Bioinformatics lecture notes
  • Introduction
  • Introduction to biology (for computer scientists)
  • Ethical considerations
  • Finding patterns in DNA
    • Introduction to pattern discovery
    • Looking for frequent k-mers
    • Leveraging biology
    • Finding genes
  • Exact string matching
    • Introduction to exact string matching
    • Semi-numerical matching
    • The Z algorithm
    • The KMP algorithm
  • Multiple sequence alignment
    • Introduction to multiple sequence alignment
    • Motif finding
  • String indexing
    • Introduction to string indexing
    • Introduction to suffix trees
    • Suffix trees: beyond the basics
    • Suffix arrays
    • The Burrows-Wheeler transform and the FM-index
  • Inexact alignment
    • Introduction to inexact alignment
    • Inexact alignment calculation with dynamic programming
    • Example: filling the dynamic programming table
    • Modeling alignment as a graph
    • Backtracking through the dynamic programming table
    • From edit distance to alignment scores
    • Local alignment
    • Exercises
  • Advanced inexact alignment
    • Gap penalties
    • Sequence alignment in linear space
    • Sequence alignment with bounded error
  • Proteomics data analysis
    • Introduction to proteomic data analysis
    • From peptides to theoretical spectra
    • Cyclopeptide sequencing
    • Dealing with errors in experimental spectra
  • Data clustering
    • Introduction to data clustering
    • K-means clustering
    • Hierarchical clustering
  • Phylogenetic analysis
    • Introduction to phylogenetic inference
    • Distance-based phylogenetic analysis
    • Trait-based phylogenetic inference
  • Sequence assembly
    • Introduction to sequence assembly
    • Graph formulations of sequence assembly
    • Finding Eulerian tours
  • Gene finding and annotation
    • Introduction to sequence annotation
    • Gene finding
    • Introduction to Hidden Markov Models
    • Taxonomic and functional annotation
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Introduction

NextIntroduction to biology (for computer scientists)

Last updated 3 months ago

This book will contain a collection of lecture notes largely based on undergraduate and graduate classes taught by at the University of Maryland. The goal is to eventually also include a collection of exercises/problem sets that can be used for formative or summative assessments.

Acknowledgements

A large part of the undergraduate curriculum described in the lecture notes was inspired by and 's book. Several lecture notes (particularly related to sequence alignment statistics and motif finding) are based on presentations by . Additional major contributions were made by Jacquelyn Michaelis while co-teaching an undergraduate bioinformatics class.

The initial version of the lecture notes was created with the help of students taking a graduate-level bioinformatics class:

Noam Auslander Joshua Brulé Hyongtae Cho Mark Daly Wikum Dinalankara Geet Duggal Ted Gibbons Jonathan Gluck Raul Guerra Chris M. Hill Sam Huang Assaf Magen Jesse Moll Koyel Mukherjee Emre Sefer Ferhan Ture Xiao Wang Derrick Wood

Mihai Pop
Pavel Pevzner
Philip Compeau
Bioinformatics Algorithms
Stephen Altschul