Browsing by Author "Fan, Xian"

Now showing 1 - 3 of 3
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    Calculating Variant Allele Fraction of Structural Variation in Next Generation Sequencing by Maximum Likelihood
    (2015-04-23) Fan, Xian; Nakhleh, Luay K.; Kavraki, Lydia; Jermaine, Chris; Chen, Ken
    Cancer cells are intrinsically heterogeneous. Multiple clones with their unique variants co-exist in tumor tissues. The variants include point mutations and structural variations. Point mutations, or single nucleotide variants are those variants on one base; structural variations are variations involving sequence with length not smaller than 50 bases. Approaches to estimate the number of clones and their respective percentages from point mutations have been recently proposed. However, structural variations, although involving more reads than point mutations, have not been quantitatively studied in characterizing cancer heterogeneity. I describe in this thesis a maximum likelihood approach to estimate variant allele fraction of a putative structural variation, as a step towards the characterization of tumor heterogeneity. A software tool, BreakDown, implemented in Perl realizing this statistical model is publicly available. I studied the performance of BreakDown through both simulated and real data, and found BreakDown outperformed other methods such as THetA in estimating variant allele fractions.
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    Detecting Structural Variations with Illumina, PacBio and Optical Maps Data by Computational Approaches
    (2018-04-20) Fan, Xian; Nakhleh, Luay; Chen, Ken
    Detecting structural variations (SV) is important in deciphering variations in human DNA and the cause of genetic disease such as cancer. Computational approaches to detect SVs are made possible by sequencing technologies. As different sequencing technologies render data with different characteristics, computational approaches are designed in a way that is specific to a certain technology. In this thesis I studied three technologies: Illumina, PacBio and Optical Maps. As Illumina and PacBio reads have complementary advantages and disadvantages of read length and error rate, I proposed a new approach, HySA, that combines Illumina and PacBio to detect SV. HySA was able to detect SVs that cannot be detected by the approaches for either only Illumina or only PacBio. However, due to the repetitiveness of the human DNA as well as the existence of complex SVs, it is still challenging for HySA to detect some SVs on the repetitive regions or complex SVs. To overcome that, I proposed a new approach to detect SVs by Optical Maps data, which is advantageous over Illumina and PacBio in read length, despite its lack of sequence and unique error profile. The SVs detected by Optical Maps alone complement those from Illumina and PacBio. In all, the two approaches I proposed help push towards a more complete characterization of SVs in human DNA.
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    Towards accurate characterization of clonal heterogeneity based on structural variation
    (BioMed Central, 2014) Fan, Xian; Zhou, Wanding; Chong, Zechen; Nakhleh, Luay; Chen, Ken
    Recent advances in deep digital sequencing have unveiled an unprecedented degree of clonal heterogeneity within a single tumor DNA sample. Resolving such heterogeneity depends on accurate estimation of fractions of alleles that harbor somatic mutations. Unlike substitutions or small indels, structural variants such as deletions, duplications, inversions and translocations involve segments of DNAs and are potentially more accurate for allele fraction estimations. However, no systematic method exists that can support such analysis. In this paper, we present a novel maximum-likelihood method that estimates allele fractions of structural variants integratively from various forms of alignment signals. We develop a tool, BreakDown, to estimate the allele fractions of most structural variants including medium size (from 1 kilobase to 1 megabase) deletions and duplications, and balanced inversions and translocations. Evaluation based on both simulated and real data indicates that our method systematically enables structural variants for clonal heterogeneity analysis and can greatly enhance the characterization of genomically instable tumors.