Medical Biology - Research Publications

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    Socrates: identification of genomic rearrangements in tumour genomes by re-aligning soft clipped reads
    Schroeder, J ; Hsu, A ; Boyle, SE ; Macintyre, G ; Cmero, M ; Tothill, RW ; Johnstone, RW ; Shackleton, M ; Papenfuss, AT (OXFORD UNIV PRESS, 2014-04-15)
    MOTIVATION: Methods for detecting somatic genome rearrangements in tumours using next-generation sequencing are vital in cancer genomics. Available algorithms use one or more sources of evidence, such as read depth, paired-end reads or split reads to predict structural variants. However, the problem remains challenging due to the significant computational burden and high false-positive or false-negative rates. RESULTS: In this article, we present Socrates (SOft Clip re-alignment To idEntify Structural variants), a highly efficient and effective method for detecting genomic rearrangements in tumours that uses only split-read data. Socrates has single-nucleotide resolution, identifies micro-homologies and untemplated sequence at break points, has high sensitivity and high specificity and takes advantage of parallelism for efficient use of resources. We demonstrate using simulated and real data that Socrates performs well compared with a number of existing structural variant detection tools. AVAILABILITY AND IMPLEMENTATION: Socrates is released as open source and available from CONTACT: Supplementary information: Supplementary data are available at Bioinformatics online.
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    Inferring structural variant cancer cell fraction
    Cmero, M ; Yuan, K ; Ong, CS ; Schröder, J ; PCAWG Evolution and Heterogeneity Working Group, ; Corcoran, NM ; Papenfuss, T ; Hovens, CM ; Markowetz, F ; Macintyre, G ; PCAWG Consortium, (Nature Research (part of Springer Nature), 2020-02-05)
    We present SVclone, a computational method for inferring the cancer cell fraction of structural variant (SV) breakpoints from whole-genome sequencing data. SVclone accurately determines the variant allele frequencies of both SV breakends, then simultaneously estimates the cancer cell fraction and SV copy number. We assess performance using in silico mixtures of real samples, at known proportions, created from two clonal metastases from the same patient. We find that SVclone's performance is comparable to single-nucleotide variant-based methods, despite having an order of magnitude fewer data points. As part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we use SVclone to reveal a subset of liver, ovarian and pancreatic cancers with subclonally enriched copy-number neutral rearrangements that show decreased overall survival. SVclone enables improved characterisation of SV intra-tumour heterogeneity.