Anagha Joshi


EMBO long-term fellow
Research associate
Department of Haematology, University of Cambridge, Cambridge Institute for Medical Research

PeriodEmployment and Education
Mar. 10 - Now Postdoctoral fellow (CIMR, University of Cambridge, U.K.)
Apr. 06 - Feb. 10 PhD Bioinformatics (University of Gent, Belgium)
Apr. 04 - Mar. 06 Research assistant (IISc, Bangalore, India)
Sep. 04 - Dec. 04 Research assistant (University of Pune, India)
Jul. 01 - Jun. 03 MSc Physics (University of Pune, India)
Jul. 99 - Jun. 01 BSc Physics (University of Pune, India)


Research Interest

Keywords: Bioinformatics, ChIP-seq data, gene expression data, systems biology, data integration

Understanding how gene regulatory networks affect development and disease is one of the key questions in biology. In recent years, in order to get a holistic view, high-throughput data has become a preferred tool instead of characterizing individual genes. Making sense of large-scale datasets still remains a challenge. I aim to contribute to this end by developing new ways of large-scale data analysis and data integration.

During my PhD, I developed algorithms for building transcription regulatory networks using gene expression and high-throughput interaction data. Initially I developed a Gibbs sampler based clustering method, 'Ganesh' which was further integrated into a network inference method 'LeMoNe', which reconstructs gene regulatory networks from expression data. We validated LeMoNe in the eukaryotic model organism S. cerevisae and then used it to build transcription regulatory networks across diverse species, from a simple prokaryotic system, E. coli, to cancer gene expression data in humans. The algorithm was also used to study post-transcriptional regulation in S. cerevisae. Aiming towards understanding how different regulatory processes come together, I realized a need for data integration and developed a Cytoscape plug-in to find regulatory path motifs in perturbational data called 'Pathicular'.

While working in Prof. Gottgens' group, I developed a parsimony-based method to infer developmental hierarchies from gene expression data. We generated ChIP-seq compendia in blood as well as Embryonic Stem Cells. Using this data I developed a web-tool 'GSCA' (Gene Set Control Analysis) to infer combinatorial transcriptional control behind correlated gene sets. I also developed 'PeakCompare', a web-tool for experimentalists to provide a global context to their ChIP-seq experiment.




Fellowships

EMBO long term fellowship (Cambridge, UK).
Marie- Curie early career development fellowship (Ghent, Belgium).
CSIR Junior research fellowship (Bangalore, India).


Publications

21)    Joshi A. et. al. PeakCompare provides a global context to genome-wide binding patterns of transcription factor(s). (In preparation)


20)    Joshi A. et. al. Gene Set Control Analysis (GSCA): Unravelling combinatorial transcriptional control. (In preparation)


19)    Martello1 G, Sugimoto T., Diamanti E, Joshi A, Hannah R., Ohtsuka S., Gottgens B., Niwa H., Smith A. (2011) ESRRB is the pivotal target of the GSK3 /TCF3 axis mediating embryonic stem cell self-reniewal. (submitted)


18)    Tanaka Y.*, Joshi A.*, Wilson N., Kinston S., Nishikawa S., Gottgens B. (2011) The transcriptional programme controlled by Runx1 during early haematopoietic development. (submitted)


17)    Mutasa-Gottgens E., Joshi A., Holmes H., Heddden P., Gottgens B. (2011) Genome scale analysis of vernalization and gibberellin responses in sugar beet shoot apices. (submitted)


16)    Chan W. I. , Hannah R , Dawson MA , Pridans C., Foster D. , Joshi A. , Gottgens B. , van Deursen J., Huntly B. (2011) The transcriptional coactivator Cbp regulates self-renewal and differentiation in adult hematopoietic stem cells. Mol Cell Biol. 2011 Oct 17.


