Comparative Genomics and Visualisation - Part 1

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Slides from a Comparative Genomics and Visualisation course (part 1) presented at the University of Dundee, 7th March 2014. Other materials are available at GitHub …

Slides from a Comparative Genomics and Visualisation course (part 1) presented at the University of Dundee, 7th March 2014. Other materials are available at GitHub (https://github.com/widdowquinn/Teaching)

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  • 1. Compara've  Genomics  and   Visualisa'on  –  Part  1   Leighton  Pritchard  
  • 2. Part  1   l What  is  compara've  genomics?   l Levels  of  genome  comparison   l  bulk,  whole  sequence,  features   l A  Brief  History  of  Compara've  Genomics   l  experimental  compara;ve  genomics   l Computa'onal  Compara've  Genomics   l  Bulk  proper;es   l  Whole  genome  comparisons   l Part  2   l  Genome  feature  comparisons  
  • 3. What  is  Compara've  Genomics?   The  combina'on  of  genomic  data  and   compara've  and  evolu'onary  biology  to   address  ques'ons  of  genome  structure,   evolu'on  and  func'on.  
  • 4. What  is  Compara've  Genomics?     “Nothing  in  biology  makes  sense,  except   in  the  light  of  evolu9on”   Theodosius  Dobzhansky  
  • 5. Why  Compara've  Genomics?   l Genomes  describe  heritable  characteris;cs   l Related  organisms  share  ancestral  genomes   l Func;onal  elements  encoded  in  genomes   are  common  to  related  organisms   l Func;onal  understanding  of  model  systems   (E.  coli,  A.  thaliana,  D.  melanogaster)  can  be   transferred  to  non-­‐model  systems  on  the   basis  of  genome  comparisons   l Genome  comparisons  can  be  informa;ve,   even  for  distantly-­‐related  organisms  
  • 6. Why  Compara've  Genomics?   l BUT:   l  Context:  epigene;cs,  ;ssue   differen;a;on,  mesoscale  systems,  etc.   l  Phenotypic  plas'city:  responses  to   temperature,  stress,  environment,  etc.  
  • 7. Why  Compara've  Genomics?   l Genomic  differences  can  underpin  phenotypic   (morphological  or  physiological)  differences.   l Where  phenotypes  or  other  organism-­‐level   proper;es  are  known,  comparison  of   genomes  may  give  mechanis;c  or  func;onal   insight  into  differences  (e.g.  GWAS).   l Genome  comparisons  aid  iden;fica;on  of   func;onal  elements  on  the  genome.   l Studying  genomic  changes  reveals   evolu;onary  processes  and  constraints.    
  • 8. Why  Compara've  Genomics?   Adapted  from  Hardison  (2003)  PLoS  Biol.  doi:10.1371/journal.pbio.0000058   species   'me   contemporary   organisms   l  Comparison  within  species  (e.g.  isolate-­‐level  –  or  even  within  individuals):   which  genome  features  may  account  for  unique  characteris;cs  of  organisms/ tumours?  Epigene;cs  in  an  individual.  
  • 9. Why  Compara've  Genomics?   genus   'me   contemporary   organisms   l Comparison  within  genus  (e.g.  species-­‐level):  what  genome  features   show  evidence  of  selec;ve  pressure,  and  in  which  species?  
  • 10. Why  Compara've  Genomics?   subgroup   'me   contemporary   organisms   l Comparison  within  subgroup  (e.g.  genus-­‐level):  what  are  the  core  set   of  genome  features  that  define  a  subgroup  or  genus?  
  • 11. The  E.coli  long-­‐term  evolu'on  experiment   l Run  by  the  Lenski  lab,  Michigan  State  University  since  1988   l  hVp://myxo.css.msu.edu/ecoli/   l 12  flasks,  citrate  usage  selec;on   l 50,000  genera;ons  of  Escherichia  coli!   l  Cultures  propagated  every  day   l  Every  500  genera;ons  (75  days),     mixed-­‐popula;on  samples  stored   l  Mean  fitness  es;mated  at  500     genera;on  intervals   Jeong  et  al.  (2009)  J.  Mol.  Biol.  doi:10.1016/j.jmb.2009.09.052   Barrick  et  al.  (2009)  Nature  doi:10.1038/nature08480   Wiser  et  al.  (2013)  Science.  doi:10.1126/science.1243357  
  • 12. Compara've  Genomics  in  the  News   Sankaraman  et  al.  (2014)  Nature.  doi:10.1038/nature12961   l Neanderthal  alleles:   l  Aid  adapta;on  outwith  Africa   l  Associated  with  disease  risk   l  Reduce  male  fer;lity  
  • 13. Levels  of  Genome  Comparison   Genomes  are  complex,  and  can  be   compared  on  a  range  of  conceptual  levels   -­‐  both  prac'cally  and  in  silico.  
