A combinatorial approach for analyzing intra-tumor heterogeneity from high-throughput sequencing data
Motivation: High-throughput sequencing of tumor samples has shown that most tumors exhibit extensive intra-tumor heterogeneity, with multiple subpopulations of tumor cells containing different somatic mutations. Recent studies have quantified this intra-tumor heterogeneity by clustering mutations in...
Saved in:
| Published in | Bioinformatics (Oxford, England) Vol. 30; no. 12; pp. i78 - i86 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Oxford University Press
15.06.2014
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 1367-4803 1367-4811 1367-4811 |
| DOI | 10.1093/bioinformatics/btu284 |
Cover
| Abstract | Motivation: High-throughput sequencing of tumor samples has shown that most tumors exhibit extensive intra-tumor heterogeneity, with multiple subpopulations of tumor cells containing different somatic mutations. Recent studies have quantified this intra-tumor heterogeneity by clustering mutations into subpopulations according to the observed counts of DNA sequencing reads containing the variant allele. However, these clustering approaches do not consider that the population frequencies of different tumor subpopulations are correlated by their shared ancestry in the same population of cells.
Results: We introduce the binary tree partition (BTP), a novel combinatorial formulation of the problem of constructing the subpopulations of tumor cells from the variant allele frequencies of somatic mutations. We show that finding a BTP is an NP-complete problem; derive an approximation algorithm for an optimization version of the problem; and present a recursive algorithm to find a BTP with errors in the input. We show that the resulting algorithm outperforms existing clustering approaches on simulated and real sequencing data.
Availability and implementation: Python and MATLAB implementations of our method are available at http://compbio.cs.brown.edu/software/
Contact: braphael@cs.brown.edu
Supplementary information: Supplementary data are available at Bioinformatics online. |
|---|---|
| AbstractList | Motivation: High-throughput sequencing of tumor samples has shown that most tumors exhibit extensive intra-tumor heterogeneity, with multiple subpopulations of tumor cells containing different somatic mutations. Recent studies have quantified this intra-tumor heterogeneity by clustering mutations into subpopulations according to the observed counts of DNA sequencing reads containing the variant allele. However, these clustering approaches do not consider that the population frequencies of different tumor subpopulations are correlated by their shared ancestry in the same population of cells. Results: We introduce the binary tree partition (BTP), a novel combinatorial formulation of the problem of constructing the subpopulations of tumor cells from the variant allele frequencies of somatic mutations. We show that finding a BTP is an NP-complete problem; derive an approximation algorithm for an optimization version of the problem; and present a recursive algorithm to find a BTP with errors in the input. We show that the resulting algorithm outperforms existing clustering approaches on simulated and real sequencing data. Availability and implementation: Python and MATLAB implementations of our method are available at http://compbio.cs.brown.edu/software/ Contact:
braphael@cs.brown.edu Supplementary information:
