Ritesh Krishna  Ritesh Krishna photo         

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Computational Genomics Lead, Senior Research Scientist, Senior Inventor
IBM Research Laboratory, UK



Dr. Ritesh Krishna is an interdisciplinary scientist specializing in informatics for multi-omics based life sciences research. Ritesh leads the Computational Genomics efforts at the IBM Research UK.

Ritesh obtained his PhD in Computer Science from the University of Warwick, where he developed computational methods for functional analysis of large-scale gene expression data. From there, he moved to the University of Liverpool for his Post-dcotoral research where he was associated with the Institute of Integrative Biology, Centre for Genomics Research and the Institute of Infection and Global Health.

Research Interests

Interdisciplinary collaborative programmes between multiple Life Sciences streams and Computer Science, Distributed computing for life sciences, AI and machine learning for Life Sciences, Bioinformatics and System Biology, Multi-omics with focus on Genomics and Proteomics, Big Data, Large scale network inference.

Research Grants

  • A computational cloud framework for the study of gene families, BBSRC (BB/N023145/1)


Derivation of Process Algebraic Models of Biochemical Systems: pi-calculus, Stochastic Simulation of Biochemical Reactions, VDM Verlag,ISBN-13: 978-3639200188, 2009.



V Elisseev, L J Gardiner, R Krishna, Scalable in-memory processing of omics workflows, Computational and Structural Biotechnology Journal, 2022

L J Gardiner, R Krishna, Bluster or Lustre: Can AI improve crops and plants health?, Plants 10 (12), 2021

L J Gardiner, R Rusholme-Pilcher, J Colmer, H Rees, J M Crescente, A Carrieri, S Duncan, E Pyzer-Knapp, R Krishna, A Hall, Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function, PNAS 118 (32), 2021

L J Gardiner, N Haiminen, F Utro, L Parida, E Seabolt, R Krishna, J H Kaufman. Re-purposing software for functional characterization of the microbiome, Microbiome 9(4), 2021

R Krishna, V Elisseev, User Centric Genomics Infrastructure: trends and technologies, Genome, 2020

L J Gardiner, A Carrieri, J Wilshaw, S Checkley, E Pyzer-Knapp, R Krishna, Using human in vitro transcriptome analysis to build trustworthy machine learning models for prediction of animal drug toxicity, Scientific Reports 10(1), 2020

F Utro, N Haiminen, E Siragusa, L J Gardiner, Ed Seabolt, R Krishna, J H Kaufman, L Parida, Hierarchically Labeled Database Indexing Allows Scalable Characterization of Microbiomes, iScience, https://doi.org/10.1016/j.isci.2020.100988, 2020.

I Goodhead, F Blow, P Brownridge, M Hughes, J Kenny, R Krishna, L McLean, P Pongchaikul, R Beynon, A C Darby, Large-scale and significant expression from pseudogenes in Sodalis glossinidius – a facultative bacterial endosymbiont, Microbial Genomics,, DOI 10.1099/mgen.0.000285, 2020

L J Gardiner, A Carrieri, J Wilshaw, S Checkley, E Pyzer-Knapp, R Krishna, Combining human cell line transcriptome analysis and Bayesian inference to build trustworthy machine learning models for prediction of animal toxicity in drug development, Machine Learning for Health (ML4H) at NeurIPS 2019

S Whiteford, A E Van't Hof, R Krishna, T Marubbi, S Widdison, I J Saccheri, M Guest, N I Morrison, A C Darby, Recovering individual haplotypes and a contiguous genome assembly from pooled long read sequencing of the diamondback moth (Lepidoptera: Plutellidae), BioRxiv, 2019

R Krishna, V. Elisseev, S. Antao, BaaS - Bioinformatics as a Service, Lecture Notes in Computer Science, Euro-Par 2018 Parallel Processing Workshops, pp-601-612, 2018.

F Cipcigan, A P Carrieri, E O Pyzer-Knapp, R Krishna, Y Hsiao, M Winn, M G Ryadnov, C Edge, G Martyna, J Crain, Accelerating molecular discovery through data and physical sciences: Applications to peptide-membrane interactions, The Journal of Chemical Physics, doi:10.1063/1.5027261, 2018.

J Turner*, R Krishna*, A E V Hof*, E R Sutton, K Matzen, A C Darby, The sequence of a male-specific genome region containing the sex determination switch in Aedes aegypti, Parasites & Vectors, 11:549, 2018. * (equal contribution)

J. A. Hodgson, D. W. Wallis, R. Krishna, S. J. Cornell, How to manipulate landscapes to improve the potential for range expansion, Methods in Ecology and Evolution, doi:10.1111/2041-210X.12614, 2016.

