MCB Seminar | Mahima Sanyal on Sept. 28

The Molecular and Cellular Biology Seminar series features Mahima Sanyal  on Sept. 28 from 4:35 to 5:55 p.m.

• Contact Dr. Sarah Wyatt , MCB director, for more information.

Sanyal is a graduate student in Biological Sciences and  Molecular Cellular Biology .

Abstract: The first observations that different human genomes showed variations were found to be due to the rare changes in the quantity and structure of the chromosomes ¹ . Now, the 1000 Genomes Project has elucidated nearly 88 million variants in healthy individuals ² . Structural variants are a class of genomic DNA alterations that arise from processes like insertion, deletion, inversion, duplication, and translocation which may possibly lead to copy number changes ³ . Advances in Next Generation Sequencing have resulted in a torrential development of new algorithms that cater to detect the increasing complexity of variations in different genomes ⁴ . Development of genome graphs is a new in silico approach which creates a map of chromosomal segments that result in structural variation ⁵ . Previous methods of detection like paired-end mapping and split-read based approaches depend on using existing knowledge and suffer disadvantages of low resolution and high false negatives in regions with high genomic repeats ⁴ . To overcome the drawbacks , methods like read depth-based and combinatorial approaches have been designed to detect novel variants ⁴ . The latter methods have formed the basis of genome graphs. Identification of genome rearrangements is crucial for predicting pathogenesis like cancer and Parkinsons ¹ . Even though methods of structural variant detection have become routine, classification of variants do not have a clear approach resulting in the inability to accurately attribute genomic alterations to any phenotype or pathogenesis. To address this, Hadi et al. ⁵ have developed a tool “Jabba” that uses genome graphs to create distinct representations of structural variant groups across thousands of cancer genomes. Improved methods to detect and classify chromosomal structural variants can aid in understanding how genomes evolve, providing better means of prediction of disease occurrence and progression.

References

1.Alkan, C., Coe, B. P. & Eichler, E. E. Genome structural variation discovery and genotyping. Nat Rev Genet 12 , 363–376 (2011).

2.Auton, A. et al. A global reference for human genetic variation. Nature 526 , 68–74 (2015).

3.Freeman, J. L. et al. Copy number variation: New insights in genome diversity. Genome Res. 16 , 949–961 (2006).

4.Zhao, M., Wang, Q., Wang, Q., Jia, P. & Zhao, Z. Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives. BMC Bioinformatics 14 , S1 (2013).

5.Hadi, K. et al. Distinct Classes of Complex Structural Variation Uncovered across Thousands of Cancer Genome Graphs. Cell 183 , 197-210.e32 (2020).