Genome Sequencing & DNA Sequence Analysis

Transcription

1 7.91 / 7.36 / BE.490 Lecture #1 Feb. 24, 2004 Genome Sequencing & DNA Sequence Analysis Chris Burge

2 What is a Genome? A genome is NOT a bag of proteins

3 What s in the Human Genome?

4 Outline of Unit II: DNA/RNA Sequence Analysis Reading* 2/24 2/26 Genome Sequencing & DNA Sequence Analysis M Ch. 3 DNA Sequence Comparison & Alignment M Ch. 7 3/2 DNA Motif Modeling & Discovery M Ch. 4 3/4 Markov and Hidden Markov Models for DNA M Ch. 4 3/9 DNA Sequence Evolution M Ch. 6 3/11 RNA Structure Prediction & Applications M Ch. 5 3/16 Literature Discussion TBA * M = Mount, Bioinformatics: Sequence and Genome Analysis

5 Feedback to Instructor Examples from past years: Comic font looks stupid Burge uses too much genomics jargon Better synergy between Yaffe/Burge sections Asks questions to the class, student answers, but I didn t hear/understand the answer

6 DNA vs Protein Sequence Analysis Protein Sequence Analysis DNA Sequence Analysis - emphasis on chemistry - emphasis on regulation - protein structure - RNA structure - selection is everywhere - signal vs noise (statistics) - multiple alignment - motif finding - comparative proteomics - comparative genomics - data: O(10^8) aa - data: O(10^10) nt Read your probability/statistics primer!

7 Genome Sequencing & DNA Sequence Analysis The Language of Genomics Shotgun Sequencing - Progress: genomes, transcriptomes, etc. DNA Sequence Alignment I - How to choose a mismatch penalty Comparative Genomics Examples - PipMaker, Phylogenetic Shadowing

8 Recent Media Attention

9 Genomespeak Bork, Peer, and Richard Copley. " Genome Speak." Nature 409 (15 February 2001): 815. Learn to speak genomic

10 In the following article, note the use of the following genomic terms: euchromatic, whole-genome shotgun sequencing, sequence reads, 5.11-fold coverage, plasmid clones, whole-genome assembly, regional chromosome assembly. Venter, JC, MD Adams, EW Myers, PW Li, RJ Mural, GG Sutton, HO Smith, "The Sequence of The Human Genome." Science 291, no (16 February 2001):

11 Types of Nucleotides ribonucleotides deoxyribonucleotides dideoxyribonucleotides

12 DNA Sequencing Adapted from Fig. 4.2 of Genomes by T. A. Brown, John Wiley & Sons, NY, 1999

13 Shotgun Sequencing a BAC or a Genome Sonicate, Subclone 200 kb (NIH) 3 Gb (Celera) Subclones Sequence, Assemble Shotgun Contigs What would cause problems with assembly?

14 Shotgun Coverage (Poisson distribution) Sequence N reads, 500 bp each, from a 200kb BAC Coverage/read p = 500/200,000 = Total coverage C = Np Y = no. of reads covering the point x P(Y=k) = (N!/(N-k)!k!) p k (1-p) N-k e -c c k / k!. x P(Y=0)= e -c Examples: e e What could cause reality to differ from theory?

15 Clickable Genomes Eukaryotes Protists Eubacteria S. cerevisiae Plasmodium E. coli S. pombe Giardia B. subtilis C. elegans S. aureus Drosophila (several) Anopheles (>100) Ciona Archaea Arabidopsis Methanococcus Human Sulfolobus Phages/Viruses Mouse Lots Tetraodon (total of ~16) Fugu Zebrafish Neurospora Aspergillus Organelles Lots

16 Large-scale Transcript Sequencing Please see the following example article that uses large-scale transcript sequencing. Okazaki, Y, M Furuno, T Kasukawa, J Adachi, H Bono, S Kondo, "Analysis of The Mouse Transcriptome Based On Functional Annotation of 60,770 Full-length cdnas." Nature 420, no (5 December 2002):

17 EST Sequencing dbest release No. of public entries: 20,039,613 Summary by Organism - as of February 20, 2004 Homo sapiens (human) 5,472,005 Mus musculus + domesticus (mouse) 4,055,481 Rattus sp. (rat) 583,841 Triticum aestivum (wheat) 549,926 Ciona intestinalis 492,511 Gallus gallus (chicken) 460,385 Danio rerio (zebrafish) 450,652 Zea mays (maize) 391,417 Xenopus laevis (African clawed frog) 359,901 Hordeum vulgare + subsp. vulgare (barley) 352,924 Source: NCBI -

