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We present a cancer genomic analysis pipeline which takes as input sequencing reads for both germline and tumor genomes and outputs filtered lists of all genetic mutations in the form of short ranked list of the most affected genes in the tumor, using either the Complete Genomics or Illumina platforms. A novel reporting and ranking system has been developed that makes use of publicly available datasets and literature specific to each patient, including new methods for using publicly available expression data in the absence of proper control data. Previously implicated small and large variations (including gene fusions) are reported in addition to probable driver mutations. Relationships between cancer and the sequenced tumor genome are highlighted using a network-based approach that integrates known and predicted protein-protein, protein-TF, and protein-drug interaction data. By using an integrative approach, effects of genetic variations on gene expression are used to provide further evidence of driver mutations. This pipeline has been developed with the aim to be used in assisting in the analysis of pediatric tumors, as an unbiased and automated method for interpreting sequencing results along with identifying potentially therapeutic drugs and their targets. We present results that agree with previous literature and highlight specific findings in a few patients.
Genomics, Cancer, Bioinformatics, Proteins, Assembly, Sequential analysis, Tumors,next generation sequencing, Pediatric cancer, genome analysis
Michael Zeller, Christophe N. Magnan, Vishal R. Patel, Paul Rigor, Leonard Sender, Pierre Baldi, "A Genomic Analysis Pipeline and Its Application to Pediatric Cancers", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 11, no. , pp. 826-839, Sept.-Oct. 2014, doi:10.1109/TCBB.2014.2330616
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