Integrative genomic analysis by interoperation of bioinformatics tools in GenomeSpace.

Complex biomedical analyses require the use of multiple software tools in concert and remain challenging for much of the biomedical research community. We introduce GenomeSpace (http://www.genomespace.org), a cloud-based, cooperative community resource that currently supports the streamlined interaction of 20 bioinformatics tools and data resources. To facilitate integrative analysis by non-programmers, it offers a growing set of 'recipes', short workflows to guide investigators through high-utility analysis tasks.

Comparative analysis of RNA sequencing methods for degraded or low-input samples.

RNA-seq is an effective method for studying the transcriptome, but it can be difficult to apply to scarce or degraded RNA from fixed clinical samples, rare cell populations or cadavers. Recent studies have proposed several methods for RNA-seq of low-quality and/or low-quantity samples, but the relative merits of these methods have not been systematically analyzed. Here we compare five such methods using metrics relevant to transcriptome annotation, transcript discovery and gene expression. Using a single human RNA sample, we constructed and sequenced ten libraries with these methods and compared them against two control libraries. We found that the RNase H method performed best for chemically fragmented, low-quality RNA, and we confirmed this through analysis of actual degraded samples. RNase H can even effectively replace oligo(dT)-based methods for standard RNA-seq. SMART and NuGEN had distinct strengths for measuring low-quantity RNA. Our analysis allows biologists to select the most suitable methods and provides a benchmark for future method development.

Predicting in vitro drug sensitivity using Random Forests.

MOTIVATION: Panels of cell lines such as the NCI-60 have long been used to test drug candidates for their ability to inhibit proliferation. Predictive models of in vitro drug sensitivity have previously been constructed using gene expression signatures generated from gene expression microarrays. These statistical models allow the prediction of drug response for cell lines not in the original NCI-60. We improve on existing techniques by developing a novel multistep algorithm that builds regression models of drug response using Random Forest, an ensemble approach based on classification and regression trees (CART). RESULTS: This method proved successful in predicting drug response for both a panel of 19 Breast Cancer and 7 Glioma cell lines, outperformed other methods based on differential gene expression, and has general utility for any application that seeks to relate gene expression data to a continuous output variable. IMPLEMENTATION: Software was written in the R language and will be available together with associated gene expression and drug response data as the package ivDrug at http://r-forge.r-project.org.

3D Chromosome Regulatory Landscape of Human Pluripotent Cells.

In this study, we describe the 3D chromosome regulatory landscape of human naive and primed embryonic stem cells. To devise this map, we identified transcriptional enhancers and insulators in these cells and placed them within the context of cohesin-associated CTCF-CTCF loops using cohesin ChIA-PET data. The CTCF-CTCF loops we identified form a chromosomal framework of insulated neighborhoods, which in turn form topologically associating domains (TADs) that are largely preserved during the transition between the naive and primed states. Regulatory changes in enhancer-promoter interactions occur within insulated neighborhoods during cell state transition. The CTCF anchor regions we identified are conserved across species, influence gene expression, and are a frequent site of mutations in cancer cells, underscoring their functional importance in cellular regulation. These 3D regulatory maps of human pluripotent cells therefore provide a foundation for future interrogation of the relationships between chromosome structure and gene control in development and disease.

Activation of proto-oncogenes by disruption of chromosome neighborhoods.

Oncogenes are activated through well-known chromosomal alterations such as gene fusion, translocation, and focal amplification. In light of recent evidence that the control of key genes depends on chromosome structures called insulated neighborhoods, we investigated whether proto-oncogenes occur within these structures and whether oncogene activation can occur via disruption of insulated neighborhood boundaries in cancer cells. We mapped insulated neighborhoods in T cell acute lymphoblastic leukemia (T-ALL) and found that tumor cell genomes contain recurrent microdeletions that eliminate the boundary sites of insulated neighborhoods containing prominent T-ALL proto-oncogenes. Perturbation of such boundaries in nonmalignant cells was sufficient to activate proto-oncogenes. Mutations affecting chromosome neighborhood boundaries were found in many types of cancer. Thus, oncogene activation can occur via genetic alterations that disrupt insulated neighborhoods in malignant cells.

Early-branching gut fungi possess a large, comprehensive array of biomass-degrading enzymes.

The fungal kingdom is the source of almost all industrial enzymes in use for lignocellulose bioprocessing. We developed a systems-level approach that integrates transcriptomic sequencing, proteomics, phenotype, and biochemical studies of relatively unexplored basal fungi. Anaerobic gut fungi isolated from herbivores produce a large array of biomass-degrading enzymes that synergistically degrade crude, untreated plant biomass and are competitive with optimized commercial preparations from Aspergillus and Trichoderma. Compared to these model platforms, gut fungal enzymes are unbiased in substrate preference due to a wealth of xylan-degrading enzymes. These enzymes are universally catabolite-repressed and are further regulated by a rich landscape of noncoding regulatory RNAs. Additionally, we identified several promising sequence-divergent enzyme candidates for lignocellulosic bioprocessing.

IGF2BP2/IMP2-Deficient mice resist obesity through enhanced translation of Ucp1 mRNA and Other mRNAs encoding mitochondrial proteins.

Although variants in the IGF2BP2/IMP2 gene confer risk for type 2 diabetes, IMP2, an RNA binding protein, is not known to regulate metabolism. Imp2(-/-) mice gain less lean mass after weaning and have increased lifespan. Imp2(-/-) mice are highly resistant to diet-induced obesity and fatty liver and display superior glucose tolerance and insulin sensitivity, increased energy expenditure, and better defense of core temperature on cold exposure. Imp2(-/-) brown fat and Imp2(-/-) brown adipocytes differentiated in vitro contain more UCP1 polypeptide than Imp2(+/+) despite similar levels of Ucp1 mRNA; the Imp2(-/-)adipocytes also exhibit greater uncoupled oxygen consumption. IMP2 binds the mRNAs encoding Ucp1 and other mitochondrial components, and most exhibit increased translational efficiency in the absence of IMP2. In vitro IMP2 inhibits translation of mRNAs bearing the Ucp1 untranslated segments. Thus IMP2 limits longevity and regulates nutrient and energy metabolism in the mouse by controlling the translation of its client mRNAs.