Biology has been transformed by the rapid development of computing and the concurrent rise of data-rich approaches such as, omics or high-resolution imaging. However, there is a persistent computational skills gap in the biomedical research workforce. Inherent limitations of classroom teaching and institutional core support highlight the need for accessible ways for researchers to explore developments in computational biology. An analysis of the Scripps Research Genomics Core revealed increases in the total number and diversity of experiments: the share of experiments other than bulk RNA- or DNA-sequencing increased from 34% to 60% within 10 years, requiring more tailored computational analyses. These challenges were tackled by forming a volunteer-led affinity group of approximately 300 academic biomedical researchers interested in computational biology, referred to as the Computational Biology and Bioinformatics (CBB) affinity group. This adaptive group has provided continuing education and networking opportunities through seminars, workshops, and coding sessions while evolving along with the needs of its members. A survey of CBB's impact confirmed the group's events increased the members' exposure to computational biology educational and research events (79% respondents) and networking opportunities (61% respondents). Thus, volunteer-led affinity groups may be a viable complement to traditional institutional resources for enhancing the application of computing in biomedical research.

MOTIVATION: Omics Pipe (http://sulab.scripps.edu/omicspipe) is a computational framework that automates multi-omics data analysis pipelines on high performance compute clusters and in the cloud. It supports best practice published pipelines for RNA-seq, miRNA-seq, Exome-seq, Whole-Genome sequencing, ChIP-seq analyses and automatic processing of data from The Cancer Genome Atlas (TCGA). Omics Pipe provides researchers with a tool for reproducible, open source and extensible next generation sequencing analysis. The goal of Omics Pipe is to democratize next-generation sequencing analysis by dramatically increasing the accessibility and reproducibility of best practice computational pipelines, which will enable researchers to generate biologically meaningful and interpretable results. RESULTS: Using Omics Pipe, we analyzed 100 TCGA breast invasive carcinoma paired tumor-normal datasets based on the latest UCSC hg19 RefSeq annotation. Omics Pipe automatically downloaded and processed the desired TCGA samples on a high throughput compute cluster to produce a results report for each sample. We aggregated the individual sample results and compared them to the analysis in the original publications. This comparison revealed high overlap between the analyses, as well as novel findings due to the use of updated annotations and methods. AVAILABILITY AND IMPLEMENTATION: Source code for Omics Pipe is freely available on the web (https://bitbucket.org/sulab/omics_pipe). Omics Pipe is distributed as a standalone Python package for installation (https://pypi.python.org/pypi/omics_pipe) and as an Amazon Machine Image in Amazon Web Services Elastic Compute Cloud that contains all necessary third-party software dependencies and databases (https://pythonhosted.org/omics_pipe/AWS_installation.html).

The benefits of virtualization are typically considered to be server consolidation, (leading to the reduction of power and cooling costs) increased availability, isolation, ease of operating system deployment and simplified disaster recovery. High Performance Computing (HPC) environments pose one main challenge for virtualization: the need to maximize throughput with minimal loss of CPU and I/O efficiency. However, virtualization is usually evaluated in terms of enterprise workloads and assumes that servers are underutilized and can be consolidated. In this paper we evaluate the performance of several virtual machine technologies in the context of HPC. A fundamental requirement of current high performance workloads is that both CPU and I/O must be highly efficient for tasks such as MPI jobs. This work benchmarks two virtual machine monitors, Open VZ and KVM, specifically focusing on I/O throughput since CPU efficiency has been extensively studied [1]. Open VZ offers near native I/O performance. Amazon's EC2 “ClusterCompute Node” product is also considered for comparative purposes and performs quite well. The EC2 “Cluster ComputeNode” product utilizes the Xen hyper visor in hvm mode and 10Gbit/s Ethernet for high throughput communication. Therefore, we also briefly studied Xen on our hardware platform (in hvmmode) to determine if there are still areas of improvement in KVM that allow EC2 to outperform KVM (with InfiniBand host channel adapters operating at 20 Gbit/s) in MPI benchmarks. We conclude that KVM's I/O performance is sub optimal, potentially due to memory management problems in the hyper visor. Amazon's EC2 service is promising, although further investigation is necessary to understand the effects of network based storage on I/O throughput in compute nodes. Amazon's offering may be attractive for users searching for “InfiniBand-like” performance without the upfront investment required to build an InfiniBand cluster or users wishing to dynamically expand their cluster during periods of high demand.

Neurophysiologists are often faced with the problem of evaluating the quality of a code for a sensory or motor variable, either to relate it to the performance of the animal in a simple discrimination task or to compare the codes at various stages along the neuronal pathway. One common belief that has emerged from such studies is that sharpening of tuning curves improves the quality of the code, although only to a certain point; sharpening beyond that is believed to be harmful. We show that this belief relies on either problematic technical analysis or improper assumptions about the noise. We conclude that one cannot tell, in the general case, whether narrow tuning curves are better than wide ones; the answer depends critically on the covariance of the noise. The same conclusion applies to other manipulations of the tuning curve profiles such as gain increase.