MOTIVATION: Intrinsically disordered regions of proteins play an essential role in the regulation of various biological processes. Key to their regulatory function is the binding of molecular recognition features (MoRFs) to globular protein domains in a process known as a disorder-to-order transition. Predicting the location of MoRFs in protein sequences with high accuracy remains an important computational challenge. METHOD: In this study, we introduce MoRFCHiBi, a new computational approach for fast and accurate prediction of MoRFs in protein sequences. MoRFCHiBi combines the outcomes of two support vector machine (SVM) models that take advantage of two different kernels with high noise tolerance. The first, SVMS, is designed to extract maximal information from the general contrast in amino acid compositions between MoRFs, their surrounding regions (Flanks), and the remainders of the sequences. The second, SVMT, is used to identify similarities between regions in a query sequence and MoRFs of the training set. RESULTS: We evaluated the performance of our predictor by comparing its results with those of two currently available MoRF predictors, MoRFpred and ANCHOR. Using three test sets that have previously been collected and used to evaluate MoRFpred and ANCHOR, we demonstrate that MoRFCHiBi outperforms the other predictors with respect to different evaluation metrics. In addition, MoRFCHiBi is downloadable and fast, which makes it useful as a component in other computational prediction tools. AVAILABILITY AND IMPLEMENTATION: http://www.chibi.ubc.ca/morf/.

MOTIVATION: Detection of single nucleotide polymorphisms (SNPs) has been a major application in processing second generation sequencing (SGS) data. In principle, SNPs are called on single base differences between a reference genome and a sequence generated from SGS short reads of a sample genome. However, this exercise is far from trivial; several parameters related to sequencing quality, and/or reference genome properties, play essential effect on the accuracy of called SNPs especially at shallow coverage data. In this work, we present Slider II, an alignment and SNP calling approach that demonstrates improved algorithmic approaches enabling larger number of called SNPs with lower false positive rate. In addition to the regular alignment and SNP calling, as an optional feature, Slider II is capable of utilizing information about known SNPs of a target genome, as priors, in the alignment and SNPs calling to enhance it's capability of detecting these known SNPs and novel SNPs and mutations in their vicinity.

MOTIVATION: A plethora of alignment tools have been created that are designed to best fit different types of alignment conditions. While some of these are made for aligning Illumina Sequence Analyzer reads, none of these are fully utilizing its probability (prb) output. In this article, we will introduce a new alignment approach (Slider) that reduces the alignment problem space by utilizing each read base's probabilities given in the prb files. RESULTS: Compared with other aligners, Slider has higher alignment accuracy and efficiency. In addition, given that Slider matches bases with probabilities other than the most probable, it significantly reduces the percentage of base mismatches. The result is that its SNP predictions are more accurate than other SNP prediction approaches used today that start from the most probable sequence, including those using base quality.

The increase in the number of time-series gene expression experiments drives an increase in the development of analysis methods that aim to reverse engineer genetic regulatory networks. Methods have previously been developed that test all combinations of gene interactions in a gene expression time-series dataset for those that closely fit a model of gene regulation. We describe an efficient method for reducing the computational cost of this approach by pruning the search space of candidates least likely to fit a regulatory model. Our approach represents changes in expression levels as directional slope information and discards combinations whose slope relationships are most likely outside the scope of the given model of regulatory relationships. We demonstrate a 70% reduction in the computational time required to process three S. cerevisiae microarray time-series datasets.