Pool of Potential Papers to Present

• Determinants of expression variability.
  • Alemu et al., Nucleic acids research, 2014
• Integrative analysis of metabolomics and transcriptomics data: a unified model framework to identify underlying system pathways.
  • Brink-Jensen et al., PloS one, 2013
• Galaxy Integrated Omics: Web-based standards-compliant workflows for proteomics informed by transcriptomics.
  • Fan et al., Molecular & cellular proteomics : MCP, 2015
• Genetic and epigenetic fine mapping of causal autoimmune disease variants
  • Farh et al., Nature, 2014
• Inferring dynamic gene regulatory networks in cardiac differentiation through the integration of multi-dimensional data.
  • Gong et al., BMC bioinformatics, 2015
• p53-regulated networks of protein, mRNA, miRNA and lncRNA expression revealed by integrated pSILAC and NGS analyses.
  • Hunten et al., Molecular & cellular proteomics : MCP, 2015
• A pilot study utilizing multi-omic molecular profiling to find potential targets and select individualized treatments for patients with previously treated metastatic breast cancer.
  • Jameson et al., Breast cancer research and treatment, 2014
• Meta-analysis of pathway enrichment: combining independent and dependent omics data sets.
  • Kaever et al., PloS one, 2014
• Copy number variation detection and genotyping from exome sequence data.
  • Krumm et al., Genome research, 2012
• A network biology workflow to study transcriptomics data of the diabetic liver.
  • Kutmon et al., BMC genomics, 2014
• Integrative analysis of haplotype-resolved epigenomes across human tissues
  • Leung et al., Nature, 2015
• RNA-seq differential expression studies: more sequence or more replication?
  • Liu et al., Bioinformatics (Oxford, England), 2014
• A dynamic alternative splicing program regulates gene expression during terminal erythropoiesis.
  • Pimentel et al., Nucleic acids research, 2014
• Framework for the Integration of Genomics, Epigenomics and Transcriptomics in Complex Diseases.
  • Pineda et al., Human heredity, 2015
• Cell-of-origin chromatin organization shapes the mutational landscape of cancer
  • Polak et al., Nature, 2015
• Assessing copy number from exome sequencing and exome array CGH based on CNV spectrum in a large clinical cohort.
  • Retterer et al., Genetics in medicine : official journal of the American College of Medical Genetics, 2015
• TopKLists: a comprehensive R package for statistical inference, stochastic aggregation, and visualization of multiple omics ranked lists.
  • Schimek et al., Statistical applications in genetics and molecular biology, 2015
• MVDA: a multi-view genomic data integration methodology.
  • Serra et al., BMC bioinformatics, 2015
• Integration of somatic mutation, expression and functional data reveals potential driver genes predictive of breast cancer survival.
  • Suo et al., Bioinformatics (Oxford, England), 2015
• pwOmics: An R package for pathway-based integration of time-series omics data using public database knowledge
  • A. Wachter & T. Beissbarth, Bioinformatics, 2015