Sequence Alignment. GS , Introduc8on to Bioinforma8cs The University of Texas GSBS program, Fall 2013

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1 Sequence Alignment GS , Introduc8on to Bioinforma8cs The University of Texas GSBS program, Fall 2013 Ken Chen, Ph.D. Department of Bioinforma8cs and Computa8onal Biology UT MD Anderson Cancer Center 1

2 Acknowledgements Slides benefited from Canadian Bioinforma8cs Workshops ( Stuart M. Brown, NYU School of Medicine Wikipedia Unknown online contributors 2

3 Outline Sequence alignment (50 mins) Reading: Bioinforma8cs and Func8onal Genomics, Chapter 3-6 (page ) Break (10 mins) NGS alignment (30 mins) Lab (50 minutes) 3

4 What is a sequence alignment? tcctctgcctctgccatcat---caaccccaaagt!! tcctgtgcatctgcaatcatgggcaaccccaaagt! Wikipedia: a sequence alignment is a way of arranging the sequences of DNA, RNA, or protein to iden8fy regions of similarity that may be a consequence of func8onal, structural, or evolu8onary rela8onships between the sequences. Ques8ons: What is the process? Arrange the sequences of DNA, RNA, or protein What is the purpose? Iden8fy func8onal, structural, or evolu8onary rela8onships among sequences 4

5 Understanding our genome PP

6 Understanding genomic varia8ons SNPs, haplotypes and tag SNPs. 6

7 What differences are expected? Genome evolu*on SNV (or SNP): ATA AGA Inser*on: AA AGA Dele*on: ACTG ACG Indel: an inser*on or a dele*on DNV (or DNP): ATGA AGCA MNV (or MNP): ATGTA AGCGA 7

8 What differences are expected? reference Structural Variation (SV) Large Dele8on Tandem Duplica8on VNTR Dispersed Duplica8on Novel Inser8ons SINE/LINE Inser8ons Inversions Transloca8on Complex 8

9 What makes a good alignment? Two sequences 1. TGTG! 2. TAG! Subject: Query: TGTG!.! T-AG! TGTG!.! TA-G! T-AG!.! TGTG! TA-G!.! TGTG! Ques8ons: Are these 4 alignments equally good? T- >A, G- >A, which is more likely in your study? Transi8on/Transversion Ra8o? Muta8on mechanism? Deamina8on? T- A, A- G, which is more likely? Sequence context? Two species, which one of these two sequence is ancestral? 9

10 Muta8on spectra in colon cancer cell- lines 10

11 Why is it a problem? Why is it a challenging problem? 1. Massive amount of data 2. Solu8on (hypothesis) space is huge (infinite) Finding needles in a haystack Why is it s8ll a problem? 1. Not all possible hypotheses are examined Good at subs8tu8ons and indels, but poor at structural varia8ons 2. Non- unique solu8ons? 3. No solu8on (twilight zone)? When sequences are very different. 4. Scoring systems not appropriate for the problem 5. Efficiency 11

12 A mathema8cal defini8on for pair- Two sequences: wise alignment All possible alignments in between: Each alignment (hypothesis): Has a score: F(a) X = [x 1, x 2,..., x m ],Y = [y 1, y 2,..., y n ] a A(X,Y ) The alignment problem is to find: a * = argmax a A(X,Y ) F(a) A(X,Y ) 12

13 a * = argmax a A(X,Y ) How to compute scores? F(a) Sequence 1 Sequence 2 actaccagttcatttgatacttctcaaa taccattaccgtgttaactgaaaggacttaaagact Example: A simple scoring system A G C T A G C T Match: 1 Mismatch: 0 Score = 5 13

14 What about indels? Ques8ons: 1. Are shorter indels always more likely than longer indels? TGTAG!! T---G! Affine gap penal8es (Gotoh, 1982):! γ(n) = d + (n 1) e!!!!!!!! gap! gap!!!open!!extend! Nature Gene*cs 44, (2012) In Coding Region? Frame- shil/inframe γ(n) d e Zhan et al. BMC Genomics :557 doi: / n 14

