Research Details
Computational Analysis of Genome Complexity, Transcription Regulation and Cellular Phenotypes
The group attempts to understand the complexities of gene and genome architecture, transcriptome regulation and their relevance for phenotypic properties of cells. This group develops and applies integrative bioinformatics, probabilistic and computational systems biology methods, which are used in the analysis of sequences and gene expression data, ranging from short regulatory macromolecules, individual genes and gene pairs, to the entire genome, gene regulatory networks and pathways.

In collaborations with other groups within the Bioinformatics Institute, other A*STAR institutes and notable overseas groups, this group focuses on the challenges and unsolved problems of gene expression events in normal and cancerous cells; protein-DNA interactions in transcription regulation; computational genome cartography of transcription factors binding sites; local and distant regulations of transcriptional machinery; direct gene targets for transcription factors; and genome and transcriptome complexity.

This group translates their study of gene expression pattern and analysis of macromolecular structures and interactions to support the classification of diseases, biological validation of in silico findings, individual prognosis of relapse events, and the identification of novel biologically essential and clinically significant risk factors and its reliable combinations.

Currently, the group is developing computational and statistical methods for understanding regulations of transcriptional machinery; identification of novel evolutionarily conserved regulatory sequences in the human genome; analytical and computational tools for analysis of human genome architectures - including cis-sense anti-sense gene pairs, data mining, pattern recognition, annotation; and computational algorithms to improve diagnostics, individual prediction and optimization of treatment assignment for breast and lung cancers.

1. Computational genome cartography of DNA-transcription factor binding based on ChIP-PET and ChIP-Seq data (p53, STAT1, c-Myc etc: C.L. Wei et al (2006) Cell; V.A. Kuznetsov et al, Genome Bioinformatics, 19, 2007)

Statistical prediction of reliable binding sites

Modeling of DNA-transcription factor binding avidity function based on scale-dependent network statistics [VA Kuznetsov, (2003), Signal Processing]
2. Analysis of human genome architecture, transcriptome complexity, co-expression of sense-antisense gene pairs (V.A. Kuznetsov et al, 2006)
3. Functional and Network Bioinformatics: Identification of (proto-oncogene (c-myc) direct down-regulated gene targets in differentiation and B-cell activation pathways [K.I.Zeller et al,(2006) PNAS USA]   4. Integrative Clinical Bioinformatics: Computational classification, prediction and prognosis of human cancers. Genetic reclassification of histologic grade II breast cancer [A.V.Ivshina et al, (2006); Cancer Res.; E.T.Liu, V.A.Kuznetsov, L.D.Miller, Cancer Cell, (2006)]
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