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  1. Computational Genomics:Genome assembly Andrey Kislyuk 25 January 2010

  2. Why do we need to assemble genomes? • DNA sequencing methods can’t sequence more than about 1000 nt at a time • Sanger method (1975) • chain termination with labeled ddNTPs • Maxam-Gilbert method (1976) • cleaving agents • Both require primers for DNA Polymerase, but we can’t make the primers until we know the sequence! • Both limited to ~1000 nt on the gel/capillary • Both require cloning and/or PCR amplification • Large-scale sequencing: primer walking • Slow, costly, and error-prone: not practical beyond ~10Kbp 5 June 2014·Computational Genomics

  3. Shotgun sequencing • Whole genome shotgun sequencing (1995) • Hydroshearing: prepare several libraries of random fragments approx. 2, 5, 10, 50… Kbp long • Cloning: use bacterial plasmids to grow DNA – problems arise if DNA contains a gene harmful to the host bacteria • Picking, amplification • Sanger sequencing, capillary electrophoresis, read out fluorescent dyes with a laser – 4 different colors • Result: lots of ~1000 nt Sanger reads • Assemble them with pairwise sequence alignment • Multiple coverage corrects errors • Seems straightforward now, but many did not believe it could be done! 5 June 2014·Computational Genomics

  4. How much shotgun sequencing? • So, can we really sequence the whole genome with this? (No, we can’t.) • Lander and Waterman (1988): • Assuming random distribution of reads and ignoring repeat resolution issues, Define: • G = genome length • L = length of a single read • Then overall coverage is C = LN/G • N = number of reads sequenced • T = minimum overlap to align the reads together • Coverage for any given base obeys the Poisson distribution: • The number of gaps (bases with 0 coverage) is: 5 June 2014·Computational Genomics

  5. How much shotgun sequencing? 5 June 2014·Computational Genomics

  6. How much shotgun sequencing? 5 June 2014·Computational Genomics

  7. Pioneers of sequence assembly • J. Craig Venter’s group at TIGR (later JCVI) • Created TIGR Assembler, Celera Assembler, and associated tools • Jim Kent (UC Santa Cruz) • Created GigAssembler • Allowed the Human Genome Project to compete with Celera • Philip Green’s group at the University of Washington • Created Phred, Phrap and Consed tools • Sequencing centers: JCVI, Sanger Institute, Whitehead/MIT, DOE JGI, Baylor HGSC, WUSTL 5 June 2014·Computational Genomics

  8. Why do we need to assemble genomes? • 2nd Generation sequencing methods • Cheaper and more processive (sequence more data), but shorter read length • 454 Pyrosequencing: 200-600 nt average read length • Illumina: 50-70 nt average read length • ABI SOLiD: 50 nt average read length • Same idea: randomly hydrosheared library • Random reads from across the genome form a big puzzle 5 June 2014·Computational Genomics

  9. Next Generation Sequencing Technologies Sequencing by synthesis • 2nd generation • 454 Pyrosequencing • Solexa/Illumina • SOLiD • 3rd generation • Single-molecule sequencing • Nanopore sequencing 5 June 2014·Computational Genomics

  10. 454 Pyrosequencing A + PCR Reagents + Emulsion Oil B Mix DNA library & capture beads (limited dilution) Create “Water-in-oil” emulsion “Break micro-reactors” Isolate DNA containing beads Perform emulsion PCR 5 June 2014·Computational Genomics

  11. 454 Pyrosequencing 44 μm Load enzyme beads Load beads into PicoTiter™Plate PicoTiter™Plate Diameter = 44 μm Depth = 55 μm Well size = 75 pl Well density = 480 wells mm-2 1.6 million wells per slide 5 June 2014·Computational Genomics

  12. 454 Pyrosequencing Sequencing by synthesis Photonsgenerated are captured by CCD camera Reagent flow Margulies et al., 2005