15 How to explore the hypothesis space? a * = argmax a A(X,Y ) F(a) Q: How to align?!! TGTG! TAG!! 1. This looks best:!!t!!!!t!! 2. Which one is better?!!tg!!!tg!!!!!.!!t-!!!ta!! 3! Promising hypotheses only 15

16 Alignment path matrix Scoring system: Match=1, mismatch=0, gap=- 1 T! G T G T - 1 -T! -T! 1 T-! -T! A - 2 -T! T-! G - 3 Each cell represents the best par8al alignment 16

17 Alignment path matrix Scoring system: Match=1, mismatch=0, gap=- 1 T! G T G T T! 0 T! A TG! T-! TA! 1 TA! T-G! G - 3 TA-! TG! T-! TGT! T-A! 17

18 Fill in matrix and remember the choices Scoring system: Match=1, mismatch=0, gap=- 1 T! G T G T A G

19 Trace Back from the end Scoring system: Match=1, mismatch=0, gap=- 1 T! G T G Subject T Q uer y A G GTGT! GA-T! flip TGTG! T-AG! 19

20 Dynamic Programming When a large search space can be structured into a succession of stages, such that: " " " the initial stage contains trivial solutions to sub-problems" each partial solution can be calculated by recurring a fixed number of partial solutions in an earlier stage" the final stage contains the overall solution Question:" Does it guarantee to find the best solution?" 20

21 The Needleman- Wunsch algorithm 1. Create a table of size (m+1)x(n+1) for sequences X and Y of lengths m and n, 2. Fill table entries (m:1) and (1:n) with the values: i F i,1 = σ (x k, ), F 1, j = σ (, y k ) k=1 3. Star8ng from the top lel, compute each entry using the recursive rela8on: " $ F i, j = max# $ % $ 4. Perform the trace- back procedure from the bopom- right corner j k=1 F i 1, j 1 +σ (x i, y j ) F i 1, j +σ (x i, ) F i, j 1 +σ (, y j ) & $ ' $ ( $ (1970, J Mol Biol. 48(3):443-53) Limita8ons: subs8tu8ons and indels only 21

22 Local/global alignment Global alignments, which apempt to align every residue in every sequence, are most useful when the sequences in the query set are similar and of roughly equal size. Needleman Wunsch algorithm Local alignments are more useful for dissimilar sequences that are suspected to contain regions of similarity or similar sequence mo8fs within their larger sequence context. Smith Waterman algorithm 22

23 Break 23

24 Types of sequence alignment blastp: protein to protein (1) blastn: DNA to DNA (1) blastx: DNA to protein (6) tblastn: protein to DNA (6) tblastx: DNA to DNA (36) Number of possible amino acid sequences in the universe? =20 n? 24

25 BLAST algorithm 1. Make a k- leper word list of the query sequence 3. Scan the database sequences for exact matches Extend the exact matches to high- scoring segment pair (H 2. Generate a list of high scoring words: PQG, PEG, PAG, QPE, QAE,. 4. Evaluate the significance of the HSP score. 25

26 BLAST scores E = Kmne λs E value: the number of different alignments with scores equivalent to or beper than S that are expected to occur by chance in a database search m, n: length of the query sequence and length of the en8re database, respec8vely K, λ: constant derived by Karlin and Altschul, 1990; They depend upon the subs8tu8on matrix, gap penal8es, and sequence composi8on (the leper frequencies) λs ln K S' =, E = mn 2 S' ln2 S : bit score. A normalized score so alignments obtained from employing different scoring matrices in separate BLAST searches and be compared P =1 e E Small E values (0.05 or less) correspond closely to the P values 26

