Merging of multi-string BWTs with applications

Holt, James; McMillan, Leonard

doi:10.1093/bioinformatics/btu584

Cited by 42 publications

(44 citation statements)

References 26 publications

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“…The msBWTs were constructed using a hybrid combination of ropeBWT (Li 2014) and the msBWT merge algorithm (Holt and McMillan 2014) as described at https://github.com/holtjma/msbwt/wiki. The msBWTs are a lossless compressed form of the raw, sequenced reads that can be efficiently queried to find number of occurrences of and/or the associated read fragments containing any specified subsequence or k-mer.…”

Section: Methodsmentioning

confidence: 99%

Genomes of the Mouse Collaborative Cross

et al. 2017

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The Collaborative Cross (CC) is a multiparent panel of recombinant inbred (RI) mouse strains derived from eight founder laboratory strains. RI panels are popular because of their long-term genetic stability, which enhances reproducibility and integration of data collected across time and conditions. Characterization of their genomes can be a community effort, reducing the burden on individual users. Here we present the genomes of the CC strains using two complementary approaches as a resource to improve power and interpretation of genetic experiments. Our study also provides a cautionary tale regarding the limitations imposed by such basic biological processes as mutation and selection. A distinct advantage of inbred panels is that genotyping only needs to be performed on the panel, not on each individual mouse. The initial CC genome data were haplotype reconstructions based on dense genotyping of the most recent common ancestors (MRCAs) of each strain followed by imputation from the genome sequence of the corresponding founder inbred strain. The MRCA resource captured segregating regions in strains that were not fully inbred, but it had limited resolution in the transition regions between founder haplotypes, and there was uncertainty about founder assignment in regions of limited diversity. Here we report the whole genome sequence of 69 CC strains generated by paired-end short reads at 30× coverage of a single male per strain. Sequencing leads to a substantial improvement in the fine structure and completeness of the genomes of the CC. Both MRCAs and sequenced samples show a significant reduction in the genome-wide haplotype frequencies from two wild-derived strains, CAST/EiJ and PWK/PhJ. In addition, analysis of the evolution of the patterns of heterozygosity indicates that selection against three wild-derived founder strains played a significant role in shaping the genomes of the CC. The sequencing resource provides the first description of tens of thousands of new genetic variants introduced by mutation and drift in the CC genomes. We estimate that new SNP mutations are accumulating in each CC strain at a rate of 2.4 ± 0.4 per gigabase per generation. The fixation of new mutations by genetic drift has introduced thousands of new variants into the CC strains. The majority of these mutations are novel compared to currently sequenced laboratory stocks and wild mice, and some are predicted to alter gene function. Approximately one-third of the CC inbred strains have acquired large deletions (>10 kb) many of which overlap known coding genes and functional elements. The sequence of these mice is a critical resource to CC users, increases threefold the number of mouse inbred strain genomes available publicly, and provides insight into the effect of mutation and drift on common resources.

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Section: Methodsmentioning

confidence: 99%

Genomes of the Mouse Collaborative Cross

et al. 2017

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“…This is due to two factors: First, Zhang et al use LoRDEC version 0.8 with the default parameters, while we use version 0.9 with the parameters suggested for E. coli in the LoRDEC paper [6]. Second, Zhang et al use FMLRC version 0.1.2 and construct the BWT with msBWT [37], while we use version 1.0.0 and construct the BWT with RopeBWT2 [38] as recommended by the FMLRC documentation. Table 3 shows the results.…”

Section: Error Correctionmentioning

confidence: 99%

GraphAligner: Rapid and Versatile Sequence-to-Graph Alignment

Rautiainen

Marschall

2019

Preprint

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Genome graphs can represent genetic variation and sequence uncertainty. Aligning sequences to genome graphs is key to many applications, including error correction, genome assembly, and genotyping of variants in a pan-genome graph. Yet, so far this step is often prohibitively slow. We present GraphAligner, a tool for aligning long reads to genome graphs. Compared to state-of-the-art tools, GraphAligner is 12x faster and uses 5x less memory, making it as efficient as aligning reads to linear reference genomes. When employing GraphAligner for error correction, we find it to be almost 3x more accurate and over 15x faster than extant tools.Availability: Package manager: https://anaconda.org/bioconda/graphaligner and source code: https://github.com/maickrau/GraphAligner

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“…Holt and McMillan have recently extended a data structure for string compression, the multi-string Burrows–Wheeler transform (msBWT) (Bauer et al 2013), to next-generation sequencing data (Holt et al 2014). A msBWT is a compressed, indexed representation of raw, unaligned sequence reads which allows fast queries for specific sequences over very large datasets (Fig.…”

Section: Resources For Next-generation Sequencingmentioning

confidence: 99%