Search

Motivation: As the volume of next-generation sequencing (NGS) data increases, faster algorithms become necessary. Although speeding up individual components of a sequence analysis pipeline (e.g. read mapping) can reduce the computational cost of analysis, such approaches do not take full advantage of the particulars of a given problem. One problem of great interest, genotyping a known set of variants (e.g. dbSNP or Affymetrix SNPs), is important for characterization of known genetic traits and causative disease variants within an individual, as well as the initial stage of many ancestral and population genomic pipelines (e.g. GWAS). Results: We introduce lightweight assignment of variant alleles (LAVA), an NGS-based genotyping algorithm for a given set of SNP loci, which takes advantage of the fact that approximate matching of mid-size k-mers (with k = 32) can typically uniquely ide ntify loci in the human genome without full read alignment. LAVA accurately calls the vast majority of SNPs in dbSNP and Affymetrix's Genome-Wide Human SNP Array 6.0 up to about an order of magnitude faster than standard NGS genotyping pipelines. For Affymetrix SNPs, LAVA has significantly higher SNP calling accuracy than existing pipelines while using as low as ∼5 GB of RAM. As such, LAVA represents a scalable computational method for population-level genotyping studies as well as a flexible NGS-based replacement for SNP arrays. Availability and Implementation: LAVA software is available at http://lava.csail.mit.edu.

It is becoming increasingly impractical to indefinitely store raw sequencing data for later processing in an uncompressed state. In this paper, we describe a scalable compressive framework, Read-Quality-Sparsifier (RQS), which substantially outperforms the compression ratio and speed of other de novo quality score compression methods while maintaining SNP-calling accuracy. Surprisingly, RQS also improves the SNP-calling accuracy on a gold-standard, real-life sequencing dataset (NA12878) using a k-mer density profile constructed from 77 other individuals from the 1000 Genomes Project. This improvement in downstream accuracy emerges from the observation that quality score values within NGS datasets are inherently encoded in the k-mer landscape of the genomic sequences. To our knowledge, RQS is the first scalable sequence-based quality compression method that can efficiently compress quality scores of terabyte-sized and larger sequencing datasets. Availability: An implementation of our method, RQS, is available for download at: http://rqs.csail.mit.edu/. © 2014 Springer International Publishing Switzerland. Keywords: RQS; quality score; sparsification; compression; accuracy; variant calling, Hertz Foundation, National Institutes of Health (U.S.) (R01GM108348)

Background Chimeric transcripts, including partial and internal tandem duplications (PTDs, ITDs) and gene fusions, are important in the detection, prognosis, and treatment of human cancers. Results We describe Barnacle, a production-grade analysis tool that detects such chimeras in de novo assemblies of RNA-seq data, and supports prioritizing them for review and validation by reporting the relative coverage of co-occurring chimeric and wild-type transcripts. We demonstrate applications in large-scale disease studies, by identifying PTDs in MLL, ITDs in FLT3, and reciprocal fusions between PML and RARA, in two deeply sequenced acute myeloid leukemia (AML) RNA-seq datasets. Conclusions Our analyses of real and simulated data sets show that, with appropriate filter settings, Barnacle makes highly specific predictions for three types of chimeric transcripts that are important in a range of cancers: PTDs, ITDs, and fusions. High specificity makes manual review and validation efficient, which is necessary in large-scale disease studies. Characterizing an extended range of chimera types will help generate insights into progression, treatment, and outcomes for complex diseases.

One of the key problems in computational genomics is that of identifying structural variations between two sequences of genomic origin. Recently, with the advent of high-throughput sequencing of transcriptomes (RNA-seq), transcriptional structural variation studies also came into prominence. This study introduces two novel frameworks for aligning transcribed sequences to the genome with high sensitivity to structural alterations within the transcript. (1) A pairwise nucleotide-level alignment model and (2) a faster lower-sensitivity solution based on chaining homologous substrings between the transcript and the genome. A further contribution of this study is a stand-alone transcriptome-to-genome alignment tool, which can comprehensively identify and characterize transcriptional events (duplications, inversions, rearrangements and fusions); suitable for high-throughput structural variation studies involving long transcribed sequences with high similarity to their genomic origin. Reported results include experiments upon simulated datasets of transcriptional events and RNA-seq assemblies of a human prostate cancer individual.

To the Editor: Most next-generation sequencing (NGS) quality scores are space intensive, redundant and often misleading. In this Correspondence, we recover quality information directly from sequence data using a compression tool named Quartz, rendering such scores redundant and yielding substantially better space and time efficiencies for storage and analysis. Quartz is designed to operate on NGS reads in FASTQ format, but it can be trivially modified to discard quality scores in other formats for which scores are paired with sequence information. Discarding 95% of quality scores resulted, counterintuitively, in improved SNP calling, implying that compression need not come at the expense of accuracy., Hertz Foundation, National Institutes of Health (U.S.) (NIH grant GM108348)

By running standard genotype calling tools, it is possible to accurately identify the number of “wild type” and “mutant” alleles for each single-nucleotide polymorphism (SNP) site. However, in the case of two heterozygous SNP sites, genotype calling tools cannot determine whether “mutant” alleles from different SNP loci are on the same chromosome or on different homologous chromosomes (i.e. compound heterozygote).

