A large peptidome dataset improves HLA class I epitope prediction across most of the human population
Prediction of HLA epitopes is important for the development of cancer immunotherapies and vaccines. However, current prediction algorithms have limited predictive power, in part because they were not trained on high-quality epitope datasets covering a broad range of HLA alleles. To enable prediction...
Saved in:
Published in | Nature biotechnology Vol. 38; no. 2; pp. 199 - 209 |
---|---|
Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
New York
Nature Publishing Group US
01.02.2020
Nature Publishing Group |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | Prediction of HLA epitopes is important for the development of cancer immunotherapies and vaccines. However, current prediction algorithms have limited predictive power, in part because they were not trained on high-quality epitope datasets covering a broad range of HLA alleles. To enable prediction of endogenous HLA class I-associated peptides across a large fraction of the human population, we used mass spectrometry to profile >185,000 peptides eluted from 95 HLA-A, -B, -C and -G mono-allelic cell lines. We identified canonical peptide motifs per HLA allele, unique and shared binding submotifs across alleles and distinct motifs associated with different peptide lengths. By integrating these data with transcript abundance and peptide processing, we developed HLAthena, providing allele-and-length-specific and pan-allele-pan-length prediction models for endogenous peptide presentation. These models predicted endogenous HLA class I-associated ligands with 1.5-fold improvement in positive predictive value compared with existing tools and correctly identified >75% of HLA-bound peptides that were observed experimentally in 11 patient-derived tumor cell lines.
Prediction of HLA class I epitopes is improved in accuracy and breath with peptidomes from 95 mono-allelic cell lines. |
---|---|
AbstractList | Prediction of HLA epitopes is important for the development of cancer immunotherapies and vaccines. However, current prediction algorithms have limited predictive power, in part because they were not trained on high-quality epitope datasets covering a broad range of HLA alleles. To enable prediction of endogenous HLA class I-associated peptides across a large fraction of the human population, we used mass spectrometry to profile >185,000 peptides eluted from 95 HLA-A, -B, -C and -G mono-allelic cell lines. We identified canonical peptide motifs per HLA allele, unique and shared binding submotifs across alleles and distinct motifs associated with different peptide lengths. By integrating these data with transcript abundance and peptide processing, we developed HLAthena, providing allele-and-length-specific and pan-allele-pan-length prediction models for endogenous peptide presentation. These models predicted endogenous HLA class I-associated ligands with 1.5-fold improvement in positive predictive value compared with existing tools and correctly identified >75% of HLA-bound peptides that were observed experimentally in 11 patient-derived tumor cell lines.Prediction of HLA class I epitopes is improved in accuracy and breath with peptidomes from 95 mono-allelic cell lines. Prediction of HLA epitopes is important for the development of cancer immunotherapies and vaccines. However, current prediction algorithms have limited predictive power, in part because they were not trained on high-quality epitope datasets covering a broad range of HLA alleles. To enable prediction of endogenous HLA class I-associated peptides across a large fraction of the human population, we used mass spectrometry to profile >185,000 peptides eluted from 95 HLA-A, -B, -C and -G mono-allelic cell lines. We identified canonical peptide motifs per HLA allele, unique and shared binding submotifs across alleles and distinct motifs associated with different peptide lengths. By integrating these data with transcript abundance and peptide processing, we developed HLAthena, providing allele-and-length-specific and pan-allele-pan-length prediction models for endogenous peptide presentation. These models predicted endogenous HLA class I-associated ligands with 1.5-fold improvement in positive predictive value compared with existing tools and correctly identified >75% of HLA-bound peptides that were observed experimentally in 11 patient-derived tumor cell lines. Prediction of HLA epitopes is important for the development of cancer immunotherapies and vaccines. However, current prediction algorithms have limited predictive power, in part because they were not trained on high-quality epitope datasets covering a broad range of HLA alleles. To enable prediction of endogenous HLA class I-associated peptides across a large fraction of the human population, we used mass spectrometry to profile >185,000 peptides eluted from 95 HLA-A, -B, -C and -G mono-allelic cell lines. We identified canonical peptide motifs per HLA allele, unique and shared binding submotifs across alleles and distinct motifs associated with different peptide lengths. By integrating these data with transcript abundance and peptide processing, we developed HLAthena, providing allele-and-length-specific and pan-allele-pan-length prediction models for endogenous peptide presentation. These models predicted endogenous HLA class I-associated ligands with 1.5-fold improvement in positive predictive value compared with existing tools and correctly identified >75% of HLA-bound peptides that were observed experimentally in 11 patient-derived tumor cell lines. Prediction of HLA class I epitopes is improved in accuracy and breath with peptidomes from 95 mono-allelic cell lines. Prediction of HLA epitopes is important for the development of cancer immunotherapies and vaccines. However, current prediction algorithms have limited predictive power, in part because they were not trained on high-quality epitope datasets covering a broad range of HLA alleles. To enable prediction of endogenous HLA class I–associated peptides across a large fraction of the human population, we used mass spectrometry to profile >185,000 peptides eluted from 95 HLA-A, B, C and G mono-allelic cell lines. We identified canonical peptide motifs per HLA allele, unique and shared binding submotifs across alleles, and distinct motifs associated with different peptide lengths. By integrating these data with transcript abundance and peptide processing, we developed HLAthena , providing allele-and-length-specific and pan-allele-pan-length prediction models for endogenous peptide presentation. These models predicted endogenous HLA class I–associated ligands with 1.5-fold improvement in positive predictive value compared with existing tools and correctly identified >75% of HLA-bound peptides that were observed experimentally in 11 patient-derived tumor cell lines. |
Audience | Academic |
Author | Oliveira, Giacomo Rosenbluth, Jennifer M. Hartigan, Christina R. Keskin, Derin B. Bachireddy, Pavan Clauser, Karl R. Lan Zhang, Guang Carr, Steven A. Le, Phuong M. Zhang, Wandi Zervantonakis, Ioannis K. Ligon, Keith L. Hacohen, Nir Klaeger, Susan Keshishian, Hasmik Eisenhaure, Thomas Lane, William J. Sarkizova, Siranush Braun, David A. Wu, Catherine J. Stevens, Jonathan Ouspenskaia, Tamara Li, Letitia W. Justesen, Sune Law, Travis |
AuthorAffiliation | 8 Department of Cell Biology, Harvard Medical School, Boston, Massachusetts, USA 12 Center for Cancer Immunology, Massachusetts General Hospital, Boston, Massachusetts, USA 9 Immunitrack, Copenhagen E, Denmark 1 Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA 2 Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA 3 Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA 5 Harvard Medical School, Boston, Massachusetts, USA 11 Department of Computer Science, Metropolitan College, Boston University, Boston, Massachusetts, USA 4 Center for Patient Derived Models, Dana-Farber Cancer Institute, Boston, Massachusetts, USA 6 Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA 7 Division of Neuropathology, Brigham and Women’s Hospital, Boston, Massachusetts, USA 10 Department of Pathology, Brigham and Women’s Hospital, Boston, Massachusetts, USA |
AuthorAffiliation_xml | – name: 4 Center for Patient Derived Models, Dana-Farber Cancer Institute, Boston, Massachusetts, USA – name: 3 Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA – name: 9 Immunitrack, Copenhagen E, Denmark – name: 5 Harvard Medical School, Boston, Massachusetts, USA – name: 10 Department of Pathology, Brigham and Women’s Hospital, Boston, Massachusetts, USA – name: 6 Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA – name: 11 Department of Computer Science, Metropolitan College, Boston University, Boston, Massachusetts, USA – name: 8 Department of Cell Biology, Harvard Medical School, Boston, Massachusetts, USA – name: 12 Center for Cancer Immunology, Massachusetts General Hospital, Boston, Massachusetts, USA – name: 1 Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA – name: 2 Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA – name: 7 Division of Neuropathology, Brigham and Women’s Hospital, Boston, Massachusetts, USA |
Author_xml | – sequence: 1 givenname: Siranush surname: Sarkizova fullname: Sarkizova, Siranush organization: Department of Biomedical Informatics, Harvard Medical School, Broad Institute of MIT and Harvard – sequence: 2 givenname: Susan orcidid: 0000-0002-0074-5163 surname: Klaeger fullname: Klaeger, Susan organization: Broad Institute of MIT and Harvard – sequence: 3 givenname: Phuong M. surname: Le fullname: Le, Phuong M. organization: Department of Medical Oncology, Dana-Farber Cancer Institute – sequence: 4 givenname: Letitia W. surname: Li fullname: Li, Letitia W. organization: Department of Medical Oncology, Dana-Farber Cancer Institute – sequence: 5 givenname: Giacomo surname: Oliveira fullname: Oliveira, Giacomo organization: Department of Medical Oncology, Dana-Farber Cancer Institute – sequence: 6 givenname: Hasmik surname: Keshishian fullname: Keshishian, Hasmik organization: Broad Institute of MIT and Harvard – sequence: 7 givenname: Christina R. surname: Hartigan fullname: Hartigan, Christina R. organization: Broad Institute of MIT and Harvard – sequence: 8 givenname: Wandi surname: Zhang fullname: Zhang, Wandi organization: Department of Medical Oncology, Dana-Farber Cancer Institute – sequence: 9 givenname: David A. surname: Braun fullname: Braun, David A. organization: Broad Institute of MIT and Harvard, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Department of Medicine, Brigham and Women’s Hospital – sequence: 10 givenname: Keith L. surname: Ligon fullname: Ligon, Keith L. organization: Broad Institute of MIT and Harvard, Harvard Medical School, Center for Patient Derived Models, Dana-Farber Cancer Institute, Division of Neuropathology, Brigham and Women’s Hospital – sequence: 11 givenname: Pavan surname: Bachireddy fullname: Bachireddy, Pavan organization: Broad Institute of MIT and Harvard, Department of Medical Oncology, Dana-Farber Cancer Institute, Department of Medicine, Brigham and Women’s Hospital – sequence: 12 givenname: Ioannis K. orcidid: 0000-0003-2386-9553 surname: Zervantonakis fullname: Zervantonakis, Ioannis K. organization: Department of Cell Biology, Harvard Medical School – sequence: 13 givenname: Jennifer M. orcidid: 0000-0003-1314-6976 surname: Rosenbluth fullname: Rosenbluth, Jennifer M. organization: Department of Cell Biology, Harvard Medical School – sequence: 14 givenname: Tamara surname: Ouspenskaia fullname: Ouspenskaia, Tamara organization: Broad Institute of MIT and Harvard – sequence: 15 givenname: Travis orcidid: 0000-0002-7399-3299 surname: Law fullname: Law, Travis organization: Broad Institute of MIT and Harvard – sequence: 16 givenname: Sune surname: Justesen fullname: Justesen, Sune organization: Immunitrack – sequence: 17 givenname: Jonathan surname: Stevens fullname: Stevens, Jonathan organization: Department of Pathology, Brigham and Women’s Hospital – sequence: 18 givenname: William J. orcidid: 0000-0002-1097-5229 surname: Lane fullname: Lane, William J. organization: Harvard Medical School, Department of Pathology, Brigham and Women’s Hospital – sequence: 19 givenname: Thomas surname: Eisenhaure fullname: Eisenhaure, Thomas organization: Broad Institute of MIT and Harvard – sequence: 20 givenname: Guang surname: Lan Zhang fullname: Lan Zhang, Guang organization: Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Department of Computer Science, Metropolitan College, Boston University – sequence: 21 givenname: Karl R. surname: Clauser fullname: Clauser, Karl R. organization: Broad Institute of MIT and Harvard – sequence: 22 givenname: Nir orcidid: 0000-0002-2349-2656 surname: Hacohen fullname: Hacohen, Nir email: nhacohen@mgh.harvard.edu organization: Broad Institute of MIT and Harvard, Department of Medical Oncology, Dana-Farber Cancer Institute, Center for Cancer Immunology, Massachusetts General Hospital – sequence: 23 givenname: Steven A. orcidid: 0000-0002-7203-4299 surname: Carr fullname: Carr, Steven A. email: scarr@broadinstitute.org organization: Broad Institute of MIT and Harvard – sequence: 24 givenname: Catherine J. orcidid: 0000-0002-3348-5054 surname: Wu fullname: Wu, Catherine J. email: cwu@partners.org organization: Broad Institute of MIT and Harvard, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Department of Medicine, Brigham and Women’s Hospital – sequence: 25 givenname: Derin B. orcidid: 0000-0002-8496-6181 surname: Keskin fullname: Keskin, Derin B. email: derin_keskin@dfci.harvard.edu organization: Broad Institute of MIT and Harvard, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Department of Medicine, Brigham and Women’s Hospital, Department of Computer Science, Metropolitan College, Boston University |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31844290$$D View this record in MEDLINE/PubMed |
BookMark | eNqNkl2L1DAUhousuB_6A7yRgDcKds1HJ2luhGFRd2Bgwa_bkKannSxtUpN00X9vZmddZ0RFAknT87xvk573tDhy3kFRPCX4nGBWv44VWdSixESWmFFaygfFCVlUvCRc8qP8jG-rC35cnMZ4jTHmFeePimNG6qqiEp8UsESDDj2gCaZkWz8CanXSERKy4xT8DUR0uV4iM-gY0QrBZJOfMh6gtSZZ75A2wefa6GNCvkNpA2gzj9qhyU_zoLfM4-Jhp4cIT-7Ws-Lzu7efLi7L9dX71cVyXRoucCo5kaKVRJBa1FXDGlMTyQjRsmlrSYwhglKjddXljeY1b0llaNOAbgiVTb1gZ8Wbne80NyO0BlwKelBTsKMO35XXVh1WnN2o3t8ogXGNJc4GL-4Mgv86Q0xqtNHAMGgHfo6KMiokExVlGX3-G3rt5-Dy9RStBBeckpr8k2KLbMcw3qN6PYCyrvP5dGb7abXkhFW5j0Jk6vwPVB4tjNbkaHQ2vz8QvDwQZCbBt9TrOUa1-vjh_9mrL4fsqz22maN1EPMUbb9JcSc5wMkOvw1KgO6-IQSrbZDVLsgqB1ltg6xk1jzb7-S94mdyM0B3QMwl10P49WP_7voDMC773A |
