The Team:

Sali Farhan, Ph.D.

Neuro Bioinformatics Core Facility title: Scientific Director

Hospital title:  Assistant Professor in the Department of Neurology and Neurosurgery, and the Department of Human Genetics.

Biography: Sali Farhan is an Assistant Professor in the Department of Neurology and Neurosurgery and is cross-appointed in the Department of Human Genetics at McGill University. She is also a Clinical Molecular Geneticist at the McGill University Health Centre. Her primary research focus is on the genetics of neurodegeneration, with a central focus on amyotrophic lateral sclerosis. She has contributed to the identification of several disease genes using exome sequencing including DNAJC7 in ALS, TMTC3 in intellectual disability and epilepsy, NFS1 in a novel mitochondrial disorder, and several others. She leverages the use of next-generation sequencing technology to identify genomic signatures in ALS and neurodegeneration and has helped create the ALS Knowledge Portal (alskp.org), an open-source genomics database of ~4000 ALS patients and ~8000 ethnically matched controls. Sali also collaborates closely with neurobiologists to model mutations observed in human diseases. Working with local and international clinicians and scientists, her research vision is to bridge clinical genomics with the research lab to translate research findings back into the clinical lab to best serve patients.

Dan Spiegelman

Neuro Bioinformatics Core Facility title: Bioinformatician

Biography: After graduating with an M.Sc. in Microbiology from McGill University in 2006, Dan joined Guy Rouleau’s laboratory at the University of Montreal as a research assistant tasked with serving as a bridge between a small group of informaticians and a large group of wet-lab biologists on a Genome Canada large-scale sequencing project. Dan came to McGill in 2013 when Dr. Rouleau left UdeM to join McGill as the new director of the MNI. As the last remaining member of Dr. Rouleau’s bioinformatics team still at the Neuro, Dan serves as the institutional memory and the point of continuity between the Bioinformatics Core Facility and its Rouleau Lab ancestor.
Though he never received formal training in informatics, long-term exposure to trained professionals slowly but surely called him over to the computational side, as informatics became increasingly central to the activities of a high-profile genetics lab. Today his responsibilities run the gamut of Core Facility activities, from data management to large-scale production, pipeline development and maintenance, and external project consulting. Dan’s skill profile is that of a consummate generalist; his areas of expertise are in the generalist’s toolkit: Linux shell scripting, data management and the High-Performance Computing environment. 

Georgina Jiménez Ambriz, Ph.D.

Neuro Bioinformatics Core Facility title: Bioinformatician

Biography: Georgina has a PhD in ecological genetics from the Université de Montpellier, France. She has worked as an associate professor at the Faculty of Sciences in the National Autonomous University of Mexico where she studied genetics and evolutionary trends of organism’s adaptation to perturbed environments. She got a master in bioinformatics from the “Université de Montréal.” Within her masters, she made an internship in the “Laboratoire de Santé Publique du Québec” and studied metagenomics analysis and machine learning algorithms to analyze the association between health conditions and patient microbiome data.
Georgina has developed skills in bioinformatics, biostatistics, machine learning, population genetics and analysis of polygenic traits. As a member of the bioinformatics core, she assists and collaborates with researchers and students in their genomic analytical needs and in the development of new workflows.

Saeid Amiri, Ph.D

Neuro Bioinformatics Core Facility title: Bioinformatician

Biography: His skill lies at the intersection of machine learning, statistical genetics, and bioinformatics. As a person with a background in statistics, Saeid enjoys developing statistical methods for genomic data analyses, in particular, for high-dimensional data.