Computational Biology and Bioinformatics


Mohammad Mahdi Karimi, Ph.D.
Head of Bioinformatics, Biomedical Research Centre

mkarimi@brc.ubc.ca
604-822-7805

 


The overall research objective of the Computational Biology and Bioinformatics Lab (CBBL) at the Biomedical Research Centre (BRC) is to develop and apply new bioinformatics methods to delineate and interrogate genetic and epigenetic regulatory networks, derived from analysis of high-throughput biological data sets from human and mouse, with a focus on the impact of these processes on human health and disease.

To achieve this goal, the CBBL takes advantage of immediate access to sequencing data generated by MiSeq and NextSeq Illumina sequencers as well protein expression information from CyTOF at the BRC and aims to:

(1) develop innovative and groundbreaking bioinformatics approaches for genomic, epigenomic, and transcriptomic analyses of next-generation sequencing (NGS) data

(2) transform these methods to software applications and analysis toolboxes that assist biologists in their statistical analyses of high-throughput biological data

(3) integrate and multiplex high-throughput biological data generated by various “omics” platforms at the BRC including Illumina sequencers and the CyTOF.

We are particularly interested in developing bioinformatics tools to study the following research areas:

Allele-specific analysis of disease related epigenomic data
In mammalian genomes, a small number of genes termed “imprinted,” are expressed in a parent-of-origin-specific manner, that is, only a single parental allele is expressed for these genes. There is a growing body of evidence that the expression of imprinted genes is influenced by the epigenetic machinery, where one allele is marked by active epigenetic marks and the other with repressive marks. Mono-allelic expression is not always an indication of imprinting. There are clear cases in the human and mouse genomes where SNPs/Indels in regulatory regions show differential patterns of DNA methylation, causing allele-specific (AS) differences in expression. The assessment of epigenomic status using sequencing based methods, such as ChIP-seq, provides an unprecedented opportunity to identify and correlate allelic differences with epigenomic status. Our specific aim is to incorporate allelic variation data into our epigenomic analysis pipeline, allowing for the identification of AS epigenomics variations in both human and mouse.

Epigenetic quantitative trait loci (EpiQTLs) mapping in human cancers
EpiQTL analyses identify genomic loci where genetic variations change an epigenetic profile either locally (in cis) or distantly (in trans). We are interested in developing a bioinformatics framework making use of AS information to detect EpiQTLs for human cancers. This study is able to further elucidate the causal associations underlying cancer risk alleles found to be expression quantitative trait loci (eQTLs) by showing their potential overlaps with EpiQTLs. Knowing that a large fraction of cancer-related SNPs are located in noncoding regions with unknown functional role, the comprehensive analysis of EpiQTLs and eQTLs will improve our understanding of these SNPs, thereby allowing for the identification of novel therapeutic targets for human cancers.

Global analysis of retrotroelement-mediated aberrant gene expression in cancers
Epigenomic patterns are profoundly altered in cancer. The genomes of cancer cells are characterized by localized regions of de novo hypermethylation, frequently in CpG island promoters of tumor suppressor genes and microRNA genes. Paradoxically repetitive elements, which make up over 40% of the human genome, are frequently hypomethylated in different types of cancers. While DNA demethylation of such repetitive elements is widespread in cancer, the role of hypomethylation of these elements in tumorigenesis remains controversial. We plan to design a new statistical framework: 1) to determine the magnitude of retroelement-mediated aberrant gene expression in cancers using RNAseq data and 2) to investigate histone marks and DNA methylation at candidate genes and retroelements. Together, these analyses will enable us to delineate the perturbed regulatory mechanisms or epigenetic pathways responsible for retroelement driven aberrant gene transcription.

Interactive visual analysis tools for next generation sequencing data analysis
Our lab is actively collaborating with computer scientists at Simon Fraser University (SFU) and Vancouver Institute for Visual Analytics (VIVA) to design and apply new visual analytic tools for exploration and analyses of NGS data. More specifically, we are developing visualization tools in consultation with several biologists to provide an integrated environment for analysis of sequencing data such as ChIP-seq and RNA-seq, etc. Our previous published tools enable a highly accessible and simplified approach to the importing of NGS data and subsequent normalization, clustering, and visualization in an interactive manner.

Selected Publications

Younesy H, Nielsen CB, Lorincz MC, Jones SJM, Karimi MM*
& Moller M*(*Co-corresponding author)
ChAsE: Chromatin analysis and exploration tool
Bioinformatics, doi: 10.1093/bioinformatics/btw382, (2016).

 

Sharif J, Endo T, Nakayama M, Karimi MM, Shimada M, Katsuyama K, Goyal P, BrindAmour J, Sun M, Sun Z, Ishikura T, Mizutani-Koseki Y, Ohara O, Shinkai Y, Nakanishi M, Xie H, Lorincz MC & Koseki H.
Protracted NP95 binding to hemimethylated DNA disrupts SETDB1-mediated proviral silencing.Cell Stem Cell,

 

Artem Babaian, Mark T. Romanish, Liane Gagnier, Mohammad M. Karimi, Christian Steidl, Dixie L. Mager. Onco-exaptation of an Endogenous Retroviral LTR Drives IRF5 Expression in Hodgkin Lymphoma.Oncogene, doi:10.1038/onc.2015.308 (2015).