27 Basic Local Alignment Search Tools 27

28 Example CCGCAGGCAACCGCCAATTTCACTGCCAAGGTTCGTTGGCAGACCGTCCTGGCTTCAAAACGACCG ATAACGGTAAGTCTTGGCACGTAGGTGGTTATTTGATCGTTGGGATGATTGTGGTTAACAACAATC TACATACACATTTTCATATGACCGCCTTCGTTAATAAGCTTATATAGACATAAATATATAAGGTGC CATGTATTTAACAGAGCAGATTATGGACAGGCCAAAACCTAGAACAGTAAAGGAACTAGCAGACAC TCTTGTGATTCCTTTAATAGATTTGTTGATACCTTGTAAATTTTGCAATAGATTTTTATCTTATTT TGAGCTACTTAATTTTGATCACAAGTGTTTACAGCTTATTTGGACAGAGGAGGATTTGGTGTATGG ACTCTGTAGTAGCTGTGCTTATGCGTCTGCACAGTTAGAATTTACACATTTTTTTCAATTTGCTGT AGTTGGAAAAGATATAGAAACTGTAGAAGGAACAGCTATTGGAAATATTTGTATTAGGTGTCGCTA CTGTTTTAAGTTATTAGACTTAGTGGAGAAGT! hpp://blast.ncbi.nlm.nih.gov/blast.cgi >gi ref NC_ Human papillomavirus type 9, complete genome 28

29 The next generation sequencing paired-end alignment Levy et al., 2007; Wheeler et al., 2008; Bentley et al., 2008; Ley et al., 2008 DNA Samples NGS Meyerson et al.,

30 Challenges in aligning NGS reads against human genomes Large size (3Gbp) 45% Repeats Transposons Segmental duplica8ons Simple repeats (VNTR, homo- polymers) Large structural polymorphism The human leukocyte an8gen (HLA) regions on chr6 Gap or unfinished regions peri- centromere, sub- telomere ~5Mb unique to ethnic groups (e.g., African, Asian) Finishing errors (1/10,000 bp) Reference represents minor alleles 30

31 Challenges in aligning NGS reads Short reads bp (versus a very long reference) Non- unique alignment Sensi8ve to sequencing errors Massive amount of short reads 1.5 billion 100 bp reads to cover human genome 50 8mes (50x) 1 Illumina 600GB 100 bp run = 6 billion reads Small insert size bp libraries < AluY(300bp), L1Hs(6000 bp) 31

32 Hashing (full- text search) 32

33 Burrows Wheeler Transforma8on 33

34 Illumina AB SOLiD Roche 454 Helicos gapped all alignments multithreaded Bowtie X X X X BWA X X X X X X BFAST X X X X X X X Corona Lite X X ELAND X GenomeMapper X X X X gnumap X X X X karma X X X * MAQ X X MOSAIK X X X X X X X MrFAST X X X MrsFAST X X Novoalign X X X * RMAP X X SeqMap X X X SHRiMP X X X X X X Slider X X SOAP2 X X X SSAHA2 X X X X SOCS X X SXOligoSearch X X X X Zoom X X * X 34 Slides by M. Stromberg

35 NGS sequence data FASTQ format Readname uniquely id a read Sequencing informa8on: sequencer, lane, date, and etc. Paired end status, barcode, and etc. Nucleo8de sequence {A,C,G,T,N} Base Quality ASC II symbols, each represen8ng an integer quality score of the corresponding base, i.e., how likely the corresponding base is an error, as determined from image processing (intensity, chas8ty etc.) $ zcat G1-1_ATCACG_L001_R1_002.fastq.gz 1:N:0:ATCACG! CGGAAAAAAACGGAATTATCGAATGGAATCGAAGAGAATCATCGAATGGACCCGAATGGAATCATCTCATGGAATGGAATGGAATAATCCATGGACTCGA! +! CCCFFFFFGGHHHJJJIJIEECDGIIIIJIIIGCEHGIJJGEHCCF9FHGGGIDHFFFFDDCEDEDCCDEDDDDD@>CDDC:@CCAACD:AAC>>ACC@A! $ zcat G1-1_ATCACG_L001_R2_002.fastq.gz 2:N:0:ATCACG! ACTCGATGATTCCATTCGATTCCATTCGATGATGATTGCATTCGAGTCCATGGATTATTCCATTCCATTCCATGAGATGATTCCATTCGGGTCCATTCGA! +! CCCFFFFFHHHHHJDIJJJJJJJIIJJJIFJJJIJEIJGHIJIJIJHIJIGCEGIDIJJJIJJJJEIJJJIIIIIJJGIICHIJIIIGGGIGHHHFFFF@! 35

