Events Calendar

Which tools for experimental analysis should you choose?

High throughout RNA sequencing allows genome-wide investigation of gene expression and regulation. However, designing an experiment and choosing the right tools for analysis can be challenging. This session will introduce methods for analyzing and visualizing RNA-seq data: quality control, alignment-based quantification, transcriptome assembly and differential expression analysis.

This course is part of a series of trainings in biomedical data science offered by the Center for Quantitative Methods and Data Science, Tufts CTSI, and Tufts University Data Intensive Studies Center. For the full list of trainings in this series, please visit the Center for Quantitative Methods and Data Science webpage.

Faculty

This workshop will be taught by  Rebecca Batorsky, PhD and Albert Tai, PhD.

Rebecca Batorsky is a Senior Bioinformatics Scientist in Research Technology, part of Tufts Technology Services and a DISC fellow. She earned her PhD in Physics in 2012 from Tufts University, where she focused on mathematical and computational modeling of virus evolution. Before becoming staff at Tufts, she worked as a bioinformatics software developer at a clinical genomics start-up company. Dr. Batorsky works to enable researchers to answer biological questions with data-driven methods, such as analysis of high-throughput DNA and RNA sequencing data. She is especially interested in developing methods to use multiple `omics technologies to give insight into biological pathways and processes.

Albert Tai is a Research Assistant Professor of Immunology at Tufts University. His research work focuses on providing current research technology to basic research community within and outside of the University, including next generation sequencing (NGS), high throughout screen (HTS), high content screen (HGS), robotics automation and flow cytometry. These technologies, especially NGS and HCS, generates significant amount of data and require specialized analytical approaches. A part of his research centers on creating or optimizing these analytical approaches, via utilizing existing software/pipeline and/or developing new ones. Furthermore, research projects that utilize multiple technologies, or multi-omics, are becoming more popular, a mean to allow association and visualization of multi-omics data is also of interest.

Who should attend

Basic statistical knowledge is required. Computational experience will be helpful, but is not required.

Details

Wednesday, July 14
2:00-3:30PM
Online (a link will be sent to those who register).

Registration

To attend, please register here.

You may also register for the other trainings in this series:

• June 23: Study Design for Biomedical Data Science
• June 30: Introduction to Sequence-Based Transcriptomic Analysis
• July 21: Bioinformatics Analysis of Single-Cell RNA Sequencing Data
• July 28: Functional and Enrichment Analysis Methods for RNAseq Data

The July 2021 Clinical Research Staff Quarterly Training will focus on hot topics in IRB and research compliance.

Big changes are coming to IRB forms and submission processes. Join us on July 15 for the next session of the Tufts Medical Center (Tufts MC) and Tufts CTSI Professional Education Clinical Research Staff Quarterly Training series. You’ll get the inside scoop on what is new, what you will be required to do, and best practices for working with the Tufts IRB. If you are involved in study start-up activities or interact with research participants or IRB submissions, these sessions are for you! These quarterly trainings are also a great way to get connected to the community of clinical research professionals at Tufts MC.

The July session continues our focus on topics in research compliance specific to Tufts MC. Whether you are an experienced coordinator, or new to Tufts MC, there will be something here that is relevant to your work.

Featured Speakers

Christine Choy, IRB Supervisor and Database Administrator

Caitlin Farley, IRB Administrator II

Carly Tucker, Clinical Research Compliance Manager

Featured Topics

Diversity Enrollment, Biospecimen Banking, and More

The IRB office recently updated their forms and templates to reflect new processes to highlight and improve enrollment of diverse populations, their updated Biospecimen banking (formerly tissue banking) policy, and much more. You will learn about all of these changes; which forms, templates, and policies were revised, created, and eliminated; and how these changes will affect current and new studies.

Best Practices for IRB Submissions and Responding to IRB Comments

The IRB office will provide tips and guidance for a smooth and efficient IRB review of your projects. You will learn best practices for submitting studies to the Tufts IRB, responding to comments and requested revisions, and getting your study approved as quickly as possible.

Third Party Vendor Risk Assessment Update

This short update will cover the EVA page for approved research vendors including: where to find the list, how to best understand and use the approved vendors list, and other tips and tricks for this new vendor review.

Who should attend

Clinical research staff are encouraged to attend.

Details

Thursday, July 15
1:00-2:00PM
Online via Zoom (a link will be sent to those who register).

Registration

To attend, please register here.

What are methods for performing common workflows on scRNAseq data to characterize sub-populations of cell profiles?

Single-cell RNA sequencing (scRNA-seq) allows for transcriptome-wide profiling of individual cells present in a tissue sample. While conceptually similar, scRNSeq and “bulk” RNAseq projects differ so greatly in their overall study design, goals, and statistical caveats that their analytical investigation is distinct. In this session, we will introduce methods for performing common workflows on scRNAseq data to characterize sub-populations of cell profiles, including: data preprocessing and normalization, dimensionality reduction, clustering, and visualization.

This course is part of a series of trainings in biomedical data science offered by the Center for Quantitative Methods and Data Science, Tufts CTSI, and Tufts University Data Intensive Studies Center. For the full list of trainings in this series, please visit the Center for Quantitative Methods and Data Science webpage.

