Abbott, JC

Butcher, SA

Dumas, ME

Holmes, E

Hoyles, L

Nicholson, JK

English

Spiral - Imperial College Digital Repository

Taxonomic	  data	  could	  be	  used	  to	  split	  pa2ents	  into	  disease	  and	  healthy	  groups	  (Figure	  4).	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  Selec2on	  of	  tools	  for	  use	  in	  the	  pipeline	  was	  made	  following	  assessment	  of	  numerous	  op2ons;	  for	  example,	  a	  number	  of	  de-­‐novo	  assemblers	  were	  assessed	  on	  both	  raw	  assembly	  sta2s2cs	  and	  a	  measure	  of	  poten2ally	  chimeric	  con2gs	  produced,	  based	  upon	  the	  number	  of	  genera	  the	  reads	  associated	  with	  each	  con2g	  originated	  from	  (Figure	  6).	  	  	  	  	  	  	  	  Figure	  6.	  Results	  of	  comparison	  of	  de-­‐novo	  metagenome	  assemblers	  Although	  various	  tools	  have	  been	  selected	  for	  the	  different	  stages	  of	  the	  pipeline,	  the	  modular	  nature	  of	  the	  pipeline	  permits	  the	  ready	  replacement	  of	  these	  with	  alterna2ves	  should	  beFer	  or	  more	  appropriate	  methods	  become	  available	  in	  future.	  Availability.	  Code	  is	  s2ll	  being	  finalized,	  but	  will	  be	  available	  from	  www.ic.ac.uk/bioinfsupport/soLware	  when	  complete.	  We	  have	  developed	  a	  modular	  pipeline	  (Figure	  1)	  	  for	  analysis	  of	  metagenomic	  sequence	  data.	  The	  pipeline	  is	  undergoing	  valida2on	  using	  read	  data	  from	  a	  recent	  metagenomic	  study	  on	  the	  microbiome	  associated	  with	  liver	  cirrhosis	  [1].	  	  	  	  	  	  	  	  	  	  Figure	  1.	  IMP:	  Imperial	  Metagenomics	  Pipeline.	  TrimGalore	  was	  used	  to	  quality	  trim	  data.	  Reads	  were	  filtered	  into	  human,	  viral,	  parasite,	  fungi,	  plant	  and	  bacterial/archaeal	  ‘bins’	  using	  BWA	  [2]	  to	  map	  the	  reads	  against	  reference	  genomes,	  including	  the	  human	  genome,	  all	  available	  viral,	  protozoan	  and	  helminth,	  and	  plant	  genomes,	  and	  a	  selec2on	  of	  gut-­‐associated	  fungi	  chosen	  based	  on	  the	  availability	  of	  whole	  genomes	  of	  species	  listed	  by	  [3].	  Reads	  not	  assigned	  to	  any	  of	  the	  aforemen2oned	  groups	  were	  assumed	  to	  be	  of	  archaeal/bacterial	  origin.	  For	  the	  archaeal/bacterial	  data,	  taxonomic	  affilia2ons	  and	  abundance	  data	  were	  determined	  using	  MetaPhlAn	  2.0	  [4].	  Bacterial/archaeal	  reads	  were	  assembled	  into	  con2gs	  using	  IDBA-­‐UD	  [5]	  in	  two	  phases	  –	  firstly	  on	  a	  per-­‐sample	  basis,	  then	  using	  a	  pool	  of	  reads	  which	  remained	  unassembled	  following	  the	  first	  round	  of	  assembly.	  Puta2ve	  genes	  were	  determined	  using	  MetaGeneMark	  [6,7],	  then	  clustered	  at	  the	  protein	  level	  using	  UCLUST	  7.0.1090	  [8]	  to	  create	  a	  non-­‐redundant	  gene	  catalog.	  Func2onal	  classifica2on	  of	  gene	  clusters	  was	  carried	  out	  by	  searching	  	  cluster	  centroids	  against	  a	  database	  of	  KEGG	  proteins	  (release	  2015-­‐05-­‐1)	  with	  USEARCH	  [8],	  and	  func2onal	  domains/pathway	  associa2ons	  using	  InterProScan	  [9].	  Sequence	  data	  for	  83	  healthy	  controls	  and	  98	  pa2ents	  were	  processed	  (Figure	  2).	  Human	  data	  were	  not	  analysed	  further	  because	  of	  ethical	  considera2ons.	  Plant,	  fungal	  and	  parasite	  data	  are	  currently	  presented	  on	  a	  presence/absence	  basis	  (Figure	  5),	  but	  the	  modular	  nature	  of	  the	  pipeline	  means	  analyses	  of	  these	  datasets	  can	  be	  easily	  expanded	  in	  the	  future.	  