  • 14. Three  broad  levels  of  comparison   l Bulk  Proper;es   l  chromosome/plasmid  counts  and  sizes,     l  nucleo;de  content,  etc.   l Whole  Genome  Sequence   l  sequence  similarity   l  organisa;on  of  genomic  regions  (synteny),  etc.   l Genome  Features/Func;onal  Components   l  numbers  and  types  of  features  (genes,  ncRNA,  regulatory   elements,  etc.)   l  organisa;on  of  features  (synteny,  operons,  regulons,  etc.)   l  complements  of  features   l  selec;on  pressure,  etc.  
  • 15. A  Brief  History  of  Experimental   Compara've  Genomics   You  don’t  have  to  sequence  genomes  to   compare  them  (but  it  helps).  
  • 16. Genome  Comparisons  Predate  NGS   l Sequence  data  was  not  always  cheap  and  abundant   l Prac;cal,  experimental  genome  comparisons  were  needed  
  • 17. Bulk  Genome  Property  Comparisons   Values  calculated  for  individual  genomes,   and  subsequently  compared.  
  • 18. Bulk  Genome  Proper'es   l  Large-­‐scale  summary  measurements   l  Measure  genomes  independently  –  compare  values  later   l  Number  of  chromosomes   l  Ploidy   l  Chromosome  size   l  Nucleo;de  (A,  C,  G,  T)  frequency/percentage  
  • 19. Chromosome  Counts/Size   l  The  chromosome  counts/ploidy  of  organisms  can  vary  widely   l  Escherichia  coli:  1  (but  plasmids…)   l  Rice  (Oryza  sa6va):  24  (but  mitochondria,  plas;ds  etc…)   l  Human  (Homo  sapiens):  46,  diploid   l  Adders-­‐tongue  (Ophioglossum  re6culatum):  up  to  1260   l  Domes;c  (but  not  wild)  wheat  soma;c  cells  hexaploid,  gametes  haploid   l  Physical  genome  size  (related  to  sequence  length)     can  also  vary  greatly   l  Genome  size  and  chromosome  count     do  not  indicate  organism  ‘complexity’   l  S;ll  surprises  to  be  found  in  physical   study  of  chromosomes!  (e.g.  Hi-­‐C)   Kamisugi  et  al.  (1993)  Chromosome  Res.  1(3):  189-­‐96   Wang  et  al.  (2013)  Nature  Rev  Genet.  doi:10.1038/nrg3375  
  • 20. Nucleo'de  Content   l Experimental  approaches  for  accurate  measurement   l  e.g.  use  radiolabelled  monophosphates,  calculate  propor;ons  using   chromatography   Karl  (1980)  Microbiol.  Rev.  44(4)  739-­‐796   Krane  et  al.  (1991)  Nucl.  Acids  Res.  doi:10.1093/nar/19.19.5181  
  • 21. Whole  Genome  Comparisons   Comparisons  of  one  whole  or  drac   genome  with  another  (or  many  others)  
  • 22. Whole  Genome  Comparisons   l  Requires  two  genomes:  “reference”  and  “comparator”   l  Experiment  produces  a  compara;ve  result,  dependent  on  the   choice  of  genomes   l  Methods  mostly  based  around  direct  or  indirect  DNA   hybridisa;on   l  DNA-­‐DNA  hybridisa;on   l  Compara;ve  Genomic  Hybridisa;on  (CGH)   l  Array  Compara;ve  Genomic  Hybridisa;on  (aCGH)  
  • 23. DNA-­‐DNA  Hybridisa'on  (DDH)   l Several  methods  based  around  the  same  principle   1.  Denature  organism  A,  B     genomic  DNA  mixture   2.  Allow  to  anneal  –  hybrids  result     (reassocia;on  ≈  similarity)   Morelló-­‐Mora  &  Amann  (2001)  FEMS  Microbiol.  Rev.  doi:10.1016/S0168-­‐6445(00)00040-­‐1  
  • 24. DNA-­‐DNA  Hybridisa'on  (DDH)   l  Several  methods  -­‐  same  principle   1.  Find  homoduplex  Tm1   2.  Denature  reference,  comparator   gDNA  +  mix   3.  Allow  to  anneal  –  hybrids  result     (reassocia;on  ≈  similarity),  find     heteroduplex  Tm2   4.  ∆Tm  =  Tm1  –  Tm2   5.  High  ∆T  implies  greater  genomic   difference  (fewer  H-­‐bonds)   l  Proxy  for  sequence  similarity   Morelló-­‐Mora  &  Amann  (2001)  FEMS  Microbiol.  Rev.  doi:10.1016/S0168-­‐6445(00)00040-­‐1  
  • 25. DNA-­‐DNA  Hybridisa'on  (DDH)   l Used  for  taxonomic  classifica;on  in  prokaryotes  from  1960s   l Sibley  &  Ahlquist  redefined  bird  and  primate  phylogeny  with     DDH  in  1980s:  Homo  shares  more  recent  common  ancestor  with  Pan   than  with  Gorilla  (this  was  previously  in  dispute)   Sibley  &  Ahlquist  (1984)  J.  Mol.  Evol.  doi:10.1007/BF02101980  
