Comprehensive whole-genome structural variation detection is challenging with current approaches. With diploid cells as DNA source and the presence of numerous repetitive elements, short-read DNA sequencing cannot be used to detect structural variation efficiently. In this report, we show that genome mapping with long, fluorescently labeled DNA molecules imaged on nanochannel arrays can be used for whole-genome structural variation detection without sequencing. While whole-genome haplotyping is not achieved, local phasing (across >150-kb regions) is routine, as molecules from the parental chromosomes are examined separately. In one experiment, we generated genome maps from a trio from the 1000 Genomes Project, compared the maps against that derived from the reference human genome, and identified structural variations that are >5 kb in size. We find that these individuals have many more structural variants than those published, including some with the potential of disrupting gene function or regulation.
We present a new method, OMSV, for accurately and comprehensively identifying structural variations (SVs) from optical maps. OMSV detects both homozygous and heterozygous SVs, SVs of various types and sizes, and SVs with or without creating or destroying restriction sites. We show that OMSV has high sensitivity and specificity, with clear performance gains over the latest method. Applying OMSV to a human cell line, we identified hundreds of SVs >2 kbp, with 68 % of them missed by sequencing-based callers. Independent experimental validation confirmed the high accuracy of these SVs. The OMSV software is available at http://yiplab.cse.cuhk.edu.hk/omsv/.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-017-1356-2) contains supplementary material, which is available to authorized users.
Human genomes contain structural variations (SVs) that are associated with various phenotypic variations and diseases. SV detection by sequencing is incomplete due to limited read length.Nanochannel-based optical mapping (OM) allows direct observation of SVs up to hundreds of kilobases in size on individual DNA molecules, making it a promising alternative technology for identifying large SVs. SV detection from optical maps is non-trivial due to complex types of error present in OM data, and no existing methods can simultaneously handle all these complex errors and the wide spectrum of SV types. Here we present a novel method, OMSV, for accurate and comprehensive identification of SVs from optical maps. OMSV detects both homozygous and heterozygous SVs, SVs of various types and sizes, and SVs with and without creating/destroying restriction sites. In an extensive series of tests based on real and simulated data, OMSV achieved both high sensitivity and specificity, with clear performance gains over the latest existing method. Applying OMSV to a human cell line, we identified hundreds of SVs >2kbp, with 65% of them missed by sequencing-based callers. Independent experimental validations confirmed the high accuracy of these SVs. We also demonstrate how OMSV can incorporate sequencing data to determine precise SV break points and novel sequences in the SVs not contained in the reference. We provide OMSV as open-source software to facilitate systematic studies of large SVs.
We present a novel tool SOAPfusion for fusion discovery with paired-end RNA-Seq reads. SOAPfusion is accurate and efficient for fusion discovery with high sensitivity (≥93%), low false-positive rate (≤1.36%), even the coverage is as low as 10×, highlighting its ability to detect fusions efficiently at low sequencing cost. From real data of Universal Human Reference RNA (UHRR) samples, SOAPfusion detected 7 novel fusion genes, more than other existing tools and all genes have been validated through reverse transcription-polymerase chain reaction followed by Sanger sequencing. SOAPfusion thus proves to be an effective method with precise applicability in search of fusion transcripts, which is advantageous to accelerate pathological and therapeutic cancer studies.
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