  13. Raw sequencer output 4-mer 3-mer Measures the presence or absence of each nucleotide at any given position TACG Flow Order 2-mer KEY (TCAG) 1-mer • Sanger: Trace (usually .ab1/.scf file format) • 454: Flowgram (.sff file format) 5 June 2014·Computational Genomics

  14. Assembly algorithms • Paradigms • Overlap-Layout-Consensus • De Bruijn graphs 5 June 2014·Computational Genomics

  15. Differences between an overlap graph and a de Bruijn graph Schatz M C et al. Genome Res. 2010;20:1165-1173 ©2010 by Cold Spring Harbor Laboratory Press

  16. 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 ..ACGATTACAATAGGTT.. Credit: Serafim Batzoglou

  17. Overlap: A pairwise alignment problem • 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 Credit: Serafim Batzoglou

  18. Overlapping Reads T GA TACA | || || TAGA TAGT • 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 TAGATTACACAGATTAC ||||||||||||||||| TAGATTACACAGATTAC Credit: Serafim Batzoglou

  19. Overlapping Reads and Repeats • A k-mer that appears N times initiates N2 comparisons • For an Alu that appears 106 times  1012 comparisons – too much • Solution: Discard all k-mers that appear more than t Coverage, (t ~ 10) Credit: Serafim Batzoglou


  21. Finding Overlapping Reads (cont’d) • Correcterrors using multiple alignment C: 20 C: 20 C: 35 C: 35 C: 0 T: 30 C: 35 C: 35 TAGATTACACAGATTACTGA C: 40 C: 40 TAGATTACACAGATTACTGA TAG TTACACAGATTATTGA TAGATTACACAGATTACTGA TAGATTACACAGATTACTGA A: 15 A: 15 A: 25 A: 25 - A: 0 A: 40 A: 40 A: 25 A: 25 • Score alignments • Accept alignments with good scores Credit: Serafim Batzoglou

  22. Layout • Repeats are a major challenge • Do two aligned fragments really overlap, or are they from two copies of a repeat? Credit: Serafim Batzoglou

  23. The k-mer uniqueness ratio Schatz M C et al. Genome Res. 2010;20:1165-1173 ©2010 by Cold Spring Harbor Laboratory Press

  24. Merge Reads into Contigs repeat region Merge reads up to potential repeat boundaries Credit: Serafim Batzoglou

  25. Merge Reads into Contigs (cont’d) repeat region • Ignore non-maximal reads • Merge only maximal reads into contigs Credit: Serafim Batzoglou

  26. Merge Reads into Contigs (cont’d) repeat boundary??? • Ignore “hanging” reads when detecting repeat boundaries sequencing error b a Credit: Serafim Batzoglou

  27. Merge Reads into Contigs (cont’d) ????? Unambiguous • Insert non-maximal reads whenever unambiguous Credit: Serafim Batzoglou

  28. Link Contigs into Supercontigs (aka scaffolds) Normal density Too dense: Overcollapsed? (Myers et al. 2000) Inconsistent links: Overcollapsed? Credit: Serafim Batzoglou

  29. Link Contigs into Supercontigs (cont’d) Find all links between unique contigs Connect contigs incrementally, if  2 links Credit: Serafim Batzoglou

  30. Link Contigs into Supercontigs (cont’d) Fill gaps in supercontigs with paths of overcollapsed contigs Credit: Serafim Batzoglou

  31. Link Contigs into Supercontigs (cont’d) d ( A, B ) Contig A Contig B • Define G = ( V, E ) • V := contigs • E := ( A, B ) such that d( A, B ) < C • Reason to do so: Efficiency; full shortest paths cannot be computed Credit: Serafim Batzoglou

  32. Link Contigs into Supercontigs (cont’d) Contig A Contig B Define T: contigs linked to either A or B Fill gap between A and B if there is a path in G passing only from contigs in T Credit: Serafim Batzoglou

