Data Science Training Program
Ayushi Agrawal, PhD
Michela Traglia, PhD
Gene expression is central to cell biology. Disease pathways often involve changes in the expression levels of at least some genes. RNA-seq has become one of the most popular experimental methods to quantify gene expression levels. This 2-day hands-on workshop will provide an introduction to a typical bulk RNA-seq protocol and focus on the data analysis steps for recovering actionable insights.
You’ll learn how to perform quality checks of raw data in the typical format provided by sequencing centers, how to process the data by trimming adapters, how to map trimmed reads to a reference genome, and how to tally gene-wise counts.
In addition, the course will cover how to analyze high-throughput data using a webtool and using a high-performance compute (HPC) cluster, various methods of quality control, and how to interpret data formats such as FASTQ, BAM, and GFF.
No prior RNA-seq experience is required.
Novice: This is an introductory workshop in the RNA-Seq Analysis series. No prior experience required. No prerequisites.
Visit the workshop site for more details and materials.
AudienceGladstone and UCSF
The Gladstone Data Science Training Program was started in 2018 to provide trainees with learning opportunities and hands-on workshops to improve their skills in bioinformatics and computational analysis. This program offers a series of workshops throughout the year to enable trainees to gain new skills and get support with their questions and data.
Diversity, Equity, and Inclusion
At Gladstone, we are committed to providing events and professional development activities that resonate with our community’s diverse members. Our goal is to develop creative programming that encompasses a wide variety of ideas and perspectives to inspire, educate, and engage with everyone within our walls.
We want to effect positive change through our events and activities by providing a platform for discussions on important topics related to increasing diversity and inclusiveness in the sciences.