36 Sequence Alignment/Map (SAM) format One entry (line) per alignment One read can have mul8ple entry Flag: encodes 11 boolean values strand of alignment is this unique alignment? CIGAR: encodes mismatches in alignment Bases QVs SRR _2 16 chr M * 0 0 Read name Flag Ref. seq Ref. pos Map quality CIGAR string Matepair info CCTTGTTTGGAAGTAGGGTTTTGCACCTGGAACC FGGEGFG??;DDDDDFAFDDFF?AFGGEEGFAGG Module 34 bioinformatics.ca

37 SAM/BAM format SAM = text, BAM = binary Header Ref Seq Length Alignment tool Module 34 bioinformatics.ca

38 Mapping quality of reads Mul8ple solu8on: How likely each one is, compara8vely? Is there a best one? P(pos read,refseq) = P(read refseq, pos) P(read, pos refseq) pos Li H., Ruan J., and D. R Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res 38

39 BWA: alignment via Burrows- Wheeler transforma8on Program: bwa (alignment via Burrows-Wheeler transformation)! Version: r16! Contact: Heng Li Usage: bwa <command> [options]!! Command: index index sequences in the FASTA format! aln gapped/ungapped alignment! samse generate alignment (single ended)! sampe generate alignment (paired ended)! bwasw BWA-SW for long queries!! fa2pac convert FASTA to PAC format! pac2bwt generate BWT from PAC! pac2bwtgen alternative algorithm for generating BWT! bwtupdate update.bwt to the new format! pac_rev generate reverse PAC! bwt2sa generate SA from BWT and Occ! pac2cspac convert PAC to color-space PAC! stdsw standard SW/NW alignment! 39

40 (Sequence Alignment/Map) samtools Program: samtools (Tools for alignments in the SAM format)! Version: dev (r982:313)!! Usage: samtools <command> [options]!! Command: view SAM<->BAM conversion! sort sort alignment file! mpileup multi-way pileup! depth compute the depth! faidx index/extract FASTA! tview text alignment viewer! index index alignment! idxstats BAM index stats (r595 or later)! fixmate fix mate information! flagstat simple stats! calmd recalculate MD/NM tags and '=' bases! merge merge sorted alignments! rmdup remove PCR duplicates! reheader replace BAM header! cat concatenate BAMs! targetcut cut fosmid regions (for fosmid pool only)! phase phase heterozygotes! 40

41 Integra8ve Genome Viewer Nature Biotechnology 29, (2011) doi: /nbt

42 Lab (50 minutes) hpp:// hpp://odin.mdacc.tmc.edu/~kchen3/seqlab/seqlab.htm 1. Iden8fy sequence altera8ons that evolved the 2 nd sequence from the 1 st (10-15 min) Compare 3 algorithms BLAST, Needleman, and Smith- Waterman Interpreta8on of alignment results 2. NGS sequence alignment (25-30 min) Generate NGS synthe8c data using wgsim Perform reference guided alignment using bwa View and understand alignment using samtools View alignment in IGV 42

