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CSCI2950-‐C Topics in Computa8onal Biology

Lecture 1

Ben Raphael September 12, 2011

hDp://cs.brown.edu/courses/csci2950-‐c/

Topics in Computa8onal Biology

Computer Science

Mathema8cs & Sta8s8cs

Biology

Computa(onal biology: develop computa8onal techniques to solve biological problems.

Computa(onal molecular biology: develop computa8onal techniques to solve biological problems related to DNA, RNA, and/or proteins.

Ecology? Neuroscience? Botany?

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Technology Transforming Biology Un8l late 20th Century

Hypothesis Genera8on and Valida8on

21th Century and Beyond

High throughput technologies

Hypothesis Genera8on and Valida8on

Algorithms

Human Genome Sequenced 2000-‐2003

June 26, 2000

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Biology 101

Molecular Biology 101

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Central Dogma


Three fundamental molecules. 1)  DNA –  Informa8on storage.

2)  RNA –  Old view: Mostly a

“messenger”. –  New view: Performs many

important func8ons. 3)  Proteins –  Perform most cellular func8ons

(biochemistry, signaling, control, etc.)

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DNA

…ACGTGACTGAGGACCGTG CGACTGAGACTGACTGGGT CTAGCTAGACTACGTTTTA TATATATATACGTCGTCGT ACTGATGACTAGATTACAG ACTGATTTAGATACCTGAC TGATTTTAAAAAAATATT…

Double helix Two strands.

Each strand composed of sequence of covalently bonded nucleo(des (bases). Four possible nucleo8des: A, C, T, G

DNA sequence: string from 4 character alphabet

DNA 3’ 5’

5’ 3’

Bases (nucleo(des) in each strand are complementary: A ßà C, T ßàG Watson-‐Crick base-‐pairing

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RNA •  RNA: single stranded, 4 leDer alphabet: A, C, U, G (U = T)

•  Can fold into structures due to base complementarity.

•  Many classes of RNA –  mRNA (messenger: transcrip8on

•  DNA à RNA à protein) –  tRNA (transfer RNA: transla8on

•  mRNA à protein –  miRNA –  snoRNA –  piwiRNA –  Etc…

Protein

•  String of amino acids: 20 leDer alphabet

•  Folds into 3D structures to perform various func8ons in cells

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Molecules of DNA, RNA, and protein interact with each other in 8me and space

Computer Science! algorithms on strings, trees, graphs

Molecules of DNA, RNA, and protein are built from an “alphabet” of “characters”.

Computa8onal Molecular Biology

1.  Measurement/Technology. 2.  Comparison. 3.  Predic8on.

For DNA, RNA, protein:

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Measurement Derive the DNA sequence of a human. Which RNA molecules or proteins are

present in a cell at a given 8me?

No technology to answer such ques8ons exactly.


Measurement Technology + Algorithms

Biological knowledge

Genome Sequencing and Comparison

Human Genome Project

“Next-‐genera8on” DNA sequencing

DNA sequencing

… ACGTATTAGG …

Species

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Comparison What are the similari8es/difference

between sequences? e.g. from human and chimpanzee? [String comparison ßà Algorithms]



Predic8on What differences in DNA determine

specific traits? [Gene8cs] Which muta8ons in DNA cause cancer? Does a protein bind to DNA? To what sequence? How does the sequence of RNA or protein determine

its 3D structure? Do two proteins bind/interact? …


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Why Study Computa8onal Biology?

Interdisciplinary Biology Computer Science Mathema8cs Sta8s8cs

= FUN!

WSJ: Jan, 26, 2009.

Why choose just 1?

Course goals

•  Learn applica8ons of computer science, mathema8cs, and sta8s8cs to biology.

•  Learn some new CS/mathema8cs. •  Learn to read and cri8que research papers. – Understand how to formulate a biological problem as a computa8onal problem.

•  Undertake a research project –  Course project: Use your skills to solve a biological problem.

