Data Science Training Program
May 15-May 16, 2023

Ayushi Agrawal

Bioinformatician II

Michela Traglia

Statistician III

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 a quality check of the raw data in the typical format provided by sequencing centers, process it by trimming adapters, map trimmed reads to a reference genome, and tally gene-wise counts. You’ll learn to analyze high-throughput data using docker, learn about various methods of quality control at each step, and interpret data formats such as FASTQ, BAM, and GFF. No prior RNA-seq experience is required.

Visit the workshop site for more details and materials.

Novice: This is an introductory workshop in the RNA-Seq Analysis series. No prior experience required. No prerequisites.

Details

Dates
May 15-May 16, 2023
Time
9:00am-12:00pm PDT
Location
Online
Contact(s)





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.