18 * -omes and -omics Glycome??? Ribonome? Proteome Variome Transcriptome Genome Mass spec, Y2H,? SNPs, haplotypes ESTs, cdnas, microarrays Genome sequences *Warning: some of the words on this slide may not be in Webster s dictionary

19 DNA Sequence Alignment I How does DNA alignment differ from protein alignment? Query: 1 ttgacctagatgagatgtcgttcacttttactgagctacagaaaa 45 Subject: 403 ttgatctagatgagatgccattcacttttactgagctacagaaaa 447 Use BLASTN instead of BLASTP

20 Nucleotidenucleotide BLAST Web Server (BLASTN)

21 DNA Sequence Alignment II Translating searches: translate in all possible reading frames search peptides against protein database (BLASTP) ttgacctagatgagatgtcgttcactttactgagctacagaaaa ttg acc tag atg aga tgt cgt tca ctt tta ctg agc tac aga aaa L T x M R C R S L L L S Y R K t tga cct aga tga gat gtc gtt cac ttt tac tga gct aca gaa aa x P R x D V V H F Y x S T E tt gac cta gat gag atg tcg ttc act ttt act gag cta cag aaa a D L D E M S F T F T E L Q K Also consider reading frames on complementary DNA strand

22 DNA Sequence Alignment III Common flavors of BLAST: Program Query Database BLASTP aa aa BLASTN nt nt BLASTX nt ( aa) aa TBLASTN aa nt ( aa) TBLASTX nt ( aa) nt ( aa) PsiBLAST aa (aa msa) aa Which would be best for searching ESTs against a genome?

23 DNA Sequence Alignment IV Which alignments are significant? Q: 1 ttgacctagatgagatgtcgttcacttttactgagctacagaaaa 45 S: 403 ttgatctagatgagatgccattcacttttactgagctacagaaaa 447 Identify high scoring segments whose score S exceeds a cutoff x using dynamic programming. Scores follow an extreme value distribution: P(S > x) = 1 - exp[-kmn e -λx ] For sequences of length m, n where K, λ depend on the score matrix and the composition of the sequences being compared (Same theory as for protein sequence alignments)

24 From M. Yaffe Lecture #2 Notes (cont) Probability values for the extreme value distribution (A) and the normal distribution (B). The area under each curve is 1. A. 0.4 B. The random sequence alignment scores would give rise to an extreme value distribution like a skewed gaussian Called Gumbel extreme value Yev Yn distribution X X For a normal distribution with a mean m and a variance σ, the height of the curve is described by Y=1/(σ 2π) exp[-(x-m) 2 /2σ 2 ] For an extreme value distribution, the height of the curve is described by Y=exp[-x-e -x ] and P(S>x) = 1-exp[-e -λ(x-u)] where u=(ln Kmn)/λ Can show that mean extreme score is ~ log 2 (nm), and the probability of getting a score that exceeds some number of standard deviations x is: P(S>x)~ Kmne -λx. ***K and λ are tabulated for different matrices **** For the less statistically inclined: E~ Kmne -λs

25 DNA Sequence Alignment V i How is λ related to the score matrix? λ is the unique positive solution to the equation*: p p j e λs ij = 1 i i,j p = frequency of nt i, s ij = score for aligning an i,j pair What kind of an equation is this? (transcendental) What would happen to λ if we doubled all the scores? (reduced by half) What does this tell us about the nature of λ? (scaling factor) *Karlin & Altschul, 1990

26 DNA Sequence Alignment VI What scoring matrix to use for DNA? Usually use simple match-mismatch matrices: i j: A C G T A 1 m m m C m 1 m m s i,j : G T m m m m 1 m m 1 m = mismatch penalty (must be negative)

27 DNA Sequence Alignment VII How to choose the mismatch penalty? Use theory of High Scoring Segment composition* High scoring alignments will have composition: q ij =p i p j e λs ij where q ij = frequency of i,j pairs ( target frequencies ) p i, p j = freq of i, j bases in sequences being compared What would happen to the target frequencies if we doubled all of the scores? *Karlin & Altschul, 1990