As the more recent next-generation sequencing (NGS) technologies provide longer read sequences, the use of sequencing datasets for complete haplotype phasing is fast becoming a reality, allowing haplotype reconstruction of a single sequenced genome. Nearly all previous haplotype reconstruction studies have focused on diploid genomes and are rarely scalable to genomes with higher ploidy. Yet computational investigations into polyploid genomes carry great importance, impacting plant, yeast and fish genomics, as well as the studies of the evolution of modern-day eukaryotes and (epi)genetic interactions between copies of genes. In this paper, we describe a novel maximum-likelihood estimation framework, HapTree, for polyploid haplotype assembly of an individual genome using NGS read datasets. We evaluate the performance of HapTree on simulated polyploid sequencing read data modeled after Illumina sequencing technologies. For triploid and higher ploidy genomes, we demonstrate that HapTree substantially improves haplotype assembly accuracy and efficiency over the state-of-the-art; moreover, HapTree is the first scalable polyplotyping method for higher ploidy. As a proof of concept, we also test our method on real sequencing data from NA12878 (1000 Genomes Project) and evaluate the quality of assembled haplotypes with respect to trio-based diplotype annotation as the ground truth. The results indicate that HapTree significantly improves the switch accuracy within phased haplotype blocks as compared to existing haplotype assembly methods, while producing comparable minimum error correction (MEC) values. A summary of this paper appears in the proceedings of the RECOMB 2014 conference, April 2–5., National Science Foundation (U.S.) (NSF/NIH BIGDATA Grant R01GM108348-01), National Science Foundation (U.S.) (Graduate Research Fellowship), Simons Foundation

Background: Chimeric transcripts, including partial and internal tandem duplications (PTDs, ITDs) and gene fusions, are important in the detection, prognosis, and treatment of human cancers. Results: We describe Barnacle, a production-grade analysis tool that detects such chimeras in de novo assemblies of RNA-seq data, and supports prioritizing them for review and validation by reporting the relative coverage of co-occurring chimeric and wild-type transcripts. We demonstrate applications in large-scale disease studies, by identifying PTDs in MLL, ITDs in FLT3, and reciprocal fusions between PML and RARA, in two deeply sequenced acute myeloid leukemia (AML) RNA-seq datasets. Conclusions: Our analyses of real and simulated data sets show that, with appropriate filter settings, Barnacle makes highly specific predictions for three types of chimeric transcripts that are important in a range of cancers: PTDs, ITDs, and fusions. High specificity makes manual review and validation efficient, which is necessary in large-scale disease studies. Characterizing an extended range of chimera types will help generate insights into progression, treatment, and outcomes for complex diseases., Simon Fraser University. Bioinformatics for Combating Infectious Disease Project, Simon Fraser University (Graduate Fellowship), Pacific Century Institute (Graduate Scholarship), Genome Canada (Firm), Canadian Institutes of Health Research, Genome British Columbia (Firm) (Grant #121AML), Provincial Health Services Authority (British Columbia, Canada), BC Cancer Foundation

Motivation: Computational identification of genomic structural variants via high-throughput sequencing is an important problem for which a number of highly sophisticated solutions have been recently developed. With the advent of high-throughput transcriptome sequencing (RNA-Seq), the problem of identifying structural alterations in the transcriptome is now attracting significant attention. In this article, we introduce two novel algorithmic formulations for identifying transcriptomic structural variants through aligning transcripts to the reference genome under the consideration of such variation. The first formulation is based on a nucleotide-level alignment model; a second, potentially faster formulation is based on chaining fragments shared between each transcript and the reference genome. Based on these formulations, we introduce a novel transcriptome-to-genome alignment tool, Dissect (DIScovery of Structural Alteration Event Containing Transcripts), which can identify and characterize transcriptomic events such as duplications, inversions, rearrangements and fusions. Dissect is suitable for whole transcriptome structural variation discovery problems involving sufficiently long reads or accurately assembled contigs. Results: We tested Dissect on simulated transcripts altered via structural events, as well as assembled RNA-Seq contigs from human prostate cancer cell line C4-2. Our results indicate that Dissect has high sensitivity and specificity in identifying structural alteration events in simulated transcripts as well as uncovering novel structural alterations in cancer transcriptomes., Pacific Institute for the Mathematical Sciences (Fellowship)