CitedBy_id | crossref_primary_10_1016_j_coi_2022_102172 crossref_primary_10_3390_antib9040065 crossref_primary_10_3389_fimmu_2022_812393 crossref_primary_10_1002_imed_1021 crossref_primary_10_1016_j_isci_2023_108613 crossref_primary_10_1038_s41586_022_04682_5 crossref_primary_10_1073_pnas_2007246117 crossref_primary_10_1093_pnasnexus_pgad400 crossref_primary_10_1093_bib_bbab493 crossref_primary_10_1007_s10811_021_02383_6 crossref_primary_10_1093_bioinformatics_btad468 crossref_primary_10_3389_fimmu_2020_01181 crossref_primary_10_3389_fimmu_2021_634441 crossref_primary_10_1002_jmv_28860 crossref_primary_10_1016_j_cels_2022_12_002 crossref_primary_10_1038_s41571_020_00460_2 crossref_primary_10_1038_s41467_024_48322_0 crossref_primary_10_1371_journal_ppat_1010337 crossref_primary_10_1016_j_celrep_2023_113596 crossref_primary_10_1038_s41587_022_01424_w crossref_primary_10_1038_s41573_023_00809_z crossref_primary_10_1038_s41467_021_24562_2 crossref_primary_10_3390_ijms23031913 crossref_primary_10_3389_fimmu_2023_1108303 crossref_primary_10_1038_s41586_023_06706_0 crossref_primary_10_1038_s41467_022_34904_3 crossref_primary_10_1016_j_mcpro_2023_100631 crossref_primary_10_1016_j_vaccine_2022_06_062 crossref_primary_10_1158_2326_6066_CIR_22_0621 crossref_primary_10_1158_2159_8290_CD_23_1199 crossref_primary_10_1016_j_xcrm_2021_100194 crossref_primary_10_1093_bib_bbae302 crossref_primary_10_1093_bioinformatics_btab628 crossref_primary_10_1016_j_tig_2020_10_001 crossref_primary_10_1021_acscentsci_3c01544 crossref_primary_10_1016_j_mcpro_2022_100228 crossref_primary_10_1002_cti2_1226 crossref_primary_10_1136_jitc_2023_008306 crossref_primary_10_1093_database_baae014 crossref_primary_10_1073_pnas_2204078119 crossref_primary_10_3389_fimmu_2021_782152 crossref_primary_10_1186_s12882_022_02779_7 crossref_primary_10_3390_pharmaceutics15020622 crossref_primary_10_1038_s41416_021_01353_6 crossref_primary_10_1158_2159_8290_CD_21_0502 crossref_primary_10_1074_mcp_RA120_002201 crossref_primary_10_7554_eLife_75670 crossref_primary_10_1080_2162402X_2021_1955545 crossref_primary_10_3389_fonc_2023_1147590 crossref_primary_10_1002_cai2_26 crossref_primary_10_1038_s41587_021_01021_3 crossref_primary_10_1016_j_mcpro_2021_100178 crossref_primary_10_1016_j_celrep_2021_109305 crossref_primary_10_1038_s41467_021_23713_9 crossref_primary_10_1038_s43018_021_00210_y crossref_primary_10_3389_fgene_2021_602196 crossref_primary_10_7554_eLife_65365 crossref_primary_10_1021_acs_jproteome_3c00229 crossref_primary_10_1172_JCI150535 crossref_primary_10_3389_fimmu_2023_1265044 crossref_primary_10_1126_sciimmunol_adh1781 crossref_primary_10_1111_nyas_14526 crossref_primary_10_1016_j_crmeth_2021_100084 crossref_primary_10_3389_fimmu_2022_944872 crossref_primary_10_1073_pnas_2216697120 crossref_primary_10_3389_fonc_2021_790004 crossref_primary_10_1038_s41598_024_58777_2 crossref_primary_10_1016_j_smim_2023_101727 crossref_primary_10_1038_s41591_022_01786_3 crossref_primary_10_1016_j_ijbiomac_2024_131356 crossref_primary_10_3390_vaccines11071174 crossref_primary_10_1021_acs_jcim_3c01667 crossref_primary_10_3389_fimmu_2023_1210044 crossref_primary_10_1136_jitc_2023_007073 crossref_primary_10_1107_S2052252524002768 crossref_primary_10_3390_biomedicines10040822 crossref_primary_10_1093_bib_bbad504 crossref_primary_10_1038_s41467_022_30867_7 crossref_primary_10_1016_j_smim_2023_101733 crossref_primary_10_3389_fimmu_2021_674021 crossref_primary_10_2174_0113816128286593240226060318 crossref_primary_10_1016_j_cels_2024_03_001 crossref_primary_10_1111_tan_14574 crossref_primary_10_1093_bib_bbae154 crossref_primary_10_3389_fcimb_2021_642583 crossref_primary_10_1038_s41586_022_04839_2 crossref_primary_10_1038_s42003_021_02716_8 crossref_primary_10_1021_acs_jproteome_1c00590 crossref_primary_10_1093_nar_gkad1068 crossref_primary_10_37349_ei_2023_00091 crossref_primary_10_1016_j_iotech_2021_100052 crossref_primary_10_1167_iovs_62_14_3 crossref_primary_10_3389_fimmu_2022_878762 crossref_primary_10_1038_s41587_021_01038_8 crossref_primary_10_1016_j_clim_2022_109219 crossref_primary_10_2139_ssrn_3902781 crossref_primary_10_1021_acsomega_0c01278 crossref_primary_10_26508_lsa_202302255 crossref_primary_10_1016_j_lfs_2023_121374 crossref_primary_10_4049_jimmunol_2001409 crossref_primary_10_1002_pmic_202000143 crossref_primary_10_3390_cancers13040743 crossref_primary_10_1128_mSystems_00310_21 crossref_primary_10_1093_nar_gkac965 crossref_primary_10_1093_narcan_zcae002 crossref_primary_10_3389_fimmu_2023_1142573 crossref_primary_10_1111_cas_16118 crossref_primary_10_7554_eLife_79144 crossref_primary_10_1016_j_ctrv_2022_102429 crossref_primary_10_1158_0008_5472_CAN_21_0301 crossref_primary_10_3389_fimmu_2023_1269335 crossref_primary_10_1172_jci_insight_163040 crossref_primary_10_3389_fimmu_2021_644637 crossref_primary_10_1016_j_mcpro_2023_100506 crossref_primary_10_1016_j_xcrm_2024_101516 crossref_primary_10_1186_s12967_023_04821_0 crossref_primary_10_1016_j_cels_2020_11_005 crossref_primary_10_1016_j_crmeth_2022_100293 crossref_primary_10_1111_cas_14602 crossref_primary_10_1016_j_smim_2023_101758 crossref_primary_10_3390_ijms22115940 crossref_primary_10_1016_j_crmeth_2023_100479 crossref_primary_10_1016_j_jep_2020_113382 crossref_primary_10_1016_j_mcpro_2021_100133 crossref_primary_10_1016_j_celrep_2022_110916 crossref_primary_10_1016_j_mcpro_2022_100432 crossref_primary_10_1080_14760584_2021_1935248 crossref_primary_10_1172_JCI151666 crossref_primary_10_1016_j_smim_2023_101766 crossref_primary_10_1158_2159_8290_CD_23_1536 crossref_primary_10_1038_s41577_023_00934_1 crossref_primary_10_3390_vaccines11030548 crossref_primary_10_1681_ASN_2022060644 crossref_primary_10_1093_bib_bbad150 crossref_primary_10_3389_fimmu_2022_1067463 crossref_primary_10_1002_pmic_201900335 crossref_primary_10_1002_pmic_202100171 crossref_primary_10_3389_fimmu_2022_833017 crossref_primary_10_1002_pmic_201900334 crossref_primary_10_1016_j_annonc_2021_08_2153 crossref_primary_10_3390_vaccines12050498 crossref_primary_10_1016_j_cels_2020_06_010 crossref_primary_10_1080_14760584_2022_2012456 crossref_primary_10_1038_s43018_021_00226_4 crossref_primary_10_3389_fimmu_2021_658372 crossref_primary_10_1186_s40364_023_00478_5 crossref_primary_10_1371_journal_pone_0252198 crossref_primary_10_1016_j_cell_2021_05_046 crossref_primary_10_1038_s41598_020_77466_4 crossref_primary_10_1186_s12943_023_01844_5 crossref_primary_10_1016_j_cbi_2022_110220 crossref_primary_10_1016_j_celrep_2024_114325 crossref_primary_10_3390_cells10092379 crossref_primary_10_1158_2767_9764_CRC_23_0121 crossref_primary_10_1021_acs_jproteome_0c00457 crossref_primary_10_1038_s41587_022_01464_2 crossref_primary_10_1093_nar_gkaa379 crossref_primary_10_1186_s12859_020_03946_z crossref_primary_10_1016_j_mcpro_2021_100116 crossref_primary_10_1038_s42256_023_00694_6 crossref_primary_10_3389_fimmu_2023_1212136 crossref_primary_10_1016_j_mcpro_2021_100111 crossref_primary_10_1186_s12859_023_05606_4 crossref_primary_10_1007_s00281_022_00980_2 crossref_primary_10_1038_s41586_021_03520_4 crossref_primary_10_1016_j_jaut_2023_103070 crossref_primary_10_1172_jci_insight_146356 crossref_primary_10_1016_j_ccell_2023_08_013 crossref_primary_10_1016_j_jhepr_2022_100576 crossref_primary_10_1136_jitc_2023_006889 crossref_primary_10_1146_annurev_cancerbio_060820_111701 crossref_primary_10_3389_fgene_2023_1082168 crossref_primary_10_1038_s41541_023_00795_8 crossref_primary_10_3389_fimmu_2023_1105420 crossref_primary_10_1016_j_immuno_2023_100030 crossref_primary_10_3389_fimmu_2021_682103 crossref_primary_10_1002_eji_202350449 crossref_primary_10_1038_s41586_020_03054_1 crossref_primary_10_1038_s41467_022_34033_x crossref_primary_10_1186_s13073_020_00767_w crossref_primary_10_1016_j_xcrm_2021_100221 crossref_primary_10_3389_fimmu_2022_847756 crossref_primary_10_1016_j_jconrel_2022_05_005 crossref_primary_10_12688_f1000research_132538_1 crossref_primary_10_1093_bioinformatics_btab479 crossref_primary_10_1038_s41577_023_00937_y crossref_primary_10_1093_bib_bbae133 crossref_primary_10_1093_nar_gkac776 crossref_primary_10_1128_JVI_00081_21 crossref_primary_10_1038_s41571_023_00789_4 crossref_primary_10_1146_annurev_chembioeng_101420_125021 crossref_primary_10_1038_s41573_021_00387_y crossref_primary_10_1038_s41598_023_40000_3 crossref_primary_10_1038_s43018_023_00552_9 crossref_primary_10_1093_intimm_dxac046 crossref_primary_10_3390_cancers14163972 crossref_primary_10_1042_BST20220782 crossref_primary_10_1016_j_mcpro_2024_100798 crossref_primary_10_1038_s42256_020_00260_4 