J. Comput. Biol. - Computational techniques for human genome resequencing using mated gapped reads.

Tópicos

{ sequenc(1873) structur(1644) protein(1328) }
{ problem(2511) optim(1539) algorithm(950) }
{ detect(2391) sensit(1101) algorithm(908) }
{ result(1111) use(1088) new(759) }
{ model(3404) distribut(989) bayesian(671) }
{ framework(1458) process(801) describ(734) }
{ structur(1116) can(940) graph(676) }
{ error(1145) method(1030) estim(1020) }
{ studi(1119) effect(1106) posit(819) }
{ spatial(1525) area(1432) region(1030) }
{ use(1733) differ(960) four(931) }
{ process(1125) use(805) approach(778) }
{ system(1976) rule(880) can(841) }
{ motion(1329) object(1292) video(1091) }
{ featur(1941) imag(1645) propos(1176) }
{ data(3963) clinic(1234) research(1004) }
{ perform(999) metric(946) measur(919) }
{ system(1050) medic(1026) inform(1018) }
{ method(1969) cluster(1462) data(1082) }
{ method(1219) similar(1157) match(930) }
{ imag(2830) propos(1344) filter(1198) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ risk(3053) factor(974) diseas(938) }
{ visual(1396) interact(850) tool(830) }
{ blood(1257) pressur(1144) flow(957) }
{ patient(2837) hospit(1953) medic(668) }
{ first(2504) two(1366) second(1323) }
{ cancer(2502) breast(956) screen(824) }
{ estim(2440) model(1874) function(577) }
{ can(774) often(719) complex(702) }
{ imag(1947) propos(1133) code(1026) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ featur(3375) classif(2383) classifi(1994) }
{ network(2748) neural(1063) input(814) }
{ imag(2675) segment(2577) method(1081) }
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{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
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{ case(1353) use(1143) diagnosi(1136) }
{ howev(809) still(633) remain(590) }
{ studi(1410) differ(1259) use(1210) }
{ research(1085) discuss(1038) issu(1018) }
{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
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{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ 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) }
{ sampl(1606) size(1419) use(1276) }
{ 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) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ high(1669) rate(1365) level(1280) }
{ use(976) code(926) identifi(902) }
{ drug(1928) target(777) effect(648) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }

Resumo

Unchained base reads on self-assembling DNA nanoarrays have recently emerged as a promising approach to low-cost, high-quality resequencing of human genomes. Because of unique characteristics of these mated pair reads, existing computational methods for resequencing assembly, such as those based on map-consensus calling, are not adequate for accurate variant calling. We describe novel computational methods developed for accurate calling of SNPs and short substitutions and indels (<100 bp); the same methods apply to evaluation of hypothesized larger, structural variations. We use an optimization process that iteratively adjusts the genome sequence to maximize its a posteriori probability given the observed reads. For each candidate sequence, this probability is computed using Bayesian statistics with a simple read generation model and simplifying assumptions that make the problem computationally tractable. The optimization process iteratively applies one-base substitutions, insertions, and deletions until convergence is achieved to an optimum diploid sequence. A local de novo assembly procedure that generalizes approaches based on De Bruijn graphs is used to seed the optimization process in order to reduce the chance of converging to local optima. Finally, a correlation-based filter is applied to reduce the false positive rate caused by the presence of repetitive regions in the reference genome.

Resumo Limpo

unchain base read selfassembl dna nanoarray recent emerg promis approach lowcost highqual resequenc human genom uniqu characterist mate pair read exist comput method resequenc assembl base mapconsensus call adequ accur variant call describ novel comput method develop accur call snps short substitut indel bp method appli evalu hypothes larger structur variat use optim process iter adjust genom sequenc maxim posteriori probabl given observ read candid sequenc probabl comput use bayesian statist simpl read generat model simplifi assumpt make problem comput tractabl optim process iter appli onebas substitut insert delet converg achiev optimum diploid sequenc local de novo assembl procedur general approach base de bruijn graph use seed optim process order reduc chanc converg local optima final correlationbas filter appli reduc fals posit rate caus presenc repetit region refer genom

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