  • 26. Compara've  Genomic  Hybridisa'on   l  Two  genomes:  “reference”  and  “test”  are  labelled  (red  and  green  –     a  bad  conven6on  to  choose,  for  visualisa6on),  then  hybridised  against  a   third  “normal”  genome   l  Differences  in  red/green  intensity  mapped  by  microscopy  correspond  to   rela;ve  rela;onship  of  reference  and  test  to  “normal”  genome   l  Comparisons  within  species  (or  individual,  for  tumours);  copy  number   varia'ons  (CNV)   l  Labour-­‐intensive,  low-­‐resolu;on  
  • 27. Compara've  Genomic  Hybridisa'on   l Image  analysis  required  –  intensity  along  medial  axis.   Kallioniemi  et  al.  (1992)  Science  doi:10.1126/science.1359641   Fraga  et  al.  (2005)  Proc.  Natl.  Acad.  Sci.  USA  doi:10.1073/pnas.0500398102   Epigene'cs:  hybridising     methylated  DNA  
  • 28. Array  Compara've  Genomic  Hybridisa'on   l  Uses  DNA  microarrays:  thousands  of  short  DNA  probes  (genome     fragments)  immobilised  on  a  surface   l  gDNA,  cDNA,  etc.  fluorescently-­‐labelled  and  hybridised  to  the  array   l  Smaller  sample  sizes  cf.  CGH,     automatable,  high-­‐throughput,  high-­‐res   l  Iden'fies  copy  number  varia'on  (CNV)   and  segmental  duplica'on   Pollack  et  al.  (1999)  Nat.  Genet.  doi:10.1038/12640  
  • 29. Genome  Feature  Comparisons   Comparisons  on  the  basis  of  a  restricted   set  of  genome  features  
  • 30. Chromosomal  Rearrangements   l  Genomes  are  dynamic,  and  undergo  large-­‐scale  changes   l  Hybridisa;on  used  to  map  genome  rearrangement/duplica;on   l  Separate  chromosomes  electrophore;cally   l  Apply  single  gene  hybridising  probes   l  Reciprocal  hybridisa;ons  indicate  transloca;ons   Fischer  et  al.  (2000)  Nature.  doi:10.1038/35013058  
  • 31. Diagnos'c  PCR/MLST   l  Define  a  set  of  regions  (usually  genes):   l  conserved  enough  that  PCR  primers  can   be  designed  to  amplify  the  same  region   in  mul;ple  organisms   l  and:   l  divergent  enough  that  hybridising   probes  can  dis;nguish  between  groups   l  or:   l  sequence  the  amplifica;on  products   l  Sequence  variants  given  numbers   l  Number  profiles  define  groups   l  Track  evolu;on  by  minimum  spanning   trees  (MST)   l  hVp://pubmlst.org/   Maiden  et  al.  (2006)  Ann.  Rev.  Microbiol.  doi:10.1146/annurev.micro.59.030804.121325  
  • 32. l  aCGH  can  also  be  applied  across  species  for  classifica'on/diagnos'cs:   l  Microarray  probes  represent  genes     from  one  or  more  organisms   l  “Off-­‐species”  gDNA  fragmented,     labelled,  and  hybridised   l  Hybridisa;on  ≈  sequence     similarity  ≈  gene  presence   l  Heatmap  of  217  Staphylococcus   aureus  isolates  on  7-­‐strain  array.   l  columns=isolates   l  yellow/red=gene  present   l  blue/white/grey=gene  absent   l  Lower  bars  coloured  by  lineage  and  host     (green=caVle,  blue=horse,  purple=human)   Array  Compara've  Genomic  Hybridisa'on   Sung  et  al.  (2008)  Microbiol.  doi:10.1099/mic.0.2007/015289-­‐0  
  • 33. But  This  Happened…   l High-­‐throughput  sequencing  
  • 34. …And  Then  It  Rained  Sequence  Data   l  Modern  high-­‐throughput  sequencing  (454,  Illumina)  completely   changed  the  landscape.   l  Complete,  (mainly)  accurate  sequence     data  much  cheaper,  enabling:   l  more  precise  sequence  comparison   l  novel  analyses,  insights  and     visualisa;ons   l  Genomic  &  exomic  comparisons   l  19/2/2014  at  GOLD:   l  3,011  “finished”  genomes   l  9,891  “permanent  drar”  genomes   l  19/2/2014  at  NCBI  WGS:   l  17,023  whole  genome  projects  
  • 35. …And  Then  It  Rained  Sequence  Data   l In  2012,  GOLD  added  3736  genomes,  NCBI  added  4585   l Mostly  prokaryotes  (archaea  and  bacteria)   l We’re  a  liVle  ahead  of  Su’s  (Scripps,  La  Jolla)  projec;ons   Figures  and  code  from:  hlp://sulab.org/2013/06/sequenced-­‐genomes-­‐per-­‐year/    
  • 36. Computa'onal  Compara've  Genomics   Massively  enabled  by  high-­‐throughput   sequencing,  much  more  powerful  and   precise.  
