Lab Members

Program in Bioinformatics and Integrative Biology 
Program in Molecular Medicine
                                                                                                                                       
Since the completion of the human genome I have been working on identifying and functionally characterizing non-coding regulatory elements. While at the Broad Institute I developed computational techniques to integrate comparative genomics and functional genomics data. This effort resulted in Siphy, an algorithm to identify both reduced and skewed substitution rate that are typical of functional elements and Scripture, one of the first algorithms to assemble transcripts from RNA-Seq data. 

Since moving my laboratory to the University of Massachusetts Medical School I complemented our computational genomics approach with a wet laboratory. I still continue to maintain Siphy and contribute to all computational tools in the lab (https://github.com/garber-lab/siphy).

                                                                                                                                           
Elisa is interested in the cell-type specific control of gene expression and its misregulation in disease. Her work in the Garber lab has focused on comparative genomics to understand enhancer evolution and gene regulation. Elisa is currently using Single Cell RNA-Seq to explore transcriptional changes in disease at cell-type resolution.

                                                                                                                                           
Pranitha is interested in understanding the role of cis-regulatory elements in gene regulation.  Her research focuses on integrating temporal epigenetic, transcriptomic and higher order data to identify the regulatory circuits. She also uses comparative genomics and population genetic approaches to gain insights into the molecular roles of the putative regulatory elements.

                                                                                                                                           
Jake is interested in dissecting the heterogeneity in gene expression. He uses Single Cell RNA-sequencing data to get at the underlying distributions in gene expression that are the result of different cis and trans regulatory elements within the population of cells. He aims to apply this technology to autoimmune diseases of the skin to track the transcriptional profile changes within the cell types of the skin throughout the progression of the diseases. And identify the transcriptional programs and signaling networks in each individual cell type that may lead to the initiation and progression of the autoimmune reaction. 

                                                                                                                                           
Yuming is focused on systematically characterizing the influence of flu virus on the nasal tract using single cell RNA sequencing on cells from human nasal wash sample.

                                                                                                                                           
Yuqing is interested in understanding the transcriptional and epigenetic differences between skin autoimmune diseases. She is currently focusing on comparing two clinically hard-to-distinguish diseases, lupus and dermatomyositis, by scRNA-seq and scATAC-seq.

                                                                                                                                           
Alan built inDrop system at Garber lab. He is focusing on using single-cell RNA-Seq (scRNA-Seq) to identify cell types and quantify the gene expression profiles, with the goal of determining the cause and mechanism of disease. In addition to his single-cell work, he wrote and supports ESAT, our End-sequencing Analysis Toolkit (https://github.com/garber-lab/ESAT), which was designed specifically for quantifying gene expression of RNA-Seq data from end-sequencing methods. The code has been adapted for single-cell analysis, since most scRNA-Seq technologies are 3’-based.

Bioinformatics Core
Program in Molecular Medicine
                                                                                                                                         
Alper designs and implements reusable, robust and production grade bioinformatics analysis pipelines and pipeline generation tools for processing next-generation sequencing data that support researchers in UMass Medical School.

                                                                                                                                           
Onur is interested in creating easy to use pipelines for processing highthroughput data.He is currently developing user-friendly servers for creating custom pipelines (https://dolphinnext.umassmed.edu/).

Students/Post-Docs/Research Staff

                                                                                                                                           
We are actively recruiting talented people into our group at various positions. Please email Manuel with a current CV, including publications and research experience, and a brief statement of interests.