Back close

Identification of Genomic Rearrangements across Organisms Leading to Evolutionary Insights

Project Incharge:Dr. Indulekha T. S.
Identification of Genomic Rearrangements across Organisms Leading to Evolutionary Insights

Genomes of organisms undergo rearrangements and mutations over time, and genome evolutions are studied well by understanding this dynamism. Recombination, transposition and mutation are the three important processes that lead to these genomic changes. Genome rearrangements describe changes in the genetic linkage relationship of large chromosomal regions, involving reversals, transpositions, block interchanges, deletions, insertions, fissions, fusions and translocations etc. Many algorithms for calculating rearrangement scenarios between two genomes have been proposed. The calculated rearrangement scenario is often common for the same pair of permutations. Hence, deciding which calculated rearrangement scenario is more biologically meaningful is significant. Rearrangements have been shown to be responsible for numerous heritable diseases, evolution and specialization. The chromosomal regions affected by these rearrangements are called breakpoints, while those which have not been rearranged are called synteny blocks. To gain a better understanding of the evolutionary forces that affect genome architecture, Homologous synteny blocks (HSBs) and chromosome evolutionary breakpoint regions (EBRs) can be identified.

Related Projects

Autonomous Guidance System for Visually Impaired in a Library
Autonomous Guidance System for Visually Impaired in a Library
Furfuryl Derivatives of Meldrum’s Acid: Synthesis, Characterisation and Biological Studies
Furfuryl Derivatives of Meldrum’s Acid: Synthesis, Characterisation and Biological Studies
Drug Target Identification using Protein-Protein Interaction Networks 
Drug Target Identification using Protein-Protein Interaction Networks 
A Machine Learning Approach for Early Prediction of Blood Culture Positivity in Neutropenia Patients Using Medical History and Hematological Parameters
A Machine Learning Approach for Early Prediction of Blood Culture Positivity in Neutropenia Patients Using Medical History and Hematological Parameters
Towards Next-generation Adaptable Computing
Towards Next-generation Adaptable Computing
Admissions Apply Now