15)    Joshi A., Van de Peer Y., Michoel T. Structural and functional organization of RNA regulons in the posttranscriptional regulatory network of yeast. Nucleic Acids Res. 2011 Aug 12


14)    Michoel T., Joshi A., Nachtergaele B., Van de Peer Y. Enrichment and aggregation of topological motifs are independent organizational principles of integrated interaction networks. Mol Biosyst. 2011 Oct 1;7(10):2769-78.


13Tijssen M.R., Cvejic A., Joshi A., Hannah R.L., Ferreira R., Forrai A., Bellissimo D.C., Oram S.H., Smethurst P.A., Wilson N.K., Wang X., Ottersbach K., Stemple D.L., Green A.R., Ouwehand W.H., Gottgens B. (2011) “Genome-Wide Analysis of Simultaneous GATA1/2, RUNX1, FLI1, and SCL Binding in Megakaryocytes Identifies Hematopoietic Regulators” Dev Cell 20(5):597-609


12Hannah R., Joshi A., Wilson N.K., Kinston S., Gottgens B. (2011) “A Compendium of Genome-wide Haematopoietic Transcription Factor Maps supports the Identification of Gene Regulatory Control Mechanisms” Exp Hematology 39(5): 531-541


11)    Joshi A., Gottgens B. (2011) “Maximum Parsimony Analysis of Gene Expression Profiles Permits the Reconstruction of Developmental Cell Lineage Trees” Dev Biol 353(2): 440-447


10)    Michoel T., Joshi A., Bonnet E., Vermeirssen V., Van de Peer Y. Towards system level modeling of functional modules and regulatory pathways using genome-scale data. Proceedings of the Seventh International Workshop on Computational Systems Biology (WCSB 2010) 71-74. Luxembourg, Luxembourg.


9)    Bonnet E, Tatari M, Joshi A, Michoel T, Marchal K, Berx G, Van de Peer Y. Module network inference from a cancer gene expression data set identifies microRNA regulated modules. PLoS One. 2010 Apr 14;5(4):e10162.


8)    Joshi A, Van Parys T, Peer YV, Michoel T. Characterizing regulatory path motifs in integrated networks using perturbational data. Genome Biol. 2010;11(3):R32.


7)    Vermeirssen V, Joshi A, Michoel T, Bonnet E, Casneuf T, Van de Peer Y. Transcription regulatory networks in Caenorhabditis elegans inferred through reverse-engineering of gene expression profiles constitute biological hypotheses for metazoan development. Mol Biosyst. 2009 Dec;5(12):1817-30.


6)    Michoel T, De Smet R, Joshi A, Van de Peer Y, Marchal K. Comparative analysis of module-based versus direct methods for reverse-engineering transcriptional regulatory networks. BMC Syst Biol. 2009 May 7;3:49.


5)    Michoel T, De Smet R, Joshi A, Marchal K, Van de Peer Y. Reverse-engineering transcriptional modules from gene expression data. Ann N Y Acad Sci. 2009 Mar;1158:36-43.


4)    Joshi A, De Smet R, Marchal K, Van de Peer Y, Michoel T. Module networks revisited: computational assessment and prioritization of model predictions. Bioinformatics. 2009 Feb 15;25(4):490-6.


3)    Joshi A, Van de Peer Y, Michoel T. Analysis of a Gibbs sampler method for model-based clustering of gene expression data. Bioinformatics. 2008 Jan 15;24(2):176-83.


2)    Michoel T, Maere S, Bonnet E, Joshi A, Saeys Y, Van den Bulcke T, Van Leemput K, van Remortel P, Kuiper M, Marchal K, Van de Peer Y. Validating module network learning algorithms using simulated data. BMC Bioinformatics. 2007 May 3;8 Suppl 2:S5.


1)    Thakur KG, Joshi A, Gopal B. Structural and biophysical studies on two promoter recognition domains of the extra-cytoplasmic function sigma factor sigma(C) from Mycobacterium tuberculosis. J Biol Chem. 2007 Feb 16;282(7):4711-8.

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