Supplementary data are available at Bioinformatics online. High-throughput sequencing of tumor samples has shown that most tumors exhibit extensive intra-tumor heterogeneity, with multiple subpopulations of tumor cells containing different somatic mutations. Recent studies have quantified this intra-tumor heterogeneity by clustering mutations into subpopulations according to the observed counts of DNA sequencing reads containing the variant allele. However, these clustering approaches do not consider that the population frequencies of different tumor subpopulations are correlated by their shared ancestry in the same population of cells. We introduce the binary tree partition (BTP), a novel combinatorial formulation of the problem of constructing the subpopulations of tumor cells from the variant allele frequencies of somatic mutations. We show that finding a BTP is an NP-complete problem; derive an approximation algorithm for an optimization version of the problem; and present a recursive algorithm to find a BTP with errors in the input. We show that the resulting algorithm outperforms existing clustering approaches on simulated and real sequencing data. Python and MATLAB implementations of our method are available at http://compbio.cs.brown.edu/software/ . High-throughput sequencing of tumor samples has shown that most tumors exhibit extensive intra-tumor heterogeneity, with multiple subpopulations of tumor cells containing different somatic mutations. Recent studies have quantified this intra-tumor heterogeneity by clustering mutations into subpopulations according to the observed counts of DNA sequencing reads containing the variant allele. However, these clustering approaches do not consider that the population frequencies of different tumor subpopulations are correlated by their shared ancestry in the same population of cells.MOTIVATIONHigh-throughput sequencing of tumor samples has shown that most tumors exhibit extensive intra-tumor heterogeneity, with multiple subpopulations of tumor cells containing different somatic mutations. Recent studies have quantified this intra-tumor heterogeneity by clustering mutations into subpopulations according to the observed counts of DNA sequencing reads containing the variant allele. However, these clustering approaches do not consider that the population frequencies of different tumor subpopulations are correlated by their shared ancestry in the same population of cells.We introduce the binary tree partition (BTP), a novel combinatorial formulation of the problem of constructing the subpopulations of tumor cells from the variant allele frequencies of somatic mutations. We show that finding a BTP is an NP-complete problem; derive an approximation algorithm for an optimization version of the problem; and present a recursive algorithm to find a BTP with errors in the input. We show that the resulting algorithm outperforms existing clustering approaches on simulated and real sequencing data.RESULTSWe introduce the binary tree partition (BTP), a novel combinatorial formulation of the problem of constructing the subpopulations of tumor cells from the variant allele frequencies of somatic mutations. We show that finding a BTP is an NP-complete problem; derive an approximation algorithm for an optimization version of the problem; and present a recursive algorithm to find a BTP with errors in the input. We show that the resulting algorithm outperforms existing clustering approaches on simulated and real sequencing data.Python and MATLAB implementations of our method are available at http://compbio.cs.brown.edu/software/ .AVAILABILITY AND IMPLEMENTATIONPython and MATLAB implementations of our method are available at http://compbio.cs.brown.edu/software/ . Motivation: High-throughput sequencing of tumor samples has shown that most tumors exhibit extensive intra-tumor heterogeneity, with multiple subpopulations of tumor cells containing different somatic mutations. Recent studies have quantified this intra-tumor heterogeneity by clustering mutations into subpopulations according to the observed counts of DNA sequencing reads containing the variant allele. However, these clustering approaches do not consider that the population frequencies of different tumor subpopulations are correlated by their shared ancestry in the same population of cells. Results: We introduce the binary tree partition (BTP), a novel combinatorial formulation of the problem of constructing the subpopulations of tumor cells from the variant allele frequencies of somatic mutations. We show that finding a BTP is an NP-complete problem; derive an approximation algorithm for an optimization version of the problem; and present a recursive algorithm to find a BTP with errors in the input. We show that the resulting algorithm outperforms existing clustering approaches on simulated and real sequencing data. Availability and implementation: Python and MATLAB implementations of our method are available at http://compbio.cs.brown.edu/software/ Contact: braphael@cs.brown.edu Supplementary information: Supplementary data are available at Bioinformatics online. |
