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Combinatorial algorithms for structural variation detection in high-throughput sequenced genomes.

ABSTRACT: Recent studies show that along with single nucleotide polymorphisms and small indels, larger structural variants among human individuals are common. The Human Genome Structural Variation Project aims to identify and classify deletions, insertions, and inversions (>5 Kbp) in a small number of normal individuals with a fosmid-based paired-end sequencing approach using traditional sequencing technologies. The realization of new ultra-high-throughput sequencing platforms now makes it feasible to detect the full spectrum of genomic variation among many individual genomes, including cancer patients and others suffering from diseases of genomic origin. Unfortunately, existing algorithms for identifying structural variation (SV) among individuals have not been designed to handle the short read lengths and the errors implied by the "next-gen" sequencing (NGS) technologies. In this paper, we give combinatorial formulations for the SV detection between a reference genome sequence and a next-gen-based, paired-end, whole genome shotgun-sequenced individual. We describe efficient algorithms for each of the formulations we give, which all turn out to be fast and quite reliable; they are also applicable to all next-gen sequencing methods (Illumina, 454 Life Sciences [Roche], ABI SOLiD, etc.) and traditional capillary sequencing technology. We apply our algorithms to identify SV among individual genomes very recently sequenced by Illumina technology.

SUBMITTER: Hormozdiari F

PROVIDER: S-EPMC2704429 | biostudies-literature | 2009 Jul

REPOSITORIES: biostudies-literature

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Combinatorial algorithms for structural variation detection in high-throughput sequenced genomes.

Hormozdiari Fereydoun F Alkan Can C Eichler Evan E EE Sahinalp S Cenk SC

Genome research 20090515 7

Recent studies show that along with single nucleotide polymorphisms and small indels, larger structural variants among human individuals are common. The Human Genome Structural Variation Project aims to identify and classify deletions, insertions, and inversions (>5 Kbp) in a small number of normal individuals with a fosmid-based paired-end sequencing approach using traditional sequencing technologies. The realization of new ultra-high-throughput sequencing platforms now makes it feasible to det ...[more]

PMID: 19447966

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Project description:BackgroundStructural variations play an important role in bacterial genomes. They can mediate genome adaptation quickly in response to the external environment and thus can also play a role in antibiotic resistance. The detection of structural variations in bacteria is challenging, and the recognition of even small rearrangements can be important. Even though most detection tools are aimed at and benchmarked on eukaryotic genomes, they can also be used on prokaryotic genomes. The key features of detection are the ability to detect small rearrangements and support haploid genomes. Because of the limiting performance of a single detection tool, combining the detection abilities of multiple tools can lead to more robust results. There are already available workflows for structural variation detection for long-reads technologies and for the detection of single-nucleotide variation and indels, both aimed at bacteria. Yet we are unaware of structural variations detection workflows for the short-reads sequencing platform. Motivated by this gap we created our workflow. Further, we were interested in increasing the detection performance and providing more robust results.ResultsWe developed an open-source bioinformatics pipeline, ProcaryaSV, for the detection of structural variations in bacterial isolates from paired-end short sequencing reads. Multiple tools, starting with quality control and trimming of sequencing data, alignment to the reference genome, and multiple structural variation detection tools, are integrated. All the partial results are then processed and merged with an in-house merging algorithm. Compared with a single detection approach, ProcaryaSV has improved detection performance and is a reproducible easy-to-use tool.ConclusionsThe ProcaryaSV pipeline provides an integrative approach to structural variation detection from paired-end next-generation sequencing of bacterial samples. It can be easily installed and used on Linux machines. It is publicly available on GitHub at https://github.com/robinjugas/ProcaryaSV .

Project description:We present a novel method utilizing "saltless" pH gradient weak cation exchange-hydrophilic interaction liquid chromatography directly coupled to electron transfer dissociation (ETD) mass spectrometry for the automated on-line high throughput characterization of hypermodified combinatorial histone codes. This technique, performed on a low resolution mass spectrometer, displays an improvement over existing methods with an approximately 100-fold reduction in sample requirements and analysis time. The scheme presented is capable of identifying all of the major combinatorial histone codes present in a sample in a 2-h analysis. The large N-terminal histone peptides are eluted by the pH and organic solvent weak cation exchange-hydrophilic interaction liquid chromatography gradient and directly introduced via nanoelectrospray ionization into a benchtop linear quadrupole ion trap mass spectrometer equipped with ETD. Each polypeptide is sequenced, and the modification sites are identified by ETD fragmentation. The isobaric trimethyl and acetyl modifications are resolved chromatographically and confidently distinguished by the synthesis of mass spectrometric and chromatographic information. We demonstrate the utility of the method by complete characterization of human histone H3.2 and histone H4 from butyrate-treated cells, but it is generally applicable to the analysis of highly modified peptides. We find this methodology very useful for chromatographic separation of isomeric species that cannot be separated well by any other chromatographic means, leading to less complicated tandem mass spectra. The improved separation and increased sensitivity generated novel information about much less abundant forms. In this method demonstration we report over 200 H3.2 forms and 70 H4 forms, including forms not yet detected in human cells, such as the remarkably highly modified histone H3.2 K4me3K9acK14acK18acK23acK27acK36me3. Such detail provided by our proteomics platform will be essential for determining how histone modifications occur and act in combination to propagate the histone code during transcriptional events and could greatly enable sequencing of the histone component of human epigenomes.

Dataset Information

Combinatorial algorithms for structural variation detection in high-throughput sequenced genomes.

Publications

Combinatorial algorithms for structural variation detection in high-throughput sequenced genomes.

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