Dataset Information

Read clouds uncover variation in complex regions of the human genome.

ABSTRACT: Although an increasing amount of human genetic variation is being identified and recorded, determining variants within repeated sequences of the human genome remains a challenge. Most population and genome-wide association studies have therefore been unable to consider variation in these regions. Core to the problem is the lack of a sequencing technology that produces reads with sufficient length and accuracy to enable unique mapping. Here, we present a novel methodology of using read clouds, obtained by accurate short-read sequencing of DNA derived from long fragment libraries, to confidently align short reads within repeat regions and enable accurate variant discovery. Our novel algorithm, Random Field Aligner (RFA), captures the relationships among the short reads governed by the long read process via a Markov Random Field. We utilized a modified version of the Illumina TruSeq synthetic long-read protocol, which yielded shallow-sequenced read clouds. We test RFA through extensive simulations and apply it to discover variants on the NA12878 human sample, for which shallow TruSeq read cloud sequencing data are available, and on an invasive breast carcinoma genome that we sequenced using the same method. We demonstrate that RFA facilitates accurate recovery of variation in 155 Mb of the human genome, including 94% of 67 Mb of segmental duplication sequence and 96% of 11 Mb of transcribed sequence, that are currently hidden from short-read technologies.

SUBMITTER: Bishara A

PROVIDER: S-EPMC4579342 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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Read clouds uncover variation in complex regions of the human genome.

Bishara Alex A Liu Yuling Y Weng Ziming Z Kashef-Haghighi Dorna D Newburger Daniel E DE West Robert R Sidow Arend A Batzoglou Serafim S

Genome research 20150818 10

Although an increasing amount of human genetic variation is being identified and recorded, determining variants within repeated sequences of the human genome remains a challenge. Most population and genome-wide association studies have therefore been unable to consider variation in these regions. Core to the problem is the lack of a sequencing technology that produces reads with sufficient length and accuracy to enable unique mapping. Here, we present a novel methodology of using read clouds, ob ...[more]

PMID: 26286554

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Project description:BackgroundIn mammals, Imprinting Control Regions (ICRs) regulate a subset of genes in a parent-of-origin-specific manner. In both human and mouse, previous studies identified a set of CpG-rich motifs occurring as clusters in ICRs and germline Differentially Methylated Regions (gDMRs). These motifs consist of the ZFP57 binding site (ZFBS) overlapping a subset of MLL binding units known as MLL morphemes. MLL or MLL1 (Mixed Lineage Leukemia 1) is a relatively large multidomain protein that plays a central role in the regulation of transcription. The structures of both MLL1 and MLL2 include a domain (MT) that binds CpG-rich DNA and a conserved domain (SET) that methylates lysine 4 in histone H3 producing H3K4me3 marks in chromatin.ResultsSince genomic imprinting impacts many developmental and key physiological processes, we followed a previous bioinformatics strategy to pinpoint ICR positions in the Bos taurus genome. Initial genome-wide analyses involved finding the positions of ZFP57 binding sites, and the CpG-rich motifs (ZFBS-morph overlaps) along cattle chromosomal DNA. By creating plots displaying the density of ZFBS-morph overlaps, we removed background noise and thus improved signal detection. With the density-plots, we could view the positions of peaks locating known and candidate ICRs in cattle DNA. Our evaluations revealed the correspondence of peaks in plots to reported known and inferred ICRs/DMRs in cattle. Beside peaks pinpointing such ICRs, the density-plots also revealed additional peaks. Since evaluations validated the robustness of our approach, we inferred that the additional peaks may correspond to candidate ICRs for imprinted gene expression.ConclusionOur bioinformatics strategy offers the first genome-wide approach for systematically localizing candidate ICRs. Furthermore, we have tailored our datasets for upload onto the UCSC genome browser so that researchers could find known and candidate ICRs with respect to a wide variety of annotations at all scales: from the positions of Single Nucleotide Polymorphisms (SNPs), to positions of genes, transcripts, and repeated DNA elements. Furthermore, the UCSC genome browser offers tools to produce enlarged views: to uncover the genes in the vicinity of candidate ICRs and thus discover potential imprinted genes for experimental validations.

Project description:BackgroundShort-read resequencing of genomes produces abundant information of the genetic variation of individuals. Due to their numerous nature, these variants are rarely exhaustively validated. Furthermore, low levels of undetected variant miscalling will have a systematic and disproportionate impact on the interpretation of individual genome sequence information, especially should these also be carried through into in reference databases of genomic variation.ResultsWe find that sequence variation from short-read sequence data is subject to recurrent-yet-intermittent miscalling that occurs in a sequence intrinsic manner and is very sensitive to sequence read length. The miscalls arise from difficulties aligning short reads to redundant genomic regions, where the rate of sequencing error approaches the sequence diversity between redundant regions. We find the resultant miscalled variants to be sensitive to small sequence variations between genomes, and thereby are often intrinsic to an individual, pedigree, strain or human ethnic group. In human exome sequences, we identify 2-300 recurrent false positive variants per individual, almost all of which are present in public databases of human genomic variation. From the exomes of non-reference strains of inbred mice, we identify 3-5000 recurrent false positive variants per mouse - the number of which increasing with greater distance between an individual mouse strain and the reference C57BL6 mouse genome. We show that recurrently miscalled variants may be reproduced for a given genome from repeated simulation rounds of read resampling, realignment and recalling. As such, it is possible to identify more than two-thirds of false positive variation from only ten rounds of simulation.ConclusionIdentification and removal of recurrent false positive variants from specific individual variant sets will improve overall data quality. Variant miscalls arising are highly sequence intrinsic and are often specific to an individual, pedigree or ethnicity. Further, read length is a strong determinant of whether given false variants will be called for any given genome - which has profound significance for cohort studies that pool datasets collected and sequenced at different points in time.

Dataset Information

Read clouds uncover variation in complex regions of the human genome.

Publications

Read clouds uncover variation in complex regions of the human genome.

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