  • 37. Three  broad  levels  of  comparison   l Bulk  Proper;es   l  chromosome/plasmid  counts  and  sizes,     l  nucleo;de  content,  etc.   l Whole  Genome  Sequence   l  sequence  similarity   l  organisa;on  of  genomic  regions  (rearrangements),  etc.   l Genome  Features/Func;onal  Components   l  numbers  and  types  of  features  (genes,  ncRNA,  regulatory   elements,  etc.)   l  organisa;on  of  features  (synteny,  operons,  regulons,  etc.)   l  complements  of  features   l  selec;on  pressure,  etc.  
  • 38. Bulk  Genome  Property  Comparisons   Values  calculated  for  individual  genomes,   and  subsequently  compared.  
  • 39. Nucleo'de  Frequencies/Genome  Size   l Very  easy  to  calculate  from  complete  or  drar  genome  sequence   l  (or  in  a  region  of  genome  sequence)   l GC  content/chromosome  size  can  be  characteris;c  of  an   organism   l [ACTIVITY]   l  bacteria_size_gc  iPython  notebook   l  ipython notebook –-pylab inline  in   bacteria_size  directory  
  • 40. Blobology   l Metazoan  sequence  data  can  be  contaminated  by  microbial   symbionts.   l  Host  and  symbiont  DNA  have  different  %GC  (and  are  present  in   different  amounts/coverage)   l  Preliminary  genome  assembly,  followed   by  read  mapping   l  Plot  con;g  coverage  against     %GC  =  Blobology   l  hVp://nematodes.org/bioinforma;cs/blobology/   Kumar  &  Blaxter  (2011)  Symbiosis  doi:10.1007/s13199-­‐012-­‐0154-­‐6  
  • 41. Nucleo'de  k-­‐mers   l  Sequence  data  is  required  to  determine  k-­‐mers   l  Nucleo;de  frequencies:     l  A,  C,  G,  T   l  Dinucleo;de  frequencies:     l  AA,  AC,  AG,  AT,  CA,  CC,  CG,  CT,  GA,  GC,  GG,  GT,  TA,  TC,  TG,  TT   l  Trinucleo;de  frequencies:   l  64  trinucleo;des   l  k-­‐nucleo;de  frequencies:   l  4k  k-­‐mers   l  [ACTIVITY]   l  runApp(“shiny/nucleotide_frequencies”)in  RStudio  
  • 42. k-­‐mer  Spectra   l k-­‐mer  spectrum:   l  Frequency  distribu;on  of  observed  k-­‐mer  counts   l  Most  species  have  a  unimodal  k-­‐mer  spectrum   Chor  et  al.  (2009)  Genome  Biol.  doi:10.1186/gb-­‐2009-­‐10-­‐10-­‐r108  
  • 43. k-­‐mer  Spectra   l  k-­‐mer  spectrum:   l  All  mammals  tested  (and  some  other)  species  have  a  mul;modal  k-­‐mer   spectrum   l  Genomic  regions  differ  in  this  property   Chor  et  al.  (2009)  Genome  Biol.  doi:10.1186/gb-­‐2009-­‐10-­‐10-­‐r108  
  • 44. Average  Nucleo'de  Iden'ty  (ANI)   l ANI  introduced  as  a  subs;tute  for  DDH  in  2007:   l  70%  iden;ty  (DDH)  =  “gold  standard”     prokaryo;c  species  boundary   l  70%  iden;ty  (DDH)  ≈  95%  iden;ty  (ANI)   Goris  et  al.  (2007)  Int.  J.  System.  Evol.  Biol.  doi:10.1099/ijs.0.64483-­‐0  
  • 45. Average  Nucleo'de  Iden'ty  (ANI)   l ANI  introduced  as  a  subs;tute  for  DDH  in  2007:   l  70%  iden;ty  (DDH)  =  “gold  standard”     prokaryo;c  species  boundary   l  70%  iden;ty  (DDH)  ≈  95%  iden;ty  (ANI)   l Original  method  emulates  physical   experiment:   1.  break  genome  into  1020nt  fragments   2.  align  fragments  using  BLASTN   3.  ANI  =  mean  iden;ty  of  all  BLASTN     matches  with  >30%  iden;ty  over  70%   alignable  length   Goris  et  al.  (2007)  Int.  J.  System.  Evol.  Biol.  doi:10.1099/ijs.0.64483-­‐0  
  • 46. Average  Nucleo'de  Iden'ty  (ANI)   l ANI  introduced  as  a  subs;tute  for  DDH  in  2007:   l  70%  iden;ty  (DDH)  =  “gold  standard”  prokaryo;c  species  boundary   l  70%  iden;ty  (DDH)  ≈  95%  iden;ty  (ANI)   l ANIm  and  TETRA  introduced  (2009)   1.  Align  sequences  using  NUCmer   2.  ANI  =  mean  %iden;ty  of  matches   l TETRA:   1.  Calculate  tetranucleo;de  frequencies   2.  Determine  each  tetramer  devia;on  from  expecta;on  (Z-­‐score)   3.  TETRA  =  Pearson  correla;on  coefficient  of  tetramer  Z-­‐scores   Richter  &  Rosselló-­‐Móra  (2009)  Proc.  Natl.  Acad.  Sci.  USA  doi:10.1073/pnas.0906412106  
