Multi-class computational evolution: development, benchmark evaluation and application to RNA-Seq biomarker discovery.

Background: A computational evolution system (CES) is a knowledge discovery engine that can identify subtle, synergistic relationships in large datasets. Pareto optimization allows CESs to balance accuracy with model complexity when evolving classifiers. Using Pareto optimization, a CES is able to identify a very small number of features while maintaining high classification accuracy.

Mutation analysis with random DNA identifiers (MARDI) catalogs Pig-a mutations in heterogeneous pools of CD48-deficient T cells derived from DMBA-treated rats.

Identification of mutations induced by xenotoxins is a common task in the field of genetic toxicology. Mutations are often detected by clonally expanding potential mutant cells and genotyping each viable clone by Sanger sequencing. Such a "clone-by-clone" approach requires significant time and effort, and sometimes is even impossible to implement.

Evaluating the Stability of RNA-Seq Transcriptome Profiles and Drug-Induced Immune-Related Expression Changes in Whole Blood.

Methods were developed to evaluate the stability of rat whole blood expression obtained from RNA sequencing (RNA-seq) and assess changes in whole blood transcriptome profiles in experiments replicated over time. Expression was measured in globin-depleted RNA extracted from the whole blood of Sprague-Dawley rats, given either saline (control) or neurotoxic doses of amphetamine (AMPH). The experiment was repeated four times (paired control and AMPH groups) over a 2-year span.

An Iterative Leave-One-Out Approach to Outlier Detection in RNA-Seq Data.

The discrete data structure and large sequencing depth of RNA sequencing (RNA-seq) experiments can often generate outlier read counts in one or more RNA samples within a homogeneous group. Thus, how to identify and manage outlier observations in RNA-seq data is an emerging topic of interest. One of the main objectives in these research efforts is to develop statistical methodology that effectively balances the impact of outlier observations and achieves maximal power for statistical testing.

XLPM: efficient algorithm for the analysis of protein-protein contacts using chemical cross-linking mass spectrometry.

Background: Chemical cross-linking is used for protein-protein contacts mapping and for structural analysis. One of the difficulties in cross-linking studies is the analysis of mass-spectrometry data and the assignment of the site of cross-link incorporation. The difficulties are due to higher charges of fragment ions, and to the overall low-abundance of cross-link species in the background of linear peptides.