Dr. Song Liu has overall responsibility for shared resource direction and oversight, and coordinates bioinformatics support assignments for all work done by the resource personnel. He supervises the faculty members, staff bioinformaticians and research assistants in the Resource and prepares written documentation as required by the profession, the Institute and various regulatory agencies.
His responsibilities also include promoting the resource through both internal and external presentations, and coordinating the efforts with other Shared Resources (e.g., Genomics Shared Resource). Dr. Song Liu serves on Roswell Park’s Information Technology (IT) Advisory Board Research Committee, Education Roundtable Committee, Internal Funding Review Committee, Genomics Shared Resource Advisory Committee, and Center for Personalized Medicine Steering Committee. Dr. Song Liu is Vice Chair for Bioinformatics and Professor of the Roswell Park Department of Biostatistics and Bioinformatics, and Director of the Master of Science program in Bioinformatics and Biostatistics through Roswell’s graduate division.
Dr. Jianmin Wang assists Dr. Song Liu in the day-to-day operation of the Bioinformatics Shared Resource. In collaboration with Dr. Song Liu, he reviews the bioinformatics section of new protocols, grants, and manuscripts, directs the experimental design and analysis plan of clinical, laboratory, and population-based studies utilizing high-resolution, high-throughput platforms, oversees ongoing bioinformatics support regarding the project, and presents on the progress of the Resource to the Advisory Committee. His responsibilities also include directing the development of analysis pipelines for various next-generation sequencing, high-density microarray, and proteomics applications utilizing the high-performance computing facility, and working on solutions to integrate different analytic tools and unify the analysis interface. In collaboration with the IT department, he is actively involved in the design and deployment of various data warehousing and computing resources. Dr. Wang serves on Roswell Park’s IT Advisory Board Research Committee.
Dr. Wang joined Roswell Park in 2012 and his primary research interest is to develop novel computational and statistical methods to meet the demands of analyzing ever-increasing cutting edge large scale omics data, such as high throughput microarray data, next generation sequencing (NGS) data, and proteomics data, in both basic biological and oncological researches. His research effort has made significant contribution to the improvement of 1) the methods for parallel mammalian genome assembly, 2) understanding alternative splicing under evolution context, 3) detection of somatic structural variations (SVs) and copy number variations (CNVs) in cancer studies using NGS data, and 4) bioinformatics analysis of quantitative proteomics.
Dr. Lei Wei joined the faculty of Roswell Park in 2013 as Assistant Professor of Oncology. Specialized in genetic variation and somatic mutation detection through creating and utilizing sophisticated computational and statistical methods of Next-Generation Sequencing (NGS), he has developed his research interests which lie within integrative analysis of large-scale, multi-dimensional data to unveil the molecular mechanism of cancer initiation, progression and prognosis.
Prior to his arrival at Buffalo, he joined the bioinformatics team of the Pediatric Cancer Genome Project (PCGP), St. Jude Children's Research Hospital and Washington University in 2010. Since then, he has been working as the Disease Coordinator and Lead Analyst of four major cancer types, analyzed over 150 pairs of Whole-Genome Sequencing (WGS) data, and numerous exome, RNASeq and targeted sequencing data sets. Besides human cancers, he dissected NGS data of mouse model and influenza virus, and developed analytic pipelines with high sensitivity and specificity, which have been adopted by several genome centers to analyze data for multiple projects, and yield a number of published and in-progress high-impact publications.
An example of a recent publication is “The genomic landscape of hypodiploid acute lymphoblastic leukemia” published in Nature Genetics (45: 242-52, 2013). He has also served as a core member of the pilot Clinical Sequencing group at St. Jude Children’s Research Hospital.
Dr. Martin Morgan earned his undergraduate and Master's degrees in Botany at the University of Toronto. Dr. Morgan's PhD studies at the University of Chicago involved the evolutionary consequences of frequency-dependent selection, and of multilocus deleterious mutation.
Dr. Morgan spent 10 years as an Assistant and then Associate Professor at Washington State University, before joining the Fred Hutchinson Cancer Research Center in 2005. At the Hutch, Dr. Morgan worked on the Bioconductor project for the analysis and comprehension of high-throughput genomic data; he has led Bioconductor since 2008. Dr. Morgan recently moved to Roswell Park Comprehensive Cancer Center in Buffalo, NY, where the Bioconductor project is now based.
Dr. Qianqian Zhu joined the faculty of Roswell Park in April of 2012 as Assistant Professor of Oncology. She has an interdisciplinary training in biostatistics, bioinformatics, and human genetics, and has accumulated extensive collaboration experience with biologists and clinicians in genetic epidemiology studies utilizing approaches of GWAS and NGS.
Dr. Zhu completed her postdoctoral training in statistical genetics at Center for Human Genetic Variation at Duke University from 2010 to 2012. Prior to that, she received her PhD education in Bioinformatics and MA education in Biostatistics from the University at Buffalo (UB). She has authored a number of publications in premier journals, including American Journal of Human Genetics, Genome Biology, and Bioinformatics. Dr. Zhu’s primary research interest is in developing statistically sound and computationally efficient methods to pinpoint the causal genetic variants of human diseases utilizing high-throughput genetics and genomics data.
