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Computer developer with over 25 years of broad experience in programming, systems…
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63rd Annual Meeting of The American Society of Human Genetics October 22, 2013
Identifying the source of newly presenting tumor fails to yield definitive results in a significant number of cases, with cancer of unknown primary organ (CUP) accounting for 3-5% of all cancer diagnoses. Tumors are commonly classified via visual inspection alongside of immunohistochemistry and expression profiling, neither of which can consistently identify a molecular signature by which to guide treatment. Misclassifying a cancer’s primary site can have wide-ranging effects, including: 1)…
Identifying the source of newly presenting tumor fails to yield definitive results in a significant number of cases, with cancer of unknown primary organ (CUP) accounting for 3-5% of all cancer diagnoses. Tumors are commonly classified via visual inspection alongside of immunohistochemistry and expression profiling, neither of which can consistently identify a molecular signature by which to guide treatment. Misclassifying a cancer’s primary site can have wide-ranging effects, including: 1) masking the true primary cancer, 2) misinforming the patient treatment plan 3) obfuscating clinical trial results by enrolling misclassified subjects. Here we present a method for using cost-effective, high-throughput sequencing exome data to find the molecular signatures of various tissue-specific cancers. We do so by first scoring the genes from individual exomes based upon the level of mutational burden. We then perform an unsupervised analysis to reduce the dimensionality of the scored genes. Using exomes from The Cancer Genome Atlas, we apply this method to classify cancers into tissue-specific clusters. This model can thus be used with new tumor exomes to aid in identifying the tissue-of-origin of the primary cancer.
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The Open Bioinformatics Journal July 1, 2013
High throughput sequencing has resulted in extreme growth rates of sequencing data. At our lab, we generate terabytes of data every day. It is usually seen as required for data output to be “cleaned” and processed in various ways prior to use for common tasks such as variant calling, expression quantification, and assembly. Two common tasks associated with HTS are adapter trimming and paired-end joining. We have developed two tools at Expression Analysis, Inc. to address these common tasks:…
High throughput sequencing has resulted in extreme growth rates of sequencing data. At our lab, we generate terabytes of data every day. It is usually seen as required for data output to be “cleaned” and processed in various ways prior to use for common tasks such as variant calling, expression quantification, and assembly. Two common tasks associated with HTS are adapter trimming and paired-end joining. We have developed two tools at Expression Analysis, Inc. to address these common tasks: fastq-mcf and fastq-join. We compare the performance of these tools to similar open-source utilities, both in terms of resource efficiency, and effectiveness.
Courses
Advanced Genomics and Genetics Analyses
AS.410.713
Molecular Phylogenetic Techniques
AS.410.640
Projects
Nov 2011 - Present
Sequencing processing utilities, statistics for alignment and sequencing, variant calling
Jun 2003 - Present
SMX is a text-embedded macro processing language distrbuted under a BSD-style open source license.
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