1. http://cs273a.stanford.edu [BejeranoFall13/14] 1 MW 12:50-2:05pm in Beckman B302 Profs: Serafim Batzoglou & Gill Bejerano TAs: Harendra Guturu & Panos Achlioptas CS273A Lecture 4: NON-protein coding genes (ncRNA)

2. http://cs273a.stanford.edu [BejeranoFall13/14] 2 Announcements HW1 will be out by Friday. We’ll announce it over the mailing list/Piazza.

3. http://cs173.stanford.edu [BejeranoWinter12/13] 3 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

4. http://cs273a.stanford.edu [BejeranoFall13/14] 4 “non coding” RNAs (ncRNA)

5. 5 Central Dogma of Biology:

6. 6 Active forms of “non coding” RNA reverse transcription (of eg retrogenes) long non-coding RNA microRNA rRNA, snRNA, snoRNA

7. 7 What is ncRNA? Non-coding RNA (ncRNA) is an RNA that functions without being translated to a protein. Known roles for ncRNAs: –RNA catalyzes excision/ligation in introns. –RNA catalyzes the maturation of tRNA. –RNA catalyzes peptide bond formation. –RNA is a required subunit in telomerase. –RNA plays roles in immunity and development (RNAi). –RNA plays a role in dosage compensation. –RNA plays a role in carbon storage. –RNA is a major subunit in the SRP, which is important in protein trafficking. –RNA guides RNA modification. –RNA can do so many different functions, it is thought in the beginning there was an RNA World, where RNA was both the information carrier and active molecule.

8. http://cs273a.stanford.edu [BejeranoFall13/14] 8 “non coding” RNAs (ncRNA) Small structural RNAs (ssRNA)

9. 9 AAUUGCGGGAAAGGGGUCAA CAGCCGUUCAGUACCAAGUC UCAGGGGAAACUUUGAGAUG GCCUUGCAAAGGGUAUGGUA AUAAGCUGACGGACAUGGUC CUAACCACGCAGCCAAGUCC UAAGUCAACAGAUCUUCUGU UGAUAUGGAUGCAGUUCA ssRNA Folds into Secondary and 3D Structures Cate, et al. (Cech & Doudna). (1996) Science 273:1678. Waring & Davies. (1984) Gene 28: 277. Computational Challenge: predict 2D & 3D from sequence.

10. For example, tRNA

11. tRNA Activity

12. ssRNA structure rules Canonical basepairs: –Watson-Crick basepairs: G ≡ C A = U –Wobble basepair: G − U Stacks: continuous nested basepairs. (energetically favorable) Non-basepaired loops: –Hairpin loop –Bulge –Internal loop –Multiloop Pseudo-knots

13. Ab initio RNA structure prediction: lots of Dynamic Programming Objective: Maximizing the number of base pairs (Nussinov et al, 1978) simple model:  (i, j) = 1 iff GC|AU|GU fancier model: GC > AU > GU

14. Dynamic programming  Compute S(i,j) recursively (dynamic programming) –Compares a sequence against itself in a dynamic programming matrix  Three steps

15. Initialization Example: GGGAAAUCC GGGAAAUCC G0 G00 G00 A00 A00 A00 U00 C00 C00 the main diagonal the diagonal below L: the length of input sequence

16. Recursion GGGAAAUCC G0000 G00000 G00000 A0000? A0001 A0011 U0000 C000 C00 Fill up the table (DP matrix) -- diagonal by diagonal j i

17. Traceback GGGAAAUCC G000000123 G000000123 G00000122 A0000111 A000111 A00111 U0000 C000 C00 The structure is: What are the other “optimal” structures?

18. Pseudoknots drastically increase computational complexity

19. http://cs273a.stanford.edu [BejeranoFall13/14] 19 Objective: Minimize Secondary Structure Free Energy at 37 O C: Mathews, Disney, Childs, Schroeder, Zuker, & Turner. 2004. PNAS 101: 7287. Instead of  (i, j), measure and sum energies:

20. Zuker’s algorithm MFOLD: computing loop dependent energies

21. Bafna1 RNA structure Base-pairing defines a secondary structure. The base-pairing is usually non-crossing. In this example the pseudo-knot makes for an exception.

