1. http://cs273a.stanford.edu [BejeranoFall14/15] 1 MW 12:50-2:05pm in Beckman B100 Profs: Serafim Batzoglou & Gill Bejerano CAs: Jim Notwell & Sandeep Chinchali CS273A Lecture 2: Protein Coding Genes

2. http://cs273a.stanford.edu [BejeranoFall14/15] 2 Announcements http://cs273a.stanford.edu/ – Course guidelines, office hours, etc. – Lecture 1 is posted – Problem set 1 rolls out next week Course communications via Piazza – Auditors please sign up too The first tutorial this Friday in Beckman B-200 from 1:00pm-2:00pm. It's the only one some students should consider skipping. While they may be familiar with the first half of the Molecular Biology 101 lecture, we also cover gene regulation and genome rearrangements. CAs will be sending out a Doodle poll via Piazza to identify ideal times for office hours. Students can contact them if they have any questions.

3. http://cs273a.stanford.edu [BejeranoFall14/15] 3 Class Goals Meet your genome (learn to surf, learn the surf) Understand genomic tools (theory, applications) DIY (pose questions, write & run tools, understand answers)

4. http://cs273a.stanford.edu [BejeranoFall14/15] 4 Class Topics (0) Genome context: cells, DNA, central dogma (1) Genome content / genome function: genes, gene regulation, repeats, epigenetics (2) Genome sequencing: technologies, assembly/analysis, technology dependence (3) Genome evolution: evolution = mutation + selection, modes of evolution, comparative genomics, ultraconservation, exaptation (4) Population genomics: Tracking human migration patterns via neutral evolution (5) Genomics of human disease: disease susceptibility, cancer genomics, personal genomics (6) Genome “output” (organism) evolution: Evolutionary developmental biology (“evo-devo”)

