Bioinformatics -- Computational Biology  --  Genomic Technology -- Molecular Engineering & Computing

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Supported by NSF

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Supported by NIH

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Bayes Networks and Graphical Models in Molecular Biology

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Some Useful Advice

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Boston University Biocomputing Research Briefs

 Bioinformatics Graduate Fellowships   

Postdoctoral Positions Application Information


Our Research  

The genomics revolution that includes the Human Genome Project, massive bacterial sequencing, whole-genome gene expression profiling, SNP sequencing and other biotechnology breakthoughs has had a remarkable impact on life sciences. We now face a daunting challenge of assigning function to the newly identified genes or understanding the role of previously unexplored regions of genomes. Initial analysis of most bacterial genomes suggests that we cannot assign (even) broad function to one third of a typical bacterial organism and the challenge is even higher for high-order eukaryotes such as human cells. Our lab is developing novel technologies for classifying these un-annotated genes into broad functional categories. For bacterial organisms we are interested in genes that are involved in host-pathogen interactions and response or genes involved in polyketides production. Such genes can become natural targets for drugs or vaccine development. Or perhaps genes involves in sporulation or DNA repair. For human cells we would like to identify the genes involves in important regulatory activity, signal transduction, apoptosis, cell-differentiation and other fundamental biological tasks.

Our goal in the lab (directed by Professor Simon Kasif) is to collaborate with biologists in the design of novel biological experiments that can be coupled with innovative computational analysis and modeling in order to improve our understanding of gene function. Biological organisms are qualitatively modelled as probabilistic networks of genes and proteins generating cascades of induction and regulation of other genes and proteins. In addition to identifying new genes and assigning them broad biological functions, the lab is involved in building qualitative or probabilistic computational frameworks that can integrate experimental data and provide us with the ability to predict with high accuracy the behavior of cells in normal or perturbed conditions. Such analytical capabilities will have a strong impact on drug design and screening, diagnostics as well as basic biological and medical sciences.

The lab is affiliated with 

Boston University Bioinformatics Program 

Boston University Biomedical Engineering Department

People in the LAB

Our Collaborators

Boston University Biocomputing Research


Functional Gene Clusters, Operons with applications to bacterial microarray analys, pathway reconstruction, functional annotation, syntetic biology, system biology

Functional Gene Clusters, Operons with applications to bacterial microarray analys, pathway reconstruction, functional annotation, system biology... based on graph matching

Variability in Microbial Organisms, Mosaic Genes, Segmentally Variable Genes, New Variable Domain Identification

Variability in Microbial Organisms, Fast Evolving Genes

Multiplex PCR Server: Genotyping, PCR Assays, Genome Walks

Bi-Clustering Server for Gene Expression Analysis

Transcriptional Analysis of Signalling Pathways: Available on Request

Structural Analysis of SNP Data (with Jie Liang's group)

Comparative Genome Analysis and Gene Identification: available by request

Functional Annotation, Protein-Protein Interaction Analysis Software: available by request

OC1 Decision Tree System, Multivariate Splits, Similar to CART, first system to implement sorting on attributes to improve speed, for data mining and machine leanring applications

Rank Gene a system for identifying diagnostic genes in microarray data , useful to locate genes that differentiate different samples such as cancer vs normal

Bioinformatics -- Computational Biology  --  Genomic Technologies -- Virtual Life  

 Computational Learning -- Molecular  Computing & Engineering

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