22. S  aSu S  cSg S  gSc S  uSa S  a S  c S  g S  u S  SS 1. A CFG S  aSu  acSgu  accSggu  accuSaggu  accuSSaggu  accugScSaggu  accuggSccSaggu  accuggaccSaggu  accuggacccSgaggu  accuggacccuSagaggu  accuggacccuuagaggu 2. A derivation of “accuggacccuuagaggu” 3. Corresponding structure Stochastic context-free grammar

23. Cool algorithmics. Unfortunately… – Random DNA (with high GC content) often folds into low-energy structures. – We will mention powerful newer methods later on.

24. ssRNA transcription ssRNAs like tRNAs are usually encoded by short “non coding” genes, that transcribe independently. Found in both the UCSC “known genes” track, and as a subtrack of the RepeatMasker track http://cs273a.stanford.edu [BejeranoFall13/14] 24

25. http://cs273a.stanford.edu [BejeranoFall13/14] 25 “non coding” RNAs (ncRNA) microRNAs (miRNA/miR)

26. http://cs273a.stanford.edu [BejeranoFall13/14] 26 MicroRNA (miR) mRNA ~70nt~22nt miR match to target mRNA is quite loose.  a single miR can regulate the expression of hundreds of genes.

27. http://cs273a.stanford.edu [BejeranoFall13/14] 27 MicroRNA Transcription mRNA

28. http://cs273a.stanford.edu [BejeranoFall13/14] 28 MicroRNA Transcription mRNA

29. http://cs273a.stanford.edu [BejeranoFall13/14] 29 MicroRNA (miR) mRNA ~70nt~22nt miR match to target mRNA is quite loose.  a single miR can regulate the expression of hundreds of genes. Computational challenges: Predict miRs. Predict miR targets.

30. http://cs273a.stanford.edu [BejeranoFall13/14] 30 MicroRNA Therapeutics mRNA ~70nt~22nt miR match to target mRNA is quite loose.  a single miR can regulate the expression of hundreds of genes. Idea: bolster/inhibit miR production to broadly modulate protein production Hope: “right” the good guys and/or “wrong” the bad guys Challenge: and not vice versa.

31. http://cs273a.stanford.edu [BejeranoFall13/14] 31 Other Non Coding Transcripts

32. lncRNAs (long non coding RNAs) http://cs273a.stanford.edu [BejeranoFall13/14] 32 Don’t seem to fold into clear structures (or only a sub-region does). Diverse roles only now starting to be understood. Example: bind splice site, cause intron retention.  Hard to detect or predict function computationally (currently)

33. http://cs273a.stanford.edu [BejeranoFall13/14] 33 lncRNAs come in many flavors

34. X chromosome inactivation in mammals X XX Y X Dosage compensation

35. Xist – X inactive-specific transcript Avner and Heard, Nat. Rev. Genetics 2001 2(1):59-67

36. http://cs273a.stanford.edu [BejeranoFall13/14] 36 Transcripts, transcripts everywhere Human Genome Transcribed* (Tx) Tx from both strands* * True size of set unknown

37. Or are they? http://cs273a.stanford.edu [BejeranoFall13/14] 37

38. http://cs273a.stanford.edu [BejeranoFall13/14] 38 The million dollar question Human Genome Transcribed* (Tx) Tx from both strands* * True size of set unknown Leaky tx? Functional?

39. System output measurements We can measure non/coding gene expression! (just remember – it is context dependent) 1. First generation mRNA (cDNA) and EST sequencing: http://cs273a.stanford.edu [BejeranoFall13/14] 39 In UCSC Browser:

40. 2. Gene Expression Microarrays (“chips”) http://cs273a.stanford.edu [BejeranoFall13/14] 40

41. 3. RNA-seq “Next” (2nd) generation sequencing. http://cs273a.stanford.edu [BejeranoFall13/14] 41

42. http://cs273a.stanford.edu [BejeranoFall13/14] 42 Gene Finding II: technology dependence Challenge: “Find the genes, the whole genes, and nothing but the genes” We started out trying to predict genes directly from the genome. When you measure gene expression, the challenge changes: Now you want to build gene models from your observations. These are both technology dependent challenges. The hybrid: what we measure is a tiny fraction of the space-time state space for cells in our body. We want to generalize from measured states and improve our predictions for the full compendium of states.

