We develop quantitative methods and software for genomics and epigenetics. We focus on microarrays and next generation sequencing. We are interested in leveraging our expertise in handling these data to help translate scientific research into medical practice with a focus on cancer.Specific problems we are currently working on include:
- Next Generation Sequencing: Developing analysis tools for various applications.
- Quantitative Epigenetics: Analyzing high throughput DNA methylation data.
- RMA, fRMA, GCRMA: Preprocessing algorithms for Affymetrix arrays.
- CRLMM: Genotyping and copy number tools for Affymetrix and Illumina SNP chips.
- minfi, CHARM: Microarray method for high-throughput measurement of DNA methylation.
- Expression Barcode: Single array classification based on calling expressed genes
Dr. Irizarry received his bachelor's in mathematics in 1993 from the University of Puerto Rico and went on to receive a Ph.D. in statistics in 1998 from the University of California, Berkeley. His thesis work was on Statistical Models for Music Sound Signals. He joined the faculty of the Department of Biostatistics in the Bloomberg School of Public Health in 1998 and was promoted to Professor in 2007. He is now a Professor in the Biostatistics and Computational Biology at the Dana Farber Cancer Center and a Professor of Biostatistics at Harvard School of Public Health. For the past fourteen years, Dr. Irizarry's work has focused on Genomics and Computational Biology problems. In particular, he has worked on the analysis and pre-processing of microarray, next-generation sequencing, and genomic data. He is currently interested in leveraging his knowledge in translational work, e.g. developing diagnostic tools and discovering biomarkers.
Montaño CM, Irizarry RA, Kaufmann WE, Talbot K, Gur RE, Feinberg AP, Taub MA. Measuring cell-type specific differential methylation in human brain tissue. Genome Biol. 2013 Aug 30;14(8):R94. [Epub ahead of print]
Wu G, Yustein JT, McCall MN, Zilliox M, Irizarry RA, Zeller K, Dang CV, Ji H. ChIP-PED enhances the analysis of ChIP-seq and ChIP-chip data. Bioinformatics. 2013 May 1;29(9):1182-9. doi: 10.1093/bioinformatics/btt108.
Aryee MJ, Liu W, Engelmann JC, Nuhn P, Gurel M, Haffner MC, Esopi D, Irizarry RA, Getzenberg RH, Nelson WG, Luo J, Xu J, Isaacs WB, Bova GS, Yegnasubramanian S. DNA methylation alterations exhibit intraindividual stability and interindividual heterogeneity in prostate cancer metastases. Sci Transl Med. 2013 Jan 23;5(169):169ra10. doi: 10.1126/scitranslmed.3005211.
McCall MN, Jaffee HA, Irizarry RA. fRMA ST: frozen robust multiarray analysis for Affymetrix Exon and Gene ST arrays. Bioinformatics. 2012 Dec 1;28(23):3153-4. doi: 10.1093/bioinformatics/bts588.
Beuf KD, Schrijver JD, Thas O, Criekinge WV, Irizarry RA, Clement L. Improved base-calling and quality scores for 454 sequencing based on a Hurdle Poisson model. BMC Bioinformatics. 2012 Nov 15;13:303. doi: 10.1186/1471-2105-13-303.
Bravo HC, Pihur V, McCall M, Irizarry RA, Leek JT. Gene expression anti-profiles as a basis for accurate universal cancer signatures. BMC Bioinformatics. 2012 Oct 22;13:272. doi: 10.1186/1471-2105-13-272.
Hansen KD, Langmead B, Irizarry RA. BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions. Genome Biol. 2012 Oct 3;13(10):R83.
Herb BR, Wolschin F, Hansen KD, Aryee MJ, Langmead B, Irizarry R, Amdam GV, Feinberg AP. Reversible switching between epigenetic states in honeybee behavioral subcastes. Nat Neurosci. 2012 Oct;15(10):1371-3. doi: 10.1038/nn.3218.
Shi W, Wahba G, Irizarry RA, Bravo HC, Wright SJ. The partitioned LASSO-patternsearch algorithm with application to gene expression data. BMC Bioinformatics. 2012 May 15;13:98. doi: 10.1186/1471-2105-13-98.
Hansen KD, Irizarry RA, Wu Z. Removing technical variability in RNA-seq data using conditional quantile normalization. Biostatistics. 2012 Apr;13(2):204-16. doi: 10.1093/biostatistics/kxr054.
Rafael Irizarry Lab