crossref_primary_10_1093_bioinformatics_btad780 crossref_primary_10_12688_f1000research_26935_1 crossref_primary_10_1126_science_adk0777 crossref_primary_10_1038_s41467_023_37547_0 crossref_primary_10_1111_biom_13717 crossref_primary_10_1126_science_abc8697 crossref_primary_10_3389_fimmu_2021_705772 crossref_primary_10_1016_j_cellimm_2023_104707 crossref_primary_10_3389_fimmu_2024_1386160 crossref_primary_10_3389_fimmu_2020_599558 crossref_primary_10_3389_fimmu_2021_662443 crossref_primary_10_1016_j_mcpro_2023_100563 crossref_primary_10_1016_j_tips_2021_01_006 crossref_primary_10_1038_s41588_024_01785_9 crossref_primary_10_4049_jimmunol_2300232 crossref_primary_10_1038_s43018_023_00591_2 crossref_primary_10_1042_BCJ20200910 crossref_primary_10_1111_imr_13233 crossref_primary_10_1016_j_contre_2022_100015 crossref_primary_10_1158_2326_6066_CIR_21_0727 crossref_primary_10_3390_cancers14051243 crossref_primary_10_1093_bioinformatics_btab131 crossref_primary_10_1182_blood_2021012882 crossref_primary_10_1093_pnasnexus_pgac124 crossref_primary_10_1016_j_isci_2021_103107 crossref_primary_10_1021_acsomega_2c02425 crossref_primary_10_1038_s41587_022_01566_x crossref_primary_10_1038_s41467_020_18204_2 crossref_primary_10_1016_j_semcancer_2023_02_007 crossref_primary_10_25208_vdv1269 crossref_primary_10_1073_pnas_2100542118 crossref_primary_10_1016_j_isci_2022_104975 crossref_primary_10_3390_biomedicines11071937 crossref_primary_10_3389_fimmu_2022_835454 crossref_primary_10_1016_j_immuni_2021_03_001 crossref_primary_10_1021_jasms_1c00076 crossref_primary_10_1016_j_cell_2023_12_013 crossref_primary_10_1136_jitc_2023_008104 crossref_primary_10_1007_s11427_020_1888_1 crossref_primary_10_1016_j_mcpro_2022_100266 crossref_primary_10_1038_s41571_020_00455_z crossref_primary_10_1158_2326_6066_CIR_20_0526 crossref_primary_10_3389_fimmu_2021_656451 crossref_primary_10_1016_j_isci_2022_103850 crossref_primary_10_3389_fimmu_2021_735609 crossref_primary_10_1016_j_coi_2023_102342 crossref_primary_10_3389_fimmu_2024_1394003 crossref_primary_10_1142_S2424913020500095 crossref_primary_10_3389_fimmu_2020_585385 crossref_primary_10_1021_acs_jproteome_1c00842 crossref_primary_10_1038_s43018_021_00197_6 crossref_primary_10_1038_s41586_021_03704_y crossref_primary_10_1038_s41586_021_04061_6 crossref_primary_10_1038_s41467_023_40129_9 crossref_primary_10_3389_fimmu_2021_635521 crossref_primary_10_3390_molecules25225409 crossref_primary_10_1074_mcp_TIR120_002048 crossref_primary_10_3389_fimmu_2023_1288105 crossref_primary_10_1038_s41587_020_0577_1 crossref_primary_10_1016_j_coi_2022_102176 |
Cites_doi | 10.3389/fimmu.2019.00141 10.18632/oncotarget.22487 10.1186/s13073-016-0288-x 10.1038/s41586-018-0792-9 10.1126/sciimmunol.aar3947 10.1182/blood-2014-04-567933 10.1074/mcp.TIR117.000383 10.1074/mcp.RA118.000877 10.1073/pnas.1707658114 10.1016/S0952-7915(98)80124-6 10.1126/science.aaf4384 10.1007/s002510050595 10.1038/nbt.4313 10.1038/nri3370 10.1007/BF01025492 10.1016/j.immuni.2017.02.007 10.4049/jimmunol.1800914 10.1172/JCI88590 10.4049/jimmunol.165.6.3260 10.1038/ncomms13404 10.1016/j.humimm.2013.06.025 10.1126/science.1546328 10.1093/nar/gku1161 10.4049/jimmunol.1300292 10.1034/j.1399-0039.2000.550314.x 10.1016/S0171-2985(11)80548-6 10.1177/1087057108329453 10.4049/jimmunol.1002629 10.1093/nar/gku1056 10.1002/1098-2272(200101)20:1<87::AID-GEPI8>3.0.CO;2-R 10.1038/nature22991 10.1093/nar/gku938 10.4049/jimmunol.1700893 10.1016/j.humimm.2008.05.001 10.1126/sciimmunol.aaw1622 10.1007/BF00172063 10.1016/j.cels.2018.05.014 10.4049/jimmunol.1700938 10.1007/s00251-018-1058-2 10.1371/journal.pone.0000796 10.21105/joss.00861 10.1074/mcp.M114.042812 |
ContentType | Journal Article |
Copyright | The Author(s), under exclusive licence to Springer Nature America, Inc. 2019 COPYRIGHT 2020 Nature Publishing Group 2019© The Author(s), under exclusive licence to Springer Nature America, Inc. 2019 The Author(s), under exclusive licence to Springer Nature America, Inc. 2019. |
Copyright_xml | – notice: The Author(s), under exclusive licence to Springer Nature America, Inc. 2019 – notice: COPYRIGHT 2020 Nature Publishing Group – notice: 2019© The Author(s), under exclusive licence to Springer Nature America, Inc. 2019 – notice: The Author(s), under exclusive licence to Springer Nature America, Inc. 2019. |
DBID | CGR CUY CVF ECM EIF NPM AAYXX CITATION N95 XI7 IOV ISR 3V. 7QO 7QP 7QR 7T7 7TK 7TM 7X7 7XB 88A 88E 88I 8AO 8FD 8FE 8FG 8FH 8FI 8FJ 8FK 8G5 ABJCF ABUWG AFKRA AZQEC BBNVY BENPR BGLVJ BHPHI C1K CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ GUQSH HCIFZ K9. L6V LK8 M0S M1P M2O M2P M7P M7S MBDVC P64 PQEST PQQKQ PQUKI PTHSS Q9U RC3 7X8 5PM |
DOI | 10.1038/s41587-019-0322-9 |
DatabaseName | Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed CrossRef Gale_Business Insights: Global Business Insights: Essentials Opposing Viewpoints Resource Center Science In Context ProQuest Central (Corporate) Biotechnology Research Abstracts Calcium & Calcified Tissue Abstracts Chemoreception Abstracts Industrial and Applied Microbiology Abstracts (Microbiology A) Neurosciences Abstracts Nucleic Acids Abstracts ProQuest_Health & Medical Collection ProQuest Central (purchase pre-March 2016) Biology Database (Alumni Edition) Medical Database (Alumni Edition) Science Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) Research Library (Alumni Edition) Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central ProQuest Central Essentials Biological Science Collection AUTh Library subscriptions: ProQuest Central Technology Collection ProQuest Natural Science Collection Environmental Sciences and Pollution Management ProQuest One Community College ProQuest Central Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student Research Library Prep SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) ProQuest Engineering Collection Biological Sciences Health & Medical Collection (Alumni Edition) PML(ProQuest Medical Library) ProQuest research library ProQuest Science Journals Biological Science Database Engineering Database Research Library (Corporate) Biotechnology and BioEngineering Abstracts ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition Engineering Collection ProQuest Central Basic Genetics Abstracts MEDLINE - Academic PubMed Central (Full Participant titles) |
DatabaseTitle | MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) CrossRef Research Library Prep ProQuest Central Student ProQuest Central Essentials Nucleic Acids Abstracts SciTech Premium Collection Environmental Sciences and Pollution Management Health Research Premium Collection Natural Science Collection Biological Science Collection Chemoreception Abstracts Industrial and Applied Microbiology Abstracts (Microbiology A) ProQuest Medical Library (Alumni) Engineering Collection Engineering Database ProQuest Science Journals (Alumni Edition) ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database Neurosciences Abstracts ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Engineering Research Database ProQuest One Academic Calcium & Calcified Tissue Abstracts Technology Collection Technology Research Database ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College Research Library (Alumni Edition) ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Biology Journals (Alumni Edition) ProQuest Central Genetics Abstracts ProQuest Engineering Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea ProQuest Research Library ProQuest Central Basic ProQuest Science Journals ProQuest SciTech Collection ProQuest Medical Library Materials Science & Engineering Collection ProQuest Central (Alumni) MEDLINE - Academic |
DatabaseTitleList | Research Library Prep Research Library Prep MEDLINE - Academic MEDLINE |