43 Lab session 1 pair- wise alignment Pair- wise nucleo*de sequence alignment Compare the following 2 sequences ACGGGCTGAGTGGAGAGCTGTGACCCACAGAGATC GTAATCACGGGCTGAGTGTCACAGCTCTCCCCACAGAGATCGTGAAG using and NCBI Nucleo8de BLAST (bl2seq), Needleman- Wunsch Algorithm, Smith- Waterman Algorithm Ques8ons: Did you get same results from the 3 aligners? Why or why not? What events are required to evolve seq2 from seq1? 43

44 BLAST 44

45 Needleman- Wunsch 45

46 Smith- Waterman 46

47 Lab session 1 BLAST ACGGGCTGAGTGGAGAGCTGTGACCCACAGAGATC!! 1. Inversion! ACGGGCTGAGTGGAGAGCTGTGACCCACAGAGATC! ACGGGCTGAGTGTCACAGCTCTCCCCACAGAGATC!! 2. Insertions! ACGGGCTGAGTGGAGAGCTGTGACCCACAGAGATC! GTAATCACGGGCTGAGTGTCACAGCTCTCCCCACAGAGATCGTGAAG!!! 47

48 Session 2: Iden8fy DNA origin 48

49 Which species has the following DNA sequence?!aatcttaaaagcaggttatataggctaaatagaactaatcattgttttagacatacttat!!tgactctaagaggaaagatgaagtactatgttttaaagaatattatattacagaattata!!gaaattagatctcttacctaaactcttcataatgcttgctctgataggaaaatgagatct!!actgttttcctttacttactacacctcagatatatttcttcatgaagacctcacagtaaa!!aataggtgattttggtctagctacagtgaaatctcgatggagtgggtcccatcagtttga!!acagttgtctggatccattttgtggatggtaagaattgaggctatttttccactgattaa!!atttttggccctgagatgctgctgagttactagaaagtcattgaaggtctcaactatagt!!attttcatagttcccagtattcacaaaaatcagtgttcttattttttatgtaaatagatt!!ttttaacttttttctttacccttaaaacgaatattttgaaaccagtttc!! 49

50 Session 2: NGS sequence alignment Open a terminal, under /Applica8ons/U8li8es in Finder mkdir Desktop/seqaln Download the reference sequence hpp://odin.mdacc.tmc.edu/~kchen3/seqlab/hg18.chr20.10k_200k.fa by holding the control key and click on the hyperlink and select Save Linked File As it is in Desktop/seqaln You may need to rename the downloaded file: mv hg18.chr20.10k_200k.fa.txt hg18.chr20.10k_200k.fa Download bwa, save in Desktop/seqaln Download samtools, save in Desktop/seqaln Download wgsim, save in Desktop/seqaln cd Desktop/seqlan chmod +x bwa samtools wgsim Produce synthe8c reads from the reference sequence./wgsim - N hg18.chr20.10k_200k.fa sim_1.fq sim_2.fq > sim.muta8on.txt Take a look at sim_1.fq and sim_2.fq. Can you tell which reads are paired? Alignment synthe8c reads to the reference using bwa./bwa index hg18.chr20.10k_200k.fa./bwa aln hg18.chr20.10k_200k.fa sim_1.fq > sim_1.sai./bwa aln hg18.chr20.10k_200k.fa sim_2.fq > sim_2.sai./bwa sampe hg18.chr20.10k_200k.fa sim_1.sai sim_2.sai sim_1.fq sim_2.fq > sim.sam Examine sim.sam, e.g., more sim.sam Do you know the meaning of each column? Can you tell which reads are paired? Prepare bam file./samtools view - b - S sim.sam > sim.bam./samtools sort sim.bam sim.sorted./samtools index sim.sorted.bam What are the file sizes of sim.bam, and sim.sam? How much difference? View bam file in IGV launch igv, double- clicking Finder/Applica8ons/IGV 2.1, File/Import Genome, select fasta from /Desktop/seqaln/hg18.chr20.10k_200k.fa, and create a name hg18.chr20sim load sim.sorted.bam navigate in IGV Can you save an image? 50

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