The effects of disease mutations on protein structure and function have been extensively investigated, and many predictors of the functional impact of single amino acid substitutions are publicly available. The majority of these predictors are based on protein structure and evolutionary conservation, following the assumption that disease mutations predominantly affect folded and conserved protein regions. However, the prevalence of the intrinsically disordered proteins (IDPs) and regions (IDRs) in the human proteome together with their lack of fixed structure and low sequence conservation raise a question about the impact of disease mutations in IDRs. Here, we investigate annotated missense disease mutations and show that 21.7% of them are located within such intrinsically disordered regions. We further demonstrate that 20% of disease mutations in IDRs cause local disorder-to-order transitions, which represents a 1.7-2.7 fold increase compared to annotated polymorphisms and neutral evolutionary substitutions, respectively. Secondary structure predictions show elevated rates of transition from helices and strands into loops and vice versa in the disease mutations dataset. Disease disorder-to-order mutations also influence predicted molecular recognition features (MoRFs) more often than the control mutations. The repertoire of disorder-to-order transition mutations is limited, with five most frequent mutations (R→W, R→C, E→K, R→H, R→Q) collectively accounting for 44% of all deleterious disorder-to-order transitions. As a proof of concept, we performed accelerated molecular dynamics simulations on a deleterious disorder-to-order transition mutation of tumor protein p63 and, in agreement with our predictions, observed an increased α-helical propensity of the region harboring the mutation. Our findings highlight the importance of mutations in IDRs and refine the traditional structure-centric view of disease mutations. The results of this study offer a new perspective on the role of mutations in disease, with implications for improving predictors of the functional impact of missense mutations.

Probe design is the most important step for any microarray based assay. Accurate and efficient probe design and selection for the target sequence is critical in generating reliable and useful results. Several different approaches for probe design are reported in literature and an increasing number of bioinformatics tools are available for the same. However, based on the reported low accuracy, determining the hybridization efficiency of the probes is still a big computational challenge. Present study deals with the extraction of various novel features related to sequence composition, thermodynamics and secondary structure that may be essential for designing good probes. A feature selection method has been used to assess the relative importance of all these features. In this paper, we validate the importance of various features currently used for designing an oligonucleotide probe. Finally, a classification methodology is presented that can be used to predict the hybridization quality of a probe.

Recent sequencing of the human and other mammalian genomes has brought about the necessity to align them, to identify and characterize their commonalities and differences. Programs that align whole genomes generally use a seed-and-extend technique, starting from exact or near-exact matches and selecting a reliable subset of these, called anchors, and then filling in the remaining portions between the anchors using a combination of local and global alignment algorithms, but their choices for the parameters so far have been primarily heuristic. We present a statistical framework and practical methods for selecting a set of matches that is both sensitive and specific and can constitute a reliable set of anchors for a one-to-one mapping of two genomes from which a whole-genome alignment can be built. Starting from exact matches, we introduce a novel per-base repeat annotation, the $Z$-score, from which noise and repeat filtering conditions are explored. Dynamic programming-based chaining algorithms are also evaluated as context-based filters. We apply the methods described here to the comparison of two progressive assemblies of the human genome, NCBI build 28 and build 34 http://genome.ucsc.edu), and show that a significant portion of the two genomes can be found in selected exact matches, with very limited amount of sequence duplication.

Many short DNA motifs such as transcription factor binding sites (TFBS) and splice sites exhibit strong local as well as non-local dependence. We introduce permuted variable length Markov models (PVLMM) which could capture the potentially important dependencies among positions, and apply them to the problem of detecting splice and TFB sites. They have been satisfactory from the viewpoint of prediction performance, and also give ready biological interpretations of the sequence dependence observed. The issue of model selection is also studied.