•  Write a proposal (NIH style)

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Course Organiza8on

Seminar style 1) Introductory lectures by me: 30-‐50% of class mee8ngs. 2) Student-‐led paper presenta8ons and discussion. 50-‐70%

3) One computer assignment: next-‐gen sequencing data. 4) Project. Groups of 1-‐3 students. Proposal: NIH style Ini8al proposal Midterm report Final report Project presenta8on

Course Organiza8on See handout •  Prerequisites •  Grading •  Department Requirements

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Topics in Computa8onal Biology

Networks Cancer

Genomes




DNA sequencing

… ACGTATTAGG …

Species

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DNA Replica8on and Muta8on

… CGTAATTAG …

… CGTCATTAG …

Species

Gene

8c

Soma8

c

Compara8ve genomics Differences between species?


Personal genomics Gene8c basis for traits of individuals?

Cancer genomics Which soma8c muta8ons lead to cancer?

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Course Topics

1.   Measurement of genomes

Human genome: ~3 billion leDers

Algorithms

Next-‐genera(on DNA sequencing Genome and Muta(ons

10-‐100’s million reads: 30-‐1000 leDers

… GGTAGTTAG …

… TATAATTAG …

… AGCCATTAG …

… CGTACCTAG …

… CATTCAGTAG …

… GGTAAACTAG …

Course Topics

Genome Millions -‐billions nucleo8des

Next-‐genera8on DNA sequencing

10-‐100’s million reads Reads: 30-‐1000 nucleo8des

… GGTAGTTAG …

… TATAATTAG …

… AGCCATTAG …

… CGTACCTAG …

… CATTCAGTAG …

… GGTAAACTAG …

1. “Resequencing” algorithms (also RNA-‐Seq, ChIP-‐Seq, *-‐Seq)

2. De novo assembly algorithms

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Course Topics

gene

Algorithms

Iden8fy func8onal variants

Cancer gene8cs Which soma8c muta8ons lead to cancer?


2. Interpreta(on of genomes

Course Topics

3. Beyond the genome: epigene8cs


Algorithms

Next-‐genera(on DNA sequencing Genome Modifica(ons

DNA Methyla(on Packaging in chroma(n

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Three fundamental molecules. 1)  DNA –  Informa8on storage.

2)  RNA –  Old view: Mostly a

“messenger”. –  New view: Performs many

important func8ons. 3)  Proteins –  Perform most cellular func8ons

(biochemistry, signaling, control, etc.)

What molecules can we measure?

Sequencing (expensive, high quality) Hybridiza8on (inexpensive,lower qual)

Sequencing (expensive, high qual) Hybridiza8on (noisy, lower qual)

Mass spectrometry (low quality) Hybridiza8on (very low quality!)

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DNA sequencing

How we obtain the sequence of nucleo8des of a species

…ACGTGACTGAGGACCGTG CGACTGAGACTGACTGGGT CTAGCTAGACTACGTTTTA TATATATATACGTCGTCGT ACTGATGACTAGATTACAG ACTGATTTAGATACCTGAC TGATTTTAAAAAAATATT…

DNA Sequencing Goal: Find the complete sequence of A, C, G, T’s in a DNA molecule. Human genome: String of 3 billion nucleo8des.

Challenge: There is no machine that takes long DNA as an input, and gives the complete sequence as output

Can only sequence <1000 leDers at a 8me

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Shotgun Sequencing

cut many times at random (shotgun)

genomic segment

Get one or two reads from each

segment ~500 bp ~500 bp

Fragment Assembly

Cover region with ~N-fold redundancy Overlap reads and extend to reconstruct the

original genomic region

reads

First Topic: Algorithms to solve fragment assembly problem

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Progress in Genome Sequencing

454 Illumina ABI SOLiD



~500 bp ~500 bp

~25-100 bp ~25-100 bp

Old technology

New technologies

Progress in Genome Sequencing

454 Illumina ABI SOLiD



~500 bp ~500 bp

~25-100 bp ~25-100 bp

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“Third-‐genera8on” DNA Sequencing

ACGTGCATGxxxxxxxxxxxxxTGCTAGCTGGxxxxxxxxxxxxxxCCTATGGTA

subread subread subread advance advance 3-‐strobe

Strobe sequencing: Mul8ple reads per fragment

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Ritz, Bashir, and Raphael (2010). Bioinforma8cs.