5. http://cs273a.stanford.edu [BejeranoFall14/15] 5 TTATATTGAATTTTCAAAAATTCTTACTTTTTTTTTGGATGGACGCAAAGAAGTTTAATAATCATATTACATGGCATTACCACCATATA CATATCCATATCTAATCTTACTTATATGTTGTGGAAATGTAAAGAGCCCCATTATCTTAGCCTAAAAAAACCTTCTCTTTGGAACTTTC AGTAATACGCTTAACTGCTCATTGCTATATTGAAGTACGGATTAGAAGCCGCCGAGCGGGCGACAGCCCTCCGACGGAAGACTCTCCTC CGTGCGTCCTCGTCTTCACCGGTCGCGTTCCTGAAACGCAGATGTGCCTCGCGCCGCACTGCTCCGAACAATAAAGATTCTACAATACT AGCTTTTATGGTTATGAAGAGGAAAAATTGGCAGTAACCTGGCCCCACAAACCTTCAAATTAACGAATCAAATTAACAACCATAGGATG ATAATGCGATTAGTTTTTTAGCCTTATTTCTGGGGTAATTAATCAGCGAAGCGATGATTTTTGATCTATTAACAGATATATAAATGGAA AAGCTGCATAACCACTTTAACTAATACTTTCAACATTTTCAGTTTGTATTACTTCTTATTCAAATGTCATAAAAGTATCAACAAAAAAT TGTTAATATACCTCTATACTTTAACGTCAAGGAGAAAAAACTATAATGACTAAATCTCATTCAGAAGAAGTGATTGTACCTGAGTTCAA TTCTAGCGCAAAGGAATTACCAAGACCATTGGCCGAAAAGTGCCCGAGCATAATTAAGAAATTTATAAGCGCTTATGATGCTAAACCGG ATTTTGTTGCTAGATCGCCTGGTAGAGTCAATCTAATTGGTGAACATATTGATTATTGTGACTTCTCGGTTTTACCTTTAGCTATTGAT TTTGATATGCTTTGCGCCGTCAAAGTTTTGAACGATGAGATTTCAAGTCTTAAAGCTATATCAGAGGGCTAAGCATGTGTATTCTGAAT CTTTAAGAGTCTTGAAGGCTGTGAAATTAATGACTACAGCGAGCTTTACTGCCGACGAAGACTTTTTCAAGCAATTTGGTGCCTTGATG AACGAGTCTCAAGCTTCTTGCGATAAACTTTACGAATGTTCTTGTCCAGAGATTGACAAAATTTGTTCCATTGCTTTGTCAAATGGATC ATATGGTTCCCGTTTGACCGGAGCTGGCTGGGGTGGTTGTACTGTTCACTTGGTTCCAGGGGGCCCAAATGGCAACATAGAAAAGGTAA AAGAAGCCCTTGCCAATGAGTTCTACAAGGTCAAGTACCCTAAGATCACTGATGCTGAGCTAGAAAATGCTATCATCGTCTCTAAACCA GCATTGGGCAGCTGTCTATATGAATTAGTCAAGTATACTTCTTTTTTTTACTTTGTTCAGAACAACTTCTCATTTTTTTCTACTCATAA CTTTAGCATCACAAAATACGCAATAATAACGAGTAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGA TAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTT GGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAG...TTGCGAA GTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAA TGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGA TACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGT TCTTGGCAAGTTGCCAACTGACGAGATGCAGTTTCCTACGCATAATAAGAATAGGAGGGAATATCAAGCCAGACAATCTATCATTACAT TTAAGCGGCTCTTCAAAAAGATTGAACTCTCGCCAACTTATGGAATCTTCCAATGAGACCTTTGCGCCAAATAATGTGGATTTGGAAAA AGAGTATAAGTCATCTCAGAGTAATATAACTACCGAAGTTTATGAGGCATCGAGCTTTGAAGAAAAAGTAAGCTCAGAAAAACCTCAAT ACAGCTCATTCTGGAAGAAAATCTATTATGAATATGTGGTCGTTGACAAATCAATCTTGGGTGTTTCTATTCTGGATTCATTTATGTAC AACCAGGACTTGAAGCCCGTCGAAAAAGAAAGGCGGGTTTGGTCCTGGTACAATTATTGTTACTTCTGGCTTGCTGAATGTTTCAATAT CAACACTTGGCAAATTGCAGCTACAGGTCTACAACTGGGTCTAAATTGGTGGCAGTGTTGGATAACAATTTGGATTGGGTACGGTTTCG TTGGTGCTTTTGTTGTTTTGGCCTCTAGAGTTGGATCTGCTTATCATTTGTCATTCCCTATATCATCTAGAGCATCATTCGGTATTTTC TTCTCTTTATGGCCCGTTATTAACAGAGTCGTCATGGCCATCGTTTGGTATAGTGTCCAAGCTTATATTGCGGCAACTCCCGTATCATT AATGCTGAAATCTATCTTTGGAAAAGATTTACAATGATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGT TCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATG TTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATA CCTATTCTTGACATGATATGACTACCATTTTGTTATTGTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATG TTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTA AGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGA TTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATA GTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATG CTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACT TAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGAT TGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAAT

6. Organism – Cell - Genome http://cs273a.stanford.edu [BejeranoFall14/15] 6 10 13 different cells in an adult human. The cell is the basic unit of life. DNA = linear molecule inside the cell that carries instructions needed throughout the cell’s life ~ long string(s) over a small alphabet Alphabet of four (nucleotides/bases) {A,C,G,T} Strings of length 10 4 -10 11...ACGTACGACTGACTAGCATCGACTACGACTAGCAC... “instruction” Genome:

7. http://cs273a.stanford.edu [BejeranoFall14/15] 7 One Cell, One Genome, One Replication Every cell holds a copy of all its DNA = its genome. The human body is made of ~10 13 cells. All originate from a single cell through repeated cell divisions. cell genome = all DNA chicken ≈ 10 13 copies (DNA) of egg (DNA) chicken egg cell division DNA strings = Chromosomes

8. http://cs273a.stanford.edu [BejeranoFall14/15] 8 The Biggest Challenge in Genomics… … is computational: How does this encode this ProgramOutput This “coding” question has profound implications for our lives

