Orchestrating high-throughput genomic analysis with Bioconductor.

Bioconductor is an open-source, open-development software project for the analysis and comprehension of high-throughput data in genomics and molecular biology. The project aims to enable interdisciplinary research, collaboration and rapid development of scientific software. Based on the statistical programming language R, Bioconductor comprises 934 interoperable packages contributed by a large, diverse community of scientists. Packages cover a range of bioinformatic and statistical applications. They undergo formal initial review and continuous automated testing. We present an overview for prospective users and contributors.

Correlations in connected random graphs.

We study the properties of the giant connected component in random graphs with arbitrary degree distribution. We concentrate on the degree-degree correlations. We show that the adjoining nodes in the giant connected component are correlated and derive analytic formulas for the joint nearest-neighbor degree probability distribution. Using those results we describe correlations in maximal entropy connected random graphs. We show that connected graphs are disassortative and that correlations are strongly related to the presence of one-degree nodes (leaves). We propose an efficient algorithm for generating connected random graphs. We illustrate our results with several examples.

Drug-perturbation-based stratification of blood cancer.

As new generations of targeted therapies emerge and tumor genome sequencing discovers increasingly comprehensive mutation repertoires, the functional relationships of mutations to tumor phenotypes remain largely unknown. Here, we measured ex vivo sensitivity of 246 blood cancers to 63 drugs alongside genome, transcriptome, and DNA methylome analysis to understand determinants of drug response. We assembled a primary blood cancer cell encyclopedia data set that revealed disease-specific sensitivities for each cancer. Within chronic lymphocytic leukemia (CLL), responses to 62% of drugs were associated with 2 or more mutations, and linked the B cell receptor (BCR) pathway to trisomy 12, an important driver of CLL. Based on drug responses, the disease could be organized into phenotypic subgroups characterized by exploitable dependencies on BCR, mTOR, or MEK signaling and associated with mutations, gene expression, and DNA methylation. Fourteen percent of CLLs were driven by mTOR signaling in a non-BCR-dependent manner. Multivariate modeling revealed immunoglobulin heavy chain variable gene (IGHV) mutation status and trisomy 12 as the most important modulators of response to kinase inhibitors in CLL. Ex vivo drug responses were associated with outcome. This study overcomes the perception that most mutations do not influence drug response of cancer, and points to an updated approach to understanding tumor biology, with implications for biomarker discovery and cancer care.

Cell-to-cell expression variability followed by signal reinforcement progressively segregates early mouse lineages.

It is now recognized that extensive expression heterogeneities among cells precede the emergence of lineages in the early mammalian embryo. To establish a map of pluripotent epiblast (EPI) versus primitive endoderm (PrE) lineage segregation within the inner cell mass (ICM) of the mouse blastocyst, we characterized the gene expression profiles of individual ICM cells. Clustering analysis of the transcriptomes of 66 cells demonstrated that initially they are non-distinguishable. Early in the segregation, lineage-specific marker expression exhibited no apparent correlation, and a hierarchical relationship was established only in the late blastocyst. Fgf4 exhibited a bimodal expression at the earliest stage analysed, and in its absence, the differentiation of PrE and EPI was halted, indicating that Fgf4 drives, and is required for, ICM lineage segregation. These data lead us to propose a model where stochastic cell-to-cell expression heterogeneity followed by signal reinforcement underlies ICM lineage segregation by antagonistically separating equivalent cells.

Long-range disassortative correlations in generic random trees.

We explicitly calculate the distance dependent correlation functions in a maximal entropy ensemble of random trees. We show that correlations remain disassortative at all distances and vanish only as a second inverse power of the distance. We discuss in detail the example of scale-free trees where the diverging second moment of the degree distribution leads to some interesting phenomena.