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: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Medicine Engineering Agriculture Biology |
EISSN | 1546-1696 |
EndPage | 209 |
ExternalDocumentID | A613408777 10_1038_s41587_019_0322_9 31844290 |
Genre | Research Support, Non-U.S. Gov't Journal Article Research Support, N.I.H., Extramural |
GeographicLocations | Massachusetts |
GeographicLocations_xml | – name: Massachusetts |
GrantInformation_xml | – fundername: U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI) grantid: T32HG002295 funderid: https://doi.org/10.13039/100000051 – fundername: NCI NIH HHS grantid: U01 CA214125 – fundername: NCI NIH HHS grantid: P01 CA206978 – fundername: NCI NIH HHS grantid: P01 CA229092 – fundername: NCI NIH HHS grantid: P50 CA240243 – fundername: NCI NIH HHS grantid: K08 CA248458 – fundername: NCI NIH HHS grantid: R21 CA216772 – fundername: NCI NIH HHS grantid: R01 CA155010 – fundername: NCI NIH HHS grantid: P50 CA101942 – fundername: NCI NIH HHS grantid: U24 CA224331 – fundername: NCI NIH HHS grantid: T32 CA009172 |
GroupedDBID | --- -~X .55 .GJ 0R~ 123 29M 2FS 2XV 36B 39C 3V. 4.4 4R4 53G 5BI 5M7 5RE 5S5 70F 7X7 88A 88E 88I 8AO 8CJ 8FE 8FG 8FH 8FI 8FJ 8G5 8R4 8R5 A8Z AAEEF AAHBH AAIKC AAMNW AARCD AAYOK AAZLF ABAWZ ABDBF ABEFU ABJCF ABJNI ABLJU ABOCM ABUWG ABVXF ACBTR ACGFO ACGFS ACGOD ACIWK ACMJI ACPRK ADBBV ADFRT AENEX AFBBN AFFNX AFKRA AFRAH AFSHS AGAYW AGEZK AGHTU AHBCP AHMBA AHOSX AHSBF AIBTJ ALFFA ALIPV ALMA_UNASSIGNED_HOLDINGS AMTXH ARMCB ASPBG AVWKF AXYYD AZFZN AZQEC BAAKF BBNVY BENPR BGLVJ BHPHI BKKNO BKOMP BPHCQ BVXVI C0K CCPQU D1J DB5 DU5 DWQXO EAD EAP EAS EBC EBS EE. EJD EMB EMK EMOBN ESTFP ESX EXGXG F5P FA8 FEDTE FQGFK FSGXE FYUFA G8K GNUQQ GUQSH GX1 HCIFZ HMCUK HVGLF HZ~ IAG IAO IEA IEP IH2 IHR INH INR IOV ISR ITC KOO L6V LGEZI LK8 LOTEE M0L M1P M2O M2P M7P M7S ML0 MVM N95 NADUK NEJ NNMJJ NXXTH O9- ODYON P2P PQQKQ PROAC PSQYO PTHSS Q2X QF4 QM4 QN7 QO4 RNS RNT RNTTT RVV RXW SHXYY SIXXV SJN SNYQT SV3 TAE TAOOD TBHMF TDRGL TN5 TSG TUS U5U UKHRP X7M XI7 XOL Y6R YZZ ZGI ZHY ZXP ~KM AAYZH CGR CUY CVF ECM EIF NPM AAYXX CITATION AADEA AAEXX ABEEJ ADZGE AADWK AAJMP AAYJO ABGIJ ACBMV ACBRV ACBYP ACIGE ACTTH ACVWB ADMDM ADQMX AEDAW AEFTE AGGBP AGPPL AHGBK AJDOV NYICJ XFK ZA5 7QO 7QP 7QR 7T7 7TK 7TM 7XB 8FD 8FK C1K FR3 K9. MBDVC P64 PQEST PQUKI Q9U RC3 7X8 5PM |
ID | FETCH-LOGICAL-c670t-6197d91718784b3bc819311a9bd891cc1722caa4f91ca686d14c2bbeab129b853 |
IEDL.DBID | BENPR |
ISSN | 1087-0156 |
IngestDate | Tue Sep 17 21:19:17 EDT 2024 Sat Oct 26 05:19:33 EDT 2024 Thu Oct 10 20:58:44 EDT 2024 Thu Oct 10 20:52:29 EDT 2024 Thu Feb 22 23:27:49 EST 2024 Fri Feb 02 04:11:11 EST 2024 Thu Aug 01 20:20:49 EDT 2024 Thu Aug 01 20:12:27 EDT 2024 Tue Oct 08 13:40:18 EDT 2024 Thu Sep 12 19:38:31 EDT 2024 Thu Oct 24 10:04:43 EDT 2024 Fri Oct 11 20:46:38 EDT 2024 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 2 |
Language | English |
License | Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms Reprints and permissions information is available at www.nature.com/reprints. |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c670t-6197d91718784b3bc819311a9bd891cc1722caa4f91ca686d14c2bbeab129b853 |
Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 D.B.K., C.J.W., N.H. and S.C. directed the overall study design. S.S. performed computational analyses and developed predictive models. S.K., C.R.H., H.K. and K.R.C. generated the MS data and performed data analysis. D.B.K. and G.L.Z. selected the HLA alleles for analysis; D.B.K., P.M.L. and L.W.L. generated the single HLA-allele cell lines and performed data generation. D.B.K., G.O., K.L., D.B., P.M.L. and L.W.L. developed the patient-derived tumor cell lines; I.K.Z. and J.M.R. generated and provided cells from an ovarian cancer PDX model; P.B. provided CLL samples for analysis. W.Z. provided expert technical assistance. T.E. generated RNA-seq data for mono-allelic cell lines; T.O. and T.L. generated and quantified Ribo-seq data. J.S. and W.L. performed HLA typing and validation of all cell lines. S.J. performed HLA-binding validation assays. S.S., S.K., N.H., C.J.W. and D.B.K. wrote the manuscript, with contributions from all co-authors. Lead Contact: cwu@partners.org Denotes equal contribution Author Contributions |
ORCID | 0000-0002-0074-5163 0000-0002-7399-3299 0000-0003-1314-6976 0000-0003-2386-9553 0000-0002-1097-5229 0000-0002-3348-5054 0000-0002-8496-6181 0000-0002-2349-2656 0000-0002-7203-4299 |
OpenAccessLink | https://pubmed.ncbi.nlm.nih.gov/PMC7008090 |
PMID | 31844290 |
PQID | 2352323001 |
PQPubID | 47191 |
PageCount | 11 |
ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_7008090 proquest_miscellaneous_2327937423 proquest_journals_2476762181 proquest_journals_2352323001 gale_infotracmisc_A613408777 gale_infotracacademiconefile_A613408777 gale_incontextgauss_ISR_A613408777 gale_incontextgauss_IOV_A613408777 gale_businessinsightsgauss_A613408777 crossref_primary_10_1038_s41587_019_0322_9 pubmed_primary_31844290 springer_journals_10_1038_s41587_019_0322_9 |
PublicationCentury | 2000 |
PublicationDate | 2020-02-01 |
PublicationDateYYYYMMDD | 2020-02-01 |
PublicationDate_xml | – month: 02 year: 2020 text: 2020-02-01 day: 01 |
PublicationDecade | 2020 |
PublicationPlace | New York |
PublicationPlace_xml | – name: New York – name: United States |
PublicationSubtitle | The Science and Business of Biotechnology |
PublicationTitle | Nature biotechnology |
PublicationTitleAbbrev | Nat Biotechnol |
PublicationTitleAlternate | Nat Biotechnol |
PublicationYear | 2020 |
Publisher | Nature Publishing Group US Nature Publishing Group |
Publisher_xml | – name: Nature Publishing Group US – name: Nature Publishing Group |
References | Kaur (CR24) 2017; 8 Rammensee, Bachmann, Emmerich, Bachor, Stevanović (CR45) 1999; 50 Zhang (CR11) 2017; 8 Rammensee, Friede, Stevanoviíc (CR44) 1995; 41 Jurtz (CR3) 2017; 199 Di Marco (CR28) 2017; 199 Solberg (CR14) 2008; 69 Chong (CR37) 2018; 17 Liepe (CR29) 2016; 354 Gragert, Madbouly, Freeman, Maiers (CR13) 2013; 74 Nielsen (CR21) 2007; 2 Parham, Moffett (CR20) 2013; 13 Keskin (CR26) 2019; 565 Rolfs, Solntsev, Shortreed, Frey, Smith (CR32) 2018; 18 Bulik-Sullivan (CR7) 2018; 37 Sette, Sidney (CR18) 1998; 10 Bremel, Homan (CR39) 2010; 6 Gfeller (CR6) 2018; 201 Bassani-Sternberg (CR34) 2016; 7 de Kruijf (CR10) 2010; 185 O’Donnell (CR5) 2018; 7 Rolfs, Müller, Shortreed, Smith, Bassani-Sternberg (CR33) 2019; 4 Lefranc (CR1) 2015; 43 Pearson (CR16) 2016; 126 Vita (CR17) 2015; 43 Harndahl (CR41) 2009; 14 Rist (CR22) 2013; 191 Faridi (CR30) 2018; 3 Abelin (CR4) 2017; 46 Maenaka (CR23) 2000; 165 Kidera, Konishi, Oka, Ooi, Scheraga (CR38) 1985; 4 Dawson, Ozgur, Sari, Ghanayem, Kostyu (CR12) 2001; 20 Robinson, Malik, Parham, Bodmer, Marsh (CR19) 2000; 55 Schuster (CR35) 2017; 114 Girdlestone (CR36) 1995; 193 Kim, Sidney, Pinilla, Sette, Peters (CR46) 2009; 10 CR40 Nielsen, Andreatta (CR8) 2016; 8 Ott (CR15) 2017; 547 Hunt (CR43) 1992; 255 Celik, Simper, Hiemisch, Blasczyk, Bade-Döding (CR25) 2018; 70 Mylonas (CR31) 2018; 17 Robinson (CR2) 2015; 43 Rajasagi (CR9) 2014; 124 Javitt (CR27) 2019; 10 Bassani-Sternberg, Pletscher-Frankild, Jensen, Mann (CR42) 2015; 14 H Rammensee (322_CR45) 1999; 50 R-L Zhang (322_CR11) 2017; 8 P Parham (322_CR20) 2013; 13 J Girdlestone (322_CR36) 1995; 193 MJ Rist (322_CR22) 2013; 191 M Nielsen (322_CR8) 2016; 8 HG Rammensee (322_CR44) 1995; 41 M Nielsen (322_CR21) 2007; 2 V Jurtz (322_CR3) 2017; 199 L Gragert (322_CR13) 2013; 74 A Javitt (322_CR27) 2019; 10 K Maenaka (322_CR23) 2000; 165 EM de Kruijf (322_CR10) 2010; 185 DV Dawson (322_CR12) 2001; 20 C Chong (322_CR37) 2018; 17 H Pearson (322_CR16) 2016; 126 B Bulik-Sullivan (322_CR7) 2018; 37 Z Rolfs (322_CR32) 2018; 18 M Rajasagi (322_CR9) 2014; 124 Y Kim (322_CR46) 2009; 10 R Vita (322_CR17) 2015; 43 A Sette (322_CR18) 1998; 10 JG Abelin (322_CR4) 2017; 46 DB Keskin (322_CR26) 2019; 565 J Liepe (322_CR29) 2016; 354 TJ O’Donnell (322_CR5) 2018; 7 PA Ott (322_CR15) 2017; 547 RD Bremel (322_CR39) 2010; 6 P Faridi (322_CR30) 2018; 3 M Di Marco (322_CR28) 2017; 199 R Mylonas (322_CR31) 2018; 17 G Kaur (322_CR24) 2017; 8 AA Celik (322_CR25) 2018; 70 M-P Lefranc (322_CR1) 2015; 43 M Bassani-Sternberg (322_CR34) 2016; 7 D Gfeller (322_CR6) 2018; 201 322_CR40 J Robinson (322_CR19) 2000; 55 J Robinson (322_CR2) 2015; 43 M Harndahl (322_CR41) 2009; 14 OD Solberg (322_CR14) 2008; 69 Z Rolfs (322_CR33) 2019; 4 M Bassani-Sternberg (322_CR42) 2015; 14 H Schuster (322_CR35) 2017; 114 A Kidera (322_CR38) 1985; 4 DF Hunt (322_CR43) 1992; 255 |
