Hamid Beiki, Ph.D.
- Center for Cancer Research
- National Cancer Institute
- Building 41, Room B620
- Bethesda, MD 20892
- 240-858-3652
- hamid.beiki@nih.gov
RESEARCH SUMMARY
Dr. Beiki is a computational biologist with a primary interest in integrating DNA and RNA information to discover functional elements of genome and gene regulation. Through integrative analysis of high-throughput datasets, and applying novel statistical strategies, Dr. Beiki has made important contributions to the fields of transcriptomics and genomics.
Areas of Expertise
Hamid Beiki, Ph.D.
Publications
Research
Biography
Hamid Beiki, Ph.D.
Dr. Beiki began research in bioinformatics as a Ph. D student at Laboratory of Systems Biology & Bioinformatics, University of Tehran, Iran in 2011, where he built the first genome-wide bovine gene co-Expression expression network and Infection response network based on mega-analysis of publicly available Microarray datasets. He had an international visitation from September 2015 to September 2016 to Iowa State University. He then joined Dr. Christopher K Tuggle, investigating the complexity of pig transcriptome (closely related to humans in terms of anatomy, genetics, and physiology and represent an excellent animal model in many fields of biomedical research) using the integration of single-molecule long-read isoform sequencing (PacBio so-seq), Illumina RNA sequencing (RNA-seq chromatin immunoprecipitation sequencing (ChIP-seq), poly(A) selected 3′-RNA-seq and CAP Analysis of Gene Expression (CAGE) data. His work on pig genome resulted in significant improvement over current pig genome annotations, e.g., the extension of more than six thousand known gene borders (5′ end extension, 3′ end extension, or both) and detection of over 10 thousand novel genes in this species. He then joined Dr. Jim Reecy lab in 2018. Participating in Functional Annotation of Animal Genome (FAANG) project, he developed the first bovine non-coding RNAs interaction map (lncRNA-mRNA, miRNA-lncRNA and miRNA-mRNA) using integration of multi-omics data. Dr. Beiki developed a new approach to create trait similarity network using integration of Iso-seq based assembled transcriptome and publicly available Quantitative Trait Luci (QTL) data. Pursuing interests in mammalian genetics, clinical applications of sequencing, and epigenetics, he joined the LRBGE in 2021.