Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.[The Cytoscape v1.1 Core runs on all major operating systems and is freely available for download from http://www.cytoscape.org/ as an open source Java application.] Such models promise to transform biological research by providing a framework to (1) systematically interrogate and experimentally verify knowledge of a pathway; (2) manage the immense complexity of hundreds or potentially thousands of cellular components and interactions; and (3) reveal emergent properties and unanticipated consequences of different pathway configurations.Typically, models are directed toward a cellular process or disease pathway of interest (Gilman and Arkin 2002) and are built by formulating existing literature as a system of differential and/or stochastic equations. However, pathway-specific models are now being supplemented with global data gathered for an entire cell or organism, by use of two complementary approaches. First, recent technological developments have made it feasible to measure pathway structure systematically, using highthroughput screens for protein-protein (Ito et al. 2001;von Mering et al. 2002), protein-DNA (Lee et al. 2002, and genetic interactions (Tong et al. 2001). To complement these data, a second set of high-throughput methods are available to characterize the molecular and cellular states induced by pathway interactions under different experimental conditions. For instance, global changes in gene expression are measured with DNA microarrays (DeRisi et al. 1997), whereas changes in protein abundance (Gygi et al. 1999), protein phosphorylation state (Zhou et al. 2001), and metabolite concentrations (Griffin et al. 2001) may be quantified with mass spectrometry, NMR, and other advanced techniques. High-throughput data pertaining to molecular interactions and states are well matched, in...

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.

We demonstrate the first successful application of exome sequencing to discover the gene for a rare, Mendelian disorder of unknown cause, Miller syndrome (OMIM %263750). For four affected individuals in three independent kindreds, we captured and sequenced coding regions to a mean coverage of 40X, and sufficient depth to call variants at ~97% of each targeted exome. Filtering against public SNP databases and a small number of HapMap exomes for genes with two novel variants in each of the four cases identified a single candidate gene, DHODH, which encodes a key enzyme in the pyrimidine de novo biosynthesis pathway. Sanger sequencing confirmed the presence of DHODH mutations in three additional families with Miller syndrome. Exome sequencing of a small number of unrelated, affected individuals is a powerful, efficient strategy for identifying the genes underlying rare Mendelian disorders and will likely transform the genetic analysis of monogenic traits.

We analyzed the whole genome sequences of a family of four, consisting of two siblings and their parents. Family-based sequencing allowed us to delineate recombination sites precisely, identify 70% of the sequencing errors, and identify very rare SNVs. We also directly estimated a human intergeneration mutation rate of ∼1.1×10-8 per position per haploid genome. Both offspring in this family have two recessive disorders--Miller syndrome, for which the gene was concurrently identified, and primary ciliary dyskinesia, for which causative genes have been previously identified. Family-based genome analysis enabled us to narrow the candidate genes for both of these Mendelian disorders to only four. Our results demonstrate the unique value of complete genome sequencing in families.