| Author | Mahmoody, Ahmad Raphael, Benjamin J. Hajirasouliha, Iman |
| AuthorAffiliation | 1 Department of Computer Science and 2 Center for Computational Molecular Biology, Brown University, Providence, RI, 02906, USA |
| AuthorAffiliation_xml | – name: 1 Department of Computer Science and 2 Center for Computational Molecular Biology, Brown University, Providence, RI, 02906, USA |
| Author_xml | – sequence: 1 givenname: Iman surname: Hajirasouliha fullname: Hajirasouliha, Iman – sequence: 2 givenname: Ahmad surname: Mahmoody fullname: Mahmoody, Ahmad – sequence: 3 givenname: Benjamin J. surname: Raphael fullname: Raphael, Benjamin J. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24932008$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNUctu1TAQtVAr-oBPAGXJJtSOnTgRElJV8ZIqdQNra-xMEqPEDrZDdfl6fHVLoWzalUf2eficOSNHzjsk5BWjbxnt-IW23rrBhwWSNfFCp61qxTNyyngjS9EydnQ_U35CzmL8Timtad08JyeV6HhFaXtK8LIwftHWQfLBwlzAugYPZiqydgEO5t0v68bCuhSgTNuSbydMGPyIDm3aFUPwSzHZcSrTFPw2TuuWiog_NnRmz-whwQtyPMAc8eXdeU6-ffzw9epzeX3z6cvV5XVpaiFTKVglhdFtlwMyybXoB2y6ejBM9yjl0HWmHTgAxxb6TnAhQSBjFZNaNBw1PyfNQXdzK-xuYZ7VGuwCYacYVfve1MPe1KG3THx_IK6bXrA3uI_7l-zBqocvzk5q9D-VoHXbVTILvLkTCD5Hj0ktNhqcZ3Dot6hYzZumZUKwDH39r9e9yZ-lZMC7A8AEH2PAQRmb8nf93trOj0ap_2M_rYLfY3XCHw |
| CitedBy_id | crossref_primary_10_1093_bioinformatics_btaa676 crossref_primary_10_1038_s41467_019_10737_5 crossref_primary_10_1186_s13059_015_0602_8 crossref_primary_10_1093_bioinformatics_btz737 crossref_primary_10_3389_fcell_2022_938685 crossref_primary_10_1371_journal_pcbi_1005509 crossref_primary_10_1089_cmb_2016_0148 crossref_primary_10_1186_s12920_019_0626_0 crossref_primary_10_1371_journal_pone_0158569 crossref_primary_10_1002_hed_24085 crossref_primary_10_1016_j_bbcan_2017_02_001 crossref_primary_10_1089_cmb_2018_0242 crossref_primary_10_1093_bioinformatics_btx270 crossref_primary_10_1007_s10555_021_09969_z crossref_primary_10_1186_1471_2105_16_S13_S7 crossref_primary_10_1093_bioinformatics_bty683 crossref_primary_10_1371_journal_pcbi_1004416 crossref_primary_10_1186_s12864_021_07660_9 crossref_primary_10_1093_bioadv_vbae094 crossref_primary_10_1186_s12859_019_2824_3 crossref_primary_10_1371_journal_pcbi_1007451 crossref_primary_10_1186_s13059_014_0443_x crossref_primary_10_1016_j_bbcan_2015_03_005 crossref_primary_10_1155_2017_5482750 crossref_primary_10_1093_bioinformatics_btv003 crossref_primary_10_1016_j_ejor_2024_09_006 crossref_primary_10_1093_bioinformatics_btaa722 crossref_primary_10_1016_j_cels_2016_07_004 crossref_primary_10_1186_s13015_019_0152_9 crossref_primary_10_1089_cmb_2017_0101 crossref_primary_10_1038_s41598_017_16813_4 crossref_primary_10_1002_cam4_3323 crossref_primary_10_18632_oncotarget_26485 crossref_primary_10_1186_1471_2164_16_S2_S1 crossref_primary_10_1101_gr_234435_118 crossref_primary_10_1093_bib_bby084 crossref_primary_10_1186_s12885_016_2202_8 crossref_primary_10_1371_journal_pone_0208002 crossref_primary_10_1016_j_bbcan_2017_01_003 crossref_primary_10_1016_j_bj_2024_100774 crossref_primary_10_1038_nrg_2016_170 crossref_primary_10_1186_s13059_014_0470_7 crossref_primary_10_1371_journal_pone_0188878 crossref_primary_10_1186_s13059_015_0647_8 crossref_primary_10_1093_bib_bbaa188 crossref_primary_10_3390_a16070333 crossref_primary_10_1007_s40484_019_0188_3 crossref_primary_10_3389_fgene_2017_00083 crossref_primary_10_1093_bioinformatics_btz355 crossref_primary_10_1038_s41467_020_14351_8 crossref_primary_10_1371_journal_pcbi_1008400 crossref_primary_10_1186_s13059_016_0936_x