MOTIVATION: Allostery is an omnipresent mechanism of the function modulation in proteins via either effector binding or mutations in the exosites. Despite the growing number of online servers and databases devoted to prediction/classification of allosteric sites and their characteristics, there is a lack of resources for an efficient and quick estimation of the causality and energetics of allosteric communication. RESULTS: The AlloSigMA server implements a unique approach on the basis of the recently introduced structure-based statistical mechanical models of allosteric signaling. It provides an interactive framework for estimating the allosteric free energy as a result of the ligand(s) binding, mutation(s) and their combinations. Latent regulatory exosites and allosteric effect of mutations can be detected and explored, facilitating the research efforts in protein engineering and allosteric drug design. AVAILABILITY AND IMPLEMENTATION: The AlloSigMA server is freely available at http://allosigma.bii.a-star.edu.sg/home/. CONTACT: [email protected].

De novo clustering is a popular technique to perform taxonomic profiling of a microbial community by grouping 16S rRNA amplicon reads into operational taxonomic units (OTUs). In this work, we introduce a new dendrogram-based OTU clustering pipeline called CRiSPy. The key idea used in CRiSPy to improve clustering accuracy is the application of an anomaly detection technique to obtain a dynamic distance cutoff instead of using the de facto value of 97 percent sequence similarity as in most existing OTU clustering pipelines. This technique works by detecting an abrupt change in the merging heights of a dendrogram. To produce the output dendrograms, CRiSPy employs the OTU hierarchical clustering approach that is computed on a genetic distance matrix derived from an all-against-all read comparison by pairwise sequence alignment. However, most existing dendrogram-based tools have difficulty processing datasets larger than 10,000 unique reads due to high computational complexity. We address this difficulty by developing two efficient algorithms for CRiSPy: a compute-efficient GPU-accelerated parallel algorithm for pairwise distance matrix computation and a memory-efficient hierarchical clustering algorithm. Our experiments on various datasets with distinct attributes show that CRiSPy is able to produce more accurate OTU groupings than most OTU clustering applications.

UNLABELLED: Pyrosequencing technologies are frequently used for sequencing the 16S ribosomal RNA marker gene for profiling microbial communities. Clustering of the produced reads is an important but time-consuming task. We present Dynamic Seed-based Clustering (DySC), a new tool based on the greedy clustering approach that uses a dynamic seeding strategy. Evaluations based on the normalized mutual information (NMI) criterion show that DySC produces higher quality clusters than UCLUST and CD-HIT at a comparable runtime. AVAILABILITY AND IMPLEMENTATION: DySC, implemented in C, is available at http://code.google.com/p/dysc/ under GNU GPL license.

Applications of next-generation sequencing technologies have the potential to bring revolutionary changes to medicine and biology. However, coverage bias can pose a challenge to short read data analysis tools, which rely on high coverage. To address this issue we have developed a support vector machine (SVM) based method for predicting low coverage prone (LCP) regions on a given genome. The developed SVM-based prediction of LCP regions on a given genome can assist data processing procedures based on Illumina sequencing technology, such as de novo sequencing and transcriptome analysis.