Dataset Information

Sequence and structural variation in a human genome uncovered by short-read, massively parallel ligation sequencing using two-base encoding.

ABSTRACT: We describe the genome sequencing of an anonymous individual of African origin using a novel ligation-based sequencing assay that enables a unique form of error correction that improves the raw accuracy of the aligned reads to >99.9%, allowing us to accurately call SNPs with as few as two reads per allele. We collected several billion mate-paired reads yielding approximately 18x haploid coverage of aligned sequence and close to 300x clone coverage. Over 98% of the reference genome is covered with at least one uniquely placed read, and 99.65% is spanned by at least one uniquely placed mate-paired clone. We identify over 3.8 million SNPs, 19% of which are novel. Mate-paired data are used to physically resolve haplotype phases of nearly two-thirds of the genotypes obtained and produce phased segments of up to 215 kb. We detect 226,529 intra-read indels, 5590 indels between mate-paired reads, 91 inversions, and four gene fusions. We use a novel approach for detecting indels between mate-paired reads that are smaller than the standard deviation of the insert size of the library and discover deletions in common with those detected with our intra-read approach. Dozens of mutations previously described in OMIM and hundreds of nonsynonymous single-nucleotide and structural variants in genes previously implicated in disease are identified in this individual. There is more genetic variation in the human genome still to be uncovered, and we provide guidance for future surveys in populations and cancer biopsies.

SUBMITTER: McKernan KJ

PROVIDER: S-EPMC2752135 | biostudies-literature | 2009 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Sequence and structural variation in a human genome uncovered by short-read, massively parallel ligation sequencing using two-base encoding.

McKernan Kevin Judd KJ Peckham Heather E HE Costa Gina L GL McLaughlin Stephen F SF Fu Yutao Y Tsung Eric F EF Clouser Christopher R CR Duncan Cisyla C Ichikawa Jeffrey K JK Lee Clarence C CC Zhang Zheng Z Ranade Swati S SS Dimalanta Eileen T ET Hyland Fiona C FC Sokolsky Tanya D TD Zhang Lei L Sheridan Andrew A Fu Haoning H Hendrickson Cynthia L CL Li Bin B Kotler Lev L Stuart Jeremy R JR Malek Joel A JA Manning Jonathan M JM Antipova Alena A AA Perez Damon S DS Moore Michael P MP Hayashibara Kathleen C KC Lyons Michael R MR Beaudoin Robert E RE Coleman Brittany E BE Laptewicz Michael W MW Sannicandro Adam E AE Rhodes Michael D MD Gottimukkala Rajesh K RK Yang Shan S Bafna Vineet V Bashir Ali A MacBride Andrew A Alkan Can C Kidd Jeffrey M JM Eichler Evan E EE Reese Martin G MG De La Vega Francisco M FM Blanchard Alan P AP

Genome research 20090622 9

We describe the genome sequencing of an anonymous individual of African origin using a novel ligation-based sequencing assay that enables a unique form of error correction that improves the raw accuracy of the aligned reads to >99.9%, allowing us to accurately call SNPs with as few as two reads per allele. We collected several billion mate-paired reads yielding approximately 18x haploid coverage of aligned sequence and close to 300x clone coverage. Over 98% of the reference genome is covered wit ...[more]

PMID: 19546169

Similar Datasets

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.

Project description:Multi-locus sequence typing (MLST) has become the gold standard for population analyses of bacterial pathogens. This method focuses on the sequences of a small number of loci (usually seven) to divide the population and is simple, robust and facilitates comparison of results between laboratories and over time. Over the last decade, researchers and population health specialists have invested substantial effort in building up public MLST databases for nearly 100 different bacterial species, and these databases contain a wealth of important information linked to MLST sequence types such as time and place of isolation, host or niche, serotype and even clinical or drug resistance profiles. Recent advances in sequencing technology mean it is increasingly feasible to perform bacterial population analysis at the whole genome level. This offers massive gains in resolving power and genetic profiling compared to MLST, and will eventually replace MLST for bacterial typing and population analysis. However given the wealth of data currently available in MLST databases, it is crucial to maintain backwards compatibility with MLST schemes so that new genome analyses can be understood in their proper historical context.We present a software tool, SRST, for quick and accurate retrieval of sequence types from short read sets, using inputs easily downloaded from public databases. SRST uses read mapping and an allele assignment score incorporating sequence coverage and variability, to determine the most likely allele at each MLST locus. Analysis of over 3,500 loci in more than 500 publicly accessible Illumina read sets showed SRST to be highly accurate at allele assignment. SRST output is compatible with common analysis tools such as eBURST, Clonal Frame or PhyloViz, allowing easy comparison between novel genome data and MLST data. Alignment, fastq and pileup files can also be generated for novel alleles.SRST is a novel software tool for accurate assignment of sequence types using short read data. Several uses for the tool are demonstrated, including quality control for high-throughput sequencing projects, plasmid MLST and analysis of genomic data during outbreak investigation. SRST is open-source, requires Python, BWA and SamTools, and is available from http://srst.sourceforge.net.

Dataset Information

Sequence and structural variation in a human genome uncovered by short-read, massively parallel ligation sequencing using two-base encoding.

Publications

Sequence and structural variation in a human genome uncovered by short-read, massively parallel ligation sequencing using two-base encoding.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets