Evolutionary insights from suffix array-based genome sequence analysis.

Gene and protein sequence analyses, central components of studies in modern 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 proteomes 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.

A FAST pattern matching algorithm.

The advent of digital computers has made the routine use of pattern-matching possible in various applications. This has also stimulated the development of many algorithms. In this paper, we propose a new algorithm that offers improved performance compared to those reported in the literature so far. The new algorithm has been evolved after analyzing the well-known algorithms such as Boyer-Moore, Quick-search, Raita, and Horspool. The overall performance of the proposed algorithm has been improved using the shift provided by the Quick-search bad-character and by defining a fixed order of comparison. These result in the reduction of the character comparison effort at each attempt. The best- and the worst- case time complexities are also presented in this paper. Most importantly, the proposed method has been compared with the other widely used algorithms. It is interesting to note that the new algorithm works consistently better for any alphabet size.