crossref_primary_10_1016_j_tcs_2016_08_015 crossref_primary_10_1371_journal_pone_0135817 crossref_primary_10_1186_s13073_019_0643_9 crossref_primary_10_1093_bioinformatics_btu651 crossref_primary_10_1007_s12015_023_10523_3 crossref_primary_10_1038_s41598_017_13338_8 crossref_primary_10_1093_molbev_msac136 crossref_primary_10_1186_s12859_020_03736_7 crossref_primary_10_1186_s12864_019_6328_3 crossref_primary_10_1111_cas_13510 crossref_primary_10_1186_1471_2164_16_S8_S7 crossref_primary_10_1093_bioinformatics_btaa464 crossref_primary_10_1093_bioinformatics_btv261 crossref_primary_10_1137_21M1449269 crossref_primary_10_1109_TBME_2016_2560939 crossref_primary_10_1109_TCBB_2018_2865729 crossref_primary_10_1089_cmb_2021_0271 crossref_primary_10_1101_gr_220707_117 crossref_primary_10_1186_s13059_014_0473_4 |
| Cites_doi | 10.1016/j.cell.2012.04.023 10.1038/nature10933 10.1038/nature12213 10.1016/j.canlet.2012.12.028 10.1038/nmeth.2883 10.1038/nature09807 10.1186/1471-2105-15-35 10.1182/blood-2012-05-433540 10.1056/NEJMoa1113205 10.1126/science.959840 10.1038/nature10738 10.1186/gb-2013-14-7-r80 10.1137/0402008 10.1126/science.1235122 10.1038/nature12634 10.1016/j.cell.2012.02.025 10.1101/gr.151670.112 10.1038/nbt.2203 10.1016/j.cell.2012.02.028 10.1093/nar/gkt641 |
| ContentType | Journal Article |
| Copyright | The Author 2014. Published by Oxford University Press. The Author 2014. Published by Oxford University Press. 2014 |
| Copyright_xml | – notice: The Author 2014. Published by Oxford University Press. – notice: The Author 2014. Published by Oxford University Press. 2014 |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 5PM ADTOC UNPAY |
| DOI | 10.1093/bioinformatics/btu284 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic PubMed Central (Full Participant titles) Unpaywall for CDI: Periodical Content Unpaywall |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE MEDLINE - Academic CrossRef |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 3 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 1367-4811 |
| EndPage | i86 |
| ExternalDocumentID | 10.1093/bioinformatics/btu284 PMC4058927 24932008 10_1093_bioinformatics_btu284 |
| Genre | Research Support, U.S. Gov't, Non-P.H.S Research Support, Non-U.S. Gov't Journal Article Research Support, N.I.H., Extramural |
| GrantInformation_xml | – fundername: NHGRI NIH HHS grantid: R01 HG005690 – fundername: NHGRI NIH HHS grantid: R01HG5690 – fundername: NCI NIH HHS grantid: R01 CA180776 |
| GroupedDBID | --- -E4 -~X .2P .DC .I3 0R~ 1TH 23N 2WC 4.4 48X 53G 5GY 5WA 70D AAIJN AAIMJ AAJKP AAJQQ AAKPC AAMDB AAMVS AAOGV AAPQZ AAPXW AAUQX AAVAP AAVLN AAYXX ABEJV ABEUO ABGNP ABIXL ABNKS ABPQP ABPTD ABQLI ABWST ABXVV ABZBJ ACGFS ACIWK ACPRK ACUFI ACUXJ ACYTK ADBBV ADEYI ADEZT ADFTL ADGKP ADGZP ADHKW ADHZD ADMLS ADOCK ADPDF ADRDM ADRTK ADVEK ADYVW ADZTZ ADZXQ AECKG AEGPL AEJOX AEKKA AEKSI AELWJ AEMDU AENEX AENZO AEPUE AETBJ AEWNT AFFZL AFGWE AFIYH AFOFC AFRAH AGINJ AGKEF AGQXC AGSYK AHMBA AHXPO AIJHB AJEEA AJEUX AKHUL AKWXX ALMA_UNASSIGNED_HOLDINGS ALTZX ALUQC AMNDL APIBT APWMN ARIXL ASPBG AVWKF AXUDD AYOIW AZVOD BAWUL BAYMD BHONS BQDIO BQUQU BSWAC BTQHN C45 CDBKE CITATION CS3 CZ4 DAKXR DIK DILTD DU5 D~K EBD EBS EE~ EJD EMOBN F5P F9B FEDTE FHSFR FLIZI FLUFQ FOEOM FQBLK GAUVT GJXCC GROUPED_DOAJ GX1 H13 H5~ HAR HW0 HZ~ IOX J21 JXSIZ KAQDR KOP KQ8 KSI KSN M-Z MK~ ML0 N9A NGC NLBLG NMDNZ NOMLY NU- NVLIB O9- OAWHX ODMLO OJQWA OK1 OVD OVEED P2P PAFKI PEELM PQQKQ Q1. Q5Y R44 RD5 RNS ROL RPM RUSNO RW1 RXO SV3 TEORI TJP TLC TOX TR2 W8F WOQ X7H YAYTL YKOAZ YXANX ZKX ~91 ~KM ABQTQ CGR CUY CVF ECM EIF M49 NPM 7X8 5PM .-4 .GJ ABEFU ABNGD ACUKT ADTOC AFFNX AGQPQ AI. AQDSO ATTQO AZFZN C1A CAG COF ELUNK HVGLF NTWIH O0~ O~Y PB- RNI RZF RZO UNPAY VH1 ZGI |
| ID | FETCH-LOGICAL-c547t-41274cb89093173b4dfe695fc1bde77f99c8f3aa3e8ad94347a4e11217b463eb3 |
| IEDL.DBID | UNPAY |
| ISSN | 1367-4803 1367-4811 |
| IngestDate | Wed Oct 29 11:58:00 EDT 2025 Tue Sep 30 16:27:48 EDT 2025 Thu Jul 10 23:27:34 EDT 2025 Thu Apr 03 07:05:09 EDT 2025 Thu Apr 24 23:04:56 EDT 2025 Tue Jul 01 03:27:10 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 12 |
