Model-Based Analysis of Tiling-Arrays for ChIP-Chip
We propose a fast and powerful analysis algorithm, titled Modelbased Analysis of Tiling-arrays (MAT), to reliably detect regions enriched by transcription factor chromatin immunoprecipitation (ChIP) on Affymetrix tiling arrays (ChlP-chip). MAT models the baseline probe behavior by considering probe...
Saved in:
| Published in | Proceedings of the National Academy of Sciences - PNAS Vol. 103; no. 33; pp. 12457 - 12462 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
National Academy of Sciences
15.08.2006
National Acad Sciences |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0027-8424 1091-6490 1091-6490 |
| DOI | 10.1073/pnas.0601180103 |
Cover
| Abstract | We propose a fast and powerful analysis algorithm, titled Modelbased Analysis of Tiling-arrays (MAT), to reliably detect regions enriched by transcription factor chromatin immunoprecipitation (ChIP) on Affymetrix tiling arrays (ChlP-chip). MAT models the baseline probe behavior by considering probe sequence and copy number on each array. It standardizes the probe value through the probe model, eliminating the need for sample normalization. MAT uses an innovative function to score regions for ChIP enrichment, which allows robust P value and false discovery rate calculations. MAT can detect ChIP regions from a single ChIP sample, multiple ChlP samples, or multiple ChIP samples with controls with increasing accuracy. The single-array ChIP region detection feature minimizes the time and monetary costs for laboratories newly adopting ChlP-chip to test their protocols and antibodies and allows established ChlP-chip laboratories to identify samples with questionable quality that might contaminate their data. MAT is developed in open-source Python and is available at http://chip. dfci.harvard.edu/∼wli/MAT. The general framework presented here can be extended to other oligonucleotide microarrays and tiling array platforms. |
|---|---|
| AbstractList | We propose a fast and powerful analysis algorithm, titled Model-based Analysis of Tiling-arrays (MAT), to reliably detect regions enriched by transcription factor chromatin immunoprecipitation (ChIP) on Affymetrix tiling arrays (ChIP-chip). MAT models the baseline probe behavior by considering probe sequence and copy number on each array. It standardizes the probe value through the probe model, eliminating the need for sample normalization. MAT uses an innovative function to score regions for ChIP enrichment, which allows robust P value and false discovery rate calculations. MAT can detect ChIP regions from a single ChIP sample, multiple ChIP samples, or multiple ChIP samples with controls with increasing accuracy. The single-array ChIP region detection feature minimizes the time and monetary costs for laboratories newly adopting ChIP-chip to test their protocols and antibodies and allows established ChIP-chip laboratories to identify samples with questionable quality that might contaminate their data. MAT is developed in open-source Python and is available at http://chip.dfci.harvard.edu/ similar to wli/MAT. The general framework presented here can be extended to other oligonucleotide microarrays and tiling array platforms. We propose a fast and powerful analysis algorithm, titled Modelbased Analysis of Tiling-arrays (MAT), to reliably detect regions enriched by transcription factor chromatin immunoprecipitation (ChIP) on Affymetrix tiling arrays (ChlP-chip). MAT models the baseline probe behavior by considering probe sequence and copy number on each array. It standardizes the probe value through the probe model, eliminating the need for sample normalization. MAT uses an innovative function to score regions for ChIP enrichment, which allows robust P value and false discovery rate calculations. MAT can detect ChIP regions from a single ChIP sample, multiple ChlP samples, or multiple ChIP samples with controls with increasing accuracy. The single-array ChIP region