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Identifying structural variation in haploid microbial genomes from short-read resequencing data using breseq.

Barrick JE, Colburn G, Deatherage DE, Traverse CC, Strand MD, Borges JJ, Knoester DB, Reba A, Meyer AG - BMC Genomics (2014)

Bottom Line: They typically disregard information in reads mapping to repeat sequences, and significant post-processing and manual examination of their output is often required to rule out false-positive predictions and precisely describe mutational events.Finally, breseq combines predictions of new junctions and deleted chromosomal regions to output biologically relevant descriptions of mutations and their effects on genes.In these cases, breseq can discover mutations that may be responsible for important or unintended changes in genomes that might otherwise go undetected.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular Biosciences, Institute for Cellular and Molecular Biology, Center for Systems and Synthetic Biology, Center for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, TX 78712, USA. jbarrick@cm.utexas.edu.

ABSTRACT

Background: Mutations that alter chromosomal structure play critical roles in evolution and disease, including in the origin of new lifestyles and pathogenic traits in microbes. Large-scale rearrangements in genomes are often mediated by recombination events involving new or existing copies of mobile genetic elements, recently duplicated genes, or other repetitive sequences. Most current software programs for predicting structural variation from short-read DNA resequencing data are intended primarily for use on human genomes. They typically disregard information in reads mapping to repeat sequences, and significant post-processing and manual examination of their output is often required to rule out false-positive predictions and precisely describe mutational events.

Results: We have implemented an algorithm for identifying structural variation from DNA resequencing data as part of the breseq computational pipeline for predicting mutations in haploid microbial genomes. Our method evaluates the support for new sequence junctions present in a clonal sample from split-read alignments to a reference genome, including matches to repeat sequences. Then, it uses a statistical model of read coverage evenness to accept or reject these predictions. Finally, breseq combines predictions of new junctions and deleted chromosomal regions to output biologically relevant descriptions of mutations and their effects on genes. We demonstrate the performance of breseq on simulated Escherichia coli genomes with deletions generating unique breakpoint sequences, new insertions of mobile genetic elements, and deletions mediated by mobile elements. Then, we reanalyze data from an E. coli K-12 mutation accumulation evolution experiment in which structural variation was not previously identified. Transposon insertions and large-scale chromosomal changes detected by breseq account for ~25% of spontaneous mutations in this strain. In all cases, we find that breseq is able to reliably predict structural variation with modest read-depth coverage of the reference genome (>40-fold).

Conclusions: Using breseq to predict structural variation should be useful for studies of microbial epidemiology, experimental evolution, synthetic biology, and genetics when a reference genome for a closely related strain is available. In these cases, breseq can discover mutations that may be responsible for important or unintended changes in genomes that might otherwise go undetected.

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Junction candidate creation from split-read alignments that overlap. a) If two alignments of a read to the reference genome overlap, the overlapping bases at the center of the read could potentially be assigned to two separate locations in the reference sequence. b) If the read alignments in (a) had the imperfect alignments pictured here, the coordinates of each match and their overlap would be corrected as pictured by removing overlap until the remainder is a perfect match with no indels or mismatched bases. c) This type of junction candidate can be fully described by the reference coordinates defining each side of the junction breakpoint, the directions in the reference sequence each junction side continues to match from those breakpoint positions, and the number of overlapping bases in the read alignments.
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Fig2: Junction candidate creation from split-read alignments that overlap. a) If two alignments of a read to the reference genome overlap, the overlapping bases at the center of the read could potentially be assigned to two separate locations in the reference sequence. b) If the read alignments in (a) had the imperfect alignments pictured here, the coordinates of each match and their overlap would be corrected as pictured by removing overlap until the remainder is a perfect match with no indels or mismatched bases. c) This type of junction candidate can be fully described by the reference coordinates defining each side of the junction breakpoint, the directions in the reference sequence each junction side continues to match from those breakpoint positions, and the number of overlapping bases in the read alignments.

Mentions: Next, breseq creates a list of potential new sequence junctions, which may indicate that distant genomic sites in the reference sequence are juxtaposed in the sample, from the lists of split-read alignments. For all reads where no one alignment spans 90% of the total read length, breseq examines all pairs of alignments reported between the read and the reference genome. Each alignment pair uniquely specifies a junction candidate: the new sequence that would exist in the sample DNA if two discontinuous regions of the reference genome were joined (FiguresĀ 2 and 3). This approach assumes that cases where individual reads from a sample genuinely map to three or more distinct locations in the reference genome will be extremely rare, which will generally be true for short-read data from samples that have not greatly diverged from the reference genome.Figure 2


Identifying structural variation in haploid microbial genomes from short-read resequencing data using breseq.

