Short read alignment with populations of genomes

Huang, Lin; Popic, Victoria; Batzoglou, Serafim

doi:10.1093/bioinformatics/btt215

Cited by 113 publications

(105 citation statements)

References 33 publications

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“…Nonredundant search of a population of human genomes was recently shown to improve detection sensitivity using a new data structure to map reads to multiple human genomes simultaneously (Huang et al 2013). This approach used assembled human genomes for reference but excluded microbial genomes.…”

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confidence: 99%

Using populations of human and microbial genomes for organism detection in metagenomes

et al. 2015

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Identifying causative disease agents in human patients from shotgun metagenomic sequencing (SMS) presents a powerful tool to apply when other targeted diagnostics fail. Numerous technical challenges remain, however, before SMS can move beyond the role of research tool. Accurately separating the known and unknown organism content remains difficult, particularly when SMS is applied as a last resort. The true amount of human DNA that remains in a sample after screening against the human reference genome and filtering nonbiological components left from library preparation has previously been underreported. In this study, we create the most comprehensive collection of microbial and reference-free human genetic variation available in a database optimized for efficient metagenomic search by extracting sequences from GenBank and the 1000 Genomes Project. The results reveal new human sequences found in individual Human Microbiome Project (HMP) samples. Individual samples contain up to 95% human sequence, and 4% of the individual HMP samples contain 10% or more human reads. Left unidentified, human reads can complicate and slow down further analysis and lead to inaccurately labeled microbial taxa and ultimately lead to privacy concerns as more human genome data is collected.

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confidence: 99%

Using populations of human and microbial genomes for organism detection in metagenomes

et al. 2015

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“…Similar idea was also adopted in [13] for larger indel calling, but replacing the k-mer indexing of [12] with BWT-index built for the extracted context around putative indels and their nearby combinations. Recently, this context extraction idea was also proposed for pan-genome indexing [14]. Our approach is more generic than the k-mer indexing and context extracting approaches above in that the read length is not fixed.…”

Section: Related Workmentioning

confidence: 99%

“…We also compared GCSA to BWBBLE [14], a recent BWTbased read aligner for pan-genomes. Given an upper bound for read length, BWBBLE creates a new sequence for each known indel, with an amount of context before and after the indel depending on the upper bound.…”

Section: Pattern Matchingmentioning

confidence: 99%

Indexing Graphs for Path Queries with Applications in Genome Research

Sirén¹,

Välimäki

Mäkinen

2014

IEEE/ACM Trans. Comput. Biol. and Bioinf.

274

213

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We propose a generic approach to replace the canonical sequence representation of genomes with graph representations, and study several applications of such extensions. We extend the Burrows-Wheeler transform (BWT) of strings to acyclic directed labeled graphs, to support path queries as an extension to substring searching. We develop, apply, and tailor this technique to a) read alignment on an extended BWT index of a graph representing pan-genome, i.e., reference genome and known variants of it; and b) split-read alignment on an extended BWT index of a splicing graph. Other possible applications include probe/primer design, alignments to assembly graphs, and alignments to phylogenetic tree of partial-order graphs. We report several experiments on the feasibility and applicability of the approach. Especially on highly-polymorphic genome regions our pan-genome index is making a significant improvement in alignment accuracy.

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“…A major drawback of the hash-based aligners is that they require prohibitive amount of memory (see Note 3). The second generation BWT-based aligners are preferred as they consume only a limited amount of memory [38,39].…”

Section: Read Alignment To a Reference Genomementioning

confidence: 99%

Analysis of Genotyping-by-Sequencing (GBS) Data

Kagale

Koh

Clarke

et al. 2016

Methods in Molecular Biology

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Vous avez des questions? Nous pouvons vous aider. Pour communiquer directement avec un auteur, consultez la première page de la revue dans laquelle son article a été publié afin de trouver ses coordonnées. Si vous n'arrivez pas à les repérer, communiquez avec nous à PublicationsArchive-ArchivesPublications@nrc-cnrc.gc.ca. NRC Publications Record / Notice d'Archives des publications de CNRC:http://nparc.cisti-icist.nrc-cnrc.gc.ca/eng/view/object/?id=2ebe20b9-f190-4f15-a0a5-a8789df676f1 http://nparc.cisti-icist.nrc-cnrc.gc.ca/fra/voir/objet/?id=2ebe20b9-f190-4f15-a0a5-a8789df676f1 Analysis of Genotyping-by-Sequencing (GBS) Data SummaryThe development of genotyping-by-sequencing (GBS) to rapidly detect nucleotide variation at the whole genome level, in many individuals simultaneously, has provided a transformative genetic profiling technique. GBS can be carried out in species with or without reference genome sequences yields huge amounts of potentially informative data. One limitation with the approach is the paucity of tools to transform the raw data into a format that can be easily interrogated at the genetic level. In this chapter we describe bioinformatics tools developed to address this shortfall together with experimental design considerations to fully leverage the power of GBS for genetic analysis.

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Short read alignment with populations of genomes

Cited by 113 publications

References 33 publications

Using populations of human and microbial genomes for organism detection in metagenomes

Using populations of human and microbial genomes for organism detection in metagenomes

Indexing Graphs for Path Queries with Applications in Genome Research

Analysis of Genotyping-by-Sequencing (GBS) Data

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