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A sensitive short read homology search tool for paired-end read sequencing data.

ABSTRACT: Homology search is still a significant step in functional analysis for genomic data. Profile Hidden Markov Model-based homology search has been widely used in protein domain analysis in many different species. In particular, with the fast accumulation of transcriptomic data of non-model species and metagenomic data, profile homology search is widely adopted in integrated pipelines for functional analysis. While the state-of-the-art tool HMMER has achieved high sensitivity and accuracy in domain annotation, the sensitivity of HMMER on short reads declines rapidly. The low sensitivity on short read homology search can lead to inaccurate domain composition and abundance computation. Our experimental results showed that half of the reads were missed by HMMER for a RNA-Seq dataset. Thus, there is a need for better methods to improve the homology search performance for short reads.We introduce a profile homology search tool named Short-Pair that is designed for short paired-end reads. By using an approximate Bayesian approach employing distribution of fragment lengths and alignment scores, Short-Pair can retrieve the missing end and determine true domains. In particular, Short-Pair increases the accuracy in aligning short reads that are part of remote homologs. We applied Short-Pair to a RNA-Seq dataset and a metagenomic dataset and quantified its sensitivity and accuracy on homology search. The experimental results show that Short-Pair can achieve better overall performance than the state-of-the-art methodology of profile homology search.Short-Pair is best used for next-generation sequencing (NGS) data that lack reference genomes. It provides a complementary paired-end read homology search tool to HMMER. The source code is freely available at https://sourceforge.net/projects/short-pair/ .

SUBMITTER: Techa-Angkoon P

PROVIDER: S-EPMC5657049 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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A sensitive short read homology search tool for paired-end read sequencing data.

Techa-Angkoon Prapaporn P Sun Yanni Y Lei Jikai J

BMC bioinformatics 20171016 Suppl 12

<h4>Background</h4>Homology search is still a significant step in functional analysis for genomic data. Profile Hidden Markov Model-based homology search has been widely used in protein domain analysis in many different species. In particular, with the fast accumulation of transcriptomic data of non-model species and metagenomic data, profile homology search is widely adopted in integrated pipelines for functional analysis. While the state-of-the-art tool HMMER has achieved high sensitivity and ...[more]

PMID: 29072140

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Project description:BackgroundA cancer genome is derived from the germline genome through a series of somatic mutations. Somatic structural variants - including duplications, deletions, inversions, translocations, and other rearrangements - result in a cancer genome that is a scrambling of intervals, or "blocks" of the germline genome sequence. We present an efficient algorithm for reconstructing the block organization of a cancer genome from paired-end DNA sequencing data.ResultsBy aligning paired reads from a cancer genome - and a matched germline genome, if available - to the human reference genome, we derive: (i) a partition of the reference genome into intervals; (ii) adjacencies between these intervals in the cancer genome; (iii) an estimated copy number for each interval. We formulate the Copy Number and Adjacency Genome Reconstruction Problem of determining the cancer genome as a sequence of the derived intervals that is consistent with the measured adjacencies and copy numbers. We design an efficient algorithm, called Paired-end Reconstruction of Genome Organization (PREGO), to solve this problem by reducing it to an optimization problem on an interval-adjacency graph constructed from the data. The solution to the optimization problem results in an Eulerian graph, containing an alternating Eulerian tour that corresponds to a cancer genome that is consistent with the sequencing data. We apply our algorithm to five ovarian cancer genomes that were sequenced as part of The Cancer Genome Atlas. We identify numerous rearrangements, or structural variants, in these genomes, analyze reciprocal vs. non-reciprocal rearrangements, and identify rearrangements consistent with known mechanisms of duplication such as tandem duplications and breakage/fusion/bridge (B/F/B) cycles.ConclusionsWe demonstrate that PREGO efficiently identifies complex and biologically relevant rearrangements in cancer genome sequencing data. An implementation of the PREGO algorithm is available at http://compbio.cs.brown.edu/software/.

Dataset Information

A sensitive short read homology search tool for paired-end read sequencing data.

Publications

A sensitive short read homology search tool for paired-end read sequencing data.

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