Similarly,	  viral	  data	  are	  given	  on	  a	  presence/absence	  basis	  but	  are	  not	  presented	  graphically	  due	  to	  the	  complexity	  of	  the	  data	  (3092	  virus	  groups	  represented	  across	  the	  pa2ent	  samples).	  	  	  	  	  	  MetaPhlAn	  2.0	  was	  used	  to	  generate	  abundance	  data	  (Figure	  3).	  	  	  	  	  	  	  	  	  	  	  IMP:	  Imperial	  Metagenomics	  Pipeline	  for	  high-­‐throughput	  sequence	  data	  	  Lesley	  Hoyles1,2	  |	  James	  C.	  AbboF1,3	  |	  Elaine	  Holmes1	  |	  Jeremy	  K.	  Nicholson1	  |	  Marc-­‐Emmanuel	  Dumas1	  |	  Sarah	  A.	  Butcher1,3	  	  Author	  affiliaBons	  1Division	  of	  Computa2onal	  and	  Systems	  Medicine,	  Department	  of	  Surgery	  and	  Cancer,	  Faculty	  of	  Medicine,	  Imperial	  College	  London,	  London	  SW7	  2AZ,	  United	  Kingdom	  2Department	  of	  Biomedical	  Sciences,	  University	  of	  Westminster,	  115	  New	  Cavendish	  Street,	  London	  W1W	  6UW,	  United	  Kingdom	  3Centre	  for	  Integra2ve	  Systems	  Biology	  and	  Bioinforma2cs,	  Division	  of	  Molecular	  Biosciences,	  Faculty	  of	  Natural	  Sciences,	  Imperial	  College	  London,	  London	  SW7	  2AZ,	  United	  Kingdom	  Contact	  Lesley	  Hoyles	  (lesley.hoyles11@imperial.ac.uk)	  References	  1Qin	  et	  al.	  (2014).	  Nature	  513,	  59–64.	  2Li	  &	  Durbin	  (2009).	  Bioinforma	  Oxf	  Engl	  25,	  1754–1760.	  3Gouba	  &	  Drancourt	  (2015).	  Med	  Mal	  Infect	  45,	  9–16.	  	  4Segata	  et	  al.	  (2012).	  Nat	  Methods	  9,	  811–814	  .	  5Peng	  et	  al.	  (2012).	  Bioinforma	  Oxf	  Engl	  28,	  1420–1428.	  6Besemer	  &	  Borodovsky	  (1999).	  Nucleic	  Acids	  Res	  27,	  3911–3920.	  	  7Zhu	  et	  al.	  (2010).	  Nucleic	  Acids	  Res	  38,	  e132.	  	  8Edgar	  (2010).	  Bioinforma>cs	  26,	  2460–2461.	  9Jones	  et	  al.	  (2014).	  Bioinforma>cs	  30,	  1236–1240. 	  	  FLORINASH	  contract	  N°	  Health-­‐F2-­‐2009-­‐241913	  is	  supported	  by	  funding	  under	  the	  Seventh	  Research	  Framework	  Programme	  of	  the	  European	  Commission	  Figure	  2.	  Representa2on	  of	  higher	  taxa	  in	  sequence	  data.	  Figure	  5.	  Assignment	  of	  reads	  from	  samples	  to	  (a)	  plant,	  (b)	  fungal	  or	  (c)	  parasite	  genomes.	  It	  is	  notable	  that	  several	  samples	  have	  no	  fungal	  DNA	  associated	  with	  them.	  	  Figure	  3.	  MetaPhlAn	  2.0	  analyses	  of	  metagenomic	  data	  from	  [1]	  using	  IMP.	  In	  agreement	  with	  [1],	  Bacteroidetes	  and	  Firmicutes	  represented	  the	  most	  abundant	  taxa	  in	  pa2ent	  samples;	  Veillonella,	  Streptococcus,	  Clostridium	  and	  Prevotella	  were	  enriched	  in	  the	  liver-­‐cirrhosis	  group;	  and	  Eubacterium	  and	  Alis>pes	  were	  amongst	  the	  most	  dominant	  bacteria	  in	  the	  healthy	  controls.	  Figure	  4.	  Principal	  component	  analysis	  of	  genus-­‐level	  data	  generated	  from	  MetaPhlAn	  2.0	  outputs.	  ALer	  removal	  of	  three	  outliers	  from	  the	  liver-­‐cirrhosis	  group,	  pa2ents	  could	  be	  separated	  based	  on	  health	  status.	  (L)	  Scores	  plot;	  (R)	  loadings	  plot.	  	  

IMP: Imperial Metagenomics Pipeline for high-­throughput sequence data

http://spiral.imperial.ac.uk/bitstream/10044/1/24496/2/Hoyles_Metagenomics_Cambridge.pdf

IMP: Imperial Metagenomics Pipeline for high-throughput sequence data

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IMP: Imperial Metagenomics Pipeline for high-­throughput sequence data

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IMP: Imperial Metagenomics Pipeline for high-throughput sequence data