  • 47. Average  Nucleo'de  Iden'ty  (ANI)   l ANIb  discards  useful  informa;on  that  ANIm  retains   l TETRA  reflects  bulk  genome  proper;es  rather  than  selec;on  on   sequence   l  Data  for  Anaplasma  marginale  (3),  A.phagocytophilum  (4),  A.centrale  (1)   l TETRA  scores  are  prone  to  false  posi;ves;  ANIb  scores  are  prone  to   false  nega;ves  
  • 48. Average  Nucleo'de  Iden'ty  (ANI)   l Jspecies  (hVp://www.imedea.uib.es/jspecies/)     l  WebStart   l  java -jar -Xms1024m -Xmx1024m jspecies1.2.1.jar l Python  script   l  scripts/calculate_ani.py l [ACTIVITY]   l  average_nucleotide_identity/README.md  Markdown   Richter  &  Rosselló-­‐Móra  (2009)  Proc.  Natl.  Acad.  Sci.  USA  doi:10.1073/pnas.0906412106  
  • 49. Diagnos'c  PCR/MLST   l PCR/MLST  s;ll  cheap   l  (but  for  how  much  longer?)   l Use  whole  genomes  to  iden;fy  unique/ diagnos;c  regions  for  PCR/MLST   Slezak  et  al.  (2003)  Brief.  Bioinf.  doi:10.1093/bib/4.2.133   Pritchard  et  al.  (2012)  PLoS  One  doi:10.1371/journal.pone.0034498  
  • 50. Whole  Genome  Sequence  Comparisons   Comparisons  of  one  whole  or  drac   genome  sequence  with  another  (or  many   others)  
  • 51. Whole  Genome  Alignment  
  • 52. Whole  Genome  Alignment   l Which  genomes  should  you  align?  (or  not  bother  aligning)   l For  reasonable  analysis,  genomes  should:   l  derive  from  a  sufficiently  recent  common  ancestor:  so  that   homologous  regions  can  be  iden;fied.   l  derive  from  a  sufficiently  distant  common  ancestor:  so  that   sufficiently  “interes;ng”  changes  are  likely  to  have  occurred   l  help  answer  your  biological  ques;on:   „ is  your  ques;on  organism  or  phenotype  specific?   „ are  you  inves;ga;ng  a  process?   l This  may  be  more  involved  for  metazoans  (vertebrates,   arthropods,  nematodes,  etc.)  than  prokaryotes…  
  • 53. Whole  Genome  Alignment   l Naïve  alignment  algorithms  (e.g.  Needleman-­‐Wunsch/Smith-­‐ Waterman)  are  not  appropriate:   l  Do  not  handle  rearrangements   l  Computa;onally  expensive  on  large  sequences   l Many  whole-­‐genome  alignment  algorithms  proposed,  including:   l  LASTZ  (hVp://www.bx.psu.edu/~rsharris/lastz/)   l  BLAT  (hVp://genome.ucsc.edu/goldenPath/help/blatSpec.html)   l  Mugsy  (hVp://mugsy.sourceforge.net/)   l  megaBLAST  (hVp://www.ncbi.nlm.nih.gov/blast/html/megablast.html)   l  MUMmer  (hVp://mummer.sourceforge.net/)   l  LAGAN  (hVp://lagan.stanford.edu/lagan_web/index.shtml)   l  WABA,  etc…  
  • 54. Whole  Genome  Alignment   l BLAT   l  BLAT  is  broadly  similar  to  BLAST   l  Main  differences:   „ op;mised  to  find  only  exact  or  near-­‐exact  matches,  for   speed   „ indexes  the  subject  genome,  retains  the  index  and  scans   the  query     „ connects  homologous  match  regions  into  a  single  alignment   (BLAST  reports  them  separately)   „ reports  mRNA  match  intron-­‐exon  boundaries  exactly   (BLAST  tends  to  extend)   l  Advantages:  fast;  exact  exon  boundaries;  UCSC  integra;on   l  Disadvantages:  does  not  find  more  remote/very  divergent   matches   Kent  (2002)  Genome  Res.  doi:10.1101/gr.229202  
  • 55. Whole  Genome  Alignment   l megaBLAST   l  Op;mised  for  speed  over  BLASTN     (see  hVp://www.ncbi.nlm.nih.gov/blast/Why.shtml):   „ genome-­‐level  searches     „ queries  on  large  sequence  sets   „ long  alignments  of  very  similar  sequence  (sequencing  errors/SNPs)   l  Uses  Zhang  et  al.  (2000)  greedy  algorithm   l  Concatenates  queries  to  improve  performance  (“query  packing”)   „ NOTE:  this  is  good  prac'ce  for  large  query  sets!   l  Two  modes:  megaBLAST,  and  discon;nuous  megaBLAST  (dc-­‐megablast)   „ dc-­‐megablast  intended  for  more  divergent  sequences   Zhang  et  al.  (2000)  J.  Comp.  Biol.  7(1-­‐2)  203-­‐14   Korf  et  al.  (2003)  “BLAST”,  O’Reilly  &  Associates,  Sebastopol,  CA  
