DNA SEQUENCING. Shear DNA into millions of small fragments Read nucleotides at a time from the small fragments (Sanger method)

Transcription

1 GENOME ASSEMBLY

2 DNA SEQUENCING Shear DNA into millions of small fragments Read nucleotides at a time from the small fragments (Sanger method)

3 WHOLE GENOME SHOTGUN SEQUENCING Start with many copies of genome. Bacterial genome length: 5 million. Fragment them and sequence reads at both ends. Read length: 35 to 1000 bp. Find overlapping reads. ACGTAGAATCGACCATG......AACATAGTTGACGTAGAATC Merge overlapping reads into contigs....aacatagttgacgtagaatcgaccatg... Contig Contig Contig Gap Gap Coverage at this location=2

4 DE NOVO GENOME ASSEMBLY Problem: given a collection of reads, i.e. short subsequences of the genomic sequence in the alphabet A, C, G, T, completely reconstruct the genome from which the reads are derived. Challenges: Repeats in the genome ACCCAGTTGACTGGGATCCTTTTTAAAGACTGGGATTTTAACGCG CAGTTGACTG ACTGGGATCC Sample reads GACTGGGATT Sequencing errors: substitutions, insertions, deletions, and others. TTTTTATAGA (substitution), CCTT TAAACG (deletion and insertion) Size of the data, e.g. 1.5 billion reads in 110GB FASTA/Q file.

5 CHALLENGES IN FRAGMENT ASSEMBLY Repeats: A major problem for fragment assembly > 50% of human genome are repeats: - over 1 million Alu repeats (about 300 bp) - about 200,000 LINE repeats (1000 bp and longer) Repeat Repeat Repeat Green and blue fragments are interchangeable when assembling repetitive DNA

6 DE NOVO GENOME ASSEMBLY CURRENT SOLUTIONS Overlap-layout-consensus (Celera, Newbler) Suitable for low coverage, long reads Highly parallelizable De Bruijn graph construction (ALLPATHS-LG, ABySS, Velvet, SOAPdenovo, EULER-SR, SPAdes, IDBA, and HyDA) Suitable for high coverage, short reads Fast but memory-intensive Sensitive to sequencing errors Mathematically elegant repeat classification

7 OVERLAP-LAYOUT-CONSENSUS Assemblers: ARACHNE, PHRAP, CAP, TIGR, CELERA Overlap: find potentially overlapping reads Layout: merge reads into contigs and contigs into supercontigs Consensus: derive the DNA sequence and correct read errors

8 OVERLAP Find the best match between the suffix of one read and the prefix of another Due to sequencing errors, need to use dynamic programming to find the optimal overlap alignment Apply a filtration method to filter out pairs of fragments that do not share a significantly long common substring

9 OVERLAPPING READS Sort all k-mers in reads (k ~ 24) Find pairs of reads sharing a k-mer Extend to full alignment throw away if not >95% similar TACA TAGATTACACAGATTACT GA TAGT TAGATTACACAGATTACTAGA

10 OVERLAPPING READS AND REPEATS A k-mer that appears N times, initiates N 2 comparisons For an Alu that appears 10 6 times comparisons too much Solution: Discard all k-mers that appear more than t Coverage, (t ~ 10)

11 FINDING OVERLAPPING READS Create local multiple alignments from the overlapping reads TAGATTACACAGATTACTGA TAGATTACACAGATTACTGA TAG TTACACAGATTATTGA TAGATTACACAGATTACTGA TAGATTACACAGATTACTGA TAGATTACACAGATTACTGA TAG TTACACAGATTATTGA TAGATTACACAGATTACTGA

12 FINDING OVERLAPPING READS (CONT D) Correct errors using multiple alignment TAGATTACACAGATTACTGA TAGATTACACAGATTACTGA TAG TTACACAGATTATTGA TAGATTACACAGATTACTGA TAGATTACACAGATTACTGA Score alignments C: 20 C: 35 T: 30 C: 35 C: 40 A: 15 A: 25 - A: 40 A: 25 Accept alignments with good scores C: 20 C: 35 C: 0 C: 35 C: 40 A: 15 A: 25 A: 0 A: 40 A: 25

13 Repeats are a major challenge LAYOUT Do two aligned fragments really overlap, or are they from two copies of a repeat? Solution: repeat masking hide the repeats!!! Masking results in high rate of misassembly (up to 20%) Misassembly means alot more work at the finishing step

