J. Comput. Biol. - Using structural and evolutionary information to detect and correct pyrosequencing errors in noncoding RNAs.

Tópicos

{ sequenc(1873) structur(1644) protein(1328) }
{ algorithm(1844) comput(1787) effici(935) }
{ featur(1941) imag(1645) propos(1176) }
{ system(1050) medic(1026) inform(1018) }
{ analysi(2126) use(1163) compon(1037) }
{ process(1125) use(805) approach(778) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ imag(2830) propos(1344) filter(1198) }
{ general(901) number(790) one(736) }
{ search(2224) databas(1162) retriev(909) }
{ compound(1573) activ(1297) structur(1058) }
{ group(2977) signific(1463) compar(1072) }
{ estim(2440) model(1874) function(577) }
{ method(2212) result(1239) propos(1039) }
{ data(1737) use(1416) pattern(1282) }
{ measur(2081) correl(1212) valu(896) }
{ featur(3375) classif(2383) classifi(1994) }
{ imag(2675) segment(2577) method(1081) }
{ framework(1458) process(801) describ(734) }
{ problem(2511) optim(1539) algorithm(950) }
{ clinic(1479) use(1117) guidelin(835) }
{ method(1557) propos(1049) approach(1037) }
{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ visual(1396) interact(850) tool(830) }
{ spatial(1525) area(1432) region(1030) }
{ ehr(2073) health(1662) electron(1139) }
{ model(2656) set(1616) predict(1553) }
{ sampl(1606) size(1419) use(1276) }
{ first(2504) two(1366) second(1323) }
{ can(981) present(881) function(850) }
{ implement(1333) system(1263) develop(1122) }
{ activ(1452) weight(1219) physic(1104) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
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{ method(1219) similar(1157) match(930) }
{ network(2748) neural(1063) input(814) }
{ patient(2315) diseas(1263) diabet(1191) }
{ take(945) account(800) differ(722) }
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{ assess(1506) score(1403) qualiti(1306) }
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{ surgeri(1148) surgic(1085) robot(1054) }
{ error(1145) method(1030) estim(1020) }
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{ care(1570) inform(1187) nurs(1089) }
{ method(984) reconstruct(947) comput(926) }
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{ studi(1410) differ(1259) use(1210) }
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{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ 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) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ 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(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ method(1969) cluster(1462) data(1082) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

The analysis of the sequence-structure relationship in RNA molecules is not only essential for evolutionary studies but also for concrete applications such as error-correction in next generation sequencing (NGS) technologies. The prohibitive sizes of the mutational and conformational landscapes, combined with the volume of data to process, require efficient algorithms to compute sequence-structure properties. In this article, we address the correction of NGS errors by calculating which mutations most increase the likelihood of a sequence to a given structure and RNA family. We introduce RNApyro, an efficient, linear time and space inside-outside algorithm that computes exact mutational probabilities under secondary structure and evolutionary constraints given as a multiple sequence alignment with a consensus structure. We develop a scoring scheme combining classical stacking base-pair energies to novel isostericity scores and apply our techniques to correct pointwise errors in 5s and 16s rRNA sequences. Our results suggest that RNApyro is a promising algorithm to complement existing tools in the NGS error-correction pipeline.

Resumo Limpo

analysi sequencestructur relationship rna molecul essenti evolutionari studi also concret applic errorcorrect next generat sequenc ngs technolog prohibit size mutat conform landscap combin volum data process requir effici algorithm comput sequencestructur properti articl address correct ngs error calcul mutat increas likelihood sequenc given structur rna famili introduc rnapyro effici linear time space insideoutsid algorithm comput exact mutat probabl secondari structur evolutionari constraint given multipl sequenc align consensus structur develop score scheme combin classic stack basepair energi novel isoster score appli techniqu correct pointwis error s s rrna sequenc result suggest rnapyro promis algorithm complement exist tool ngs errorcorrect pipelin

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