  • 56. Whole  Genome  Alignment   l MUMmer   l  Uses  suffix  trees  for  paVern  matching:  very  fast  even  for  large  sequences   „ Finds  maximal  exact  matches   „ Memory  use  depends  only  on  reference  sequence  size   Kurtz  et  al.  (2004)  Genome  Biol.  doi:10.1186/gb-­‐2004-­‐5-­‐2-­‐r12  
  • 57. Whole  Genome  Alignment   l MUMmer   l  Uses  suffix  trees  for  paVern  matching:  very  fast  even  for  large  sequences   „ Finds  maximal  exact  matches   „ Memory  use  depends  only  on  reference  sequence  size   l  Suffix  Tree:   l  Can  be  constructed  and  searched  in  O(n)  ;me   l  Useful  algorithms  are  nontrivial   l  BANANA$   „  B  followed  by  ANANA$  only   „  A  followed  by  $,  NA$,  NANA$   „  N  followed  by  A$,  ANA$   Kurtz  et  al.  (2004)  Genome  Biol.  doi:10.1186/gb-­‐2004-­‐5-­‐2-­‐r12  
  • 58. Whole  Genome  Alignment   l MUMmer   l  Process:   „ 1)  Iden;fy  a  non-­‐overlapping  subset  of  maximal  exact  matches:   oren  Maximum  Unique  Matches  (MUMs  -­‐  though  not  always   unique)   „ 2)  Cluster  into  alignment  anchors   „ 3)  Extend  between  anchors  to  produce  a  final  gapped  alignment   l  Very  flexible  approach:  a  suite  of  programs  (mummer, nucmer, promer,  …)   „  nucleo;de  and  “conceptual  protein”  (more  sensi;ve)  alignments   „  used  for  genome  comparisons,  assembly  scaffolding,  repeat   detec;on,  etc.   „  forms  the  basis  for  other  aligners/assemblers,  e.g.  Mugsy,  AMOS   Kurtz  et  al.  (2004)  Genome  Biol.  doi:10.1186/gb-­‐2004-­‐5-­‐2-­‐r12  
  • 59. Whole  Genome  Alignment   l [ACTIVITY]   l  whole_genome_alignments_A.md Markdown   l  hVps://github.com/widdowquinn/Teaching/blob/master/ Compara;ve_Genomics_and_Visualisa;on/Part_1/ whole_genome_alignment/ whole_genome_alignments_A.md  
  • 60. Mul'ple  Genome  Alignment   l Several  tools:   l  Mugsy  (hVp://mugsy.sourceforge.net/)   l  MLAGAN  (hVp://lagan.stanford.edu/lagan_web/index.shtml)   l  TBA/Mul'Z  (hVp://www.bx.psu.edu/miller_lab/)   l  Mauve  (hVp://gel.ahabs.wisc.edu/mauve/)   l Posi;onal  homology  vs.  glocal  
  • 61. Mul'ple  Genome  Alignment   l LAGAN:  rapid  alignment  of  two  homologous   genome  sequences   l  Generate  local  alignments  (anchors,  B)   l  Construct  rough  global  map     (maximal-­‐scoring  ordered  subset,  C)   „ Join  anchors  that  lie  within  a     threshold  distance,  the  same  way   l  Compute  global  alignment  by     dynamic  programming  (D)   Brudno  et  al.  (2003)  Genome  Res.  doi:10.1101/gr.926603  
  • 62. Mul'ple  Genome  Alignment   l MLAGAN:  mul;ple  genome  alignment  of  k   genomes  in  k-­‐1  alignment  steps,  using  a   phylogene;c  tree  (CLUSTAL-­‐like):   l  Make  rough  global  maps  between  each     pair  of  sequences  (step  C  in  LAGAN)   l  Progressive  mul;ple  alignment  with     anchors  (iterated)   1.  Perform  global  alignment  between     closest  pair  of  sequences  with     LAGAN:  alignments  are     “mul6-­‐sequences”   2.  Find  rough  global  maps  of  this  mul6-­‐ sequence  to  all  other  mul6-­‐sequences.   Brudno  et  al.  (2003)  Genome  Res.  doi:10.1101/gr.926603  
  • 63. Human-­‐Mouse-­‐Rat  Alignment   l Three-­‐way  progressive  alignment,  iden;fying:   l  Homologous  (H/M/R),  rodent-­‐only  (M/R)  and  human-­‐ mouse  or  human-­‐rat  (H/M,  H/R)  homologous  regions   l Three-­‐way  synteny   synteny  mapped  to  rat  genome   Brudno  et  al.  (2004)  Genome  Res.  doi:10.1101/gr.2067704   Ini'al  alignments  by  BLAT   Syntenous  regions  aligned  with  LAGAN  
  • 64. Drac  Genome  Alignment  
