Ravi Vijaya Satya

A pattern matching algorithm for codon optimization and CpG motif-engineering in DNA expression vectors

Codon optimization enhances the efficiency of DNA expression vectors used in DNA vaccination and gene therapy by increasing protein expression. Additionally, certain nucleotide motifs have experimentally been shown to be immuno-stimulatory while certain others immuno-suppressive. In this paper, we present algorithms to locate a given set of immuno-modulatory motifs in the DNA expression vectors corresponding to a given amino acid sequence and maximize or minimize the number and the context of the immuno-modulatory motifs in the DNA expression vectors. The main contribution is to use multiple pattern matching algorithms to synthesize a DNA sequence for a given amino acid sequence and a graph theoretic approach for finding the longest weighted path in a directed graph that will maximize or minimize certain motifs. This is achieved using O(n/sup 2/) time, where n is the length of the amino acid sequence. Based on this, we develop a software tool.

The Undirected Incomplete Perfect Phylogeny Problem

The incomplete perfect phylogeny (IPP) problem and the incomplete perfect phylogeny haplotyping (IPPH) problem deal with constructing a phylogeny for a given set of haplotypes or genotypes with missing entries. The earlier approaches for both of these problems dealt with restricted versions of the problems, where the root is either available or can be trivially reconstructed from the data, or certain assumptions were made about the data. In this paper, we deal with the unrestricted versions of the problems, where the root of the phylogeny is neither available nor trivially recoverable from the data. Both IPP and IPPH problems have previously been proven to be NP-complete. Here, we present efficient enumerative algorithms that can handle practical instances of the problem. Empirical analysis on simulated data shows that the algorithms perform very well both in terms of speed and in terms accuracy of the recovered data.

Constructing Near-Perfect Phylogenies with multiple homoplasy events

MOTIVATION We explore the problem of constructing near-perfect phylogenies on bi-allelic haplotypes, where the deviation from perfect phylogeny is entirely due to homoplasy events. We present polynomial-time algorithms for restricted versions of the problem. We show that these algorithms can be extended to genotype data, in which case the problem is called the near-perfect phylogeny haplotyping (NPPH) problem. We present a near-optimal algorithm for the H1-NPPH problem, which is to determine if a given set of genotypes admit a phylogeny with a single homoplasy event. The time-complexity of our algorithm for the H1-NPPH problem is O(m2(n + m)), where n is the number of genotypes and m is the number of SNP sites. This is a significant improvement over the earlier O(n4) algorithm. We also introduce generalized versions of the problem. The H(1, q)-NPPH problem is to determine if a given set of genotypes admit a phylogeny with q homoplasy events, so that all the homoplasy events occur in a single site. We present an O(m(q+1)(n + m)) algorithm for the H(1,q)-NPPH problem. RESULTS We present results on simulated data, which demonstrate that the accuracy of our algorithm for the H1-NPPH problem is comparable to that of the existing methods, while being orders of magnitude faster. AVAILABILITY The implementation of our algorithm for the H1-NPPH problem is available upon request.

Modified LZW algorithm for efficient compressed text retrieval

With increasing amount of text data being stored in the compressed format, efficient information retrieval in the compressed domain has become a major concern. Being able to randomly access the compressed data is highly desirable for efficient retrieval and is required in many applications. For example, in a library information retrieval system, only the records that are relevant to the query are displayed. We present modified LZW algorithms that support fast random access to the compressed text. Instead of fully decompressing the text and outputting the results selectively, we allow random access and partial decoding of the compressed text and displaying the relevant portion. The compression ratio can also be improved using the modified LZW algorithm. Preliminary results on the time and storage performance are given.

New Algorithms for Finding Monad Patterns in DNA Sequences

In this paper, we present two new algorithms for discovering monad patterns in DNA sequences. Monad patterns are of the form (l,d)-k, where l is the length of the pattern, d is the maximum number of mismatches allowed, and k is the minimum number of times the pattern is repeated in the given sample. The time-complexity of some of the best known algorithms to date is O(nt 2 l d σ d ), where t is the number of input sequences, n is the length of each input sequence, and σ = | ∑ | is the size of the alphabet. The first algorithm that we present in this paper takes \(O(n^{2}t^{2}l^{\frac{d}{2}})\) time and \(O(ntl^{\frac{d}{2}}\sigma^{\frac{d}{2}})\) space, and the second algorithm takes \(O(n^3t^3l^\frac{d}{2}\sigma^{\frac{d}{2}})\) time using \(O(l^\frac{d}{2}\sigma^{\frac{d}{2}})\) space. In practice, our algorithms have much better performance provided the d/l ratio is small. The second algorithm performs very well even for large values l and d as long as the d/l ratio is small.

Model of wide-angle optical field propagation using scalar diffraction theory

A generalized nonparaxial theoretical framework based on the scalar diffraction theory is developed to describe the propagation of an optical field through a linear optical system with quasi-monochromatic spatially incoherent illumination. Software implementation of this theoretical framework on single and multiple processor platforms was developed and simulated results of the imaging process through optical aberration-corrected optics are presented for both in-focus and out-of-focus imaging, validating the first-order nonparaxial model.

A flexible compressed text retrieval system using a modified LZW algorithm

Summary form only given. With an increasing amount of text data being stored in compressed format, being able to access the compressed data randomly and decode it partially is highly desirable for efficient retrieval in many applications. The efficiency of these operations depends on the compression method used. We present a modified LZW algorithm that supports efficient indexing and searching on compressed files. Our method performs in a sublinear complexity, since we only decode a small portion of the file. The proposed approach not only provides the flexibility for dynamic indexing in different text granularities, but also provides the possibility for parallel processing in both encoding and decoding sides, independent of the number of processors available. It also provides good error resilience. The compression ratio is improved using the proposed modified LZW algorithm. Test results show that our public trie method has a compression ratio of 0.34 for the TREC corpus and 0.32 with text preprocessing using a star transform with an optimal static dictionary; this is very close to the efficient word Huffman and phrase based word Huffman schemes, but has a more flexible random access ability.

Longer sequence insertions in p6 gag play a role in immune escape in HIV-1 subtype C

Methods In a prospective study, a cohort of select seropositive drug naive subjects is being monitored at YRG CARE, Chennai for a period of two years with repeated sampling at 6-month intervals. The viral RNA was extracted from plasma and Gag was amplified, followed by Sanger sequencing. The samples of interest were further subjected to next-generation sequencing using Illumina MiSeq and analyzed using the CLC Genomics Workbench software.

Search and Retrieval of Compressed Text

In recent times, we have witnessed an unprecedented growth of textual information via the Internet, digital libraries and archival text in many applications. To be able to store, manage, organize and transport the data efficiently, text compression is necessary. We also need efficient search engines to speedily find the information from this huge mass of data, especially when it is compressed. In this chapter, we present a review of text compression algorithms, with particular emphasis on the LZ family algorithms, and present our current research on the family of Star compression algorithms. We discuss ways to search the information from its compressed format and introduce some recent work on compressed domain pattern matching, with a focus on a new two-pass compression algorithm based on LZW algorithm. We present the architecture of a compressed domain search and retrieval system for archival information and indicate its suitability for implementation in a parallel and distributed environment using random access property of the two-pass LZW algorithm.