Sr. Scientist, Bioinformatics & Biostatistics at Abraxis Bioscience (Costa Mesa, CA)

Scientist, Bioinformatics & Biostatistics Profession: Biotechnology/Pharmaceuticals -> Scientific Research … in Cell Biology of Cancer, Molecular Oncology and Bioinformatics. Thorough understanding of in silico design…

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Differential co-expression framework to quantify goodness of biclusters and compare biclustering algorithms.

Publication Date: 2010 May 28 PMID: 20507637
Authors: Hui, C. K. – Karuturi, R. K.
Journal: Algorithms Mol Biol

ABSTRACT: BACKGROUND: Biclustering is an important analysis procedure to understand the biological mechanisms from microarray gene expression data. Several algorithms have been proposed to identify biclusters, but very little effort was made to compare the performance of different algorithms on real datasets and combine the resultant biclusters into one unified ranking. RESULTS: In this paper we propose differential co-expression framework and a differential co-expression scoring function to objectively quantify quality or goodness of a bicluster of genes based on the observation that genes in a bicluster are co-expressed in the conditions belonged to the bicluster and not co-expressed in the other conditions. Furthermore, we propose a scoring function to stratify biclusters into three types of co-expression. We used the proposed scoring functions to understand the performance and behavior of the four well established biclustering algorithms on six real datasets from different domains by combining their output into one unified ranking. CONCLUSIONS: Differential co-expression framework is useful to provide quantitative and objective assessment of the goodness of biclusters of co-expressed genes and performance of biclustering algorithms in identifying co-expression biclusters. It also helps to combine the biclusters output by different algorithms into one unified ranking i.e. meta-biclustering.

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Hierarchical folding of multiple sequence alignments for the prediction of structures and RNA-RNA interactions.

Publication Date: 2010 May 21 PMID: 20492641
Authors: Seemann, S. E. – Richter, A. S. – Gorodkin, J. – Backofen, R.
Journal: Algorithms Mol Biol

ABSTRACT: BACKGROUND: Many regulatory non-coding RNAs (ncRNAs) function through complementary binding with mRNAs or other ncRNAs, e.g., microRNAs, snoRNAs and bacterial sRNAs. Predicting these RNA interactions is essential for functional studies of putative ncRNAs or for the design of artificial RNAs. Many ncRNAs show clear signs of undergoing compensating base changes over evolutionary time. Here, we postulate that a non-negligible part of the existing RNA-RNA interactions contain preserved but covarying patterns of interactions. METHODS: We present a novel method that takes compensating base changes across the binding sites into account. The algorithm works in two steps on two pre-generated multiple alignments. In the first step, individual base pairs with high reliability are found using the PETfold algorithm, which includes evolutionary and thermodynamic properties. In step two (where high reliability base pairs from step one are constrained as unpaired), the principle of cofolding is combined with hierarchical folding. The final prediction of intra- and inter-molecular base pairs consists of the reliabilities computed from the constrained expected accuracy scoring, which is an extended version of that used for individual multiple alignments. RESULTS: We derived a rather extensive algorithm. One of the advantages of our approach (in contrast to other RNA-RNA interaction prediction methods) is the application of covariance detection and prediction of pseudoknots between intra- and inter-molecular base pairs. As a proof of concept, we show an example and discuss the strengths and weaknesses of the approach.

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Sequence embedding for fast construction of guide trees for multiple sequence alignment.

Publication Date: 2010 May 14 PMID: 20470396
Authors: Blackshields, G. – Sievers, F. – Shi, W. – Wilm, A. – Higgins, D. G.
Journal: Algorithms Mol Biol

ABSTRACT: BACKGROUND: The most widely used multiple sequence alignment methods require sequences to be clustered as an initial step. Most sequence clustering methods require a full distance matrix to be computed between all pairs of sequences. This requires memory and time proportional to N;2 for N sequences. When N grows larger than 10,000 or so, this becomes increasingly prohibitive and can form a significant barrier to carrying out very large multiple alignments. RESULTS: In this paper, we have tested variations on a class of embedding methods that have been designed for clustering large numbers of complex objects where the individual distance calculations are expensive. These methods involve embedding the sequences in a space where the similarities within a set of sequences can be closely approximated without having to compute all pair-wise distances. CONCLUSIONS: We show how this approach greatly reduces computation time and memory requirements for clustering large numbers of sequences and demonstrate the quality of the clusterings by benchmarking them as guide trees for multiple alignment. Source code is available for download from http://www.clustal.org/mbed.tgz.

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Physiology and Molecular Biology of Stress Tolerance in Plants

Product Description

Today, biologists all over the world speak the same scientific language of molecular biology and use the same molecular tools. More interest and attention is given to molecular biology of abiotic stress tolerance and modes of installing better tolerant mechanisms in crop plants. These studies make plants capable of sustaining their yields even under stress conditions. Further, the information gained may form the basis for its application in biotechnology and bioinformatics. This book does not only review the current status in the physiology and molecular biology of stress tolerance and its improvement in plants but will also trigger further research on this exciting topic.

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