  • 65. Drac  Genome  Alignment   l Whole  genome  alignments  useful  for  scaffolding  assemblies   l  High-­‐throughput  sequence  assemblies  come  in  fragments  (con;gs)   l  Con;gs  can  some;mes  be  ordered  if  paired  reads  or  long  read   technologies  are  used   l  Can  also  align  to  a  known  reference  genome   l MUMmer   l  Can  use  NUCmer  or,  for  more  distant  rela;ons,  PROmer   l Mauve/Progressive  Mauve   l  hVp://gel.ahabs.wisc.edu/mauve/   Darling  et  al.  (2003)  Genome  Res.  doi:10.1101/gr.2289704  
  • 66. Mauve   l  Mauve’s  alignment  algorithm   1.  Find  local  alignments  (mul;-­‐MUMs  –  seed  &   extend)   2.  Construct  phylogene;c  guide  tree  from  mul;-­‐ MUMs   3.  Select  subset  of  mul;-­‐MUMs  as  anchors.   „  Par;;on  anchors  into  Local  Collinear   Blocks  (LCBs)  –  consistently-­‐ordered   subsets   4.  Perform  recursive  anchoring  to  iden;fy   further  anchors   5.  Perform  progressive  alignment  (similar  to   CLUSTAL),  against  guide  tree   l  Mauve  Con;g  Mover  (MCM)  for  ordering  con;gs   Darling  et  al.  (2003)  Genome  Res.  doi:10.1101/gr.2289704  
  • 67. Mauve   l  Mauve  alignment  of  LCBs  in  nine  enterobacterial  genomes   l  Rearrangement  of  homologous  backbone  sequence   Darling  et  al.  (2003)  Genome  Res.  doi:10.1101/gr.2289704  
  • 68. Drac  Genome  Alignment   l [OPTIONAL  ACTIVITY]  (useful  for  exercise)   l  Alignment  and  reordering  of  drar  genome  con;gs   l  whole_genome_alignments_B.md  Markdown   l  hVps://github.com/widdowquinn/Teaching/blob/master/ Compara;ve_Genomics_and_Visualisa;on/Part_1/ whole_genome_alignment/ whole_genome_alignments_B.md   l [ACTIVITY]   l  Visualisa;on  of  whole  genome  alignment  with  Biopython   l  biopython_visualisation  iPython  notebook  
  • 69. Collinearity  and  Synteny   l Rearrangements  may  occur  post-­‐specia;on   l Different  species  s;ll  exhibit  conserva;on  of  sequence   similarity  and  order   l  Two  elements  are  collinear  if  they  lie  in  the  same  linear   sequence   l  Two  elements  are  syntenous  (syntenic)  if:   „ (orig.)  they  lie  on  the  same  chromosome   „ (mod.)  conserva;on  of  blocks  of  order  within  the  same   chromosome   l Signs  of  evolu;onary  constraints,  including  synteny,  may   indicate  func;onal  genome  regions   l More  about  this  in  Part  2,  related  to  genome  features  
  • 70. Syntenous   l example1.png  from  biopython_visualisation   ac;vity  
  • 71. Nonsyntenous   l example2.png  from  biopython_visualisation   ac;vity  
  • 72. Whole  Genome  Duplica'on   l Puffer  fish  Tetraodon  nigroviridis  (smallest  known  vertebrate  genome)   l  Whole-­‐genome  duplica;on,  subsequent  to  divergence  from  mammals.   l  Ancestral  vertebrate  genome  inferred  to  have  12  chromosomes.   Duplicated  genes  (ExoFish)  on  21  chromosomes   Jaillon  et  al.  (2004)  Nature  doi:10.1038/nature03025  
  • 73. VISTA,  mVISTA,  VISTA-­‐Point   l Alignment/visualisa;on  tools:     l  hVp://genome.lbl.gov/vista/index.shtml   l mVISTA:  align  and  compare  submiVed  sequences  (up  to  2Mbp)   l VISTA-­‐Point:  visualise  precomputed  alignments   Frazer  et  al.  (2004)  Nucl.  Acids  Res.  doi:10.1093/nar/gkh458  
  • 74. UCSC   l hVp://genome.ucsc.edu/   l Many  vertebrate/invertebrate  model  genomes   Kent  et  al.  (2002)  Genome  Res.  doi:10.1101/gr.229102  
  • 75. Conclusion   l Physical  and  computa;onal  genome  comparisons:   l  Similar  biological  ques;ons  -­‐>  similar  concepts   l Lots  of  sequence  data  in  modern  biology   l Conserva;on  ≈  evolu;onary  constraint   l Many  choices  of  algorithms/analysis  sorware   l Many  choices  of  visualisa;on  sorware/tools   l Coming  in  Part  2:  genomic  func;onal  elements  
  • 76. Credits   l This  slideshow  is  shared  under  a  Crea;ve  Commons   AVribu;on  4.0  License   hVp://crea;vecommons.org/licenses/by/4.0/)   l Copyright  is  held  by  The  James  HuVon  Ins;tute   hVp://www.huVon.ac.uk   l You  may  freely  use  this  material  in  research,  papers,  and   talks  so  long  as  acknowledgement  is  made.    