9. http://cs273a.stanford.edu [BejeranoFall14/15] 9 Class Topics (0) Genome context: cells, DNA, central dogma (1) Genome content / genome function: genes, gene regulation, repeats, epigenetics (2) Genome sequencing: technologies, assembly/analysis, technology dependence (3) Genome evolution: evolution = mutation + selection, modes of evolution, comparative genomics, ultraconservation, exaptation (4) Population genomics: Tracking human migration patterns via neutral evolution (5) Genomics of human disease: disease susceptibility, cancer genomics, personal genomics (6) Genome “output” (organism) evolution: Evolutionary developmental biology (“evo-devo”)

10. TTATATTGAATTTTCAAAAATTCTTACTTTTTTTTTGGATGGACGCAAAGAAGTTTAATAATCATATTACATGGCATTACCACCATATA CATATCCATATCTAATCTTACTTATATGTTGTGGAAATGTAAAGAGCCCCATTATCTTAGCCTAAAAAAACCTTCTCTTTGGAACTTTC AGTAATACGCTTAACTGCTCATTGCTATATTGAAGTACGGATTAGAAGCCGCCGAGCGGGCGACAGCCCTCCGACGGAAGACTCTCCTC CGTGCGTCCTCGTCTTCACCGGTCGCGTTCCTGAAACGCAGATGTGCCTCGCGCCGCACTGCTCCGAACAATAAAGATTCTACAATACT AGCTTTTATGGTTATGAAGAGGAAAAATTGGCAGTAACCTGGCCCCACAAACCTTCAAATTAACGAATCAAATTAACAACCATAGGATG ATAATGCGATTAGTTTTTTAGCCTTATTTCTGGGGTAATTAATCAGCGAAGCGATGATTTTTGATCTATTAACAGATATATAAATGGAA AAGCTGCATAACCACTTTAACTAATACTTTCAACATTTTCAGTTTGTATTACTTCTTATTCAAATGTCATAAAAGTATCAACAAAAAAT TGTTAATATACCTCTATACTTTAACGTCAAGGAGAAAAAACTATAATGACTAAATCTCATTCAGAAGAAGTGATTGTACCTGAGTTCAA TTCTAGCGCAAAGGAATTACCAAGACCATTGGCCGAAAAGTGCCCGAGCATAATTAAGAAATTTATAAGCGCTTATGATGCTAAACCGG ATTTTGTTGCTAGATCGCCTGGTAGAGTCAATCTAATTGGTGAACATATTGATTATTGTGACTTCTCGGTTTTACCTTTAGCTATTGAT TTTGATATGCTTTGCGCCGTCAAAGTTTTGAACGATGAGATTTCAAGTCTTAAAGCTATATCAGAGGGCTAAGCATGTGTATTCTGAAT CTTTAAGAGTCTTGAAGGCTGTGAAATTAATGACTACAGCGAGCTTTACTGCCGACGAAGACTTTTTCAAGCAATTTGGTGCCTTGATG AACGAGTCTCAAGCTTCTTGCGATAAACTTTACGAATGTTCTTGTCCAGAGATTGACAAAATTTGTTCCATTGCTTTGTCAAATGGATC ATATGGTTCCCGTTTGACCGGAGCTGGCTGGGGTGGTTGTACTGTTCACTTGGTTCCAGGGGGCCCAAATGGCAACATAGAAAAGGTAA AAGAAGCCCTTGCCAATGAGTTCTACAAGGTCAAGTACCCTAAGATCACTGATGCTGAGCTAGAAAATGCTATCATCGTCTCTAAACCA GCATTGGGCAGCTGTCTATATGAATTAGTCAAGTATACTTCTTTTTTTTACTTTGTTCAGAACAACTTCTCATTTTTTTCTACTCATAA CTTTAGCATCACAAAATACGCAATAATAACGAGTAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGA TAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTT GGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTTGCGAAGTT CTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGT TTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATAC CTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGTTCT TGGCAAGTTGCCAACTGACGAGATGCAGTTTCCTACGCATAATAAGAATAGGAGGGAATATCAAGCCAGACAATCTATCATTACATTTA AGCGGCTCTTCAAAAAGATTGAACTCTCGCCAACTTATGGAATCTTCCAATGAGACCTTTGCGCCAAATAATGTGGATTTGGAAAAAGA GTATAAGTCATCTCAGAGTAATATAACTACCGAAGTTTATGAGGCATCGAGCTTTGAAGAAAAAGTAAGCTCAGAAAAACCTCAATACA GCTCATTCTGGAAGAAAATCTATTATGAATATGTGGTCGTTGACAAATCAATCTTGGGTGTTTCTATTCTGGATTCATTTATGTACAAC CAGGACTTGAAGCCCGTCGAAAAAGAAAGGCGGGTTTGGTCCTGGTACAATTATTGTTACTTCTGGCTTGCTGAATGTTTCAATATCAA CACTTGGCAAATTGCAGCTACAGGTCTACAACTGGGTCTAAATTGGTGGCAGTGTTGGATAACAATTTGGATTGGGTACGGTTTCGTTG GTGCTTTTGTTGTTTTGGCCTCTAGAGTTGGATCTGCTTATCATTTGTCATTCCCTATATCATCTAGAGCATCATTCGGTATTTTCTTC TCTTTATGGCCCGTTATTAACAGAGTCGTCATGGCCATCGTTTGGTATAGTGTCCAAGCTTATATTGCGGCAACTCCCGTATCATTAAT GCTGAAATCTATCTTTGGAAAAGATTTACAATGATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGTTCT TGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTT TCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCT ATTCTTGACATGATATGACTACCATTTTGTTATTGTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTT TCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGA GATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTA TCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTT CATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTT CAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAA TAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGT ATGATAATGTTTTCAATGTAAGAGATTTCGATTATCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATAAAG 10http://cs273a.stanford.edu [BejeranoFall14/15]

