Mina, M

Sanavia, T

English

Institutional Research Information System University of Turin

 108 	   	   	   	   Poster:	  	  Biological	  Databases	  –	  BD8	  	  FastSemSim:	   fast	   and	   easy	   evaluation	   of	   semantic	   similarity	   measures	   on	   biomedical	  ontologies	  	  	  Mina	  M(1),	  Sanavia	  T(2)	  	  	  (1)	   MPBA,	   Fondazione	   Bruno	   Kessler,	   Trento	   (2)	   Department	   of	   Information	   Engineering,	  University	  of	  Padova,	  Padova	  	  	  Motivation:	  Although	   biological	   databases	   addressed	   the	   problem	   of	   providing	   consistent	  and	   formal	   descriptions	   of	   genes	   and	   proteins	   using	   different	   data	   structures,	   the	  quantification	  of	  the	  functional	  similarity	  between	  genes	  or	  proteins	  exploiting	  these	  data	  is	  still	   a	   challenge.	   Several	   semantic	   similarity	  measures	   (almost	   50)	   have	   been	   proposed	   to	  compare	  either	  the	  terms	  from	  biomedical	  ontologies	  or	  the	  genes/proteins	  annotated	  with	  them,	   using	   different	   approaches	   to	   consider	   the	   relationships	   among	   the	   annotations	  (Guzzi	   et	   al.,	   2012).	   Their	   implementations	   can	  be	  distinguished	   in	   two	   categories:	   on-­‐line	  and	  off-­‐line.	  For	  on-­‐line	  analyses,	  G-­‐SESAME	  (Zhidian	  et	  al.,	  2009),	  FunSimMat	  (Schlicker	  and	  Albrecht,	   2010	   and	   ProteInOn	   (Pesquita	   et	   al.,	   2009)	   are	   the	  most	   recently	   proposed	   and	  provide,	   beyond	   the	   classical	   information-­‐theoretic	   based	  metrics,	   new	   approaches	   based	  on	  term	  specificity.	  On	  the	  other	  hand,	  almost	  all	  off-­‐line	  tools	  are	  developed	  in	  R	  language:	  GOSemSim	   (Yu	   et	   al.,	   2010)	   and	   csbl.go	   (Ovaska	   et	   al.,	   2008),	   are	   among	   the	  most	   used.	  However,	  current	  applications	  are	  not	  specifically	  designed	  to	  manage	  huge	  amounts	  of	  data	  in	  order	  to	  support	  genome/proteome-­‐wide	  analyses.	  Recently,	  new	  integrative	  approaches	  in	  the	  analysis	  of	  high-­‐throughput	  data	  has	  proven	  that	  the	   integration	  of	  prior	  knowledge	  from	  biomedical	  ontologies	   is	  a	  useful	  resource	  to	   improve	  the	   identification	  of	  expression	  patterns	  (Di	  Camillo	  et	  al.,	  2012)	  and	  to	  provide	  more	  stable	  biomarker	  lists	  in	  classification	  problems	  (Sanavia	  et	  al.,	  2012).	  Therefore,	  more	  efforts	  are	  required	  to	  develop	  applications	  which	  are	  able	  to	  be	  both	  scalable	  across	  genome/proteome-­‐wide	  data	  and	  enough	  flexible	  to	   provide	   a	   user-­‐friendly	   platform	   to	   calculate	   these	   similarities	   and	   to	   integrate	   them	  within	  new	  computational	  pipelines.	  	  Methods:	   FastSemSim	   is	   both	   a	   Python	   library	   and	   an	   end-­‐user	   application,	   featuring	   an	  intuitive	  graphical	  user	  interface	  (GUI).	  As	  input	  data,	  the	  library	  requires	  the	  ontology	  graph	  and	  the	  gene/protein	  annotations.	  The	  current	  version	  of	  FastSemSim	  handles	  both	  obo	  and	  obo-­‐xml	  daily	  updated	  files	  and	  supports	  any	  multi-­‐rooted	  ontology	  (as	  long	  as	  it	  is	  acyclic).	  The	  library	  was	  implemented	  with	  a	  modular	  architecture:	  a	  core	  component	  includes	  all	  the	  routines	   for	  parsing	   the	  ontology	  and	   the	  annotations	   to	  extract	  common	   features	   for	   the	  measures	   (e.g.	   