with deeper sequencing depth or a higher diversity of tissue types. Such high gene coverage demonstrates the huge potential of RNA-seq data in obtaining exome sequences in non-model organisms. It raises the question of why many phylogenomic analyses that use short-read RNA-seq data only include hundreds of genes [9,24,25,26], instead of thousands or even tens of thousands of genes, as in similar studies that incorporate longer reads from Sanger or 454 sequencing [27,28]

Summary

Introduction

Error, incompleteness, and redundant assembled transcripts for each gene in de novo assembly of short reads cause noise in analyses and a large amount of missing data in the aligned matrix. With the recent and rapid advance of sequencing techniques, transcriptome sequencing (RNA-seq) has emerged as a powerful tool for obtaining large amount of functional genomic data in non-model organisms This has encouraged efforts such as the One Thousand Plants Project, or 1KP [1], and many other transcriptome projects. One of the biggest challenges is to accurately assemble the short reads from non-model organisms that do not have any reference genome, or de novo transcriptome assembly Because this is the first step in any phylogenomic analysis, problems at this stage (incomplete assembly, assembly errors, and redundancy) cause difficulties for downstream analyses including ortholog and paralog identification, alignment, and matrix construction. These problems increase the amount of missing data in the final aligned matrix, limiting the amount of useful transcriptomic data for phylogenomics

Methods

Results

Conclusion