11. 11 Genomes, Genes & Proteins The most visible instructions in our genome are Genes. Genes explain exactly HOW to synthesize any protein. Proteins are the work horses of every living cell....ACGTACGACTGACTAGCATCGACTACGACTAGCAC... gene Genome: cell protein

12. Central Dogma of Biology genome

13. Translation: The Genetic Code 13 http://cs273a.stanford.edu [BejeranoFall14/15]

14. Gene Structure http://cs273a.stanford.edu [BejeranoFall14/15] 14

15. Gene Splicing 15 http://cs273a.stanford.edu [BejeranoFall14/15]

16. The gene centric genome 16 http://cs273a.stanford.edu [BejeranoFall14/15] “The Genetic code” A gene centric term. For a gene centric world. There are in fact a number of additional genetic codes encoded in our genome..

17. Visualizing Gene Structure http://cs273a.stanford.edu [BejeranoFall14/15] 17

18. Genes in the Human Genome 18 http://cs273a.stanford.edu [BejeranoFall14/15] There are ~20,000 protein coding genes in the human genome. (Even half way through sequencing the human genome, Researchers thought there will be well over 100,000 genes). UCSC primer

19. http://cs273a.stanford.edu [BejeranoFall14/15] 19 Gene Finding I: ab initio Computational Challenge: “Find the genes, the whole genes, and nothing but the genes” Understand Biology  Write discovery tools (Our) answer depends on our understanding, data & tools CS262 Winter

20. 20 Everything in Genomics is a Moving Target The genomes (ie, assemblies) Their annotations Our understanding of Biology The portals Conclusion: write code that can be run... and rerun

21. http://cs273a.stanford.edu [BejeranoFall14/15] 21 Gene (Protein really) Functions The most visible instructions in our genome are Genes. Genes explain exactly HOW to synthesize any protein. Proteins are the work horses of every living cell....ACGTACGACTGACTAGCATCGACTACGACTAGCAC... gene Genome: cell protein Just look at the cell. Lots and lots of different functions to perform. (“Only 20,000 genes”..)