detection feature minimizes the time and monetary costs for laboratories newly adopting ChlP-chip to test their protocols and antibodies and allows established ChlP-chip laboratories to identify samples with questionable quality that might contaminate their data. MAT is developed in open-source Python and is available at http://chip. dfci.harvard.edu/∼wli/MAT. The general framework presented here can be extended to other oligonucleotide microarrays and tiling array platforms. We propose a fast and powerful analysis algorithm, titled Model-based Analysis of Tiling-arrays (MAT), to reliably detect regions enriched by transcription factor chromatin immunoprecipitation (ChIP) on Affymetrix tiling arrays (ChIP-chip). MAT models the baseline probe behavior by considering probe sequence and copy number on each array. It standardizes the probe value through the probe model, eliminating the need for sample normalization. MAT uses an innovative function to score regions for ChIP enrichment, which allows robust P value and false discovery rate calculations. MAT can detect ChIP regions from a single ChIP sample, multiple ChIP samples, or multiple ChIP samples with controls with increasing accuracy. The single-array ChIP region detection feature minimizes the time and monetary costs for laboratories newly adopting ChIP-chip to test their protocols and antibodies and allows established ChIP-chip laboratories to identify samples with questionable quality that might contaminate their data. MAT is developed in open-source Python and is available at http://chip.dfci.harvard.edu/∼wli/MAT. The general framework presented here can be extended to other oligonucleotide microarrays and tiling array platforms. We propose a fast and powerful analysis algorithm, titled Model-based Analysis of Tiling-arrays (MAT), to reliably detect regions enriched by transcription factor chromatin immunoprecipitation (ChIP) on Affymetrix tiling arrays (ChIP-chip). MAT models the baseline probe behavior by considering probe sequence and copy number on each array. It standardizes the probe value through the probe model, eliminating the need for sample normalization. MAT uses an innovative function to score regions for ChIP enrichment, which allows robust P value and false discovery rate calculations. MAT can detect ChIP regions from a single ChIP sample, multiple ChIP samples, or multiple ChIP samples with controls with increasing accuracy. The single-array ChIP region detection feature minimizes the time and monetary costs for laboratories newly adopting ChIP-chip to test their protocols and antibodies and allows established ChIP-chip laboratories to identify samples with questionable quality that might contaminate their data. MAT is developed in open-source Python and is available at http://chip.dfci.harvard.edu/~wli/MAT. The general framework presented here can be extended to other oligonucleotide microarrays and tiling array platforms. [PUBLICATION ABSTRACT] We propose a fast and powerful analysis algorithm, titled Model-based Analysis of Tiling-arrays (MAT), to reliably detect regions enriched by transcription factor chromatin immunoprecipitation (ChIP) on Affymetrix tiling arrays (ChIP-chip). MAT models the baseline probe behavior by considering probe sequence and copy number on each array. It standardizes the probe value through the probe model, eliminating the need for sample normalization. MAT uses an innovative function to score regions for ChIP enrichment, which allows robust P value and false discovery rate calculations. MAT can detect ChIP regions from a single ChIP sample, multiple ChIP samples, or multiple ChIP samples with controls with increasing accuracy. The single-array ChIP region detection feature minimizes the time and monetary costs for laboratories newly adopting ChIP-chip to test their protocols and antibodies and allows established ChIP-chip laboratories to identify samples with questionable quality that might contaminate their data. MAT is