Her areas of expertise include: 1) computational method development for causal genetic variant identification; 2) statistical and bioinformatics analysis of genome, epigenome, and transcriptome data; and 3) pharmacogenomics, genetic testing, and personalized medicine. She has conducted a number of cutting-edge studies in dissecting genetic contributors to complex human traits.
Dr. Li Yan joined the staff of Roswell Park in 2011. He completed his PhD in Physics at the University of Rochester and PhD in Biostatistics at the University at Buffalo. Dr. Yan has more than 12 years of experience in both academic and non-profit research foundation environments, collaborating with clinical and other scientific colleagues on a wide range of activities including study design, analysis plans, project reports, abstracts, manuscripts, and presentations.
He has extensive experience in many programming languages, including R and C++, as well as extensive experience in directing the design and deployment of various high-throughput platforms in cancer-oriented studies. An example of a recently developed tool is OSAT (Yan et al. BMC Genomics. 2012; 13:689. PMC3548766), which is a computational package for sample-to-batch allocations in omics experiments in order to minimize the impact of batch effects.
Dr. Tao Liu joined the faculty of Roswell Park in February 2019 as Assistant Professor of Oncology. Before that, he was an assistant professor in the Department of Biochemistry of University at Buffalo since 2013. He has the expertise and extensive experience with developing and applying bioinformatics and computational approaches for studying transcriptional and epigenetic regulation.
Dr. Tao Liu got his postdoctoral training at Dana-Farber Cancer Institute from 2007 to 2012, developed widely used open-source algorithms, including MACS (cited over 5,500 times) and worked as a member of the Data Analysis Center and Analysis Working Group of the ENCODE and modENCODE consortium. He received his Ph.D. in Bioinformatics from Institute of Biophysics, Chinese Academy of Sciences, and B.S. in Physics from Nanjing University in China. He has authored over 40 peer-reviewed publications in journals such as Genome Research, Genome Biology, Nature and Science. At Roswell Park, Dr. Tao Liu’s group focuses on building bioinformatics algorithms for single-cell genomics assays to study transcriptional and epigenetic regulatory mechanisms and the influence of the genetic variations at regulatory elements.
Dr. Qiang Hu joined the Roswell Park faculty in 2010. He graduated from Peking University with a degree in medicine and received his PhD in Bioinformatics from Tsinghua University. He is skilled in PERL and R computing languages, and has extensive experience in utilizing parallel computing in high-performance servers to process, analyze and interpret high-dimension, high-complexity data. An example of a recently developed tool is VPA (Hu et al. BMC Research Note. 2012; 5:31 PMC3293055), which is a customized package for analyzing variants with user-specified frequency pattern from NGS studies.
Yali Zhang, MPH, MS
Ms. Zhang joined the staff of the Department of Biostatistics and Bioinformatics in 2017. She obtained her MPH degree with a concentration in biostatistics from the Department of Biostatistics at University at Buffalo in 2011. After graduation, she worked as a data analyst in Department of Medicine at Roswell Park until 2017. Over the years, she has accumulated extensive experience working with clinical, epidemiological, and genomic data of various scales, including next-generation sequencing data. She is proficient in the collection, management, quality control, as well as in-depth analysis of those data using versatile statistical and bioinformatical pipelines. She has hands-on experience in managing and analyzing large datasets for numerous scientific projects by working closely with the PIs to provide analytical consults and ready-to-use analysis reports.
Dr. Mark Long came to Roswell Park in 2009 where he received his dual Master’s degree in Biological Sciences and Systems Biology from the University at Buffalo and the University of Luxembourg. He went on to complete his PhD at the University at Buffalo Roswell Park’s division in the Molecular Pharmacology and Cancer Therapeutics program. He has since remained at Roswell Park, utilizing his experience to drive his own and collaborative research projects. He joined the Biostatistics & Bioinformatics department in 2018.
He has an interdisciplinary skill set, with expertise in cancer genomics/epigenomics biology and computational approaches for the understanding and visualization of NGS data. He has extensive experience coding in R, as well as in the design, implementation, integration and interpretation of complex genomics data sets.
Eduardo Cortes, MA
Mr. Eduardo Cortes began his professional career at Roswell Park’s Biostatistics and Bioinformatics department in 2013. He earned his bachelors’ degrees in Mathematics and Philosophy at Universidad de los Andes and completed his master’s in Biostatistics at the University at Buffalo - SUNY-UB.
He has experience processing, managing and analyzing next-generation sequencing data. Tasks involve developing, maintaining and enriching QC pipelines (RNA-seq, ChIP-seq, 16S-seq).
Mr. Cortes has special interest in building and applying statistical models and methodologies to analyze and compare data supporting on-going cancer research projects. Collaboration with genomics’ related projects outside cancer research setting in the fields of translational psychiatry and neurology. Expertise building and implementing scripts in R, Python, and Bash programming languages. Deft application of parallel computing in high-performance computing environments in order to process and analyze obtained from diverse genomics’ settings.
Research Assistants, MS/MA
The Research Assistants (RAs) in the Bioinformatics Shared Resource work under the mentorship of the faculty on appropriately chosen bioinformatics projects. They are graduate trainees enrolled the University at Buffalo graduate program in Biostatistics.