bioinformatics for stem cell lecture 1 debashis sahoo, phd

Bioinformatics for Stem CellLecture 1

Debashis Sahoo, PhD

Outline

• Introduction• History of Bioinformatics• Introduction to computing• Data collection• Experiment design• Data analysis

Bioinformatics Definition

• Biological Data– Representation– Storage– Access– Processing

• bi·o·in·for·mat·ics [bahy-oh-in-fer-mat-iks]– noun ( used with a singular verb ) the retrieval and

analysis of biochemical and biological data using mathematics and computer science, as in the study of genomes.

http://www.merriam-webster.com/dictionary/bioinformatics

http://www.ncbi.nlm.nih.gov/About/primer/bioinformatics.html

http://www.ncbi.nlm.nih.gov/About/primer/bioinformatics.html

The science behind Michael Levitt's Nobel PrizeMichael Levitt, PhD, has dramatically advanced the field of structural biology by developing sophisticated computer algorithms to build models of complex biological molecules.

“It is hard for me to say confidently that, after fifty more years of explosive growth of computer science, there will still be a lot of fascinating unsolved problems at peoples' fingertips, that it won't be pretty much working on refinements of well-explored things. I can't be as confident about computer science as I can about biology. Biology easily has 500 years of exciting problems to work on, it's at that level.”

Professor Donald E. Knuth

The "father" of the analysis of algorithms

He is the author of the seminal multi-volume work The Art of Computer Programming.

HISTORICAL PERSPECTIVE

History of Bioinformatics

• Gergor Mendel (1866, Verhandlungen des naturforschenden Vereins Brünn)

• 1951 – structure for the alpha-helix and beta-sheet– Pauling and Corey (PNAS – 1951)

• 1953 - double helix model for DNA– Watson and Crick (Nature, 171: 737-738, 1953)

• 1955 – protein sequence of bovine insulin– F. Sanger.

History of Bioinformatics

• 1958 – 1990– Revolution in Computer Science and Engineering

• Computer, email, network, internet

• 1990 – BLAST– Altschul, S.F., Gish, W., Miller, W., Myers, E.W. & Lipman, D.J. (1990) "Basic

local alignment search tool." J. Mol. Biol. 215:403-410.

• 1995 - The Haemophilus influenzea genome (1.8 Mb) is sequenced.

• 1993 – 2013 – Microarrays• 2005 – 2013 – High-throughput sequencing

INTRODUCTION TO COMPUTING

What is a computer?

1 0 0 1 0 1 1 0

Controller

Read/Write head

Tape

Turing Machine (1936)Alan Turing, "On computable numbers, with an application to the Entscheidungsproblem", Proceedings of the London Mathematical Society, Series 2, 42 (1937), pp 230–265.

Modern ComputerProcessorMain Memory Disk Drives

IO controller

Display Keyboard Mouse

What is a Computer Program?

Executable file

Load to Memory

Run the program

C ProgramAssembly Program

DATA COLLECTION

Public Databases

• Gene Expression Omnibus (GEO)• Array Express• National Center for Biotechnology Information

(NCBI)• UCSC Genome Browser• The human protein atlas• Catalogue of Somatic Mutations in Cancer – COSMIC• The Cancer Genome Atlas (TCGA)

http://www.ncbi.nlm.nih.gov/geo/

http://www.ebi.ac.uk/arrayexpress

http://www.ncbi.nlm.nih.gov/pubmed

http://genome.ucsc.edu

http://www.proteinatlas.org/

http://www.sanger.ac.uk/genetics/CGP/cosmic/

https://tcga-data.nci.nih.gov/tcga/

EXPERIMENT DESIGN

To call in the statistician after the experiment is done may be no more than asking him to perform a postmortem examination : he may be able to say what the experiment died of.

- R. A. Fisher

http://graphpad.com/guides/prism/5/user-guide/prism5help.html

Independent Samples

• Statistical tests are based on the assumption that each subject was sampled independently.

• Provides maximum amount of information.• Provides better estimation of the mean.

The Gaussian Approximation

Everybody believes in the normal approximation, the experimenters because they think it is a mathematical theorem, the mathematicians because they think it is an experimental fact.

G. Lippman (1845 – 1921)

Sample Size Estimation

DATA ANALYSIS

Correlation

Hypothesis Testing

• Randomly select samples from the population• State the null hypothesis

– Distribution of values in two different populations are the same

• Perform the statistical test– T test, F test, Chi-sq test

• Get P-value– Set a threshold (usually < 0.05) for significance

Multiple Comparisons

• The Bonferroni correction– P < 0.05/N (N = number of comparisons)

• False Discovery Rate (FDR) – Q value– What fraction of all the discoveries are false?– Q = 10%, N = 100, smallest p-value < Q/N– http://genomics.princeton.edu/storeylab/qvalue/– Permutation based approaches