22. http://cs273a.stanford.edu [BejeranoFall14/15] 22 First full draft of the Human Genome 2001 Human Genome Consortium (HGC) Celera

23. http://cs273a.stanford.edu [BejeranoFall14/15] 23 Biological Functions of the Human Gene Set [HGC, 2001] Focus on the X axis:

24. http://cs273a.stanford.edu [BejeranoFall14/15] 24 Molecular Functions of the Human Gene Set [Celera, 2001]

25. http://cs273a.stanford.edu [BejeranoFall14/15] 25 Gene Sets: Cataloging biological knowledge

26. 26 Keyword lists are not enough Sheer number of terms too much to remember and sort Need standardized, stable, carefully defined terms Need to describe different levels of detail So…defined terms need to be related in a hierarchy With structured vocabularies/hierarchies Parent/child relationships exist between terms Increased depth -> Increased resolution Can annotate data at appropriate level May query at appropriate level organ system embryo cardiovascular heart …… …… …… …… Anatomy Hierarchy Organ system Cardiovascular system Heart Anatomy keywords

27. TJL-200427 Annotate genes to most specific terms

28. 1. Annotate at appropriate level, query at appropriate level 2. Queries for higher level terms include annotations to lower level terms 28 General Implementations for Vocabularies organ system embryo cardiovascular heart …… …… …… …… Hierarchy DAG chaperone regulator molecular function chaperone activator … enzyme regulator enzyme activator … … Query for this term Returns things annotated to descendents

29. Gene Sets Gene Ontology (“GO”) –Biological Process –Molecular Function –Cellular Location Pathway Databases –KEGG –BioCarta –Broad Institute Multiple others

30. http://cs273a.stanford.edu [BejeranoFall14/15] 30 Genes & Their Functions Gene (DNA) sequence determines protein (AA) sequence, which determines protein (3D) structure, which determines protein’s function.

31. http://cs273a.stanford.edu [BejeranoFall14/15] 31 Protein Folding Protein folding is the challenge of deducing protein structure from protein sequence.

32. Gene Families, Gene Names 32 http://cs273a.stanford.edu [BejeranoFall14/15] Genes (proteins) come in families. Genes of the same family have similar sequences. Which is why the fold into similar structure and perform similar functions. Genes of the same family will typically have a “family name” followed by a (sequential) number or “first name”.

33. http://cs273a.stanford.edu [BejeranoFall14/15] 33 Biological vs. Molecular Function: Pathways Proteins with very different molecular functions participate to manifest a single biological function, for example: a pathway.

34. http://cs273a.stanford.edu [BejeranoFall14/15] 34 Some “Special” Functions: Gene Regulation Gene 2,000 different proteins can bind specific DNA sequences. Proteins that regulate the transcription of other proteins are called transcription factors. Proteins DNA Protein binding site

35. http://cs273a.stanford.edu [BejeranoFall14/15] 35 The Importance of Gene Regulation The looks & capabilities of different cells are determined by the subset of genes they express. Different cell types express very different gene repertoires (from the same genome). To change its behavior a cell can change its transcriptional program. Think of it as a giant state machine…

36. http://cs273a.stanford.edu [BejeranoFall14/15] 36 “Special” Function: Cell Signaling Cells also talk with each other. They send and receive messages, and change their behavior according to messages they receive.

37. http://cs273a.stanford.edu [BejeranoFall14/15] 37 Signal Transduction Now its an even bigger state machine of individual state machines (=cells) talking with each other, orchestrating their individual activities.

38. Machine Learning (NLP) Opportunities http://cs273a.stanford.edu [BejeranoFall14/15] 38 Data Source Genes Ontology Map genes to ontology using literature [Bejerano lab project] Data TypeContent Structured DataCurated DAGs & mappings Free TextAbstracts, Full Text & Tables DiagramsNovel models & mappings Unstructured DataRaw data repos & metadata current use GOAL: attain