  • 77. Nucleo'de  Content   l A,  C,  G,  T  composi;on   l  Varies  between,  and  within  genomes   l  staining  varies  across  genomes,  due  to     varia;on  in  GC  content   l “isochores”:  regions  with  liVle   internal  GC  varia;on  (homogeneous)   „   long  a  point  of  discussion     –  difficult  to  define   l In  humans:   l  L1,  L2  isochores:  low  GC  (≲41%)   l  H1,  H2,  H3  isochores:  high  GC  (≳41%)   l  Imprecise  bulk  measurement   Sadoni  et  al.  (1999)  J.  Cell  Biol.  doi:10.1083/jcb.146.6.1211   hybridisa;on  of  H3  isochore  to  human  genome  
  • 78. DNA-­‐DNA  Hybridisa'on  (DDH)   l Used  for  taxonomic  classifica;on  in  prokaryotes  from  1960s   l Sibley  &  Ahlquist  redefined  bird  and  primate  phylogeny  with     DDH  in  1980s:     l Not  without  controversy:   „ Sugges;ons  of  data  manipula;on     (see  here)   „ Close  evolu;onary  rela;onships     difficult  to  resolve  due  to  paralogy     (more  on  paralogy  later…)   l S;ll  hanging  on  as  a  de  facto  “gold     standard”  in  microbiological  taxonomic     classifica;on.   Sibley  &  Ahlquist  (1987)  J.  Mol.  Evol.  doi:10.1007/BF02111285  
  • 79. Finding  isochores   l Isochores:  homogeneous  regions  of  %GC  content   l  Easy  to  find  with  windowed  (100kbp)   %GC  calcula;on,  from  sequenced     genomes.   l  3200  isochores  characterised  in  the     human  genome,  consistent  with  5     levels  (L1,  L2,  H1,  H2,  H3)  found     by  staining/hybridisa;on.     Costan'ni  et  al.  (2006)  Genome  Res.  doi:10.1101/gr.4910606  
  • 80. Compara've  Genomic  Hybridisa'on   l  Two  genomes:  “reference”  and  “test”  labelled  (red  and  green),   then  hybridised  against  a  “normal”  genome   l  semiquan'ta've:   l  Red:  loss  (<2  copies)  in  tumour   l  Green:  gain  (3-­‐4  copies)  in  tumour   l  Amplifica;ons  (>4  copies)  in  BOLD   l  Cases  with  the  same  Copy  Number     Aberra;on  (CNA)  are  numbered   De  Bortoli  et  al.  (2006)  BMC  Cancer  doi:10.1186/1471-­‐2407-­‐6-­‐223  
  • 81. l Early  approaches  took  a  threshold  score  (present/absent)   l Later  approaches  used  known  reference  genome  sequence     context  (HMMs,  synteny)  to  improve  presence/absence  calls   l  No  hybridisa;on  =  “absent”  or“divergent”?   l  Not  nearly  as  good  as  sequencing  directly!   Array  Compara've  Genomic  Hybridisa'on   Pritchard  et  al.  (2009)  PLoS  Comp.  Biol.  doi:10.1371/journal.pcbi.1000473  
  • 82. k-­‐mer  Spectra   l k-­‐mer  spectrum:   l  CpG  suppression  (CGs  are  uncommon  in  vertebrate  genomes),   but  (by  simula;on)  only  when  in  combina;on  with  a  par;cular   %GC,  explains  mul;modality   Chor  et  al.  (2009)  Genome  Biol.  doi:10.1186/gb-­‐2009-­‐10-­‐10-­‐r108