| Language | English |
| License | http://creativecommons.org/licenses/by-nc/3.0 The Author 2014. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com cc-by-nc |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c547t-41274cb89093173b4dfe695fc1bde77f99c8f3aa3e8ad94347a4e11217b463eb3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 The authors wish it to be known that in their opinion, the first two authors should be regarded as Joint First Authors. |
| OpenAccessLink | https://proxy.k.utb.cz/login?url=https://academic.oup.com/bioinformatics/article-pdf/30/12/i78/48927337/bioinformatics_30_12_i78.pdf |
| PMID | 24932008 |
| PQID | 1536681441 |
| PQPubID | 23479 |
| ParticipantIDs | unpaywall_primary_10_1093_bioinformatics_btu284 pubmedcentral_primary_oai_pubmedcentral_nih_gov_4058927 proquest_miscellaneous_1536681441 pubmed_primary_24932008 crossref_citationtrail_10_1093_bioinformatics_btu284 crossref_primary_10_1093_bioinformatics_btu284 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2014-06-15 |
| PublicationDateYYYYMMDD | 2014-06-15 |
| PublicationDate_xml | – month: 06 year: 2014 text: 2014-06-15 day: 15 |
| PublicationDecade | 2010 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England |
| PublicationTitle | Bioinformatics (Oxford, England) |
| PublicationTitleAlternate | Bioinformatics |
| PublicationYear | 2014 |
| Publisher | Oxford University Press |
| Publisher_xml | – name: Oxford University Press |
| References | Xu (2023012711105044200_btu284-B25) 2012; 148 Shah (2023012711105044200_btu284-B22) 2012; 486 Oesper (2023012711105044200_btu284-B19) 2013; 14 Miller (2023012711105044200_btu284-B14) Nowell (2023012711105044200_btu284-B18) 1976; 194 Salari (2023012711105044200_btu284-B20) 2013 Newburger (2023012711105044200_btu284-B16) 2013; 23 Ding (2023012711105044200_btu284-B3) 2013; 340 Jiao (2023012711105044200_btu284-B9) 2014; 15 Hou (2023012711105044200_btu284-B7) 2012; 148 Hurkens (2023012711105044200_btu284-B8) 1989; 2 Gerlinger (2023012711105044200_btu284-B5) 2012; 366 Kurihara (2023012711105044200_btu284-B12) 2006 Schuh (2023012711105044200_btu284-B21) 2012; 120 Nik-Zainal (2023012711105044200_btu284-B17) 2012; 149 Carter (2023012711105044200_btu284-B1) 2012; 30 Lawrence (2023012711105044200_btu284-B13) 2013; 499 Roth (2023012711105044200_btu284-B26) 2014; 11 Garey (2023012711105044200_btu284-B4) 1979 Strino (2023012711105044200_btu284-B23) 2013; 41 Navin (2023012711105044200_btu284-B15) 2011; 472 Ding (2023012711105044200_btu284-B2) 2012; 481 Vogelstein (2023012711105044200_btu284-B24) 2013; 339 Hajirasouliha (2023012711105044200_btu284-B6) 2007 Kandoth (2023012711105044200_btu284-B10) 2013; 502 23895164 - Genome Biol. 2013;14(7):R80 24633410 - Nat Methods. 2014 Apr;11(4):396-8 22495314 - Nature. 2012 Jun 21;486(7403):395-9 23539594 - Science. 2013 Mar 29;339(6127):1546-58 24484323 - BMC Bioinformatics. 2014;15:35 22397650 - N Engl J Med. 2012 Mar 8;366(10):883-92 22385958 - Cell. 2012 Mar 2;148(5):886-95 23770567 - Nature. 2013 Jul 11;499(7457):214-8 22608083 - Cell. 2012 May 25;149(5):994-1007 21399628 - Nature. 2011 Apr 7;472(7341):90-4 24195709 - J Comput Biol. 2013 Nov;20(11):933-44 24132290 - Nature. 2013 Oct 17;502(7471):333-9 23353056 - Cancer Lett. 2013 Nov 1;340(2):212-9 23892400 - Nucleic Acids Res. 2013 Sep;41(17):e165 22385957 - Cell. 2012 Mar 2;148(5):873-85 25102416 - PLoS Comput Biol. 2014 Aug 07;10(8):e1003665 22237025 - Nature. 2012 Jan 26;481(7382):506-10 23568837 - Genome Res. 2013 Jul;23(7):1097-108 959840 - Science. 1976 Oct 1;194(4260):23-8 22544022 - Nat Biotechnol. 2012 May;30(5):413-21 22915640 - Blood. 2012 Nov 15;120(20):4191-6 |
