In this work, we present the Genome Modeling System (GMS), an analysis information management system capable of executing automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled with reliable and repeatable analysis pipelines. The GMS also serves as a platform for bioinformatics development, allowing a large team to collaborate on data analysis, or an individual researcher to leverage the work of others effectively within its data management system. Rather than separating ad-hoc analysis from rigorous, reproducible pipelines, the GMS promotes systematic integration between the two. As a demonstration of the GMS, we performed an integrated analysis of whole genome, exome and transcriptome sequencing data from a breast cancer cell line (HCC1395) and matched lymphoblastoid line (HCC1395BL). These data are available for users to test the software, complete tutorials and develop novel GMS pipeline configurations. The GMS is available at https://github.com/genome/gms.

Abstract Summary: Despite recent progress, computational tools that identify gene fusions from next-generation whole transcriptome sequencing data are often limited in accuracy and scalability. Here, we present a software package, BreakFusion that combines the strength of reference alignment followed by read-pair analysis and de novo assembly to achieve a good balance in sensitivity, specificity and computational efficiency. Availability: http://bioinformatics.mdanderson.org/main/BreakFusion Contact: [email protected]; [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

MOTIVATION: The expansion of cancer genome sequencing continues to stimulate development of analytical tools for inferring relationships between somatic changes and tumor development. Pathway associations are especially consequential, but existing algorithms are demonstrably inadequate. METHODS: Here, we propose the PathScan significance test for the scenario where pathway mutations collectively contribute to tumor development. Its design addresses two aspects that established methods neglect. First, we account for variations in gene length and the consequent differences in their mutation probabilities under the standard null hypothesis of random mutation. The associated spike in computational effort is mitigated by accurate convolution-based approximation. Second, we combine individual probabilities into a multiple-sample value using Fisher-Lancaster theory, thereby improving differentiation between a few highly mutated genes and many genes having only a few mutations apiece. We investigate accuracy, computational effort and power, reporting acceptable performance for each. RESULTS: As an example calculation, we re-analyze KEGG-based lung adenocarcinoma pathway mutations from the Tumor Sequencing Project. Our test recapitulates the most significant pathways and finds that others for which the original test battery was inconclusive are not actually significant. It also identifies the focal adhesion pathway as being significantly mutated, a finding consistent with earlier studies. We also expand this analysis to other databases: Reactome, BioCarta, Pfam, PID and SMART, finding additional hits in ErbB and EPHA signaling pathways and regulation of telomerase. All have implications and plausible mechanistic roles in cancer. Finally, we discuss aspects of extending the method to integrate gene-specific background rates and other types of genetic anomalies. AVAILABILITY: PathScan is implemented in Perl and is available from the Genome Institute at: http://genome.wustl.edu/software/pathscan.

BACKGROUND: RNA transcripts from genomic sequences showing dyad symmetry typically adopt hairpin-like, cloverleaf, or similar structures that act as recognition sites for proteins. Such structures often are the precursors of non-coding RNA (ncRNA) sequences like microRNA (miRNA) and small-interfering RNA (siRNA) that have recently garnered more functional significance than in the past. Genomic DNA contains hundreds of thousands of such inverted repeats (IRs) with varying degrees of symmetry. But by collecting statistically significant information from a known set of ncRNA, we can sort these IRs into those that are likely to be functional. RESULTS: A novel method was developed to scan genomic DNA for partially symmetric inverted repeats and the resulting set was further refined to match miRNA precursors (pre-miRNA) with respect to their density of symmetry, statistical probability of the symmetry, length of stems in the predicted hairpin secondary structure, and the GC content of the stems. This method was applied on the Arabidopsis thaliana genome and validated against the set of 190 known Arabidopsis pre-miRNA in the miRBase database. A preliminary scan for IRs identified 186 of the known pre-miRNA but with 714700 pre-miRNA candidates. This large number of IRs was further refined to 483908 candidates with 183 pre-miRNA identified and further still to 165371 candidates with 171 pre-miRNA identified (i.e. with 90% of the known pre-miRNA retained). CONCLUSIONS: 165371 candidates for potentially functional miRNA is still too large a set to warrant wet lab analyses, such as northern blotting, on all of them. Hence additional filters are needed to further refine the number of candidates while still retaining most of the known miRNA. These include detection of promoters and terminators, homology analyses, location of candidate relative to coding regions, and better secondary structure prediction algorithms. The software developed is designed to easily accommodate such additional filters with a minimal experience in Perl.

BACKGROUND: Gene family identification from ESTs can be a valuable resource for analysis of genome evolution but presents unique challenges in organisms for which the entire genome is not yet sequenced. We have developed a novel gene family identification method based on negative selection patterns (NSP) between family members to screen EST-generated contigs. This strategy was tested on five known gene families in Arabidopsis to see if individual paralogs could be identified with accuracy from EST data alone when compared to the actual gene sequences in this fully sequenced genome. RESULTS: The NSP method uniquely identified family members in all the gene families tested. Two members of the FtsH gene family, three members each of the PAL, RF1, and ribosomal L6 gene families, and four members of the CAD gene family were correctly identified. Additionally all ESTs from the representative contigs when checked against MapViewer data successfully identify the gene locus predicted. CONCLUSION: We demonstrate the effectiveness of the NSP strategy in identifying specific gene family members in Arabidopsis using only EST data and we describe how this strategy can be used to identify many gene families in agronomically important crop species where they are as yet undiscovered.