Barrick JE, Colburn G, Deatherage DE, Traverse CC, Strand MD, Borges JJ, Knoester DB, Reba A, Meyer AG - BMC Genomics (2014)

Junction candidate creation from split-read alignments that overlap. a) If two alignments of a read to the reference genome overlap, the overlapping bases at the center of the read could potentially be assigned to two separate locations in the reference sequence. b) If the read alignments in (a) had the imperfect alignments pictured here, the coordinates of each match and their overlap would be corrected as pictured by removing overlap until the remainder is a perfect match with no indels or mismatched bases. c) This type of junction candidate can be fully described by the reference coordinates defining each side of the junction breakpoint, the directions in the reference sequence each junction side continues to match from those breakpoint positions, and the number of overlapping bases in the read alignments.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4300727&req=5

Fig2: Junction candidate creation from split-read alignments that overlap. a) If two alignments of a read to the reference genome overlap, the overlapping bases at the center of the read could potentially be assigned to two separate locations in the reference sequence. b) If the read alignments in (a) had the imperfect alignments pictured here, the coordinates of each match and their overlap would be corrected as pictured by removing overlap until the remainder is a perfect match with no indels or mismatched bases. c) This type of junction candidate can be fully described by the reference coordinates defining each side of the junction breakpoint, the directions in the reference sequence each junction side continues to match from those breakpoint positions, and the number of overlapping bases in the read alignments.
Mentions: Next, breseq creates a list of potential new sequence junctions, which may indicate that distant genomic sites in the reference sequence are juxtaposed in the sample, from the lists of split-read alignments. For all reads where no one alignment spans 90% of the total read length, breseq examines all pairs of alignments reported between the read and the reference genome. Each alignment pair uniquely specifies a junction candidate: the new sequence that would exist in the sample DNA if two discontinuous regions of the reference genome were joined (FiguresĀ 2 and 3). This approach assumes that cases where individual reads from a sample genuinely map to three or more distinct locations in the reference genome will be extremely rare, which will generally be true for short-read data from samples that have not greatly diverged from the reference genome.Figure 2

Bottom Line: They typically disregard information in reads mapping to repeat sequences, and significant post-processing and manual examination of their output is often required to rule out false-positive predictions and precisely describe mutational events.Finally, breseq combines predictions of new junctions and deleted chromosomal regions to output biologically relevant descriptions of mutations and their effects on genes.In these cases, breseq can discover mutations that may be responsible for important or unintended changes in genomes that might otherwise go undetected.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular Biosciences, Institute for Cellular and Molecular Biology, Center for Systems and Synthetic Biology, Center for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, TX 78712, USA. jbarrick@cm.utexas.edu.

ABSTRACT

Background: Mutations that alter chromosomal structure play critical roles in evolution and disease, including in the origin of new lifestyles and pathogenic traits in microbes. Large-scale rearrangements in genomes are often mediated by recombination events involving new or existing copies of mobile genetic elements, recently duplicated genes, or other repetitive sequences. Most current software programs for predicting structural variation from short-read DNA resequencing data are intended primarily for use on human genomes. They typically disregard information in reads mapping to repeat sequences, and significant post-processing and manual examination of their output is often required to rule out false-positive predictions and precisely describe mutational events.

Results: We have implemented an algorithm for identifying structural variation from DNA resequencing data as part of the breseq computational pipeline for predicting mutations in haploid microbial genomes. Our method evaluates the support for new sequence junctions present in a clonal sample from split-read alignments to a reference genome, including matches to repeat sequences. Then, it uses a statistical model of read coverage evenness to accept or reject these predictions. Finally, breseq combines predictions of new junctions and deleted chromosomal regions to output biologically relevant descriptions of mutations and their effects on genes. We demonstrate the performance of breseq on simulated Escherichia coli genomes with deletions generating unique breakpoint sequences, new insertions of mobile genetic elements, and deletions mediated by mobile elements. Then, we reanalyze data from an E. coli K-12 mutation accumulation evolution experiment in which structural variation was not previously identified. Transposon insertions and large-scale chromosomal changes detected by breseq account for ~25% of spontaneous mutations in this strain. In all cases, we find that breseq is able to reliably predict structural variation with modest read-depth coverage of the reference genome (>40-fold).

Conclusions: Using breseq to predict structural variation should be useful for studies of microbial epidemiology, experimental evolution, synthetic biology, and genetics when a reference genome for a closely related strain is available. In these cases, breseq can discover mutations that may be responsible for important or unintended changes in genomes that might otherwise go undetected.

Show MeSH
Related in: MedlinePlus