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When applied in large scale to electronic medical record data, the PheWAS approach replicates GWAS associations and reveals potentially new pleiotropic associations.
By mathematically 'silencing' spurious, indirect correlations in networks, two groups devise approaches for improving many different types of network analyses.
By mathematically 'silencing' spurious, indirect correlations in networks, two groups devise approaches for improving many different types of network analyses.
Optimized algorithms from the field of electrical-signal processing improve the identification of genomic signals from diverse high-throughput sequencing experiments, such as ChIP-seq, DNase-seq and FAIRE-seq.
Genes that cause a mutant phenotype are efficiently identified from genetic screens of model and non-model organisms from whole-genome sequencing data without requiring segregating populations, genetic maps and reference sequences.
The MuTect algorithm for calling somatic point mutations enables subclonal analysis of the whole-genome or whole-exome sequencing data being generated in large-scale cancer genomics projects.
The most comprehensive analysis to date of models of transcription-factor binding specificity reveals the best methods for predicting in vivo binding from in vitro data.