References_xml | – volume: 43 start-page: D405 year: 2015 end-page: D412 ident: CR17 article-title: The immune epitope database (IEDB) 3.0 publication-title: Nucleic Acids Res. contributor: fullname: Vita – volume: 255 start-page: 1261 year: 1992 end-page: 1263 ident: CR43 article-title: Characterization of peptides bound to the class I MHC molecule HLA-A2.1 by mass spectrometry publication-title: Science contributor: fullname: Hunt – volume: 17 start-page: 2347 year: 2018 end-page: 2357 ident: CR31 article-title: Estimating the contribution of proteasomal spliced peptides to the HLA-I ligandome publication-title: Mol. Cell. Proteom. contributor: fullname: Mylonas – volume: 10 start-page: 141 year: 2019 ident: CR27 article-title: Pro-inflammatory cytokines alter the immunopeptidome landscape by modulation of HLA-B expression publication-title: Front. Immunol. contributor: fullname: Javitt – volume: 10 start-page: 478 year: 1998 end-page: 482 ident: CR18 article-title: HLA supertypes and supermotifs: a functional perspective on HLA polymorphism publication-title: Curr. Opin. Immunol. contributor: fullname: Sidney – volume: 124 start-page: 453 year: 2014 end-page: 462 ident: CR9 article-title: Systematic identification of personal tumor-specific neoantigens in chronic lymphocytic leukemia publication-title: Blood contributor: fullname: Rajasagi – volume: 46 start-page: 315 year: 2017 end-page: 326 ident: CR4 article-title: Mass spectrometry profiling of HLA-associated peptidomes in mono-allelic cells enables more accurate epitope prediction publication-title: Immunity contributor: fullname: Abelin – volume: 74 start-page: 1313 year: 2013 end-page: 1320 ident: CR13 article-title: Six-locus high resolution HLA haplotype frequencies derived from mixed-resolution DNA typing for the entire US donor registry publication-title: Hum. Immunol. contributor: fullname: Maiers – volume: 7 start-page: 129 year: 2018 end-page: 132.e4 ident: CR5 article-title: MHCflurry: open-source class I MHC binding affinity prediction publication-title: Cell Syst. contributor: fullname: O’Donnell – volume: 13 start-page: 133 year: 2013 end-page: 144 ident: CR20 article-title: Variable NK cell receptors and their MHC class I ligands in immunity, reproduction and human evolution publication-title: Nat. Rev. Immunol. contributor: fullname: Moffett – volume: 185 start-page: 7452 year: 2010 end-page: 7459 ident: CR10 article-title: HLA-E and HLA-G expression in classical HLA class I-negative tumors is of prognostic value for clinical outcome of early breast cancer patients publication-title: J. Immunol. contributor: fullname: de Kruijf – volume: 8 year: 2017 ident: CR24 article-title: Structural and regulatory diversity shape HLA-C protein expression levels publication-title: Nat. Commun. contributor: fullname: Kaur – volume: 126 start-page: 4690 year: 2016 end-page: 4701 ident: CR16 article-title: MHC class I-associated peptides derive from selective regions of the human genome publication-title: J. Clin. Invest. contributor: fullname: Pearson – volume: 4 start-page: eaaw1622 year: 2019 ident: CR33 article-title: Comment on ‘A subset of HLA-I peptides are not genomically templated: evidence for cis- and trans-spliced peptide ligands’. publication-title: Sci. Immunol. contributor: fullname: Bassani-Sternberg – ident: CR40 – volume: 14 start-page: 173 year: 2009 end-page: 180 ident: CR41 article-title: Peptide binding to HLA class I molecules: homogenous, high-throughput screening, and affinity assays publication-title: J. Biomol. Screen. contributor: fullname: Harndahl – volume: 191 start-page: 561 year: 2013 end-page: 571 ident: CR22 article-title: HLA peptide length preferences control CD8 T cell responses publication-title: J. Immunol. contributor: fullname: Rist – volume: 199 start-page: 2639 year: 2017 end-page: 2651 ident: CR28 article-title: Unveiling the peptide motifs of HLA-C and HLA-G from naturally presented peptides and generation of binding prediction matrices publication-title: J. Immunol. contributor: fullname: Di Marco – volume: 6 year: 2010 ident: CR39 article-title: An integrated approach to epitope analysis I: dimensional reduction, visualization and prediction of MHC binding using amino acid principal components and regression approaches publication-title: Immunome Res. contributor: fullname: Homan – volume: 41 start-page: 178 year: 1995 end-page: 228 ident: CR44 article-title: MHC ligands and peptide motifs: first listing publication-title: Immunogenetics contributor: fullname: Stevanoviíc – volume: 114 start-page: E9942 year: 2017 end-page: E9951 ident: CR35 article-title: The immunopeptidomic landscape of ovarian carcinomas publication-title: Proc. Natl Acad. Sci. USA contributor: fullname: Schuster – volume: 2 start-page: e796 year: 2007 ident: CR21 article-title: NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and -B locus protein of known sequence publication-title: PLoS ONE contributor: fullname: Nielsen – volume: 193 start-page: 229 year: 1995 end-page: 237 ident: CR36 article-title: Regulation of HLA class I loci by interferons publication-title: Immunobiology contributor: fullname: Girdlestone – volume: 37 start-page: 55 year: 2018 end-page: 63 ident: CR7 article-title: Deep learning using tumor HLA peptide mass spectrometry datasets improves neoantigen identification publication-title: Nat. Biotechnol. contributor: fullname: Bulik-Sullivan – volume: 3 start-page: eaar3947 year: 2018 ident: CR30 article-title: A subset of HLA-I peptides are not genomically templated: evidence for cis- and trans-spliced peptide ligands publication-title: Sci. Immunol. contributor: fullname: Faridi – volume: 69 start-page: 443 year: 2008 end-page: 464 ident: CR14 article-title: Balancing selection and heterogeneity across the classical human leukocyte antigen loci: a meta-analytic review of 497 population studies publication-title: Hum. Immunol. contributor: fullname: Solberg – volume: 165 start-page: 3260 year: 2000 end-page: 3267 ident: CR23 article-title: Nonstandard peptide binding revealed by crystal structures of HLA-B*5101 complexed with HIV immunodominant epitopes publication-title: J. Immunol. contributor: fullname: Maenaka – volume: 70 start-page: 485 year: 2018 end-page: 494 ident: CR25 article-title: HLA-G peptide preferences change in transformed cells: impact on the binding motif publication-title: Immunogenetics contributor: fullname: Bade-Döding – volume: 10 year: 2009 ident: CR46 article-title: Derivation of an amino acid similarity matrix for peptide:MHC binding and its application as a Bayesian prior publication-title: BMC Bioinformatics contributor: fullname: Peters – volume: 4 start-page: 23 year: 1985 end-page: 55 ident: CR38 article-title: Statistical analysis of the physical properties of the 20 naturally occurring amino acids publication-title: J. Protein Chem. contributor: fullname: