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Genome sequence assembly

Genome sequence assembly. Assembly concepts and methods Mihai Pop Center for Bioinformatics and Computational Biology University of Maryland. Building a library. Break DNA into random fragments (8-10x coverage). Actual situation. Building a library.

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Genome sequence assembly

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  1. Genome sequence assembly Assembly concepts and methods Mihai Pop Center for Bioinformatics and Computational Biology University of Maryland

  2. Building a library • Break DNA into random fragments (8-10x coverage) Actual situation

  3. Building a library • Break DNA into random fragments (8-10x coverage) • Sequence the ends of the fragments • Amplify the fragments in a vector • Sequence 800-1000 (500-700) bases at each end of the fragment

  4. Assembling the fragments

  5. I II R F I II R F II I R F Forward-reverse constraints • The sequenced ends are facing towards each other • The distance between the two fragments is known (within certain experimental error) Insert R F Clone

  6. Building Scaffolds • Break DNA into random fragments (8-10x coverage) • Sequence the ends of the fragments • Assemble the sequenced ends • Build scaffolds

  7. Assembly gaps Physical gaps Sequencing gaps sequencing gap - we know the order and orientation of the contigs and have at least one clone spanning the gap physical gap - no information known about the adjacent contigs, nor about the DNA spanning the gap

  8. Unifying view of assembly Assembly Scaffolding

  9. Shotgun sequencing statistics

  10. Typical contig coverage Imagine raindrops on a sidewalk

  11. Lander-Waterman statistics L = read length T = minimum detectable overlap G = genome size N = number of reads c = coverage (NL / G) σ = 1 – T/L E(#islands) = Ne-cσ E(island size) = L((ecσ – 1) / c + 1 – σ) contig = island with 2 or more reads

  12. Example Genome size: 1 Mbp Read Length: 600 Detectable overlap: 40

  13. Experimental data Caveat: numbers based on artificially chopping up the genome of Wolbachia pipientis dMel

  14. Read coverage vs. Clone coverage 4 kbp 1 kbp Read coverage = 8X Clone (insert) coverage = 16 2X coverage in BAC-ends implies 100x coverage by BACs (1 BAC clone = approx. 100kbp)

  15. Assembly paradigms • Overlap-layout-consensus • greedy (TIGR Assembler, phrap, CAP3...) • graph-based (Celera Assembler, Arachne) • Eulerian path (especially useful for short read sequencing)

  16. TIGR Assembler/phrap Greedy • Build a rough map of fragment overlaps • Pick the largest scoring overlap • Merge the two fragments • Repeat until no more merges can be done

  17. Overlap-layout-consensus Main entity: read Relationship between reads: overlap 1 4 7 2 5 8 3 6 9 2 3 4 5 6 7 8 9 1 ACCTGA ACCTGA AGCTGA ACCAGA 1 2 3 2 3 1 1 2 3 3 1 1 2 3 1 3 2 2

  18. Paths through graphs and assembly • Hamiltonian circuit: visit each node (city) exactly once, returning to the start Genome

  19. Implementation details

  20. Overlap between two sequences overlap (19 bases) overhang (6 bases) …AGCCTAGACCTACAGGATGCGCGGACACGTAGCCAGGAC CAGTACTTGGATGCGCTGACACGTAGCTTATCCGGT… overhang % identity = 18/19 % = 94.7% • overlap - region of similarity between regions • overhang - un-aligned ends of the sequences • The assembler screens merges based on: • length of overlap • % identity in overlap region • maximum overhang size.

  21. All pairs alignment • Needed by the assembler • Try all pairs – must consider ~ n2 pairs • Smarter solution: only n x coverage (e.g. 8) pairs are possible • Build a table of k-mers contained in sequences (single pass through the genome) • Generate the pairs from k-mer table (single pass through k-mer table) k-mer

  22. REPEATS

  23. RptA RptB 3 6 9 12 2 5 8 11 1 4 7 10 13 6 4 8 10 2 12 1 13 3 11 5 9 7

  24. 4 6,10 8 2 12 1 13 3 7 11 5,9 Non-repetitive overlap graph

  25. Handling repeats • Repeat detection • pre-assembly: find fragments that belong to repeats • statistically (most existing assemblers) • repeat database (RepeatMasker) • during assembly: detect "tangles" indicative of repeats (Pevzner, Tang, Waterman 2001) • post-assembly: find repetitive regions and potential mis-assemblies. • Reputer, RepeatMasker • "unhappy" mate-pairs (too close, too far, mis-oriented) • Repeat resolution • find DNA fragments belonging to the repeat • determine correct tiling across the repeat

  26. Statistical repeat detection Significant deviations from average coverage flagged as repeats. - frequent k-mers are ignored - “arrival” rate of reads in contigs compared with theoretical value (e.g., 800 bp reads & 8x coverage - reads "arrive" every 100 bp) Problem 1: assumption of uniform distribution of fragments - leads to false positives non-random libraries poor clonability regions Problem 2: repeats with low copy number are missed - leads to false negatives

