Biomedical subjects

Anantharaman Kalyanaraman

Publications and source records attributed to Anantharaman Kalyanaraman.

3 recordsLinked to original sources

PubMed2005

Efficient algorithms and software for detection of full-length LTR retrotransposons.

LTR retrotransposons constitute one of the most abundant classes of repetitive elements in eukaryotic genomes. In this paper, we present a new algorithm for detection of full-length LTR retrotransposons in genomic sequences. The algorithm identifies regions in a genomic sequence that show structural characteristics of LTR retrotransposons. Three key components distinguish our algorithm from that of current software - (i) a novel method that preprocesses the entire genomic sequence in linear time and produces high quality pairs of LTR candidates in running time that is constant per pair, (ii) a thorough alignment-based evaluation of candidate pairs to ensure high quality prediction, and (iii) a robust parameter set encompassing both structural constraints and quality controls providing users with a high degree of flexibility. Validation of both our serial and parallel implementations of the algorithm against the yeast genome indicates both superior quality and performance results when compared to existing software.

Algorithms↗

PubMed2004

Space-conserving optimal DNA-protein alignment.

DNA-protein alignment algorithms can be used to discover coding sequences in a genomic sequence, if the corresponding protein derivatives are known. They can also be used to identify potential coding sequences of a newly sequenced genome, by using proteins from related species. Previously known algorithms either solve a simplified formulation, or sacrifice optimality to achieve practical implementation. In this paper, we present a comprehensive formulation of the DNA-protein alignment problem, and an algorithm to compute the optimal alignment in O(mn) time using only four tables of size (m + 1) x (n + 1), where m and n are the lengths of the DNA and protein sequences, respectively. We also developed a Protein and DNA Alignment program PanDA that implements the proposed solution. Experimental results indicate that our algorithm produces high quality alignments.

Algorithms↗

PubMed2003

Efficient clustering of large EST data sets on parallel computers.

Clustering expressed sequence tags (ESTs) is a powerful strategy for gene identification, gene expression studies and identifying important genetic variations such as single nucleotide polymorphisms. To enable fast clustering of large-scale EST data, we developed PaCE (for Parallel Clustering of ESTs), a software program for EST clustering on parallel computers. In this paper, we report on the design and development of PaCE and its evaluation using Arabidopsis ESTs. The novel features of our approach include: (i) design of memory efficient algorithms to reduce the memory required to linear in the size of the input, (ii) a combination of algorithmic techniques to reduce the computational work without sacrificing the quality of clustering, and (iii) use of parallel processing to reduce run-time and facilitate clustering of larger data sets. Using a combination of these techniques, we report the clustering of 168 200 Arabidopsis ESTs in 15 min on an IBM xSeries cluster with 30 dual-processor nodes. We also clustered 327 632 rat ESTs in 47 min and 420 694 Triticum aestivum ESTs in 3 h and 15 min. We demonstrate the quality of our software using benchmark Arabidopsis EST data, and by comparing it with CAP3, a software widely used for EST assembly. Our software allows clustering of much larger EST data sets than is possible with current software. Because of its speed, it also facilitates multiple runs with different parameters, providing biologists a tool to better analyze EST sequence data. Using PaCE, we clustered EST data from 23 plant species and the results are available at the PlantGDB website.

Algorithms↗