Scheraga – volume: 43 start-page: D413 year: 2015 end-page: D422 ident: CR1 article-title: IMGT®, the international ImMunoGeneTics information system® 25 years on publication-title: Nucleic Acids Res. contributor: fullname: Lefranc – volume: 18 start-page: 349 year: 2018 end-page: 358 ident: CR32 article-title: Global identification of post-translationally spliced peptides with neo-fusion publication-title: J. Proteome Res. contributor: fullname: Smith – volume: 201 start-page: 3705 year: 2018 end-page: 3716 ident: CR6 article-title: The length distribution and multiple specificity of naturally presented HLA-I ligands publication-title: J. Immunol. contributor: fullname: Gfeller – volume: 565 start-page: 234 year: 2019 end-page: 239 ident: CR26 article-title: Neoantigen vaccine generates intratumoral T cell responses in phase Ib glioblastoma trial publication-title: Nature contributor: fullname: Keskin – volume: 8 year: 2016 ident: CR8 article-title: NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets publication-title: Genome Med. contributor: fullname: Andreatta – volume: 547 start-page: 217 year: 2017 end-page: 221 ident: CR15 article-title: An immunogenic personal neoantigen vaccine for patients with melanoma publication-title: Nature contributor: fullname: Ott – volume: 8 start-page: 107441 year: 2017 end-page: 107451 ident: CR11 article-title: Predictive value of different proportion of lesion HLA-G expression in colorectal cancer publication-title: Oncotarget contributor: fullname: Zhang – volume: 17 start-page: 533 year: 2018 end-page: 548 ident: CR37 article-title: High-throughput and sensitive immunopeptidomics platform reveals profound interferonγ-mediated remodeling of the human leukocyte antigen (HLA) ligandome publication-title: Mol. Cell. Proteom. contributor: fullname: Chong – volume: 14 start-page: 658 year: 2015 end-page: 673 ident: CR42 article-title: Mass spectrometry of human leukocyte antigen class I peptidomes reveals strong effects of protein abundance and turnover on antigen presentation publication-title: Mol. Cell. Proteom. contributor: fullname: Mann – volume: 20 start-page: 87 year: 2001 end-page: 106 ident: CR12 article-title: Ramifications of HLA class I polymorphism and population genetics for vaccine development publication-title: Genet. Epidemiol. contributor: fullname: Kostyu – volume: 55 start-page: 280 year: 2000 end-page: 287 ident: CR19 article-title: IMGT/HLA database—a sequence database for the human major histocompatibility complex publication-title: Tissue Antigens contributor: fullname: Marsh – volume: 43 start-page: D423 year: 2015 end-page: D431 ident: CR2 article-title: The IPD and IMGT/HLA database: allele variant databases publication-title: Nucleic Acids Res. contributor: fullname: Robinson – volume: 199 start-page: 3360 year: 2017 end-page: 3368 ident: CR3 article-title: NetMHCpan-4.0: improved peptide–MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data publication-title: J. Immunol. contributor: fullname: Jurtz – volume: 354 start-page: 354 year: 2016 end-page: 358 ident: CR29 article-title: A large fraction of HLA class I ligands are proteasome-generated spliced peptides publication-title: Science contributor: fullname: Liepe – volume: 50 start-page: 213 year: 1999 end-page: 219 ident: CR45 article-title: SYFPEITHI: database for MHC ligands and peptide motifs publication-title: Immunogenetics contributor: fullname: Stevanović – volume: 7 year: 2016 ident: CR34 article-title: Direct identification of clinically relevant neoepitopes presented on native human melanoma tissue by mass spectrometry publication-title: Nat. Commun. contributor: fullname: Bassani-Sternberg – volume: 10 start-page: 141 year: 2019 ident: 322_CR27 publication-title: Front. Immunol. doi: 10.3389/fimmu.2019.00141 contributor: fullname: A Javitt – volume: 8 start-page: 107441 year: 2017 ident: 322_CR11 publication-title: Oncotarget doi: 10.18632/oncotarget.22487 contributor: fullname: R-L Zhang – volume: 8 year: 2016 ident: 322_CR8 publication-title: Genome Med. doi: 10.1186/s13073-016-0288-x contributor: fullname: M Nielsen – volume: 565 start-page: 234 year: 2019 ident: 322_CR26 publication-title: Nature doi: 10.1038/s41586-018-0792-9 contributor: fullname: DB Keskin – volume: 3 start-page: eaar3947 year: 2018 ident: 322_CR30 publication-title: Sci. Immunol. doi: 10.1126/sciimmunol.aar3947 contributor: fullname: P Faridi – volume: 124 start-page: 453 year: 2014 ident: 322_CR9 publication-title: Blood doi: 10.1182/blood-2014-04-567933 contributor: fullname: M Rajasagi – volume: 17 start-page: 533 year: 2018 ident: 322_CR37 publication-title: Mol. Cell. Proteom. doi: 10.1074/mcp.TIR117.000383 contributor: fullname: C Chong – volume: 17 start-page: 2347 year: 2018 ident: 322_CR31 publication-title: Mol. Cell. Proteom. doi: 10.1074/mcp.RA118.000877 contributor: fullname: R Mylonas – volume: 114 start-page: E9942 year: 2017 ident: 322_CR35 publication-title: Proc. Natl Acad. Sci. USA doi: 10.1073/pnas.1707658114 contributor: fullname: H Schuster – volume: 8 year: 2017 ident: 322_CR24 publication-title: Nat. Commun. contributor: fullname: G Kaur – volume: 10 start-page: 478 year: 1998 ident: 322_CR18 publication-title: Curr. Opin. Immunol. doi: 10.1016/S0952-7915(98)80124-6 contributor: fullname: A Sette – volume: 354 start-page: 354 year: 2016 ident: 322_CR29 publication-title: Science doi: 10.1126/science.aaf4384 contributor: fullname: J Liepe – volume: 50 start-page: 213 year: 1999 ident: 322_CR45 publication-title: Immunogenetics doi: 10.1007/s002510050595 contributor: fullname: H Rammensee – volume: 37 start-page: 55 year: 2018 ident: 322_CR7 publication-title: Nat. Biotechnol. doi: 10.1038/nbt.4313 contributor: fullname: B Bulik-Sullivan – volume: 6 year: 2010 ident: 322_CR39 publication-title: Immunome Res. contributor: fullname: RD Bremel – volume: 13 start-page: 133 year: 2013 ident: 322_CR20 publication-title: Nat. Rev. Immunol. doi: 10.1038/nri3370 contributor: fullname: P Parham – volume: 4 start-page: 23 year: 1985 ident: 322_CR38 publication-title: J. Protein Chem. doi: 10.1007/BF01025492 contributor: fullname: A Kidera – volume: 46 start-page: 315 year: 2017 ident: 322_CR4 publication-title: Immunity doi: 10.1016/j.immuni.2017.02.007 contributor: fullname: JG Abelin – volume: 201 start-page: 3705 year: 2018 ident: 322_CR6 publication-title: J. Immunol. doi: 10.4049/jimmunol.1800914 contributor: fullname: D Gfeller – volume: 126 start-page: 4690 year: 2016 ident: 322_CR16 publication-title: J. Clin. Invest. doi: 10.1172/JCI88590 contributor: fullname: H Pearson – volume: 165 start-page: 3260 year: 2000 ident: 322_CR23 publication-title: J. Immunol. doi: 10.4049/jimmunol.165.6.3260 contributor: fullname: K Maenaka – volume: 7 year: 2016 ident: 322_CR34 publication-title: Nat. Commun. doi: 10.1038/ncomms13404 contributor: fullname: M Bassani-Sternberg – volume: 74 start-page: 1313 year: 2013 ident: 322_CR13 publication-title: Hum. Immunol. doi: 10.1016/j.humimm.2013.06.025 contributor: fullname: L Gragert – volume: 255 start-page: 1261 year: 1992 ident: 322_CR43 publication-title: Science doi: 10.1126/science.1546328 contributor: fullname: DF Hunt – volume: 43 start-page: D423 year: 2015 ident: 322_CR2 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gku1161 contributor: fullname: J Robinson – volume: 191 start-page: 561 year: 2013 ident: 322_CR22 publication-title: J. Immunol. doi: 10.4049/jimmunol.1300292 contributor: fullname: MJ Rist – volume: 55 start-page: 280 year: 2000 ident: 322_CR19 publication-title: Tissue Antigens doi: 10.1034/j.1399-0039.2000.550314.x contributor: fullname: J Robinson – volume: 193 start-page: 229 year: 1995 ident: 322_CR36 publication-title: Immunobiology doi: 10.1016/S0171-2985(11)80548-6 contributor: fullname: J Girdlestone – volume: 14 start-page: 173 year: 2009 ident: 322_CR41 publication-title: J. Biomol. Screen. doi: 10.1177/1087057108329453 contributor: fullname: M Harndahl – volume: 185 start-page: 7452 year: 2010 ident: 322_CR10 publication-title: J. Immunol. doi: 10.4049/jimmunol.1002629 contributor: fullname: EM de Kruijf – volume: 43 start-page: D413 year: 2015 ident: 322_CR1 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gku1056 contributor: fullname: M-P Lefranc – volume: 20 start-page: 87 year: 2001 ident: 322_CR12 publication-title: Genet. Epidemiol. doi: 10.1002/1098-2272(200101)20:1<87::AID-GEPI8>3.0.CO;2-R contributor: fullname: DV Dawson – volume: 547 start-page: 217 year: 2017 ident: 322_CR15 publication-title: Nature doi: 10.1038/nature22991 contributor: fullname: PA Ott – volume: 43 start-page: D405 year: 2015 ident: 322_CR17 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gku938 contributor: fullname: R Vita – volume: 199 start-page: 3360 year: 2017 ident: 322_CR3 publication-title: J. Immunol. doi: 10.4049/jimmunol.1700893 contributor: fullname: V Jurtz – volume: 10 year: 2009 ident: 322_CR46 publication-title: BMC Bioinformatics contributor: fullname: Y Kim – volume: 69 start-page: 443 year: 2008 ident: 322_CR14 publication-title: Hum. Immunol. doi: 10.1016/j.humimm.2008.05.001 contributor: fullname: OD Solberg – volume: 4 start-page: eaaw1622 year: 2019 ident: 322_CR33 publication-title: Sci. Immunol. doi: 10.1126/sciimmunol.aaw1622 contributor: fullname: Z Rolfs – volume: 41 start-page: 178 year: 1995 ident: 322_CR44 publication-title: Immunogenetics doi: 10.1007/BF00172063 contributor: fullname: HG Rammensee – volume: 7 start-page: 129 year: 2018 ident: 322_CR5 publication-title: Cell Syst. doi: 10.1016/j.cels.2018.05.014 contributor: fullname: TJ O’Donnell – volume: 199 start-page: 2639 year: 2017 ident: 322_CR28 publication-title: J. Immunol. doi: 10.4049/jimmunol.1700938 contributor: fullname: M Di Marco – volume: 70 start-page: 485 year: 2018 ident: 322_CR25 publication-title: Immunogenetics doi: 10.1007/s00251-018-1058-2 contributor: fullname: AA Celik – volume: 2 start-page: e796 year: 2007 ident: 322_CR21 publication-title: PLoS ONE doi: 10.1371/journal.pone.0000796 contributor: fullname: M Nielsen – ident: 322_CR40 doi: 10.21105/joss.00861 – volume: 18 start-page: 349 year: 2018 ident: 322_CR32 publication-title: J. Proteome Res. contributor: fullname: Z Rolfs – volume: 14 start-page: 658 year: 2015 ident: 322_CR42 publication-title: Mol. Cell. Proteom. doi: 10.1074/mcp.M114.042812 contributor: fullname: M Bassani-Sternberg |
SSID | ssj0006466 |
Score | 2.7163389 |
Snippet | Prediction of HLA epitopes is important for the development of cancer immunotherapies and vaccines. However, current prediction algorithms have limited... |
SourceID | pubmedcentral proquest gale crossref pubmed springer |
SourceType | Open Access Repository Aggregation Database Index Database Publisher |
StartPage | 199 |
SubjectTerms | 631/114/2397 631/250/21 631/250/580 631/45/611 692/308/575 Agriculture Algorithms Alleles Amino Acid Motifs Analysis Antigenic determinants Bioinformatics Biomedical and Life Sciences Biomedical Engineering/Biotechnology Biomedicine Biotechnology Cancer immunotherapy Cancer vaccines Cell Line Databases, Protein Datasets Epitopes Epitopes - metabolism Genetic Loci Histocompatibility antigen HLA Histocompatibility antigens Histocompatibility Antigens Class I - metabolism HLA histocompatibility antigens Human populations Humans Immunotherapy Life Sciences Ligands Mass spectrometry Mass spectroscopy Peptide Hydrolases - metabolism Peptides Peptides - chemistry Peptides - metabolism Prediction models Predictions Proteasome Endopeptidase Complex - metabolism Proteome - metabolism Transcription Tumor cell lines Vaccines |
Title | A large peptidome dataset improves HLA class I epitope prediction across most of the human population |
URI | https://link.springer.com/article/10.1038/s41587-019-0322-9 https://www.ncbi.nlm.nih.gov/pubmed/31844290 https://www.proquest.com/docview/2352323001 https://www.proquest.com/docview/2476762181 https://www.proquest.com/docview/2327937423 https://pubmed.ncbi.nlm.nih.gov/PMC7008090 |
Volume | 38 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3db9MwED-xViB4QFC-AqMyCIQEiuZ8LHGeUItWOgQFDTb1LbIdZ5vEkrC0D_z33DlpWCpAvERVfLHi3Pnud77rHcCLIOeJ5Fq7mTSJG2qRuSrHfSVFpqVRSRjmNkF2Ec2Pww_L_WV74Fa3aZUbnWgVdVZqOiPf88M4wo2LBult9cOlrlEUXW1baOzA0KfKTQMYTg8WX446XRw10UqPC0qw3I82cc1A7NVouuzdxOXkkSU9y7Stn68YqO3kya0IqjVMsztwu0WUbNKIwF24ZooRXG96TP4cwa0rFQdHcONTG0u_B2bCvlMaOKsosSUrLwyjdNHarNi5PWkwNZt_nDBN-JodMlPh5q-Q_JKmIH4yaVfCLsp6xcqcIZZktucfq7q2YPfheHbw7d3cbZsuuDqK-QpdySTO0IfzRCxCFSiNkCHwPJmoTCSe1gh4fC0lstDTMhJR5oXaV8pIhchBofF_AIOiLMwjYIHkPFOKyzgToY-aIs65Nvgr0b7UMnDg9eaDp1VTWyO1MfFApA13UuROStxJEwdeEkvStjcnXmo6vahP5bqu0wmiktDWNnTguaWj2hYFJc80BIefT_6D6OtRj-hVS5SXyGkt2z8s4OKoZlaPcrdHiTtU94c3YpS2GqJOfUS-Afp_3PvzcCfuDjzrhmliSoorTLmmKXyqboiA2IGHjVB23xFVdYhQgzsQ98S1I6Cy4v2R4vzMlhePyYugJ99sBPv3a_2VPY__vYgncNOngwqb7r4Lg9Xl2jxFNLdSY9iJlzFexez9GIaT2XS6GLfb-BcDk0mP |
link.rule.ids | 230,315,786,790,891,12077,12786,21409,27946,27947,31743,31744,33397,33398,33768,33769,43334,43624,43829,74091,74381,74648 |
linkProvider | ProQuest |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3db9MwED_BEF8PCMZXYIBBICRQNCf28vGEKsTUQjck2FDfLNtxYBJLwtI-8N9z56RhqQDxUlX11apz57vfffQO4Lkoea65tWGhXR5KmxWhKfFe6ayw2plcytIXyB4m02P5frG36ANubV9WudaJXlEXtaUY-W4s0wQvLhqkN82PkKZGUXa1H6FxES5JIQT1zk8Xg8OF1tbnKiOeUXnlXrLOaopst0XD5T_NQ07-WD6yS5va-Zx52iyd3MiferO0fxNu9HiSTToBuAUXXLUNl7sJkz-34fq5foPbcOWgz6TfBjdh36kInDVU1lLUp45RsWjrluzExxlcy6bzCbOErtmMuQavfoPkZ7QFcZNpfxJ2WrdLVpcMkSTzE_9YMwwFuwPH---O3k7DfuRCaJOUL9GRzNMCPbgoSzNphLEIGEQU6dwUWR5Zi3AntlojAyOrkywpImljY5w2iBsMmv67sFXVlbsPTGjOC2O4TotMxqgn0pJbh-9yG2urRQCv1g9cNV1nDeUz4iJTHXcUckcRd1QewAtiieonc-JLS7GL9qteta2aICaRvrNhAM88HXW2qKh0piOYffzyH0SfP42IXvZEZY2ctrr_uwIejjpmjSh3RpR4P-14eS1GqtcPrYoR9wr0_nj05-VB2AN4OizTxlQSV7l6RVvE1NsQ4XAA9zqhHJ4jKmqJQIMHkI7EdSCgpuLjlerkm28unpIPQd98vRbs3z_rr-x58O9DPIGr06ODuZrPDj88hGsxhSx84fsObC3PVu4R4rqleewv7y9goUfL |
linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1Lb9QwEB5BERUcECyvQAGDQEigaJ1H8zihFbDsQikIWtSbZTsOVKJJaLIH_j0zjjc0K0BcVtF6YsWZ8fiz58sMwOOo5LnkWvuFNLkf66zwVYnzSmaFlkblcVxagux-sjiM3x7tHjn-U-tolWufaB11UWs6I5-GcZrgxMUFaVo6WsTHV_MXzQ-fKkhRpNWV0zgPF1LKW0Vfjc_fDF456eOWAc-IarmbrCOcUTZtcRGz_-Y-p71ZPlqjNj31maVqk0a5EUu1S9T8Klxx2JLNemO4BudMNYGLfbXJnxO4fCb34AS237uo-nUwM_adCOGsIYpLUZ8YRsTR1nTs2J45mJYt9mZME9JmS2YadAMNip9SF6RZJu1I2EnddqwuGaJKZqv_sWYoEHYDDuevD14ufFd-wddJyjvcVOZpgbu5IEuzWEVKI3iIgkDmqsjyQGuEPqGWEpUZaJlkSRHEOlTKSIUYQiEMuAlbVV2Z28AiyXmhFJdpkcUh-oy05NrgVa5DqWXkwbP1CxdNn2VD2Oh4lIleOwK1I0g7IvfgCalEuCqd-NPSOUb7Va7aVswQn8Q2y6EHj6wcZbmoyF56geWHL_8h9PnTSOipEypr1LSW7tMFHBxlzxpJ7owkca7qcfPajITzFa0IEQNHuBPkwZ-bB8P34OHQTB0TPa4y9Yq6CCnPIUJjD271Rjm8R3TaMYIO7kE6MtdBgBKMj1uq42820XhK-wm68_nasH8_1l_Vc-ffg3gA2zhvxd5y_91duBTS6YXlwO_AVne6MvcQ4nXqvp27vwAynkwA |
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+large+peptidome+dataset+improves+HLA+class+I+epitope+prediction+across+most+of+the+human+population&rft.jtitle=Nature+biotechnology&rft.au=Sarkizova%2C+Siranush&rft.au=Klaeger%2C+Susan&rft.au=Le%2C+Phuong+M&rft.au=Li%2C+Letitia+W&rft.date=2020-02-01&rft.pub=Nature+Publishing+Group&rft.issn=1087-0156&rft.eissn=1546-1696&rft.volume=38&rft.issue=2&rft.spage=199&rft_id=info:doi/10.1038%2Fs41587-019-0322-9&rft.externalDBID=ISR&rft.externalDocID=A613408777 |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1087-0156&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1087-0156&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1087-0156&client=summon |