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A Free Introduction to the World of Meaningful Scientific Data
April 6–10, 2020
5:30–6:30 p.m. (PST)
Join us for a five-day series about the booming field of bioinformatics. Every day this week we will dive into a key topic in the industry during a live-online, one-hour webinar.
Designed to give you a sense of what it takes to begin a bioinformatics career, you will hear from Silicon Valley bioinformaticians about the different tools and technologies used to make meaning from the vast trove of amassed health data. This specialized data serves as the foundation for cures and vaccines being discovered each day.
The series begins with a very brief overview of upcoming courses in the Bioinformatics certificate program at UC Santa Cruz Silicon Valley Extension and then jumps right into the technologies and tools every bioinformatician needs to get started.
You may attend one or all of the five one-hour, stand-alone sessions.
Register today and explore what it’s like to participate in life-changing discoveries all over the world.
Who This Is For
No experience necessary. These live lectures are designed for anyone interested in learning about a possible career in the fascinating field of bioinformatics. You’ll get to see if merging your interest in science with programming makes sense to you. The demand is enormous for trained professionals in the bioinformatics field. Help save lives!
What You Will Learn
- Sequencing-related tools used in bioinformatics
- Linear regression
- Introduction to R programming
- BioPython and other Python tools for biology for preliminary analyses of genomic sequence, such as the SARS-CoV-2 genome
- Bioinformatics certificate program at UCSC Silicon Valley Extension
The Week’s Agenda
Monday, April 6, 5:30–6:30 p.m. (PST)
Welcome to the World of Bioinformatics
A brief introduction for the person considering joining the field of bioinformatics with an overview of the UCSC Silicon Valley Extension Bioinformatics certificate program. Merging the best of biology and computer programming technology, this year-long program prepares you to analyze and interpret today’s deluge of biological information. Due to the coronavirus outbreak, our courses are only available fully remotely in a live-online format.
Featuring: JANANI RANGARAJAN, M.S.
Part I - Next-Gen Sequence Analysis Tools
Next-generation Sequencing (NGS): The first step in learning bioinformatics—A survey of the technology underlying NGS nucleic acid sequencing and how it can be used to generate bioinformatics for research and medical applications. We will examine how data is processed from raw sequencer output to interpretable information.
Featuring: PAUL SAUNDERS, Ph.D.
Tuesday, April 7, 5:30 p.m.–6:30 p.m. (PST)
Part II - Next-Gen Sequence Analysis Tools
Next-generation Sequencing (NGS): The first step in learning bioinformatics—A survey of the technology underlying NGS nucleic acid sequencing and how it can be used to generate bioinformatics for research and medical applications. We will examine how data is processed from raw sequencer output to interpretable information. Note: You do not have to attend Part I of this course to benefit from this discussion.
Featuring: PAUL SAUNDERS, Ph.D.
Wednesday, April 8, 5:30 p.m.–6:30 p.m. (PST)
Statistical Analysis and Modeling for Bioinformatics
Linear regression is one of the go-to modeling choices for many types of problems today and heavily used in biology. In this hour-long workshop, students will learn the fundamental theory behind linear regression and assumptions underlying this common modeling approach as well as how to properly use and interpret these models
Featuring: LAURYNAS KALESINSKAS, doctoral candidate in the Stanford University program of Biomedical Informatics.
Thursday, April 9, 5:30 p.m.–6:30 p.m. (PST)
Learn to Apply Python tools to Bioscience Data
This lecture will demonstrate how to utilize Biopython and other Python tools for biology to do a preliminary analysis of the SARS-CoV-2 genome. Basic familiarity with Python is recommended but not required.
Featuring: ADAM LAVERTU, doctoral candidate in the Stanford University program of Biomedical Informatics.
Friday, April 10, 5:30 p.m.–6:30 p.m. (PST)
Introduction to R Programming
R is a powerful tool for statistical computing and is widely used to analyze several types of biomedical datasets. In both industrial and academic research, basic R programming skill is usually the first step and a prerequisite for downstream analytical tasks. In this lecture, we will look at some examples of how the R environment can be used in the real world. We will then explore some of the essential concepts necessary to begin an introductory journey in that direction.
Featuring: BIBEK PAUDEL, PH.D.
Janani Rangarajan is a statistical data analyst at the Azzur Group and Gilead Sciences. She is chair of the UCSC Silicon Valley Extension Bioinformatics certificate program and a bioinformatics instructor.
Paul Saunders, Ph.D. is a biotechnology consultant, working on bioinformatics projects. Previously he worked for Chronomed Inc. as chief scientist developing point of care immunoassays and as a scientist at R&D Systems developing bioassay systems and recombinant bioactive signal transduction proteins. Saunders has a doctorate in Pharmacology and Toxicology and professional certificates in Genomic Data Science and Bioinformatics.
BIBEK PAUDEL, Ph.D.
Bibek Paudel, Ph.D, is a biomedical data scientist working as a postdoctoral research fellow at Stanford University. His research focuses on developing machine learning and statistical models for personalized medicine by combining biological domain knowledge and large heterogeneous datasets. He has worked extensively in research projects for text mining, large graph analytics, and social media analytics.
Adam Lavertu is a Ph.D. candidate in the Biomedical Informatics program at Stanford University. He currently conducts research in the Altman Lab on the genetics of drug metabolism in large population studies, as well as in other areas such as the use of natural language processing for identifying reports of adverse drug reactions in social media data. Formerly, he was a member of Stanford's Representations and Algorithms for Computational Molecular Biology teaching team, where he lectured on the algorithms behind sequencing read alignment and de novo genome assembly. He received his bachelor’s degree in Computational Biology from Colby College.
Larry Kalesinskas is a Ph.D. candidate in Biomedical Informatics at Stanford University advised by Purvesh Khatri. His work is focused on developing statistical and machine learning methods for use on single-cell epigenetic data to understand the effects of disease and aging on histone modifications. He has a bachelor’s degree in Bioinformatics and Biology from Loyola University Chicago where he worked on microbial metagenomic research.