| References_xml | – start-page: 524 volume-title: STACS year: 2007 ident: 2023012711105044200_btu284-B6 article-title: On completing latin squares – volume: 149 start-page: 994 year: 2012 ident: 2023012711105044200_btu284-B17 article-title: The life history of 21 breast cancers publication-title: Cell doi: 10.1016/j.cell.2012.04.023 – volume: 486 start-page: 395 year: 2012 ident: 2023012711105044200_btu284-B22 article-title: The clonal and mutational evolution spectrum of primary triple-negative breast cancers publication-title: Nature doi: 10.1038/nature10933 – volume: 499 start-page: 214 year: 2013 ident: 2023012711105044200_btu284-B13 article-title: Mutational heterogeneity in cancer and the search for new cancer-associated genes publication-title: Nature doi: 10.1038/nature12213 – volume: 340 start-page: 212 year: 2013 ident: 2023012711105044200_btu284-B3 article-title: Advances for studying clonal evolution in cancer publication-title: Cancer Lett. doi: 10.1016/j.canlet.2012.12.028 – volume: 11 start-page: 396 year: 2014 ident: 2023012711105044200_btu284-B26 article-title: PyClone: statistical inference of clonal population structure in cancer publication-title: Nature Methods doi: 10.1038/nmeth.2883 – volume: 472 start-page: 90 year: 2011 ident: 2023012711105044200_btu284-B15 article-title: Tumour evolution inferred by single-cell sequencing publication-title: Nature doi: 10.1038/nature09807 – volume-title: Computers and Intractability: A Guide to the Theory of NP-Completeness year: 1979 ident: 2023012711105044200_btu284-B4 – volume: 15 start-page: 35 year: 2014 ident: 2023012711105044200_btu284-B9 article-title: Inferring clonal evolution of tumors from single nucleotide somatic mutations publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-15-35 – volume: 120 start-page: 4191 year: 2012 ident: 2023012711105044200_btu284-B21 article-title: Monitoring chronic lymphocytic leukemia progression by whole genome sequencing reveals heterogeneous clonal evolution patterns publication-title: Blood doi: 10.1182/blood-2012-05-433540 – volume: 366 start-page: 883 year: 2012 ident: 2023012711105044200_btu284-B5 article-title: Intratumor heterogeneity and branched evolution revealed by multiregion sequencing publication-title: N. Engl. J. Med. doi: 10.1056/NEJMoa1113205 – volume: 194 start-page: 23 year: 1976 ident: 2023012711105044200_btu284-B18 article-title: The clonal evolution of tumor cell populations publication-title: Science doi: 10.1126/science.959840 – start-page: 249 volume-title: RECOMB year: 2013 ident: 2023012711105044200_btu284-B20 article-title: Inference of tumor phylogenies with improved somatic mutation discovery – volume: 481 start-page: 506 year: 2012 ident: 2023012711105044200_btu284-B2 article-title: Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing publication-title: Nature doi: 10.1038/nature10738 – ident: 2023012711105044200_btu284-B14 article-title: Sciclone: Inferring clonal architecture and tracking the spatial and temporal patterns of tumor evolution publication-title: PLoS Comput Biol – volume: 14 start-page: R80 year: 2013 ident: 2023012711105044200_btu284-B19 article-title: Theta: inferring intra-tumor heterogeneity from high-throughput dna sequencing data publication-title: Genome Biol. doi: 10.1186/gb-2013-14-7-r80 – volume: 2 start-page: 68 year: 1989 ident: 2023012711105044200_btu284-B8 article-title: On the size of systems of sets every t of which have an SDR, with an application to the worst-case ratio of heuristics for packing problems publication-title: SIAM J. Discret. Math. doi: 10.1137/0402008 – volume: 339 start-page: 1546 year: 2013 ident: 2023012711105044200_btu284-B24 article-title: Cancer genome landscapes publication-title: Science doi: 10.1126/science.1235122 – volume: 502 start-page: 333 year: 2013 ident: 2023012711105044200_btu284-B10 article-title: Mutational landscape and significance across 12 major cancer types publication-title: Nature doi: 10.1038/nature12634 – volume: 148 start-page: 886 year: 2012 ident: 2023012711105044200_btu284-B25 article-title: Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor publication-title: Cell doi: 10.1016/j.cell.2012.02.025 – start-page: 761 volume-title: NIPS year: 2006 ident: 2023012711105044200_btu284-B12 article-title: Accelerated variational dirichlet process mixtures – volume: 23 