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.

Non-small-cell lung cancers (NSCLC) compose 80% of all lung carcinomas with squamous cell carcinomas (SCC) and adenocarcinoma representing the majority of these tumors. Although patients with early-stage NSCLC typically have a better outcome, 35% to 50% will relapse within 5 years after surgical treatment. We have profiled primary squamous cell lung carcinomas from 129 patients using Affymetrix U133A gene chips. Unsupervised analysis revealed two clusters of SCC that had no correlation with tumor stage but had significantly different overall patient survival (P = 0.036). The high-risk cluster was most significantly associated with down-regulation of epidermal development genes. Cox proportional hazard models identified an optimal set of 50 prognostic mRNA transcripts using a 5-fold cross-validation procedure. Quantitative reverse transcription-PCR and immunohistochemistry using tissue microarrays were used to validate individual gene candidates. This signature was tested in an independent set of 36 SCC samples and achieved 84% specificity and 41% sensitivity with an overall predictive accuracy of 68%. Kaplan-Meier analysis showed clear stratification of high-risk and low-risk patients [log-rank P = 0.04; hazard ratio (HR), 2.66; 95% confidence interval (95% CI), 1.01-7.05]. Finally, we combined the SCC classifier with our previously identified adenocarcinoma prognostic signature and showed that the combined classifier had a predictive accuracy of 71% in 72 NSCLC samples also showing significant differences in overall survival (log-rank P = 0.0002; HR, 3.54; 95% CI,). This prognostic signature could be used to identify patients with early-stage high-risk NSCLC who might benefit from adjuvant therapy following surgery. (Cancer Res 2006; 66(15): 7466-72)

Nephronophthisis-related ciliopathies (NPHP-RC) are recessive disorders featuring dysplasia or degeneration preferentially in kidney, retina, and cerebellum. Here we combine homozygosity mapping with candidate gene analysis by performing “ciliopathy candidate exome capture” followed by massively-parallel sequencing. We detect 12 different truncating mutations of SDCCAG8 in 10 NPHP-RC families. We demonstrate that SDCCAG8 is localized at both centrioles and directly interacts with NPHP-RC-associated OFD1. Depletion of sdccag8 causes kidney cysts and a body axis defect in zebrafish and induces cell polarity defects in 3D renal cell cultures. This work identifies SDCCAG8 loss of function as a novel cause of a retinal-renal ciliopathy and validates exome capture analysis for broadly heterogeneous single-gene disorders.