  27. Mis-assembled repeats excision collapsed tandem rearrangement

  28. SASA repeat (4776 AA, 14Kb)from Streptococcus pneumoniae MTETVEDKVSHSITGLDILKGIVAAGAVISGTVATQTKVFTNESAVLEKTVEKTDALATNDTVVLGTISTSNSASSTSLSASESASTSASESASTSASTSASTSASESASTSASTSISASSTVVGSQTAAATEATAKKVEEDRKKPASDYVASVTNVNLQSYAKRRKRSVDSIEQLLASIKNAAVFSGNTIVNGAPAINASLNIAKSETKVYTGEGVDSVYRVPIYYKLKVTNDGSKLTFTYTVTYVNPKTNDLGNISSMRPGYSIYNSGTSTQTMLTLGSDLGKPSGVKNYITDKNGRQVLSYNTSTMTTQGSGYTWGNGAQMNGFFAKKGYGLTSSWTVPITGTDTSFTFTPYAARTDRIGINYFNGGGKVVESSTTSQSLSQSKSLSVSASQSASASASTSASASASTSASASASTSASASASTSASVSASTSASASASTSASASASTSASESASTSASASASTSASASASTSASASASTSASESASTSASASASTSASESASTSASASASTSASASASTSASGSASTSTSASASTSASASASTSASASASISASESASTSASESASTSTSASASTSASESASTSASASASTSASASASTSASASASTSASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASVSASTSASASASTSASASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASASASISASESASTSASASASTSASASASTSASASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASESASTSASASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASTSASESASTSASASASTSASASASTSASASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASASASISASESASTSASASASTSASVSASTSASASASTSASESASTSASASASTSASESASTSASASASTSASASASISASESASTSASASASTSASASASTSASASASTSASESASTSTSASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASVSASTSASESASTSASASASTSASASASTSASESASTSASASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASISASESASTSASASASTSASASASTSASVSASTSASASASTSASASASISASESASTSASASASTSASASASTSASASASTSASASASISASESASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASESASTSASASASTSASASASTSASASASTSASVSASTSASESASTSASASASTSASASASTSASASASTSASESASTSASASASTSASASASTSASESASTSASASASTSASASASTSASASASTSASASASASTSASASASTSASASASTSASASASISASESASTSASESASTSTSASASTSASESASTSASASASTSASASASTSASASASTSASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASASASTSASVSASTSASASASTSASASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASASASISASESASTSASASASTSASASASTSASASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASESASTSASASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASTSASESASTSASASASTSASASASTSASASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASASASISASESASTSASASASTSASVSASTSASASASTSASESASTSASASASTSASESASTSASASASTSASASASISASESASTSASASASTSASASASTSASASASTSASESASTSTSASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASESASTSASASASTSASASASTSASASASTSASVSASTSASESASTSASASASTSASASASTSASASASTSASESASTSASASASTSASASASTSASESASTSASASASTSASASASTSASASASTSASASASASTSASASASTSASASASTSASASASISASESASTSASASASASTSASASASTSASASASTSASASASISASESASTSASESASTSTSASASTSASESASTSASASASTSASASASTSASASASTSASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASASASTSASVSASTSASASASTSASASASTSASESASTSASASTSASESASTSASASASTSASASASTSASASASTSASESASTSASASASTSASASASTSASESASTSASASASTSASASASTSASASASTSASESASTSASASASTSASESASTSASASASTSASASASTSASGSASTSTSASASTSASASASTSASASASISASESASTSASESASTSTSASASTSASESASTSASASASTSASASASTSASASASTSASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASVSASTSASASASTSASASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASASASISASESASTSASASASTSASASASTSASASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASESASTSASASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASTSASESASTSASASASTSASASASTSASASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASASASISASESASTSASASASTSASVSASTSASASASTSASESASTSASASASTSASESASTSASASASTSASASASISASESASTSASASASTSASASASTSASASASTSASESASTSTSASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASVSASTSASESASTSASASASTSASASASTSASESASTSASASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASISASESASTSASASASTSASASASTSASVSASTSASASASTSASASASISASESASTSASASASTSASASASTSASASASTSASASASISASESASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASESASTSASASASTSASASASISASESASTSASASASTSASASASTSASASASTSASESASTSTSASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASVSASTSASESASTSASASASTSASASASTSASESASTSASASASTSASESASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASISASESASTSASASASTSASASASTSASVSASTSASASASTSASASASISASESASTSASASASTSASASASTSASASASTSASASASISASESASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSASASASTSVSNSANHSNSQVGNTSGSTGKSQKELPNTGTESSIGSVLLGVLAAVTGIGLVAKRRKRDEEE

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