An ensemble approach to accurately detect somatic mutations using SomaticSeq.

SomaticSeq is an accurate somatic mutation detection pipeline implementing a stochastic boosting algorithm to produce highly accurate somatic mutation calls for both single nucleotide variants and small insertions and deletions. The workflow currently incorporates five state-of-the-art somatic mutation callers, and extracts over 70 individual genomic and sequencing features for each candidate site. A training set is provided to an adaptively boosted decision tree learner to create a classifier for predicting mutation statuses. We validate our results with both synthetic and real data. We report that SomaticSeq is able to achieve better overall accuracy than any individual tool incorporated.

High-throughput Bayesian Network Learning using Heterogeneous Multicore Computers.

Aberrant intracellular signaling plays an important role in many diseases. The causal structure of signal transduction networks can be modeled as Bayesian Networks (BNs), and computationally learned from experimental data. However, learning the structure of Bayesian Networks (BNs) is an NP-hard problem that, even with fast heuristics, is too time consuming for large, clinically important networks (20-50 nodes). In this paper, we present a novel graphics processing unit (GPU)-accelerated implementation of a Monte Carlo Markov Chain-based algorithm for learning BNs that is up to 7.5-fold faster than current general-purpose processor (GPP)-based implementations. The GPU-based implementation is just one of several implementations within the larger application, each optimized for a different input or machine configuration. We describe the methodology we use to build an extensible application, assembled from these variants, that can target a broad range of heterogeneous systems, e.g., GPUs, multicore GPPs. Specifically we show how we use the Merge programming model to efficiently integrate, test and intelligently select among the different potential implementations.

Gene expression profiles of human inner cell mass cells and embryonic stem cells.

Human embryonic stem cell (hESC) lines are derived from the inner cell mass (ICM) of preimplantation human blastocysts obtained on days 5-6 following fertilization. Based on their derivation, they were once thought to be the equivalent of the ICM. Recently, however, studies in mice reported the derivation of mouse embryonic stem cell lines from the epiblast; these epiblast lines bear significant resemblance to human embryonic stem cell lines in terms of culture, differentiation potential and gene expression. In this study, we compared gene expression in human ICM cells isolated from the blastocyst and embryonic stem cells. We demonstrate that expression profiles of ICM clusters from single embryos and hESC populations were highly reproducible. Moreover, comparison of global gene expression between individual ICM clusters and human embryonic stem cells indicated that these two cell types are significantly different in regards to gene expression, with fewer than one half of all genes expressed in both cell types. Genes of the isolated human inner cell mass that are upregulated and downregulated are involved in numerous cellular pathways and processes; a subset of these genes may impart unique characteristics to hESCs such as proliferative and self-renewal properties.