Analyzing Biological Datasets for Non-Computational Biologists

An introductory course by UCSanDiegoX

Course Description

Are you a biologist eager to dive into the world of data analysis but lack a strong computational background? This introductory course is designed specifically for you! It bridges the gap between biology and data analysis, focusing on next-generation sequencing and utilizing Illumina's BaseSpace platform for user-friendly workflow management.

What You'll Learn

  • Genome Assembly: Reconstruct genomes by piecing together tiny fragments
  • Variant Calling: Identify mutations in genomes and their potential links to genetic diseases
  • Trio Analysis: Investigate genetic makeup across generations
  • Differential Expression Analysis: Analyze gene expressions to identify important genes
  • Best-practice workflows for biological data analysis

Prerequisites

  • Basic understanding of the Central Dogma of molecular biology
  • No strong computational background required

Who This Course Is For

  • Non-computational biologists
  • Life science researchers
  • Geneticists and molecular biologists
  • Students in biology-related fields
  • Healthcare professionals interested in genomics

Real-World Applications

  • Pathogen research
  • Genetic disease diagnosis
  • Cancer research
  • Personalized medicine
  • Agricultural biotechnology
  • Forensic science
  • Environmental biology

Syllabus

Week 1: Assembling Genomes

  • Perform genome assembly using raw whole genome sequence data
  • Assess assembled genome quality
  • Conduct annotation and gene prediction
  • Explore basic comparative genomics

Week 2: Searching for Disease-Causing Mutations

  • Perform variant calling using whole genome and whole exome sequence data
  • Compare and contrast sequencing methods

Week 3: Will Modifications of Embryos Treat Genetic Diseases?

  • Conduct variant calling on parent and child genome data
  • Perform trio analysis to identify SNV sources and de novo mutations
  • Explore compound heterozygous traits

Week 4: Analyzing Gene Expression

  • Align RNA-Seq data from different samples
  • Count gene transcripts in samples
  • Perform pairwise differential expression analysis
  • Identify genes with significant expression changes