STAT115STAT215 BIO512 BIST298

Introduction to Computational Biology and Bioinformatics

Spring 2015

Xiaole Shirley Liu

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Bioinformatics and Computational Biology

• Interdisciplinary – Statistics, Biology, Computer Science

• Applied– From freshman to postdocs– Useful training for many– The more you practice, the better you get

• Moves with technology development

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The Protein Sequence and Structure Wave

• 1955: Sanger sequenced bovine insulin

• 1970: Smith-Waterman algorithm

• 1973: PDB

• 1990: BLAST

• 1994: BLOCKS database

• 1994-: CASP

• 1997-: Proteomics

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The Microarray Wave

• Microarray contains hundreds to millions of tiny probes

• Simultaneously detect how much each gene is expressed

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ALL vs AML

• Golub et al, Science 1999.

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ALL vs AML

“Microarrays” Today

• Infer the expression value of all the genes from 1000 probes

• High throughput drug screen

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The DNA Sequencing Wave

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• 1953: DNA structure

• 1972: Recombinant DNA

• 1977: Sanger sequencing

• 1985: PCR

• 1988: NCBI

• 1990: BLAST

Sequencing in the 1970s

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The Human Genome Race

• Human Genome Project: 1990-2003– Originally 1990-2005– Boosted by technology improvement and

automation– Competition from Celera

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Human Genome Sequencing• Clone-by-clone and whole-genome shotgun

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The Human Genome Race

• Human Genome Project: 1990-2003– Originally 1990-2005– Boosted by technology improvement and

automation– Competition from Celera

• Informatics essential for both the public and private sequencing efforts– Sequence assembly and gene prediction– Working draft finished simultaneously spring

2000

Sequencing in 2001

Sequencing in 2007

Sequencing Today

• Personal genome sequencing

• HiSeq X– 900GB data / flow cell

in < 3 days, 10 * 30X human genomes, at ~$1.5-2K / sample

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Personalized Disease Susceptibility Test and Treatment

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Big Data Challenges

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All biology is becoming computational, much the same way it has became

molecular … Otherwise “low input, high throughput and no output science”

--- Sydney Brenner

2002 Nobel Prize

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Class Information

• Course website: – http://stat115.org/ – Video recording / slides online– Office hours, auditing– Background: CS, Stats, Biology

• Roughly 3 modules (2 HW each)– Transcriptome (microarrays and RNA-seq)– Gene regulation (transcriptional & epigenetic

regulation)– Human genetics and disease (GWAS / cancer)

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Class Information

• Teaching Fellows

Yang Li Stephanie Chan

• Labs: Wed 6 – 8pm, Science Center B09 – Tue 6-8pm, HSPH Kresge 209, Boston– First Lab: Fri 1/30 3-5pm (Odyssey)!

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HW and Grading

• Discussion forum: stat115.slack.com

• Submission email: harvard.stat115@gmail.com

• HW 6 * 10 or 6 * 12

• Final exams 20

• Class participation: 20

• Algorithm videos: 5

• Lecture notes: extra 5 points

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Gene Expression Microarrays

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Expression Microarrays

• Grow cells at certain condition, collect mRNA population, and label them

• Microarray has high density (thousands to millions) sequence specific probes with known location for each gene/RNA

• Sample hybridized to microarray probes by DNA (A-T, G-C) base pairing, wash non-specific binding

• Measure sample mRNA value by checking labeled signals at each probe location

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Affymetrix GeneChip Arrays

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Labeled Samples Hybridize to DNA Probes on GeneChip

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Shining Laser Light CausesTagged Fragments to Glow

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Perfect Match (PM) vs MisMatch (MM)(control for cross hybridization)

NimbleGen Arrays

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Agilent Arrays

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Microarrays

• Array comparison:– # probes / array, # probes / gene, probe length– Flexibility vs data reuse

• Why do we bother learning about microarrays now?– RNA-seq is probably preferred in new

expression experiments– The amount of useful public data– The data analysis techniques

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