Dataset Information

Efficient frequency-based de novo short-read clustering for error trimming in next-generation sequencing.

ABSTRACT: Novel massively parallel sequencing technologies provide highly detailed structures of transcriptomes and genomes by yielding deep coverage of short reads, but their utility is limited by inadequate sequencing quality and short-read lengths. Sequencing-error trimming in short reads is therefore a vital process that could improve the rate of successful reference mapping and polymorphism detection. Toward this aim, we herein report a frequency-based, de novo short-read clustering method that organizes erroneous short sequences originating in a single abundant sequence into a tree structure; in this structure, each "child" sequence is considered to be stochastically derived from its more abundant "parent" sequence with one mutation through sequencing errors. The root node is the most frequently observed sequence that represents all erroneous reads in the entire tree, allowing the alignment of the reliable representative read to the genome without the risk of mapping erroneous reads to false-positive positions. This method complements base calling and the error correction of making direct alignments with the reference genome, and is able to improve the overall accuracy of short-read alignment by consulting the inherent relationships among the entire set of reads. The algorithm runs efficiently with a linear time complexity. In addition, an error rate evaluation model can be derived from bacterial artificial chromosome sequencing data obtained in the same run as a control. In two clustering experiments using small RNA and 5'-end mRNA reads data sets, we confirmed a remarkable increase ( approximately 5%) in the percentage of short reads aligned to the reference sequence.

SUBMITTER: Qu W

PROVIDER: S-EPMC2704438 | biostudies-literature | 2009 Jul

REPOSITORIES: biostudies-literature

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Efficient frequency-based de novo short-read clustering for error trimming in next-generation sequencing.

Qu Wei W Hashimoto Shin-Ichi S Morishita Shinichi S

Genome research 20090513 7

Novel massively parallel sequencing technologies provide highly detailed structures of transcriptomes and genomes by yielding deep coverage of short reads, but their utility is limited by inadequate sequencing quality and short-read lengths. Sequencing-error trimming in short reads is therefore a vital process that could improve the rate of successful reference mapping and polymorphism detection. Toward this aim, we herein report a frequency-based, de novo short-read clustering method that organ ...[more]

PMID: 19439514

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Project description:BACKGROUND: RNA-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. RESULTS: We 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. CONCLUSIONS: With 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.

Dataset Information

Efficient frequency-based de novo short-read clustering for error trimming in next-generation sequencing.

Publications

Efficient frequency-based de novo short-read clustering for error trimming in next-generation sequencing.

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