Information	   Content),	   whereas	   all	   the	   semantic	   similarity	   measures	   were	  developed	  on	  top	  of	  the	  core	  library	  as	  independent	  modules.	  The	  library	  currently	  supports	  16	  different	  measures,	  both	  pairwise	  and	  groupwise.	  While	  groupwise	  measures	  can	  directly	  evaluate	   gene/protein	   similarities	   considering	   the	   corresponding	   sets	   of	   terms,	   pairwise	  measures	  need	  a	  “mixing	  strategy”	  (Guzzi	  et	  al.,	  2012).	  The	  three	  most	  used	  strategies	  were	  implemented:	   the	   average	   (avg)	   and	   the	  maximum	   (max)	   of	   all	   term	   pairwise	   similarities,	  and	   the	   average	   of	   similarities	   between	   best	   matching	   terms	   (Best	   Match	   Average).	  FastSemSim	   supports	   inter-­‐ontology	   and	   inter-­‐category	   (e.g.	   Molecular	   Functions	   and	  Biological	   Process	   in	  Gene	  Ontology)	   relationships,	   and	  provides	   several	   filtering	   functions	  which	   allow	   the	   user	   to	   perform	   organism-­‐specific	   analysis	   or	   to	   work	   only	   with	   specific	  evidence	   codes.	   In	   addition,	   a	   GUI	   is	   provided	   to	   easily	   calculate	   the	   similarity	  measures,	  characterized	  by	  a	  user-­‐friendly	  front-­‐end	  to	   load	  the	  ontology	  and	  the	  annotation	  files,	  to	  input	  the	  query	  and	  to	  select	  the	  output	  parameters.	  Both	  the	  library	  and	  the	  interface	  are	   109 compatible	  with	  Python	  2.x	  and	  were	  tested	  on	  Microsoft	  Windows,	  OS	  X	  and	  different	  Linux	  distributions.	  	  Results:	   Compared	   with	   the	   most	   used	   off-­‐line	   and	   on-­‐line	   available	   tools	   for	   semantic	  similarities,	   FastSemSim	   shows	   the	   highest	   coverage	   of	   implemented	   semantic	   similarity	  measures,	   enabling	   the	   systematic	   evaluation	   of	   different	   measures	   (Cho	   et	   al.,	   2013).	  Scalability	   was	   tested	   on	   Gene	   Ontology	   (GO)	   annotations	   for	   the	   categories	   Biological	  Process	  and	  Molecular	  Function	  across	  the	  proteomes	  of	  several	  organisms	  (Human,	  Mouse,	  Fly	   and	   Yeast).	   Resnik	   measure	   (Resnik,	   1999)	   combined	   with	   the	   max	   mixing	   strategy,	  conventionally	   used	   for	   protein-­‐based	   studies,	   was	   applied.	   FastSemSim	   was	   able	   to	  accomplish	   the	   analysis	   for	   all	   the	   proteomes,	   ranging	   between	   2	   minutes	   for	   the	   Yeast	  proteome	  (6380	  GO	  annotated	  proteins)	  and	  5	  hours	  and	  18	  minutes	  for	  Human	  proteome	  (45576	   GO	   annotated	   proteins).	   Available	   R	   applications	   do	   not	   provide	   an	   efficient	  implementation	   able	   to	   deal	  with	  more	   than	   1000	   genes/proteins	   efficiently.	   FastSemSim	  proved	  to	  meet	  the	  requirements	  of	  handling	  huge	  amounts	  of	  data.	  Moreover,	  the	  Python	  implementation	  and	  the	  modular	  architecture	  of	  the	  library	  can	  be	  easily	  exploited	  to	  both	  integrate	  semantic	  similarity	  within	  computational	  pipelines	  and	  extend	  the	  library	  with	  new	  measures.	  	  Contact	  email:	  mamina@fbk.eu	  	  	  	  

FastSemSim: fast and easy evaluation of semantic similarity measures on biomedical ontologies

https://iris.unito.it/bitstream/2318/1805862/1/BITS2014_Mina_Sanavia.pdf

FastSemSim: fast and easy evaluation of semantic similarity measures on biomedical ontologies

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