developed in open-source Python and is available at http://chip.dfci.harvard.edu/∼wli/MAT . The general framework presented here can be extended to other oligonucleotide microarrays and tiling array platforms. We propose a fast and powerful analysis algorithm, titled Model-based Analysis of Tiling-arrays (MAT), to reliably detect regions enriched by transcription factor chromatin immunoprecipitation (ChIP) on Affymetrix tiling arrays (ChIP-chip). MAT models the baseline probe behavior by considering probe sequence and copy number on each array. It standardizes the probe value through the probe model, eliminating the need for sample normalization. MAT uses an innovative function to score regions for ChIP enrichment, which allows robust P value and false discovery rate calculations. MAT can detect ChIP regions from a single ChIP sample, multiple ChIP samples, or multiple ChIP samples with controls with increasing accuracy. The single-array ChIP region detection feature minimizes the time and monetary costs for laboratories newly adopting ChIP-chip to test their protocols and antibodies and allows established ChIP-chip laboratories to identify samples with questionable quality that might contaminate their data. MAT is developed in open-source Python and is available at http://chip.dfci.harvard.edu/ approximately wli/MAT. The general framework presented here can be extended to other oligonucleotide microarrays and tiling array platforms.We propose a fast and powerful analysis algorithm, titled Model-based Analysis of Tiling-arrays (MAT), to reliably detect regions enriched by transcription factor chromatin immunoprecipitation (ChIP) on Affymetrix tiling arrays (ChIP-chip). MAT models the baseline probe behavior by considering probe sequence and copy number on each array. It standardizes the probe value through the probe model, eliminating the need for sample normalization. MAT uses an innovative function to score regions for ChIP enrichment, which allows robust P value and false discovery rate calculations. MAT can detect ChIP regions from a single ChIP sample, multiple ChIP samples, or multiple ChIP samples with controls with increasing accuracy. The single-array ChIP region detection feature minimizes the time and monetary costs for laboratories newly adopting ChIP-chip to test their protocols and antibodies and allows established ChIP-chip laboratories to identify samples with questionable quality that might contaminate their data. MAT is developed in open-source Python and is available at http://chip.dfci.harvard.edu/ approximately wli/MAT. The general framework presented here can be extended to other oligonucleotide microarrays and tiling array platforms. We propose a fast and powerful analysis algorithm, titled Model-based Analysis of Tiling-arrays (MAT), to reliably detect regions enriched by transcription factor chromatin immunoprecipitation (ChIP) on Affymetrix tiling arrays (ChIP-chip). MAT models the baseline probe behavior by considering probe sequence and copy number on each array. It standardizes the probe value through the probe model, eliminating the need for sample normalization. MAT uses an innovative function to score regions for ChIP enrichment, which allows robust P value and false discovery rate calculations. MAT can detect ChIP regions from a single ChIP sample, multiple ChIP samples, or multiple ChIP samples with controls with increasing accuracy. The single-array ChIP region detection feature minimizes the time and monetary costs for laboratories newly adopting ChIP-chip to test their protocols and antibodies and allows established ChIP-chip laboratories to identify samples with questionable quality that might contaminate their data. MAT is developed in open-source Python and is available at http://chip.dfci.harvard.edu/∼wli/MAT . The general framework presented here can be extended to other oligonucleotide microarrays and tiling array platforms. functional genomics genome tiling microarrays model-based