start-page: 1097 year: 2013 ident: 2023012711105044200_btu284-B16 article-title: Genome evolution during progression to breast cancer publication-title: Genome Res. doi: 10.1101/gr.151670.112 – volume: 30 start-page: 413 year: 2012 ident: 2023012711105044200_btu284-B1 article-title: Absolute quantification of somatic DNA alterations in human cancer publication-title: Nat. Biotechnol. doi: 10.1038/nbt.2203 – volume: 148 start-page: 873 year: 2012 ident: 2023012711105044200_btu284-B7 article-title: Single-cell exome sequencing and monoclonal evolution of a JAK2-negative myeloproliferative neoplasm publication-title: Cell doi: 10.1016/j.cell.2012.02.028 – volume: 41 start-page: e165 year: 2013 ident: 2023012711105044200_btu284-B23 article-title: TrAp: a tree approach for fingerprinting subclonal tumor composition publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkt641 – reference: 24633410 - Nat Methods. 2014 Apr;11(4):396-8 – reference: 959840 - Science. 1976 Oct 1;194(4260):23-8 – reference: 22608083 - Cell. 2012 May 25;149(5):994-1007 – reference: 24132290 - Nature. 2013 Oct 17;502(7471):333-9 – reference: 22495314 - Nature. 2012 Jun 21;486(7403):395-9 – reference: 23568837 - Genome Res. 2013 Jul;23(7):1097-108 – reference: 22237025 - Nature. 2012 Jan 26;481(7382):506-10 – reference: 23539594 - Science. 2013 Mar 29;339(6127):1546-58 – reference: 23770567 - Nature. 2013 Jul 11;499(7457):214-8 – reference: 22397650 - N Engl J Med. 2012 Mar 8;366(10):883-92 – reference: 22915640 - Blood. 2012 Nov 15;120(20):4191-6 – reference: 23892400 - Nucleic Acids Res. 2013 Sep;41(17):e165 – reference: 22544022 - Nat Biotechnol. 2012 May;30(5):413-21 – reference: 24195709 - J Comput Biol. 2013 Nov;20(11):933-44 – reference: 25102416 - PLoS Comput Biol. 2014 Aug 07;10(8):e1003665 – reference: 23353056 - Cancer Lett. 2013 Nov 1;340(2):212-9 – reference: 22385957 - Cell. 2012 Mar 2;148(5):873-85 – reference: 24484323 - BMC Bioinformatics. 2014;15:35 – reference: 21399628 - Nature. 2011 Apr 7;472(7341):90-4 – reference: 23895164 - Genome Biol. 2013;14(7):R80 – reference: 22385958 - Cell. 2012 Mar 2;148(5):886-95 |
| SSID | ssj0005056 |
| Score | 2.440862 |
| Snippet | Motivation: High-throughput sequencing of tumor samples has shown that most tumors exhibit extensive intra-tumor heterogeneity, with multiple subpopulations of... High-throughput sequencing of tumor samples has shown that most tumors exhibit extensive intra-tumor heterogeneity, with multiple subpopulations of tumor cells... |
| SourceID | unpaywall pubmedcentral proquest pubmed crossref |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | i78 |
| SubjectTerms | Algorithms Cluster Analysis Gene Frequency High-Throughput Nucleotide Sequencing Humans Ismb 2014 Proceedings Papers Committee Leukemia, Myeloid, Acute - genetics Mutation Neoplasms - genetics Sequence Analysis, DNA |
| Title | A combinatorial approach for analyzing intra-tumor heterogeneity from high-throughput sequencing data |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/24932008 https://www.proquest.com/docview/1536681441 https://pubmed.ncbi.nlm.nih.gov/PMC4058927 https://academic.oup.com/bioinformatics/article-pdf/30/12/i78/48927337/bioinformatics_30_12_i78.pdf |
| UnpaywallVersion | publishedVersion |
| Volume | 30 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 1367-4811 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0005056 issn: 1367-4811 databaseCode: KQ8 dateStart: 19960101 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries – providerCode: PRVEBS databaseName: Inspec with Full Text customDbUrl: eissn: 1367-4811 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0005056 issn: 1367-4811 databaseCode: ADMLS dateStart: 19980101 isFulltext: true titleUrlDefault: https://www.ebsco.com/products/research-databases/inspec-full-text providerName: EBSCOhost – providerCode: PRVBFR databaseName: Free Medical Journals customDbUrl: eissn: 1367-4811 dateEnd: 20241102 omitProxy: true ssIdentifier: ssj0005056 issn: 1367-4811 databaseCode: DIK