Poddar, Anindya and Chandra, Nagasuma and Ganapathiraju, Madhavi and Sekar, K and Klein-Seetharaman, Judith and Reddy, Rai and Balakrishnan, N (2007) Evolutionary insights from suffix array-based genome sequence analysis. In: JOURNAL OF BIOSCIENCES, 32 (5). pp. 871-881.
balki.pdf - Published Version
Restricted to Registered users only
Download (1054Kb) | Request a copy
Gene and protein sequence analyses, central components of studies in modem biology are easily amenable to string matching and pattern recognition algorithms. The growing need of analysing whole genome sequences more efficiently and thoroughly, has led to the emergence of new computational methods. Suffix trees and suffix arrays are data structures, well known in many other areas and are highly suited for sequence analysis too. Here we report an improvement to the design of construction of suffix arrays. Enhancement in versatility and scalability, enabled by this approach, is demonstrated through the use of real-life examples. The scalability of the algorithm to whole genomes renders it suitable to address many biologically interesting problems. One example is the evolutionary insight gained by analysing unigrams, bi-grams and higher n-grams, indicating that the genetic code has a direct influence on the overall composition of the genome. Further, different proteornes have been analysed for the coverage of the possible peptide space, which indicate that as much as a quarter of the total space at the tetra-peptide level is left un-sampled in prokaryotic organisms, although almost all tri-peptides can be seen in one protein or another in a proteome. Besides, distinct patterns begin to emerge for the counts of particular tetra and higher peptides, indicative of a `meaning' for tetra and higher n-grams. The toolkit has also been used to demonstrate the usefulness of identifying repeats in whole proteomes efficiently. As an example, 16 members of one COG, coded by the genome of Mycobacterium tuberculosis H37Rv have been found to contain a repeating sequence of 300 amino acids.
|Item Type:||Journal Article|
|Additional Information:||Copyright for this article belongs to Indian Academy of Sciences|
|Keywords:||biological language modelling toolkit (BLMT); genome sequence analysis;n-grams; pattern matching; suffix arrays; suffix trees; short peptide sequences genetic code bias|
|Department/Centre:||Division of Information Sciences > Supercomputer Education & Research Centre|
|Date Deposited:||31 Dec 2008 07:22|
|Last Modified:||19 Sep 2010 04:58|
Actions (login required)