Brief. Bioinformatics - Challenges in integrating Escherichia coli molecular biology data.

Tópicos

{ implement(1333) system(1263) develop(1122) }
{ extract(1171) text(1153) clinic(932) }
{ gene(2352) biolog(1181) express(1162) }
{ control(1307) perform(991) simul(935) }
{ data(1714) softwar(1251) tool(1186) }
{ perform(999) metric(946) measur(919) }
{ general(901) number(790) one(736) }
{ howev(809) still(633) remain(590) }
{ use(976) code(926) identifi(902) }
{ process(1125) use(805) approach(778) }
{ system(1050) medic(1026) inform(1018) }
{ compound(1573) activ(1297) structur(1058) }
{ model(3480) simul(1196) paramet(876) }
{ data(3008) multipl(1320) sourc(1022) }
{ detect(2391) sensit(1101) algorithm(908) }
{ can(774) often(719) complex(702) }
{ bind(1733) structur(1185) ligand(1036) }
{ framework(1458) process(801) describ(734) }
{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1976) rule(880) can(841) }
{ sequenc(1873) structur(1644) protein(1328) }
{ studi(2440) review(1878) systemat(933) }
{ motion(1329) object(1292) video(1091) }
{ concept(1167) ontolog(924) domain(897) }
{ model(2220) cell(1177) simul(1124) }
{ featur(1941) imag(1645) propos(1176) }
{ risk(3053) factor(974) diseas(938) }
{ perform(1367) use(1326) method(1137) }
{ monitor(1329) mobil(1314) devic(1160) }
{ sampl(1606) size(1419) use(1276) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
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{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
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{ featur(3375) classif(2383) classifi(1994) }
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{ imag(2675) segment(2577) method(1081) }
{ patient(2315) diseas(1263) diabet(1191) }
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{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }

Resumo

One key challenge in Systems Biology is to provide mechanisms to collect and integrate the necessary data to be able to meet multiple analysis requirements. Typically, biological contents are scattered over multiple data sources and there is no easy way of comparing heterogeneous data contents. This work discusses ongoing standardisation and interoperability efforts and exposes integration challenges for the model organism Escherichia coli K-12. The goal is to analyse the major obstacles faced by integration processes, suggest ways to systematically identify them, and whenever possible, propose solutions or means to assist manual curation. Integration of gene, protein and compound data was evaluated by performing comparisons over EcoCyc, KEGG, BRENDA, ChEBI, Entrez Gene and UniProt contents. Cross-links, a number of standard nomenclatures and name information supported the comparisons. Except for the gene integration scenario, in no other scenario an element of integration performed well enough to support the process by itself. Indeed, both the integration of enzyme and compound records imply considerable curation. Results evidenced that, even for a well-studied model organism, source contents are still far from being as standardized as it would be desired and metadata varies considerably from source to source. Before designing any data integration pipeline, researchers should decide on the sources that best fit the purpose of analysis and be aware of existing conflicts/inconsistencies to be able to intervene in their resolution. Moreover, they should be aware of the limits of automatic integration such that they can define the extent of necessary manual curation for each application.

Resumo Limpo

one key challeng system biolog provid mechan collect integr necessari data abl meet multipl analysi requir typic biolog content scatter multipl data sourc easi way compar heterogen data content work discuss ongo standardis interoper effort expos integr challeng model organ escherichia coli k goal analys major obstacl face integr process suggest way systemat identifi whenev possibl propos solut mean assist manual curat integr gene protein compound data evalu perform comparison ecocyc kegg brenda chebi entrez gene uniprot content crosslink number standard nomenclatur name inform support comparison except gene integr scenario scenario element integr perform well enough support process inde integr enzym compound record impli consider curat result evidenc even wellstudi model organ sourc content still far standard desir metadata vari consider sourc sourc design data integr pipelin research decid sourc best fit purpos analysi awar exist conflictsinconsist abl interven resolut moreov awar limit automat integr can defin extent necessari manual curat applic

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