  33. Consensus • A consensus sequence is derived from a profile of the assembled fragments • A sufficient number of reads is required to ensure a statistically significant consensus • Reading errors are corrected Credit: Serafim Batzoglou


  35. Mate pairs and paired-end reads • Mate pairs: Circularize and trim size-selected fragments during library preparation. Inserts can be approx. 1, 5, 10, 20 Kbp long. • Paired-end reads: Sequence a short amplified fragment from both ends. Fragment length is more precise but limited to about 300 bp. 5 June 2014·Computational Genomics

  36. Mate pairs/Paired-end reads 5 June 2014·Computational Genomics

  37. Paired end reads (aka mate pairs) 5 June 2014·Computational Genomics Credit: 454 Life Sciences

  38. Base Calling and Trimming • Base Calling: the process of translating the raw sequencer output into • The most likely nucleotide sequence • Confidence scores for each position • Trimming: the process of removing adapter, key, vector, and/or low quality sequence from a read 5 June 2014·Computational Genomics

  39. Reference-based assembly • Reference-based assembly • Replaces overlap detection with alignment against a similar genome • Also called mapping, mapped assembly Credit: M. Schatz 5 June 2014·Computational Genomics

  40. Reference-based assembly • Use a related genome to ease the layout task • Much faster computationally • Arranges reads with more confidence, so a better assembly is possible • Allows other types of analysis: somatic mutations, organismal SNPs, structural variation, RNA-Seq, … 5 June 2014·Computational Genomics

  41. Assembly quality control • QC/QA • Metrics: Size, number of contigs, N50 • Diagnostic procedures 5 June 2014·Computational Genomics

  42. Genome size as predicted from the assembly 5 June 2014·Computational Genomics

  43. Read length, paired-end reads, coverage • Read length and paired-end reads matter. • Long reads can span repeat regions • Paired-end reads can reach into repeat regions and bridge gaps • Combination of the two maximizes shotgun sequencing performance • Coverage also matters. • High coverage allows very high confidence in base calling • Can do repeat resolution based on coverage fitting • More likely that a read will span an ambiguous region 5 June 2014·Computational Genomics

  44. Scaffolding • If paired end reads are available, scaffolding is already done. • If not (our case)… • Sequenced relatives may exist (our case) • Use reference-based assembly to predict scaffolding • No ready-made tools available for this • Can be inaccurate • Assemblers can get confused by repeats or overlaps that are too short • May be able to join by hand • Manual gap fill • Automated gap fill (no tools exist yet) 5 June 2014·Computational Genomics

  45. Finishing • Finishing is the process of completing the chromosome sequence. • Close all gaps (usually by PCR, but large gaps in big genomes can be sent back to make BACs for resequencing) • Re-sequence areas with less than 2x, 3x, 5x coverage (depending on quality standard) – same procedure as gaps • Check and manually assemble unresolved repeat regions • Check for mis-assembly by analyzing the overlap graph • Lots of Consed work! • This is the most expensive and time-consuming part of sequencing. • Lots of small projects omit finishing and work with draft genomes 5 June 2014·Computational Genomics

  46. Assemblers we used and our results 5 June 2014·Computational Genomics

  47. Homopolymer errors 4-mer 3-mer Measures the presence or absence of each nucleotide at any given position TACG Flow Order 2-mer KEY (TCAG) 1-mer • Specific to 454 pyrosequencing • Sequencing errors usually result in frameshifts! 5 June 2014·Computational Genomics

  48. Visualization tools: Mauve 5 June 2014·Computational Genomics

  49. More topics • Currently popular assemblers • Newbler (demo) • Velvet • ALLPATHS 2 • ABYSS • SHRiMP • Celera/WGS • PHRAP • Other visualization tools (Consed, MAQ, Prospector 2, ABySS-Explorer…) • Microread assembly (Solexa and SOLiD) • de Bruijn graph assembly paradigm 5 June 2014·Computational Genomics