arXiv subjects

Manoj Pratim Samanta

Publications and source records attributed to Manoj Pratim Samanta.

4 recordsLinked to original sources

arXiv2007

Nucleotide Distribution Patterns in Insect Genomes

This work analyzed genome-wide nucleotide distribution patterns in ten insect genomes. Two internal measures were applied: (i) GC variation and (ii) third codon nucleotide preference. Although the genome size and overall GC level did not show any correlation with insect order, the internal measures usually displayed higher levels of consistency. GC variations in genomes of hymenopteran insects, honeybee and wasp, ranked highest among all eukaryotic genomes analyzed by us. Genomes of honeybee and beetle, insects of different orders with similar overall GC levels, showed significant internal differences. Honeybee genome stood out as unusual due to its high GC variation and 'left-handed' gene locations.

q-bio.GN↗

arXiv2007

Ultraconserved Sequences in the Honeybee Genome - Are GC-rich Regions Preferred?

Among all insect genomes, honeybee displays one of the most unusual patterns with interspersed long AT and GC-rich segments. Nearly 75% of the protein-coding genes are located in the AT-rich segments of the genome, but the biological significance of the GC-rich regions is not well understood. Based on an observation that the bee miRNAs, actins and tubulins are located in the GC-rich segments, this work investigated whether other highly conserved genomic regions show similar preferences. Sequences ultraconserved between the genomes of honeybee and Nasonia, another hymenopteran insect, were determined. They showed strong preferences towards locating in the GC-rich regions of the bee genome.

q-bio.GN↗

arXiv2003

Global Snapshot of Protein Interaction Network -- A Percolation Based Approach

In this paper, we study the large-scale protein interaction network of yeast uti lizing a stochastic method based upon percolation of random graphs. In order to find the global features of connectivities in the network, we introduce numeric al measures that quantify (1) how strongly a protein ties with the other parts o f the network and (2) how significantly an interaction contributes to the integr ity of the network. Our study shows that the distribution of essential proteins is distinct from the background in terms of global connectivities. This observ ation highlights a fundamental difference between the essential and the non-esse ntial proteins in the network. Furthermore, we find that the interaction data o btained from different experimental methods such as immunoprecipitation and two- hybrid techniques possess different characteristics. We discuss the biological implications of these observations.

cond-mat.stat-mech↗

arXiv2003

Redundancies in Large-scale Protein Interaction Networks

Understanding functional associations among genes discovered in sequencing projects is a key issue in post-genomic biology. However, reliable interpretation of the protein interaction data has been difficult. In this work, we show that if two proteins share significantly larger number of common interaction partners than random, they have close functional associations. Analysis of publicly available data from Saccharomyces cerevisiae reveals more than 2800 reliable functional associations, 29% of which involve at least one unannotated protein. By further analyzing these associations, we derive tentative functions for 81 unannotated proteins with high certainty.

physics.bio-ph↗