TargetMiner:

Prediction of microRNA (miRNA) target mRNAs using machine learning approaches is an important area of research. However, most of the methods suffer from either high false positive or false negative rates. One reason for this is the marked deficiency of negative examples or miRNA-non target pairs. Systematic identification of non-target mRNAs is still not addressed properly, and therefore, current machine learning approaches are compelled to rely on artificially generated negative examples for training.   ^....more

Availability: TargetMiner is now available as an online tool at www.isical.ac.in/bioinfo_miu

Contact: sanghami@isical.ac.in; rmitra_t@isical.ac.in

News on TargetMiner:Researchers from Indian Statistical Institute publish new studies and findings in the area of life sciences.

This work is funded by Swarnajayanti Fellowship scheme of the Department of Science and Technology, Government of India. This work is published in Bioinformatics, Impact Factor: 4.926, Rank: 2^nd in Mathematical and Computational Biology.

Human cancer-microRNA network

MicroRNAs are a class of small noncoding RNAs that are abnormally expressed in different cancer cells. Molecular signature of miRNAs in different malignancies suggests that these are not only actively involved in the pathogenesis of human cancer but also have a significant role in patients survival. The differential expression patterns of specific miRNAs in a specific cancer tissue type have been reported in hundreds of research articles. However limited attempt has been made to collate this multitude of information and obtain a global perspective of miRNA dysregulation in multiple cancer types. In this work a cancer-miRNA network is developed by mining the literature of experimentally verified cancer-miRNA relationships. This network throws up several new and interesting biological insights which were not evident in individual experiments, but become evident when studied in the global perspective.   ^....more

This work is funded by Swarnajayanti Fellowship scheme of the Department of Science and Technology, Government of India. This work is published in Silence and recognized as Featured article of Biomed Central.

Integrated approach for miRNA based tumor classification

MicroRNA (miRNA) expression profiling data has recently been found to be particularly important in cancer research and can be used as a diagnostic and prognostic tool. Current approaches of tumor classification using miRNA expression data do not integrate the experimental knowledge available in the literature. A judicious integration of such knowledge with effective miRNA and sample selection through a biclustering approach could be an important step in improving the accuracy of tumor classification. In this work, a novel classification technique called SFSSClass is developed that judiciously integrates a biclustering technique SAMBA for simultaneous feature (miRNA) and sample (tissue) selection (SFSS), a cancer-miRNA network that we have developed by mining the literature of experimentally verified cancer-miRNA relationships and a classifier. SFSSClass is used for classifying multiple classes of tumors and cancer cell lines. In a part of the investigation, poorly differentiated tumors (PDT) having non diagnostic histological appearance are classified while training on more differentiated tumor (MDT) samples. The proposed method is found to outperform the best known accuracy in the literature on the experimental data sets.    ^....more

This work is funded by Swarnajayanti Fellowship scheme of the Department of Science and Technology, Government of India. This work is published in BMC Bioinformatics, Impact Factor: 3.78.

Parallel pairwise local sequence alignment                                                        

Researchers are compelled to use heuristic-based pairwise sequence alignment tools instead of Smith-Waterman (SW) algorithm due to space and time constraints, thereby losing significant amount of sensitivity. Parallelization is a possible solution, though, till date, the parallelization is restricted to database searching through database fragmentation. In this work, the power of a cluster computer is utilized for developing a parallel algorithm, RPAlign, involving, first, the detection of regions that are potentially alignable, followed by their actual alignment. RPAlign is found to reduce the timing requirement by a factor of upto 9 and 99 when used with the basic local alignment search tool (BLAST) and SW, respectively, while keeping the sensitivity similar to the corresponding method. For distantly related sequences, which remain undetected by BLAST, RPAlign with SW can be used. Again, for megabase-scale sequences, when SW becomes computationally intractable, the proposed method can still align them reasonably fast with high sensitivity.    ^....more