43. 4. Spatial-temporal maps generation http://cs273a.stanford.edu [BejeranoFall13/14] 43

44. Cluster all genes for differential expression http://cs273a.stanford.edu [BejeranoFall13/14] 44 Most significantly up-regulated genes Unchanged genes Most significantly down-regulated genes Experiment Control (replicates) genes

45. Determine cut-offs, examine individual genes http://cs273a.stanford.edu [BejeranoFall13/14] 45 Most significantly up-regulated genes Unchanged genes Most significantly down-regulated genes Experiment Control (replicates) genes

46. Genes usually work in groups Biochemical pathways, signaling pathways, etc. Asking about the expression perturbation of groups of genes is both more appealing biologically, and more powerful statistically (you sum perturbations). http://cs273a.stanford.edu [BejeranoFall13/14] 46

47. ES/NES statistic + - Exper. Control Gene Set 1 Gene Set 2 Gene Set 3 Gene set 3 up regulated Gene set 2 down regulated Ask about whole gene sets http://cs273a.stanford.edu [BejeranoFall13/14]

48. TJL-200448 The Gene Sets to test come from the Ontologies

49. One approach: GSEA http://cs273a.stanford.edu [BejeranoFall13/14] 49 Dataset distribution Number of genes Gene Expression Level Gene set 3 distribution Gene set 1 distribution

50. Another popular approach: DAVID http://cs273a.stanford.edu [BejeranoFall13/14] 50 Input: list of genes of interest (without expression values).

51. Multiple Testing Correction http://cs273a.stanford.edu [BejeranoFall13/14] 51 Note that statistically you cannot just run individual tests on 1,000 different gene sets. You have to apply further statistical corrections, to account for the fact that even in 1,000 random experiments a handful may come out good by chance. (eg experiment = Throw a coin 10 times. Ask if it is biased. If you repeat it 1,000 times, you will eventually get an all heads series, from a fair coin. Mustn’t deduce that the coin is biased) run tool

52. What will you test? http://cs273a.stanford.edu [BejeranoFall13/14] 52 Also note that this is a very general approach to test gene lists. Instead of a microarray experiment you can do RNA-seq. Instead of up/down-regulated genes you can test all the genes in a personal genome where you see “bad” mutations. Any gene list can be tested. run tool

53. Coding and non-coding gene production http://cs273a.stanford.edu [BejeranoFall13/14] 53 The cell is constantly making new proteins and ncRNAs. These perform their function for a while, And are then degraded. Newly made coding and non coding gene products take their place. The picture within a cell is constantly “refreshing”. To change its behavior a cell can change the repertoire of genes and ncRNAs it makes.

54. Cell differentiation http://cs273a.stanford.edu [BejeranoFall13/14] 54 To change its behavior a cell can change the repertoire of genes and ncRNAs it makes. That is exactly what happens when cells differentiate during development from stem cells to their different final fates.

55. Human manipulation of cell fate http://cs273a.stanford.edu [BejeranoFall13/14] 55 To change its behavior a cell can change the repertoire of genes and ncRNAs it makes. We have learned (in a dish) to: 1 control differentiation 2 reverse differentiation 3 hop between different states

56. Cell replacement therapies http://cs273a.stanford.edu [BejeranoFall13/14] 56 We want to use this knowledge to provide a patient with healthy self cells of a needed type. We have learned (in a dish) to: 1 control differentiation 2 reverse differentiation 3 hop between different states (iPS = induced pluripotent stem cells)

57. How does this happen? http://cs273a.stanford.edu [BejeranoFall13/14] 57 Different cells in our body hold copies of (essentially) the same genome. Yet they express very different repertoires of proteins and non-coding RNAs. How do cells do it? A: like they do everything else: using their proteins & ncRNAs…

58. Gene Regulation http://cs273a.stanford.edu [BejeranoFall13/14] 58 Some proteins and non coding RNAs go “back” to bind DNA near genes, turning these genes on and off. Gene DNA Proteins To be continued…

59. Inferring Gene Expression Causality Measuring gene expression over time provides sets of genes that change their expression in synchrony. But who regulates whom? Some of the necessary regulators may not change their expression level when measured, and yet be essential. “Reading” enhancers can provide gene regulatory logic: If present(TF A, TF B, TF C) then turn on nearby gene X http://cs273a.stanford.edu [BejeranoFall13/14] 59

60. Review Lecture 6 Central dogma recap –Gene s, proteins and non coding RNAs RNA world hypothesis Small structural RNAs –Sequence, structure, function –Structure prediction –Transcription mode MicroRNAs –Functions –Modes of transcription lncRNAs –Xist Genome wide (and context wide) transcription –How much? –To what goals? Gene transcription and cell identity –Cell differentiation –Human manipulation of cell fates Gene regulation control http://cs273a.stanford.edu [BejeranoFall13/14] 60