 

Hamid Younesy, Torsten Möller, Matthew Lorincz, Mohammad M. Karimi*, Steven M. Jones (* Corresponding author)
VisRseq: R-based Visual framework for Analysis of Sequencing Data.
BMC Bioinformatics (Proceedings of BioVis 2015), doi:10.1186/1471-2105-16-S11-S2 (2015).

 

Julie Brind’Amour, Sheng Liu, Matthew Hudson, Carol Chen, Mohammad M Karimi, and Matthew C Lorincz
Ultra-low-input native ChIP-seq for genome-wide profiling of rare cell populations.
Nature Communications, doi: 10.1038/ncomms7033, 2015.

 

Peter J. Thompson, Vered Dulberg, Kyung-Mee Moon, Leonard J. Foster, Carol Chen, Mohammad M. Karimi, and Matthew C. Lorincz
hnRNP K coordinates transcriptional silencing by SETDB1 in embryonic stem cells.
PLoS Genetics, doi:10.1371/journal.pgen.1004933, 2015.

 

Sheng Liu, Julie Brind’Amour, Mohammad M. Karimi, Kenjiro Shirane, Aaron Bogutz, Louis Lefebvre, Hiroyuki Sasaki, Yoichi Shinkai, Matthew C Lorincz.
Setdb1 is required for persistence of H3K9me3 and repression of endogenous retroviruses in mouse primordial germ cells.
Genes & Development, doi:10.1101/gad.244848.114, 2014.

 

Lock FE, Rebollo R, Miceli-Royer K, Gagnier L, Kuah S, Babaian A, Sistiaga-Poveda M, Lai CB, Nemirovsky O, Serrano I, Steid C, Karimi MM & Mager DL.
Distinct isoform of FABP7 revealed by screening for retroelement activated genes in diffuse large B-cell lymphoma.
Proc Natl Acad Sci USA (PNAS), doi: 10.1073/pnas.1405507111, 2014.

 

Hamid Younesy, Torsten Moller, Alireza Heravi-Moussavi, Jeffrey B. Cheng, Joseph F. Costello, Matthew C. Lorincz, Mohammad M. Karimi*, and Steven J.M. Jones*.
ALEA: a toolbox for allele-specific epigenomics analysis.
Bioinformatics, doi: 10.1093/bioinformatics/btt744 (2014)

 

Kathryn Blaschke, Kevin T. Kabata, Mohammad M. Karimi, Jorge A. Zepeda-Martinez, Preeti Goyal, Sahasransu Mahaptra, Angela Tam, Diana J. Laird, Martin Hirst, Anjana Rao, Matthew C. Lorincz, and Miguel Ramalho-Santos.

Vitamin C induces Tet-dependent DNA demethylation and a blastocyst-like state in ES cells.


Nature, doi: 10.1038/nature12362 (2013)

 

H. Younesy, C.B. Nielsen, T. Moller, O. Alder, R. Cullum, M.C. Lorincz, M.M. Karimi, and S.J.M. Jones.
An Interactive Analysis and Exploration Tool for Epigenomic Data
.
Computer Graphics Forum (Proceedings of EuroVis 2013), 32(3), 2013.

 

Irina A. Maksakova, Peter J. Thompson, Preeti Goyal, Steven J.M. Jones, Prim B. Singh, Mohammad M. Karimi, and Lorincz C. Matthew.
Distinct roles of KAP1, HP1 and G9a/GLP in silencing of the two-cell-specific retrotransposon MERVL in mouse ES cells.

Epigenetics & Chromatin, doi: 10.1038/nature12362 (2013)

 

Rita Rebollo, Mohammad M. Karimi, Misha Bilenky, Liane Gagnier, Katharine Miceli-Royer, Ying Zhang, Preeti Goyal, Thomas M. Keane, Steven Jones, Martin Hirst, Matthew C. Lorincz and Dixie L. Mager. Retrotransposon-induced heterochromatin spreading in the mouse revealed by insertional polymorphisms
.
PLoS Genetics, 7(9): e1002301 (2011)

 

Karimi, M. M., P. Goyal, I. A. Maksakova, M. Bilenky, D. Leung, J. X. Tang, Y. Shinkai, D. L. Mager, S. Jones, M. Hirst, and M. C. Lorincz. 

DNA Methylation and SETDB1/H3K9me3 Regulate Predominantly Distinct Sets of Genes, Retroelements, and Chimeric Transcripts in mESCs.


Cell Stem Cell, doi: 10.1016/j.stem.2011.04.004 (2011)

 

Gupta A, Karimi MM, Manuch J, Stacho L, Zhao X .
Haplotype Inferring via Galled-Tree Networks Is NP-Complete.
Journal of Computational Biology, doi: 10.1089/cmb.2009.0117 (2010)

 

Lab Members

Hamid Younesy, Graduate Student