Single Molecule Real Time (SMRT) Sequencing from Pacific Biosciences

Course Topics

gene

Algorithms


Cancer gene8cs Which soma8c muta8ons lead to cancer?



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Soma8c Muta8ons and Cancer

Time (cell divisions)

Passenger muta8ons

Driver muta8on

“typical tumor”: ~10 driver muta8ons 100’s – 1000’s of passenger muta8ons

Sequence genome

Founder cell

Cell po

pula8o

n

Clonal Theory (Nowell 1976)

Cancer Genomes

Breast

Leukemia

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Rearrangements in Tumors Change gene structure, create novel fusion genes

Gleevec (Novar8s 2001) targets ABL-‐BCR fusion

Func8onal Muta8ons

Next-‐genera8on DNA sequencing gene : soma8c muta8on

Recurrent muta8ons/mutated genes à func8onal

91 glioblastoma mul8forme samples

Page 16 N

IH-P

A A

uthor Manuscript

NIH

-PA

Author M

anuscript N

IH-P

A A

uthor Manuscript

Figure 1. Significant copy number aberrations and pattern of somatic mutations (a) Frequency and significance of focal high-level copy-number alterations. Known and putative target genes are listed for each significant CNA, with “Number of Genes” denoting the total number of genes within each focal CNA boundary. (b–c) Distribution of the number of (b) silent and (c) non-silent mutations across the 91 GBM samples separated according to their treatment status, showing hypermutation in 7 out of the 19 treated samples. (d) Significantly mutated genes in 91 glioblastomas. The eight genes attaining a false discovery rate <0.1 are displayed here. Somatic mutations occurring in untreated samples are in dark blue; those found in statistically non-hypermutated and hypermutated samples among the treated cohort are in respectively lighter shades of blue.

Nature. Author manuscript; available in PMC 2009 April 23.

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Recurrent Muta8ons

Cancer is a disease of “pathways”.

What pathways are altered/mutated?

36%

2%

6%

18%

21%

3% 2% 5% 3%

4%

[Hanahan and Weinberg, Cell 2000]

Biological Interac8on Networks Many types: •  Protein-‐DNA (regulatory)

•  Protein-‐metabolite (metabolic)

•  Protein-‐protein (signaling)

•  RNA-‐RNA (regulatory)

•  Gene8c interac8ons (gene knockouts)

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Protein-‐Protein Interac8on Network?

•  Proteins are nodes •  Interac8ons are edges •  Edges may have weights

Yeast PPI network

H. Jeong et al. Nature 411, 41 (2001)

Problem Given:

1.  Large-‐scale interac8on network 2.  Muta8on data from mul8ple cancer

pa8ents Find: Subnetworks mutated in a significant number of samples

gene

= muta8on

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Soma8c Muta8ons and Cancer

Time (cell divisions)

Passenger muta8ons

Driver muta8on

“typical tumor”: ~10 driver muta8ons 100’s – 1000’s of passenger muta8ons

Sequence genome

Founder cell

Cell po

pula8o

n

Clonal Theory (Nowell 1976)

Given: cross-‐sec8onal data from many individuals. Goal: Infer order of muta8ons.

Course Topics

gene

Algorithms




Cases

Controls

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Course Topics

3. Beyond the genome: epigene8cs


Algorithms

Next-‐genera(on DNA sequencing Genome Modifica(ons

DNA Methyla(on Packaging in chroma(n

Course Topics

Machine Learning

Networks Cancer

Genomes

Algorithms

Systems/Programming

Data Mining/Analysis/Visualiza8on

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Reading

•  Biology background. •  Probability background. •  DNA sequencing technologies •  See course website.

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