Faculty

This workshop will be taught by Tanya Karagiannis, MS and Eric Reed, PhD.

Tanya Karagiannis is a Research Assistant at Tufts Medical Center in the Institute for Clinical Research and Health Policy Studies working with Dr. Paola Sebastiani. She has an MS in Bioinformatics from Boston University where she is also continuing her PhD in Bioinformatics under the advisement of Dr. Paola Sebastiani and Dr. Stefano Monti. Her research focus is in the application and development of single cell transcriptomic methods utilizing machine learning and Bayesian statistics, with interest in multi-omics as well.

Eric Reed is a Data Scientist in the Data Intensive Studies Center (DISC) at Tufts University. He earned an MS in Biostatistics from the University of Massachusetts Amherst in 2015 and a PhD in Bioinformatics from Boston University in 2020. Dr. Reed’s research is focused on working with biomedical researchers to implement cutting-edge high-throughput profiling techniques and develop analytical approaches to better interrogate the biological questions at hand. His dissertation work encompassed advancement of large-scale transcriptomic profiling for toxicogenomic screening. This included the benchmarking scalable library preparation techniques and development of machine learning methods and software. Through numerous collaborative projects, Dr. Reed’s work has led to contributions to various biomedical fields including environmental health, metabolic diseases, oral cancer, breast cancer, Huntington’s disease, and addiction.

Who should attend

Basic statistical knowledge is required. Computational experience will be helpful, but is not required.

Details

Wednesday, July 21
2:00-3:30PM
Online (a link will be sent to those who register).

Registration

To attend, please register here.

You may also register for the other trainings in this series:

• June 23: Study Design for Biomedical Data Science
• June 30: Introduction to Sequence-Based Transcriptomic Analysis
• July 14: Bioinformatics Analysis of Bulk RNA Sequencing Data
• July 28: Functional and Enrichment Analysis Methods for RNAseq Data

What are the three main types of functional analysis?

Functional and enrichment analyses are used to give a biological interpretation to a list of genes or proteins that may be produced from gene expression analysis. This session will introduce three main types of functional analysis and review common tools that are employed: Gene Ontology annotation and enrichment, Gene Set and Pathway enrichment, and network analysis.

This course is part of a series of trainings in biomedical data science offered by the Center for Quantitative Methods and Data Science, Tufts CTSI, and Tufts University Data Intensive Studies Center. For the full list of trainings in this series, please visit the Center for Quantitative Methods and Data Science webpage.

Faculty

This workshop will be taught by Rebecca Batorsky, PhD, Eric Reed, PhD, and Albert Tai, PhD.

Rebecca Batorsky is a Senior Bioinformatics Scientist in Research Technology, part of Tufts Technology Services and a DISC fellow. She earned her PhD in Physics in 2012 from Tufts University, where she focused on mathematical and computational modeling of virus evolution. Before becoming staff at Tufts, she worked as a bioinformatics software developer at a clinical genomics start-up company. Dr. Batorsky works to enable researchers to answer biological questions with data-driven methods, such as analysis of high-throughput DNA and RNA sequencing data. She is especially interested in developing methods to use multiple `omics technologies to give insight into biological pathways and processes.

Eric Reed is a Data Scientist in the Data Intensive Studies Center (DISC) at Tufts University. He earned an MS in Biostatistics from the University of Massachusetts Amherst in 2015 and a PhD in Bioinformatics from Boston University in 2020. Dr. Reed’s research is focused on working with biomedical researchers to implement cutting-edge high-throughput profiling techniques and develop analytical approaches to better interrogate the biological questions at hand. His dissertation work encompassed advancement of large-scale transcriptomic profiling for toxicogenomic screening. This included the benchmarking scalable library preparation techniques and development of machine learning methods and software. Through numerous collaborative projects, Dr. Reed’s work has led to contributions to various biomedical fields including environmental health, metabolic diseases, oral cancer, breast cancer, Huntington’s disease, and addiction.

Albert Tai is a Research Assistant Professor of Immunology at Tufts University. His research work focuses on providing current research technology to basic research community within and outside of the University, including next generation sequencing (NGS), high throughout screen (HTS), high content screen (HGS), robotics automation and flow cytometry. These technologies, especially NGS and HCS, generates significant amount of data and require specialized analytical approaches. A part of his research centers on creating or optimizing these analytical approaches, via utilizing existing software/pipeline and/or developing new ones. Furthermore, research projects that utilize multiple technologies, or multi-omics, are becoming more popular, a mean to allow association and visualization of multi-omics data is also of interest.

Who should attend

Basic statistical knowledge is required. Computational experience will be helpful, but is not required.

Details

Wednesday, July 28
2:00-3:30PM
Online (a link will be sent to those who register).

Registration

To attend, please register here.

You may also register for the other trainings in this series:

• June 23: Study Design for Biomedical Data Science
• June 30: Introduction to Sequence-Based Transcriptomic Analysis
• July 14: Bioinformatics Analysis of Bulk RNA Sequencing Data
• July 21: Bioinformatics Analysis of Single-Cell RNA Sequencing Data