stat115 stat225 bist512 bio298 - intro to computational biology

STAT115 STAT225 BIST512 BIO298 - Intro to Computational Biology


March 28, 2012

Daniel Fernandez

Alejandro Quiroz


1st ACTInformation theory correction

Motif Finding

The Genome Browser

Homework help Q1, Q2

INTERLUDEElectronic music with DJ Cistrome (10 min)

2nd ACTDah Cistrome

MA2C

Homework help Q3


Information Theory


Information TheoryThe amount of information transmitted through the channel is the same as the entropy (or uncertainty) associated with the source.

I.e., it is maximized when the source can produce n possible outcomes, all with equal probability (1/n). Then, the entropy is log2(n).

Thus, biologists took this concept and used it to characterize the amount of uncertainty associated with a motif, represented as a PWM. But, your TF got confused… see why!


Information Theory

INFORMATIONENTROPY

Source channel destination

ATCG

1 1 1 1 1 1 1 1 1


Information TheoryBut what happens when we want to compare the uncertainty between two sources?Or the comparison between two probability distributions, i.e, the background sequence PWM and the motif PWM?

RELATIVE ENTROPY, or, KULLBACK-LEIBLER DIVERGENCE, or

INFORMATION CONTENT


Motif Example IProkaryotic Co-expression

Objective. Find the binding sites that control the gene regulation of co-expressed genes in Mycobacterium Tuberculosis.

File. mt.fasta

Note. We assume that genes are co-expressed because they are under the control of the same transcription factor(s), and we use Gibbs sampling to try to identify the putative binding motif for this factor(s).



Motif parameters are designed to capture the features of binding sites for a classic bacterial helix-turn-helix (HTH) type transcription factor.

HTH-type TFs are typically symmetric homodimers, thus they bind to symmetric (palindromic) DNA binding sites.

Furthermore, the two HTH regions of the dimeric TF typically contact bases in two adjacent major grooves of the DNA, and thus the two halves of the palindromic binding site span well over 10 bases (the approximate number of bases per helical turn of B-form DNA).

The bases contacted by a TF are not necessarily contiguous, thus we use fragmentation to allow the Gibbs sampler to ignore positions which do not participate in the protein-DNA interaction, and are therefore not conserved as part of the binding site.

To understand what I am saying: http://melolab.org/pdidb/web/content/home search 1lmb



http://ai.stanford.edu/~xsliu/BioProspector/

http://weblogo.berkeley.edu/logo.cgi


DNA as Herederitary Material


Central Dogma of Molecular Biology

Gene Expression

Splicing


The Human Genome Project• The goal is to understand the human

genome and its role in health and disease.– “The true payoff from the HGP will be the

ability to better diagnose, treat and prevent disease”

• Francis Collins. Director of the HGP and NHGRI


Sequencing

• Thousands of researchers from 20 centers worked on the HGP

Assembly• The sequence existed as millions of clones of small

fragments• Finding overlaps and putting together “contigs” was a

huge challenge

Annotation• What does it all mean?• Where are the genes?• What do they do?


UCSC Genome browser

• http://genome.ucsc.edu/


Basic Features

• Species, assemblies

• Genome browser

• Gene sorter

• Sequence search (BLAT)

Advanced Features• Coordinate conversion

• Custom tracks

• Table Browser


UCSC Genome Browser• Consists of a suite of tools for the viewing

and mining of genomic data.


Organization of Genomic Data


Genome Gatewaystart page, basic search


Overview of the browser


The browser


Genome Gatewaystart page, basic search

Genome version Chromosome/regionGeneCytogenetic coordinatesPhenotype of interestKey words: Zinc fingers, kinase

Try the following example: AutismHow many UCSC genes are located on chromosome X?How many RefSeq are associated with Autism?

Pick the gene: AUTS2 (uc011keg.1) at chr7:70231248-70257884


base positionbase position

Gene annotationGene annotation

Tracks!Where we obtain information

Tracks!Where we obtain information


UCSC Table Browser• Retrieve the data associated with a track

in text format– To calculate intersections between tracks– To retrieve DNA sequence covered by a track.


Hhelp Q2

• How many RefSeq genes have more than 15 exons in human chromosome 1?

• How many genes on chromosome 22, on the positive strand, are associated with a disease on the OMIM db?


The CistromeUnderstanding Genetic Regulation

• CisTrOme, stands for Cis-acting regulatory elements searched across, Trans, the whole genOme. – Visit and register at http://cistrome.org/

• The objective is to map/identify the binding regions of a transcription factor across (trans) the genome in order to understand the regulatory mechanisms of gene expression in the chromosome where the gene is located (cis).


Types of Data and Peak –Calling Methods

• Chip-Chip data (Chip on Chip)

– Affymetrix one color arrays

– Nimble two color arrays

• Chip-Seq data (Chip and NGS)

– Sequencing data

(Illumina, Roche, 454)

MACSModel based

Analysis for Chip-Seq

MA2CModel based

Analysis for 2-Color arrays

MATModel based

Analysis for Tiling arrays


MA2C – Hhelp Q3 Model based Analysis for 2-Color arrays

• http://liulab.dfci.harvard.edu/MA2C/MA2C.htm

• Installation. You need Java Runtime Environment (JRE) 5.0 or higher. You can download it from http://java.sun.com

• Download the MA2C.zip and uncompress it.– Windows: open MA2C\dist\

MA2C.bat– Go to the terminal and then

MA2C/dist/ and execute the command java –Xmx600m –jar MA2C.jar (or just double click on MA2C.jar)


MA2CData Normalization

• Download the data from the homework – SDC3 zip file

• Uncompress it and open MA2C

• Upload the SampleKeyIVtoX.txt to the sample key

• Select your control group (IP channel)

• Go to normalization tab and normalize your data – default parameters are ok.


MA2CPeak Finding

• Go to the peak-detection tab.• Change the parameters accordingly• Select find peaks• Voila! the results have been ouputed to the MA2C_output

folder!

stat115 stat225 bist512 bio298 - intro to computational biology

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