1. STAT115 STAT215 BIO512 BIST298 Introduction to Computational Biology and Bioinformatics Spring 2015
Xiaole Shirley Liu
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2. 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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3. 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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4. The Microarray Wave
Microarray contains hundreds to millions of tiny probes
Simultaneously detect how much each gene is expressed
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5. ALL vs AML
Golub et al, Science 1999.
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6. ALL vs AML
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7. “Microarrays” Today
Infer the expression value of all the genes from 1000 probes
High throughput drug screen
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8. The DNA Sequencing Wave
1953: DNA structure
1972: Recombinant DNA
1977: Sanger sequencing
1985: PCR
1988: NCBI
1990: BLAST
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9. Sequencing in the 1970s
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10. The Human Genome Race Human Genome Project: 1990-2003
Originally
Boosted by technology improvement and automation
Competition from Celera
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11. Human Genome Sequencing
Clone-by-clone and whole-genome shotgun
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12. The Human Genome Race Human Genome Project: 1990-2003
Originally
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
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13. Sequencing in 2001
[Enter any extra notes here; leave the item ID line at the bottom]
Avitage Item ID: {{CE8AAEAA-A22F-47FE-A1F8-66CBC3CDB6FC}}

14. Sequencing in 2007
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Avitage Item ID: {{010D7619-E070-4F7B-BC AA639C8D}}

15. 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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16. Personalized Disease Susceptibility Test and Treatment
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17. Big Data Challenges
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18. --- Sydney Brenner 2002 Nobel Prize
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

19. STAT115

20. Class Information Course website: Roughly 3 modules (2 HW each)
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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21. 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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22. HW and Grading Discussion forum: stat115.slack.com
Submission
HW 6 * 10 or 6 * 12
Final exams 20
Class participation: 20
Algorithm videos: 5
Lecture notes: extra 5 points
Late days
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23. STAT115

24. Gene Expression Microarrays

25. 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

26. Affymetrix GeneChip Arrays

27. Labeled Samples Hybridize to DNA Probes on GeneChip

28. Shining Laser Light Causes Tagged Fragments to Glow

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

30. NimbleGen Arrays

31. Agilent Arrays

32. 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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