TopPIC Gateway: A Web Gateway for Top-Down Mass Spectrometry Data Interpretation.

Top-down mass spectrometry-based proteomics has become the method of choice for identifying and quantifying intact proteoforms in biological samples. We present a web-based gateway for TopPIC suite, a widely used software suite consisting of four software tools for top-down mass spectrometry data interpretation: TopFD, TopPIC, TopMG, and TopDiff. The gateway enables the community to use heterogeneous collection of computing resources that includes high performance computing clusters at Indiana University and virtual clusters on XSEDE's Jetstream Cloud resource for top-down mass spectral data analysis using TopPIC suite.

Building a Science Gateway For Processing and Modeling Sequencing Data Via Apache Airavata.

The amount of DNA sequencing data has been exponentially growing during the past decade due to advances in sequencing technology. Processing and modeling large amounts of sequencing data can be computationally intractable for desktop computing platforms. High performance computing (HPC) resources offer advantages in terms of computing power, and can be a general solution to these problems.

Bio and health informatics meets cloud : BioVLab as an example.

The exponential increase of genomic data brought by the advent of the next or the third generation sequencing (NGS) technologies and the dramatic drop in sequencing cost have driven biological and medical sciences to data-driven sciences. This revolutionary paradigm shift comes with challenges in terms of data transfer, storage, computation, and analysis of big bio/medical data. Cloud computing is a service model sharing a pool of configurable resources, which is a suitable workbench to address these challenges.

BioVLAB-MMIA: a cloud environment for microRNA and mRNA integrated analysis (MMIA) on Amazon EC2.

MicroRNAs, by regulating the expression of hundreds of target genes, play critical roles in developmental biology and the etiology of numerous diseases, including cancer. As a vast amount of microRNA expression profile data are now publicly available, the integration of microRNA expression data sets with gene expression profiles is a key research problem in life science research. However, the ability to conduct genome-wide microRNA-mRNA (gene) integration currently requires sophisticated, high-end informatics tools, significant expertise in bioinformatics and computer science to carry out the complex integration analysis.