Dataset Information

LeeHom: adaptor trimming and merging for Illumina sequencing reads.

ABSTRACT: The sequencing of libraries containing molecules shorter than the read length, such as in ancient or forensic applications, may result in the production of reads that include the adaptor, and in paired reads that overlap one another. Challenges for the processing of such reads are the accurate identification of the adaptor sequence and accurate reconstruction of the original sequence most likely to have given rise to the observed read(s). We introduce an algorithm that removes the adaptors and reconstructs the original DNA sequences using a Bayesian maximum a posteriori probability approach. Our algorithm is faster, and provides a more accurate reconstruction of the original sequence for both simulated and ancient DNA data sets, than other approaches. leeHom is released under the GPLv3 and is freely available from: https://bioinf.eva.mpg.de/leehom/

SUBMITTER: Renaud G

PROVIDER: S-EPMC4191382 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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leeHom: adaptor trimming and merging for Illumina sequencing reads.

Renaud Gabriel G Stenzel Udo U Kelso Janet J

Nucleic acids research 20140806 18

The sequencing of libraries containing molecules shorter than the read length, such as in ancient or forensic applications, may result in the production of reads that include the adaptor, and in paired reads that overlap one another. Challenges for the processing of such reads are the accurate identification of the adaptor sequence and accurate reconstruction of the original sequence most likely to have given rise to the observed read(s). We introduce an algorithm that removes the adaptors and r ...[more]

PMID: 25100869

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Project description:High-throughput RNA-Sequencing (RNA-Seq) has become the preferred technique for studying gene expression differences between biological samples and for discovering novel isoforms, though the techniques to analyze the resulting data are still immature. One pre-processing step that is widely but heterogeneously applied is trimming, in which low quality bases, identified by the probability that they are called incorrectly, are removed. However, the impact of trimming on subsequent alignment to a genome could influence downstream analyses including gene expression estimation; we hypothesized that this might occur in an inconsistent manner across different genes, resulting in differential bias.To assess the effects of trimming on gene expression, we generated RNA-Seq data sets from four samples of larval Drosophila melanogaster sensory neurons, and used three trimming algorithms--SolexaQA, Trimmomatic, and ConDeTri-to perform quality-based trimming across a wide range of stringencies. After aligning the reads to the D. melanogaster genome with TopHat2, we used Cuffdiff2 to compare the original, untrimmed gene expression estimates to those following trimming. With the most aggressive trimming parameters, over ten percent of genes had significant changes in their estimated expression levels. This trend was seen with two additional RNA-Seq data sets and with alternative differential expression analysis pipelines. We found that the majority of the expression changes could be mitigated by imposing a minimum length filter following trimming, suggesting that the differential gene expression was primarily being driven by spurious mapping of short reads. Slight differences with the untrimmed data set remained after length filtering, which were associated with genes with low exon numbers and high GC content. Finally, an analysis of paired RNA-seq/microarray data sets suggests that no or modest trimming results in the most biologically accurate gene expression estimates.We find that aggressive quality-based trimming has a large impact on the apparent makeup of RNA-Seq-based gene expression estimates, and that short reads can have a particularly strong impact. We conclude that implementation of trimming in RNA-Seq analysis workflows warrants caution, and if used, should be used in conjunction with a minimum read length filter to minimize the introduction of unpredictable changes in expression estimates.

Project description:MotivationIllumina Sequencing data can provide high coverage of a genome by relatively short (most often 100 bp to 150 bp) reads at a low cost. Even with low (advertised 1%) error rate, 100 × coverage Illumina data on average has an error in some read at every base in the genome. These errors make handling the data more complicated because they result in a large number of low-count erroneous k-mers in the reads. However, there is enough information in the reads to correct most of the sequencing errors, thus making subsequent use of the data (e.g. for mapping or assembly) easier. Here we use the term "error correction" to denote the reduction in errors due to both changes in individual bases and trimming of unusable sequence. We developed an error correction software called QuorUM. QuorUM is mainly aimed at error correcting Illumina reads for subsequent assembly. It is designed around the novel idea of minimizing the number of distinct erroneous k-mers in the output reads and preserving the most true k-mers, and we introduce a composite statistic π that measures how successful we are at achieving this dual goal. We evaluate the performance of QuorUM by correcting actual Illumina reads from genomes for which a reference assembly is available.ResultsWe produce trimmed and error-corrected reads that result in assemblies with longer contigs and fewer errors. We compared QuorUM against several published error correctors and found that it is the best performer in most metrics we use. QuorUM is efficiently implemented making use of current multi-core computing architectures and it is suitable for large data sets (1 billion bases checked and corrected per day per core). We also demonstrate that a third-party assembler (SOAPdenovo) benefits significantly from using QuorUM error-corrected reads. QuorUM error corrected reads result in a factor of 1.1 to 4 improvement in N50 contig size compared to using the original reads with SOAPdenovo for the data sets investigated.AvailabilityQuorUM is distributed as an independent software package and as a module of the MaSuRCA assembly software. Both are available under the GPL open source license at http://www.genome.umd.edu.Contactgmarcais@umd.edu.

Dataset Information

LeeHom: adaptor trimming and merging for Illumina sequencing reads.

Publications

leeHom: adaptor trimming and merging for Illumina sequencing reads.

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