probe analysis transcription regulation We propose a fast and powerful analysis algorithm, titled Model-based Analysis of Tiling-arrays (MAT), to reliably detect regions enriched by transcription factor chromatin immunoprecipitation (ChIP) on Affymetrix tiling arrays (ChIP-chip). MAT models the baseline probe behavior by considering probe sequence and copy number on each array. It standardizes the probe value through the probe model, eliminating the need for sample normalization. MAT uses an innovative function to score regions for ChIP enrichment, which allows robust P value and false discovery rate calculations. MAT can detect ChIP regions from a single ChIP sample, multiple ChIP samples, or multiple ChIP samples with controls with increasing accuracy. The single-array ChIP region detection feature minimizes the time and monetary costs for laboratories newly adopting ChIP-chip to test their protocols and antibodies and allows established ChIP-chip laboratories to identify samples with questionable quality that might contaminate their data. MAT is developed in open-source Python and is available at http://chip.dfci.harvard.edu/ approximately wli/MAT. The general framework presented here can be extended to other oligonucleotide microarrays and tiling array platforms. |
| Author | Liu, X. Shirley Brown, Myles Meyer, Clifford A. Gottardo, Raphael Li, Wei Carroll, Jason S. Johnson, W. Evan |
| Author_xml | – sequence: 1 givenname: W. Evan surname: Johnson fullname: Johnson, W. Evan – sequence: 2 givenname: Wei surname: Li fullname: Li, Wei – sequence: 3 givenname: Clifford A. surname: Meyer fullname: Meyer, Clifford A. – sequence: 4 givenname: Raphael surname: Gottardo fullname: Gottardo, Raphael – sequence: 5 givenname: Jason S. surname: Carroll fullname: Carroll, Jason S. – sequence: 6 givenname: Myles surname: Brown fullname: Brown, Myles – sequence: 7 givenname: X. Shirley surname: Liu fullname: Liu, X. Shirley |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/16895995$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNkc1vEzEQxS1URNPCmRNoxQGJw7Yz6_XXpVKI-KhUBIdytpxdb-PIWQd7F8h_j1eJCPRAOc1hfu_NvJkzctKH3hLyHOECQdDLbW_SBXBAlIBAH5EZgsKS1wpOyAygEqWsq_qUnKW0BgDFJDwhp8ilYkqxGaGfQmt9-dYk2xbz3vhdcqkIXXHrvOvvynmMZpeKLsRisbr-Ui5WbvuUPO6MT_bZoZ6Tr-_f3S4-ljefP1wv5jdlwxQfSmnqtpYVb6uaddhVjLeMoZC0U1XXiKWxTOByCW3DrKoFl4I3NVjODNCaQkPPCex9x35rdj-M93ob3cbEnUbQU3495dfH_FlytZdsx-XGto3th2iOsmCc_rvTu5W-C981Mi4UYDZ4fTCI4dto06A3LjXWe9PbMCadt2SCKfYgiIpipfjk-OoeuA5jzJdOusoDJUfkGXr5597HnIdHZeByDzQxpBRt9x-nYPcUjRvM4MIU3Pl_6IrDKlPjOIVqSjXmX4qMvHkA0d3o_WB_Dpl9sWfXaQjxN0wBGDDJ6S9iDdYV |
| CitedBy_id | crossref_primary_10_1101_gr_078261_108 crossref_primary_10_1182_blood_2011_03_341446 crossref_primary_10_1371_journal_pone_0053666 crossref_primary_10_4137_CIN_S295 crossref_primary_10_1093_nar_gkt912 crossref_primary_10_1093_nar_gky929 crossref_primary_10_1186_1471_2164_11_478 crossref_primary_10_1016_j_molcel_2011_08_019 crossref_primary_10_1093_nar_gkp559 crossref_primary_10_1113_JP273570 crossref_primary_10_1152_ajprenal_00586_2010 crossref_primary_10_1038_gene_2009_66 crossref_primary_10_1016_j_ygeno_2017_01_006 crossref_primary_10_1016_j_neuron_2013_05_015 crossref_primary_10_1093_bioinformatics_btp471 crossref_primary_10_1093_nar_gkq643 crossref_primary_10_2174_0929867325666180530115711 crossref_primary_10_1016_j_cell_2008_02_039 crossref_primary_10_1007_s12041_019_1152_8 crossref_primary_10_1186_s12859_018_2356_2 crossref_primary_10_1371_journal_pone_0011471 crossref_primary_10_1038_embor_2008_44 crossref_primary_10_1186_1471_2164_11_481 crossref_primary_10_1093_nar_gkab1249 crossref_primary_10_1002_mrr_20228 crossref_primary_10_1074_jbc_M110_217745 crossref_primary_10_1371_journal_pgen_1004317 crossref_primary_10_1038_gene_2011_17 crossref_primary_10_1261_rna_2808411 crossref_primary_10_1371_journal_pone_0054507 crossref_primary_10_1016_j_canlet_2011_12_003 crossref_primary_10_2217_epi_2020_0435 crossref_primary_10_1038_msb_2009_42 