Management, Analyses, and Distribution of the MaizeCODE Data on the Cloud.

MaizeCODE is a project aimed at identifying and analyzing functional elements in the maize genome. In its initial phase, MaizeCODE assayed up to five tissues from four maize strains (B73, NC350, W22, TIL11) by RNA-Seq, Chip-Seq, RAMPAGE, and small RNA sequencing. To facilitate reproducible science and provide both human and machine access to the MaizeCODE data, we enhanced SciApps, a cloud-based portal, for analysis and distribution of both raw data and analysis results. Based on the SciApps workflow platform, we generated new components to support the complete cycle of MaizeCODE data management. These include publicly accessible scientific workflows for the reproducible and shareable analysis of various functional data, a RESTful API for batch processing and distribution of data and metadata, a searchable data page that lists each MaizeCODE experiment as a reproducible workflow, and integrated JBrowse genome browser tracks linked with workflows and metadata. The SciApps portal is a flexible platform that allows the integration of new analysis tools, workflows, and genomic data from multiple projects. Through metadata and a ready-to-compute cloud-based platform, the portal experience improves access to the MaizeCODE data and facilitates its analysis.

Population-based identity-by-descent mapping combined with exome sequencing to detect rare risk variants for schizophrenia.

Genome-wide association studies (GWASs) are highly effective at identifying common risk variants for schizophrenia. Rare risk variants are also important contributors to schizophrenia etiology but, with the exception of large copy number variants, are difficult to detect with GWAS. Exome and genome sequencing, which have accelerated the study of rare variants, are expensive so alternative methods are needed to aid detection of rare variants. Here we re-analyze an Irish schizophrenia GWAS dataset (n = 3,473) by performing identity-by-descent (IBD) mapping followed by exome sequencing of individuals identified as sharing risk haplotypes to search for rare risk variants in coding regions. We identified 45 rare haplotypes (>1 cM) that were significantly more common in cases than controls. By exome sequencing 105 haplotype carriers, we investigated these haplotypes for functional coding variants that could be tested for association in independent GWAS samples. We identified one rare missense variant in PCNT but did not find statistical support for an association with schizophrenia in a replication analysis. However, IBD mapping can prioritize both individual samples and genomic regions for follow-up analysis but genome rather than exome sequencing may be more effective at detecting risk variants on rare haplotypes.

Discovery of novel human breast cancer microRNAs from deep sequencing data by analysis of pri-microRNA secondary structures.

MicroRNAs (miRNAs) are key regulators of gene expression and contribute to a variety of biological processes. Abnormal miRNA expression has been reported in various diseases including pathophysiology of breast cancer, where they regulate protumorigenic processes including vascular invasiveness, estrogen receptor status, chemotherapy resistance, invasion and metastasis. The miRBase sequence database, a public repository for newly discovered miRNAs, has grown rapidly with approximately >10,000 entries to date. Despite this rapid growth, many miRNAs have not yet been validated, and several others are yet to be identified. A lack of a full complement of miRNAs has imposed limitations on recognizing their important roles in cancer, including breast cancer. Using deep sequencing technology, we have identified 189 candidate novel microRNAs in human breast cancer cell lines with diverse tumorigenic potential. We further show that analysis of 500-nucleotide pri-microRNA secondary structure constitutes a reliable method to predict bona fide miRNAs as judged by experimental validation. Candidate novel breast cancer miRNAs with stem lengths of greater than 30 bp resulted in the generation of precursor and mature sequences in vivo. On the other hand, candidates with stem length less than 30 bp were less efficient in producing mature miRNA. This approach may be used to predict which candidate novel miRNA would qualify as bona fide miRNAs from deep sequencing data with approximately 90% accuracy.