Dataset Information

Proovread: large-scale high-accuracy PacBio correction through iterative short read consensus.

ABSTRACT: Today, the base code of DNA is mostly determined through sequencing by synthesis as provided by the Illumina sequencers. Although highly accurate, resulting reads are short, making their analyses challenging. Recently, a new technology, single molecule real-time (SMRT) sequencing, was developed that could address these challenges, as it generates reads of several thousand bases. But, their broad application has been hampered by a high error rate. Therefore, hybrid approaches that use high-quality short reads to correct erroneous SMRT long reads have been developed. Still, current implementations have great demands on hardware, work only in well-defined computing infrastructures and reject a substantial amount of reads. This limits their usability considerably, especially in the case of large sequencing projects.Here we present proovread, a hybrid correction pipeline for SMRT reads, which can be flexibly adapted on existing hardware and infrastructure from a laptop to a high-performance computing cluster. On genomic and transcriptomic test cases covering Escherichia coli, Arabidopsis thaliana and human, proovread achieved accuracies up to 99.9% and outperformed the existing hybrid correction programs. Furthermore, proovread-corrected sequences were longer and the throughput was higher. Thus, proovread combines the most accurate correction results with an excellent adaptability to the available hardware. It will therefore increase the applicability and value of SMRT sequencing.proovread is available at the following URL: http://proovread.bioapps.biozentrum.uni-wuerzburg.de.

SUBMITTER: Hackl T

PROVIDER: S-EPMC4609002 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

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proovread: large-scale high-accuracy PacBio correction through iterative short read consensus.

Hackl Thomas T Hedrich Rainer R Schultz Jörg J Förster Frank F

Bioinformatics (Oxford, England) 20140710 21

<h4>Motivation</h4>Today, the base code of DNA is mostly determined through sequencing by synthesis as provided by the Illumina sequencers. Although highly accurate, resulting reads are short, making their analyses challenging. Recently, a new technology, single molecule real-time (SMRT) sequencing, was developed that could address these challenges, as it generates reads of several thousand bases. But, their broad application has been hampered by a high error rate. Therefore, hybrid approaches t ...[more]

PMID: 25015988

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Project description:BACKGROUND: High-throughput short read sequencing is revolutionizing genomics and systems biology research by enabling cost-effective deep coverage sequencing of genomes and transcriptomes. Error detection and correction are crucial to many short read sequencing applications including de novo genome sequencing, genome resequencing, and digital gene expression analysis. Short read error detection is typically carried out by counting the observed frequencies of kmers in reads and validating those with frequencies exceeding a threshold. In case of genomes with high repeat content, an erroneous kmer may be frequently observed if it has few nucleotide differences with valid kmers with multiple occurrences in the genome. Error detection and correction were mostly applied to genomes with low repeat content and this remains a challenging problem for genomes with high repeat content. RESULTS: We develop a statistical model and a computational method for error detection and correction in the presence of genomic repeats. We propose a method to infer genomic frequencies of kmers from their observed frequencies by analyzing the misread relationships among observed kmers. We also propose a method to estimate the threshold useful for validating kmers whose estimated genomic frequency exceeds the threshold. We demonstrate that superior error detection is achieved using these methods. Furthermore, we break away from the common assumption of uniformly distributed errors within a read, and provide a framework to model position-dependent error occurrence frequencies common to many short read platforms. Lastly, we achieve better error correction in genomes with high repeat content. AVAILABILITY: The software is implemented in C++ and is freely available under GNU GPL3 license and Boost Software V1.0 license at "http://aluru-sun.ece.iastate.edu/doku.php?id = redeem". CONCLUSIONS: We introduce a statistical framework to model sequencing errors in next-generation reads, which led to promising results in detecting and correcting errors for genomes with high repeat content.

Project description:BackgroundMiscanthus sinensis Andersson is a perennial grass that exhibits remarkable lignocellulose characteristics suitable for sustainable bioenergy production. However, knowledge of the genetic resources of this species is relatively limited, which considerably hampers further work on its biology and genetic improvement.ResultsIn this study, through analyzing the transcriptome of mixed samples of leaves and stems using the latest PacBio Iso-Seq sequencing technology combined with Illumina HiSeq, we report the first full-length transcriptome dataset of M. sinensis with a total of 58.21 Gb clean data. An average of 15.75 Gb clean reads of each sample were obtained from the PacBio Iso-Seq system, which doubled the data size (6.68 Gb) obtained from the Illumina HiSeq platform. The integrated analyses of PacBio- and Illumina-based transcriptomic data uncovered 408,801 non-redundant transcripts with an average length of 1,685 bp. Of those, 189,406 transcripts were commonly identified by both methods, 169,149 transcripts with an average length of 619 bp were uniquely identified by Illumina HiSeq, and 51,246 transcripts with an average length of 2,535 bp were uniquely identified by PacBio Iso-Seq. Approximately 96 % of the final combined transcripts were mapped back to the Miscanthus genome, reflecting the high quality and coverage of our sequencing results. When comparing our data with genomes of four species of Andropogoneae, M. sinensis showed the closest relationship with sugarcane with up to 93 % mapping ratios, followed by sorghum with up to 80 % mapping ratios, indicating a high conservation of orthologs in these three genomes. Furthermore, 306,228 transcripts were successfully annotated against public databases including cell wall related genes and transcript factor families, thus providing many new insights into gene functions. The PacBio Iso-Seq data also helped identify 3,898 alternative splicing events and 2,963 annotated AS isoforms within 10 function categories.ConclusionsTaken together, the present study provides a rich data set of full-length transcripts that greatly enriches our understanding of M. sinensis transcriptomic resources, thus facilitating further genetic improvement and molecular studies of the Miscanthus species.

Dataset Information

Proovread: large-scale high-accuracy PacBio correction through iterative short read consensus.

Publications

proovread: large-scale high-accuracy PacBio correction through iterative short read consensus.

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