Kousik Kundu, PhD | Postdoctoral Fellow

Kundu, Kousik

I am mainly interested in the application of whole-genome sequencing approaches in genome-wide association studies (GWAS). My focus will be to identify novel rare genetic variants that are associated with complex traits in humans. I am also involved in developing various pipelines for next generation sequencing (NGS) data analysis. In my PhD, I have developed machine learning based methods for prediction of modular domain mediated interactions.

Publications

  • Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells.

    Chen L, Ge B, Casale FP, Vasquez L, Kwan T et al.

    Cell 2016;167;5;1398-1414.e24

  • A graph kernel approach for alignment-free domain-peptide interaction prediction with an application to human SH3 domains.

    Kundu K, Costa F and Backofen R

    Bioinformatics (Oxford, England) 2013;29;13;i335-43

  • MoDPepInt: an interactive web server for prediction of modular domain-peptide interactions.

    Kundu K, Mann M, Costa F and Backofen R

    Bioinformatics (Oxford, England) 2014;30;18;2668-9

  • Shared genetic effects on chromatin and gene expression indicate a role for enhancer priming in immune response.

    Alasoo K, Rodrigues J, Mukhopadhyay S, Knights AJ, Mann AL et al.

    Nature genetics 2018;50;3;424-431

  • GRIN3B missense mutation as an inherited risk factor for schizophrenia: whole-exome sequencing in a family with a familiar history of psychotic disorders.

    Hornig T, Grüning B, Kundu K, Houwaart T, Backofen R et al.

    Genetics research 2017;99;e1

  • An Efficient Semi-supervised Learning Approach to Predict SH2 Domain Mediated Interactions.

    Kundu K and Backofen R

    Methods in molecular biology (Clifton, N.J.) 2017;1555;83-97

  • The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease.

    Astle WJ, Elding H, Jiang T, Allen D, Ruklisa D et al.

    Cell 2016;167;5;1415-1429.e19

  • Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells.

    Chen L, Ge B, Casale FP, Vasquez L, Kwan T et al.

    Cell 2016;167;5;1398-1414.e24

  • MoDPepInt: an interactive web server for prediction of modular domain-peptide interactions.

    Kundu K, Mann M, Costa F and Backofen R

    Bioinformatics (Oxford, England) 2014;30;18;2668-9

  • Cluster based prediction of PDZ-peptide interactions.

    Kundu K and Backofen R

    BMC genomics 2014;15 Suppl 1;S5

  • A graph kernel approach for alignment-free domain-peptide interaction prediction with an application to human SH3 domains.

    Kundu K, Costa F and Backofen R

    Bioinformatics (Oxford, England) 2013;29;13;i335-43

  • Semi-supervised prediction of SH2-peptide interactions from imbalanced high-throughput data.

    Kundu K, Costa F, Huber M, Reth M and Backofen R

    PloS one 2013;8;5;e62732

Kundu, Kousik