In bioinformatics, motif discovery is one of the fundamental and important computational problems. Identifying these recurring patterns in biological sequences helps us to better understand the mechanisms that regulate gene expression. Recently several evolutionary algorithms have been developed to solve motif discovery problem, because of their efficiency in searching multidimensional solution space. HPSO, IPSO-GA, PMbPSO and PSO+ are based on particle swarm optimisation (PSO) algorithms. Among these, PSO+ is the first one to be proposed for finding gapped motifs. PSO+ is less efficient in finding gapped motifs that are located at the centre of a motif. Here, our contribution is, to find gapped motifs that are present at the centre of two conserved regions efficiently by adopting features of PSO to solve the problem. We performed experiments on simulated and real biological datasets. It is observed that our approach is able to detect known gapped TFBS more accurately and efficiently.
The identification and characterisation of genotype-phenotype mapping is a central focus of current genome wide association interaction studies (GWAIS). Revealing these relationships for exposing the hidden structures of diseases has received considerable attention by a number of researchers. However, the current statistical and computational approaches ignore many complex genetic contexts. A multifactor dimensionality reduction based on associative classification was previously proposed for detecting multi-locus single nucleotide polymorphism (SNP) interactions in GWAIS. The approach is further studied in detail by adjusting threshold levels, and adding noise to the datasets. The simulated studies demonstrated significant improvements in accuracy by adjusting threshold values over the previous approaches. The results also indicate that the approach is robust in the presence of noise. Further, the application of this approach to real world data has demonstrated higher-order interactions among five SNPs for the manifestation of breast cancer, and three SNPs for the manifestation of hypertension.
SMISS is a novel web server for protein function prediction. Three different predictors can be selected for different usage. It integrates different sources to improve the protein function prediction accuracy, including the query protein sequence, protein-protein interaction network, gene-gene interaction network and the rules mined from protein function associations. SMISS automatically switch to ab initio protein function prediction based on the query sequence when there is no homolog's in the database. It takes fasta format sequences as input; and several sequences can be submitted together without influencing the computation speed too much. PHP and Perl are two primary programming language used in the server. The CodeIgniter MVC PHP web framework and bootstrap front-end framework are used for building the server. It can be used in different platforms in standard web browser, such as Windows, Mac OS X, Linux and iOS. No plug-ins is needed for our website (availability: http://tulip.rnet.missouri.edu/profunc/).
Accurate detection of orthologs is a key aspect of comparative genomics. Orthologs can be used to predict the function of newly sequenced genes from the model organisms as they retain the same biological function through the path of evolution. In this paper, we describe different methods available for the detection of orthologs. Different computational methods comprising of phylogenetic as well as pair-wise comparison are discussed and compared. Some other methods based on synteny and protein network comparisons are also included in the paper. The study shows that phylogenetic methods of detecting orthologs are comparatively accurate and reliable than the pair-wise graph based methods but computationally more intensive and slow. These should be used only when sufficient computational power is available for operation. On the other hand, pair-wise approaches are fast and can handle large amounts of data. Synteny based methods also form a good candidate for the detection of orthologs.
SeqTrim is a pipeline designed to preprocessing sequence reads. It is easy to install and configure, flexible even if default parameters are accurate for most purposes and usable as a web interface or a standalone command line application. It identifies the sequence insert by removing low quality sequences, cloning vector, poly-A or poly-T tails, adaptors and any contaminant sequence or unwanted feature. Several input and output formats are available, which enables its inclusion in already or newly defined sequence processing work flows. It outperforms preprocessors implemented in other web servers and standalone applications at least in detecting adaptors and chimeric clones. SeqTrim is under continuous refinement to deal with most sequence events due to collaboration between biologists and computer scientists.