14 MERGE READS INTO CONTIGS Merge reads up to potential repeat boundaries

15 MERGE READS INTO CONTIGS Merge reads up to potential repeat boundaries

16 REPEATS, ERRORS, AND CONTIG LENGTHS Repeats shorter than read length are OK Repeats with more base pair differences than sequencing error rate are OK To make a smaller portion of the genome appear repetitive, try to: Increase read length

17 DE BRUIJN GRAPH EXAMPLE SHRED READS INTO K-MERS (K = 3) Read 1 Read 2 G G A C T A A A G A C C A A A T G G A G A C A C T C T A T A A A A A G A C A C C C C A C A A A A A A A T GGA GAC ACT CTA TAA AAA GAC ACC CCA CAA AAA AAT P. Pevzner, J Biomol Struct Dyn (1989) 7:63 73 R. Idury, M. Waterman, J Comput Biol (1995) 2:

18 DE BRUIJN GRAPH EXAMPLE MERGE VERTICES LABELED BY IDENTICAL K-MERS Read 1: GGA GAC ACT CTA TAA AAA Read 2: GAC ACC CCA CAA AAA AAT Resulting Graph: GGA GAC ( 2x ) ACT CTA TAA AAA ( 2x ) AAT ACC CCA CAA

19 ANOTHER EXAMPLE CONSTRUCTING THE GRAPH (K = 4) TGAG ATGA GATG CGAT CCGA TCCG (9x) ( 5x ) ( 6x ) ( 7x ) ( 7x ) ATCC ( 7x ) GATC AGAT GCTC ( 2x ) CTCT TCTA ( 2x ) CTAG ( 2x ) AAGT ( 3x ) AGTC ( 7x ) GTCG ( 9x ) TCGA ( 10x ) CGAG CGAC GAGG ( 16x ) GACG AGGC ( 16x ) ACGC GGCT ( 11x ) GCTT CTTT TTTA TTAG ( 12x ) TAGA ( 16x ) AGAG GAGA ( 9x ) ( 12x ) AGAC ( 9x ) GACA ACAA ( 5x ) A branching vertex is caused by either a repeat in the original sequence or a sequencing error

20 ANOTHER EXAMPLE CONSTRUCTING THE GRAPH (K = 4) TGAG ATGA GATG CGAT CCGA TCCG (9x) ( 5x ) ( 6x ) ( 7x ) ( 7x ) ATCC ( 7x ) GATC AGAT GCTC ( 2x ) CTCT TCTA ( 2x ) CTAG ( 2x ) AAGT ( 3x ) AGTC ( 7x ) GTCG ( 9x ) TCGA ( 10x ) CGAG CGAC GAGG ( 16x ) GACG AGGC ( 16x ) ACGC GGCT ( 11x ) GCTT CTTT TTTA TTAG ( 12x ) TAGA ( 16x ) AGAG GAGA ( 9x ) ( 12x ) AGAC ( 9x ) Sequencing errors are normally detected by a coverage cutoff threshold GACA ACAA ( 5x ) A branching vertex is caused by either a repeat in the original sequence or a sequencing error

21 EXAMPLE AFTER CONDENSATION AGATCCGATGAG AAGTCGA CGAG GCTCTAG CGACGC GAGGCT GCTTTAG TAGA AGAG GAGACAA

22 EXAMPLE AFTER ERROR REMOVAL AGATCCGATGAG AAGTCGA CGAG GAGGCT GCTTTAG TAGA AGAG GAGACAA

23 EXAMPLE AFTER RECONDENSATION AGATCCGATGAG AAGTCGAG GAGGCTTTAGA AGAG GAGACAA Any non-branching path in this graph corresponds to a contig in the original sequence. Taking the risk of following arbitrary branching paths may create chimeric species Source: Serafim Batzoglou

24 RESOLVING REPEATS USING PAIRED READS Genome Read 1 Insert size: a design parameter Read 2

25 RESOLVING REPEATS EQUIVALENT TRANSFORMATIONS Genome: S1 REPEAT S2. S3 REPEAT S4 Matches the distance in the graph, Longer than repeat length S1 S3 REPEAT REPEAT S2 S4 S1 S2 REPEAT S3 S4 P. Pevzner and H. Tang, Bioinformatics (2001) Suppl1:S

26 RESOLVING REPEATS FAILURE Spans multiple paths S1 P1 S2 REPEAT1 REPEAT2 S3 P2 S4 Mate pair transformation (Velvet, ABySS, EULER-SR) Find a unique path between mates in the graph. When multiple paths match the distance between mate-pairs, mate pair transformation fails. To resolve a repeat, insert size must be larger than the repeat length and smaller than the length of potential conjugate paths (same length paths passing through the repeat).