dateStart: 19960101 isFulltext: true titleUrlDefault: http://www.freemedicaljournals.com providerName: Flying Publisher – providerCode: PRVFQY databaseName: GFMER Free Medical Journals customDbUrl: eissn: 1367-4811 dateEnd: 20241102 omitProxy: true ssIdentifier: ssj0005056 issn: 1367-4811 databaseCode: GX1 dateStart: 19960101 isFulltext: true titleUrlDefault: http://www.gfmer.ch/Medical_journals/Free_medical.php providerName: Geneva Foundation for Medical Education and Research – providerCode: PRVAQN databaseName: PubMed Central customDbUrl: eissn: 1367-4811 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0005056 issn: 1367-4811 databaseCode: RPM dateStart: 20070101 isFulltext: true titleUrlDefault: https://www.ncbi.nlm.nih.gov/pmc/ providerName: National Library of Medicine – providerCode: PRVOVD databaseName: Journals@Ovid LWW All Open Access Journal Collection Rolling customDbUrl: eissn: 1367-4811 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0005056 issn: 1367-4811 databaseCode: OVEED dateStart: 20010101 isFulltext: true titleUrlDefault: http://ovidsp.ovid.com/ providerName: Ovid – providerCode: PRVASL databaseName: Oxford Journals Open Access Collection customDbUrl: eissn: 1367-4811 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0005056 issn: 1367-4811 databaseCode: TOX dateStart: 19850101 isFulltext: true titleUrlDefault: https://academic.oup.com/journals/ providerName: Oxford University Press – providerCode: PRVASL databaseName: Oxford Journals Open Access Collection customDbUrl: eissn: 1367-4811 dateEnd: 20220930 omitProxy: true ssIdentifier: ssj0005056 issn: 1367-4811 databaseCode: TOX dateStart: 19850101 isFulltext: true titleUrlDefault: https://academic.oup.com/journals/ providerName: Oxford University Press |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV07b9swED4kDopm6fuhPgIW6ErLFCmRGo2iQdAh7RAD7iSQFIkYdWSjlRAkc394jxZl1M7QdugmSHyI5FH3nXj3HcD7zLPSFoZT66WlQpucloXP6EQz7hABIwTZsH2eF2cz8Wmezw_ADrEwOnqFj4eQBrNYRQrRQFucxvmk69qnfJKyLF1IlQpVohrmcq90xRHhZhWWGGP5QzgqcgTsIzianX-Zfu0jsiQVapM_OV4zNsT5lHy_c9N2mRK7GuwOLL3rXXm_a9b65lovl7-prtOH8HMYdO-x8m3ctWZsb_f4IP_zrDyCBxH6kmnfymM4cM0TuNcnw7x5Cm5KsE801MNvANwVZKA7J9gm0YE85Rb1LFmE8dK2u8K7l8GZZ4V7wKExQUKoDAn8yzRmH1p3LYm-4qFm8IZ9BrPTjxcfzmhMAkFtLmRLBUO72RpV4nowyY2ovSvK3FtmaielL0urPNeaO6XrQHYntXAIIpk0ouDO8OcwalaNewkEP2dFbnzuuVJC29pMslpPuGHM5MyzLAExLGxlI0N6SNSxrPqTel7tTWQvDwmMt9XWPUXInyq8G6Smws0cTmh041bdjwrVT1GoYOMm8KKXom2TaCfz4KySgNyRr22BQBS--6RZXG4Iw0XIHZnJBNKtJP7dm7765xqv4RghpQjOdCx_A6P2e-feImxrzQkcXnyen8TN9wv97U8C |
| linkProvider | Unpaywall |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwEB6VrRBcKG9SHjISVyfr2Imd46qiqjhUHFipnCLbsdUV2-wKEqH2zA9nvHFW7PZAOXCLEj9ie5z5Jp75BuBD7lllS8Op9dJSoU1Bq9LndKoZd4iAEYJs2D7Py7O5-HRRXByAHWNhdPQKT8eQBrNYRQrRQFucxfmk68ZnfJqxPFtIlQlVoRrmcq90zRHh5jWWSLH8PTgsCwTsEzicn3-efR0isiQVapM_OV4zNsb5VHy_c9P1uRK7GuwWLL3tXfmgb9f6-qdeLv9QXadH8Gsc9OCx8i3tO5Pamz0-yP88K4_hUYS-ZDa08gQOXPsU7g_JMK-fgZsR7BMN9fAbAHcFGenOCbZJdCBPuUE9SxZhvLTrr_DuZXDmWeEecGhMkBAqQwL_Mo3Zh9Z9R6KveKgZvGGfw_z045eTMxqTQFBbCNlRwdButkZVuB5MciMa78qq8JaZxknpq8oqz7XmTukmkN1JLRyCSCaNKLkz_AVM2lXrXgHBz1lZGF94rpTQtjHTvNFTbhgzBfMsT0CMC1vbyJAeEnUs6-Gkntd7EznIQwLpttp6oAj5W4X3o9TUuJnDCY1u3ar_UaP6KUsVbNwEXg5StG0S7WQenFUSkDvytS0QiMJ3n7SLyw1huAi5I3OZQLaVxLu96fE_13gNDxFSiuBMx4o3MOm-9-4twrbOvIvb7jcpCE3m |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+combinatorial+approach+for+analyzing+intra-tumor+heterogeneity+from+high-throughput+sequencing+data&rft.jtitle=Bioinformatics+%28Oxford%2C+England%29&rft.au=Hajirasouliha%2C+Iman&rft.au=Mahmoody%2C+Ahmad&rft.au=Raphael%2C+Benjamin+J.&rft.date=2014-06-15&rft.pub=Oxford+University+Press&rft.issn=1367-4803&rft.eissn=1367-4811&rft.volume=30&rft.issue=12&rft.spage=i78&rft.epage=i86&rft_id=info:doi/10.1093%2Fbioinformatics%2Fbtu284&rft_id=info%3Apmid%2F24932008&rft.externalDocID=PMC4058927 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1367-4803&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1367-4803&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1367-4803&client=summon |