Brief. Bioinformatics - Correcting Illumina data.

Tópicos

{ clinic(1479) use(1117) guidelin(835) }
{ data(2317) use(1299) case(1017) }
{ error(1145) method(1030) estim(1020) }
{ algorithm(1844) comput(1787) effici(935) }
{ sequenc(1873) structur(1644) protein(1328) }
{ data(1714) softwar(1251) tool(1186) }
{ system(1050) medic(1026) inform(1018) }
{ medic(1828) order(1363) alert(1069) }
{ activ(1138) subject(705) human(624) }
{ patient(1821) servic(1111) care(1106) }
{ method(1969) cluster(1462) data(1082) }
{ imag(1947) propos(1133) code(1026) }
{ concept(1167) ontolog(924) domain(897) }
{ data(3963) clinic(1234) research(1004) }
{ health(3367) inform(1360) care(1135) }
{ cost(1906) reduc(1198) effect(832) }
{ analysi(2126) use(1163) compon(1037) }
{ process(1125) use(805) approach(778) }
{ model(3404) distribut(989) bayesian(671) }
{ network(2748) neural(1063) input(814) }
{ extract(1171) text(1153) clinic(932) }
{ search(2224) databas(1162) retriev(909) }
{ perform(999) metric(946) measur(919) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ studi(1119) effect(1106) posit(819) }
{ group(2977) signific(1463) compar(1072) }
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{ gene(2352) biolog(1181) express(1162) }
{ activ(1452) weight(1219) physic(1104) }
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{ record(1888) medic(1808) patient(1693) }
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{ monitor(1329) mobil(1314) devic(1160) }
{ 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) }
{ age(1611) year(1155) adult(843) }
{ signal(2180) analysi(812) frequenc(800) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
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{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
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{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
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{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Next-generation sequencing technologies revolutionized the ways in which genetic information is obtained and have opened the door for many essential applications in biomedical sciences. Hundreds of gigabytes of data are being produced, and all applications are affected by the errors in the data. Many programs have been designed to correct these errors, most of them targeting the data produced by the dominant technology of Illumina. We present a thorough comparison of these programs. Both HiSeq and MiSeq types of Illumina data are analyzed, and correcting performance is evaluated as the gain in depth and breadth of coverage, as given by correct reads and k-mers. Time and memory requirements, scalability and parallelism are considered as well. Practical guidelines are provided for the effective use of these tools. We also evaluate the efficiency of the current state-of-the-art programs for correcting Illumina data and provide research directions for further improvement.

Resumo Limpo

nextgener sequenc technolog revolution way genet inform obtain open door mani essenti applic biomed scienc hundr gigabyt data produc applic affect error data mani program design correct error target data produc domin technolog illumina present thorough comparison program hiseq miseq type illumina data analyz correct perform evalu gain depth breadth coverag given correct read kmer time memori requir scalabl parallel consid well practic guidelin provid effect use tool also evalu effici current stateoftheart program correct illumina data provid research direct improv

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