Brief. Bioinformatics - Application of second-generation sequencing to cancer genomics.

Tópicos

{ sequenc(1873) structur(1644) protein(1328) }
{ gene(2352) biolog(1181) express(1162) }
{ cancer(2502) breast(956) screen(824) }
{ health(3367) inform(1360) care(1135) }
{ can(774) often(719) complex(702) }
{ howev(809) still(633) remain(590) }
{ sampl(1606) size(1419) use(1276) }
{ care(1570) inform(1187) nurs(1089) }
{ research(1085) discuss(1038) issu(1018) }
{ research(1218) medic(880) student(794) }
{ structur(1116) can(940) graph(676) }
{ method(2212) result(1239) propos(1039) }
{ system(1976) rule(880) can(841) }
{ measur(2081) correl(1212) valu(896) }
{ imag(2830) propos(1344) filter(1198) }
{ take(945) account(800) differ(722) }
{ studi(2440) review(1878) systemat(933) }
{ chang(1828) time(1643) increas(1301) }
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{ data(1714) softwar(1251) tool(1186) }
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{ cost(1906) reduc(1198) effect(832) }
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{ method(984) reconstruct(947) comput(926) }
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{ case(1353) use(1143) diagnosi(1136) }
{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ record(1888) medic(1808) patient(1693) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ 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) }
{ group(2977) signific(1463) compar(1072) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(1969) cluster(1462) data(1082) }

Resumo

New generations of DNA sequencing technologies are enabling the systematic study of genetic derangement in cancers. Sequencing of cancer exomes or transcriptomes or even entire cancer genomes are now possible, though technical and economic challenges remain. Cancer samples are inherently heterogeneous and are often contaminated with normal DNA, placing additional demands on informatics tools for detecting genetic variation. However, even low coverage sequencing data can provide valuable information on genetic rearrangements, amplifications and losses in tumor genomes. Novel recurrent oncogenic mutations and fusion transcripts have been discovered with these technologies. In some sequenced cancer genomes, tens of thousands of genetic alterations have been discovered. While this enables the detailed dissection of mutation classes, it also presents a formidable informatics problem of sorting active 'driver' mutations from inactive 'passenger' mutations in order to prioritize these for further experimental characterization.

Resumo Limpo

new generat dna sequenc technolog enabl systemat studi genet derang cancer sequenc cancer exom transcriptom even entir cancer genom now possibl though technic econom challeng remain cancer sampl inher heterogen often contamin normal dna place addit demand informat tool detect genet variat howev even low coverag sequenc data can provid valuabl inform genet rearrang amplif loss tumor genom novel recurr oncogen mutat fusion transcript discov technolog sequenc cancer genom ten thousand genet alter discov enabl detail dissect mutat class also present formid informat problem sort activ driver mutat inact passeng mutat order priorit experiment character

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