S. D. Armstrong, D. Xia, G. S. Bah, R. Krishna, H. F. Ngangyung, E. J. LaCourse, H. J. McSorley, J. A. Kengne-Ouafo, P. W. Chounna-Ndongmo, S. Wanji, P. A. Enyong, D. W. Taylor, M. L. Blaxter, J. M. Wastling, V. N. Tanya, B. L. Makepeace,Stage-specific proteomes from Onchocerca ochengi, sister species of the human river blindness parasite, uncover adaptations to a nodular lifestyle, Molecular & Cellular Proteomics, mcp.M115.055640, 2016.

R. Krishna, D. Xia, S. Sanderson, A. Shanmugasundram, S. Vermont, A. Bernal, G. Daniel-Naguib, F. Ghali, B. P. Brunk, D. S. Roos, J.M. Wastling, A.R. Jones, A large-scale proteogenomics study of apicomplexan pathogens-Toxoplasma gondii and Neospora caninum, PROTEOMICS, doi: 10.1002/pmic.201400553, 2015.

F. Ghali, R. Krishna (joint first authors), S. Perkins, A. Collins, D. Xia, J.M. Wastling, A.R. Jones, ProteoAnnotator – Open Source Proteogenomics Annotation Software Supporting PSI Standards, PROTEOMICS, doi:10.1002/pmic.201400265, 2014.

D. Qi, R. Krishna, A. R. Jones, The jmzQuantML programming interface and validator for the mzQuantML data standard, PROTEOMICS, doi:10.1002/pmic.201300281, 2014

F. Ghali, R. Krishna, P. Lukasse, S. Martinez-Bartolome, F. Reisinger, H. Hermjakob, J. A. Vizcaino, A.R. Jones, A toolkit for the mzIdentML standard: the ProteoIDViewer, the mzidLibrary and the mzidValidator, Molecular & Cellular Proteomics, mcp.O113.029777, 2013.

(review) J.A. Medina-Aunon, R. Krishna, F. Ghali, J.P. Albar, A.R. Jones, A guide for integration of proteomics data standards into laboratory workflows, PROTEOMICS, doi: 10.1002/pmic.201200268, 2013.

(review) J.M. Wastling, S. Armstrong, R. Krishna, D. Xia, Parasites, Proteomes and Systems: has Descartes’ clock run out of time?, Parasitology, doi:10.1017/S0031182012000716 , 2012.

F. Reisinger, R. Krishna (joint first authors), F. Ghali, D. Ríos, H. Hermjakob, J. A. Vizcaíno, A. R. Jones, jmzIdentML API: A Java interface to the mzIdentML standard for peptide and protein identification data , PROTEOMICS. doi: 10.1002/pmic.201100577 , 2012.

D. C. Wedge, R. Krishna, P. Blackhurst, J. A. Siepen, A.R. Jones and S. Hubbard, FDRAnalysis: A tool for the integrated analysis of tandem mass spectrometry identification results from multiple search engines, Journal of proteome research. doi: 10.1021/pr101157s , 2011.

R. Krishna, S. Nanda, A.Kulkarni and S. Patil, A Partial Granger Causality based Method for Analysis of Parameter Interactions in Bioreactors, Computers and Chemical Engineering . doi:10.1016/j.compchemeng.2010.07.013 , 2011.

R. Krishna, C-T. Li and V. Buchanan-Wollaston, A Temporal Precedence Based Clustering Method for Gene Expression Microarray Data, BMC Bioinformatics 11:68 . doi:10.1186/1471-2105-11-68, 2010. (Appeared as Featured article, and flagged as Highly Accessed)

J. Feng, D. Yi, R. Krishna, S. Guo and V. Buchanan-Wollaston, Listen to Genes: Dealing with Microarray Data in the Frequency Domain, PLoS ONE 4(4): e5098. doi:10.1371/journal.pone.0005098, 2009.

R. Krishna, C-T. Li and V. Buchanan-Wollaston,Interaction Based Functional Clustering of Genomic Data , in Proc. IEEE International Conference on Bioinformatics and Bioengineering, doi:10.1109/BIBE.2009.28, 2009.

R. Krishna and S. Guo, A partial granger causality approach to explore causal networks derived from multi-parameter data, Lecture notes in Computer Science, Springer Berlin / Heidelberg, vol. 5307, pp. 9--27, 2008.