crossref_primary_10_1093_nar_gkq410 crossref_primary_10_1093_bioinformatics_btp469 crossref_primary_10_1101_gad_1709008 crossref_primary_10_1016_j_bbrc_2009_06_055 crossref_primary_10_1073_pnas_0810067106 crossref_primary_10_1093_bioinformatics_btq314 crossref_primary_10_1101_gad_1883910 crossref_primary_10_1016_j_metabol_2014_01_007 crossref_primary_10_1002_0471142727_mb2113s79 crossref_primary_10_1158_0008_5472_CAN_11_2091 crossref_primary_10_1186_gb_2007_8_8_r178 crossref_primary_10_1016_j_molcel_2009_07_017 crossref_primary_10_1016_j_jim_2013_06_001 crossref_primary_10_1186_1471_2164_14_82 crossref_primary_10_1261_rna_1070208 crossref_primary_10_1101_gr_111534_110 crossref_primary_10_1371_journal_pgen_1001032 crossref_primary_10_1111_j_1365_2443_2010_01423_x crossref_primary_10_1172_JCI65102 crossref_primary_10_1073_pnas_0712085105 crossref_primary_10_1371_journal_pgen_1001152 crossref_primary_10_1101_gad_1944810 crossref_primary_10_1093_bioinformatics_btm546 crossref_primary_10_1128_MCB_01038_08 crossref_primary_10_1111_j_1349_7006_2009_01299_x crossref_primary_10_1111_j_1541_0420_2007_00899_x crossref_primary_10_4049_jimmunol_182_1_44 crossref_primary_10_1073_pnas_1305823110 crossref_primary_10_1038_ncb2023 crossref_primary_10_1210_me_2009_0499 crossref_primary_10_1039_C7TX00163K crossref_primary_10_1016_j_copbio_2007_11_005 crossref_primary_10_1371_journal_pbio_1000119 crossref_primary_10_1128_mBio_00972_13 crossref_primary_10_1158_1078_0432_CCR_09_2765 crossref_primary_10_1371_journal_pone_0103243 crossref_primary_10_1093_nar_gks139 crossref_primary_10_1214_07_AOAS116 crossref_primary_10_1186_1753_6561_5_S2_S8 crossref_primary_10_1038_nprot_2013_150 crossref_primary_10_1371_journal_pgen_1003560 crossref_primary_10_1186_1471_2105_10_173 crossref_primary_10_1016_j_ygeno_2008_09_012 crossref_primary_10_1016_j_cub_2011_08_036 crossref_primary_10_1111_j_1365_313X_2009_03940_x crossref_primary_10_1016_j_copbio_2007_07_002 crossref_primary_10_1016_j_cell_2011_10_053 crossref_primary_10_1016_j_molmet_2016_11_003 crossref_primary_10_1186_gb_2009_10_10_r113 crossref_primary_10_1210_me_2010_0138 crossref_primary_10_1016_j_mce_2006_12_015 crossref_primary_10_1016_j_ymeth_2009_03_003 crossref_primary_10_1016_j_molcel_2008_10_016 crossref_primary_10_1371_journal_pcbi_1003525 crossref_primary_10_1128_MCB_05504_11 crossref_primary_10_1186_1471_2105_8_219 crossref_primary_10_1016_j_cell_2014_06_027 crossref_primary_10_1083_jcb_200901104 crossref_primary_10_1093_nar_gkl1157 crossref_primary_10_1101_gad_178079_111 crossref_primary_10_1371_journal_pone_0068712 crossref_primary_10_1093_bioinformatics_btq247 crossref_primary_10_1186_gb_2009_10_10_r107 crossref_primary_10_1186_1471_2105_11_194 crossref_primary_10_1371_journal_pone_0110973 crossref_primary_10_1101_gad_1897310 crossref_primary_10_1586_14789450_5_3_469 crossref_primary_10_1371_journal_pone_0086957 crossref_primary_10_1101_gr_085530_108 crossref_primary_10_1038_nature05563 crossref_primary_10_1242_dev_024398 crossref_primary_10_18632_oncotarget_2785 crossref_primary_10_1371_journal_pgen_1002179 crossref_primary_10_1016_j_ygeno_2011_12_005 crossref_primary_10_1371_journal_pgen_1003382 crossref_primary_10_1038_ncomms1551 crossref_primary_10_1158_0008_5472_CAN_11_1882 crossref_primary_10_1186_gb_2011_12_8_r83 crossref_primary_10_1016_j_molcel_2010_07_008 crossref_primary_10_1111_j_1600_065X_2008_00721_x crossref_primary_10_1039_b906179g crossref_primary_10_1089_cmb_2011_0102 crossref_primary_10_1002_pros_21311 crossref_primary_10_1016_j_cell_2008_01_018 crossref_primary_10_1186_gb_2008_9_9_r137 crossref_primary_10_1093_nar_gkr513 crossref_primary_10_1111_j_1541_0420_2010_01441_x crossref_primary_10_1016_j_cell_2010_09_010 crossref_primary_10_3892_ijo_2017_4019 crossref_primary_10_1093_bioinformatics_btq023 crossref_primary_10_1038_nrg2270 crossref_primary_10_1016_j_csda_2009_09_035 crossref_primary_10_1371_journal_pcbi_1000039 crossref_primary_10_1177_1362361320971085 crossref_primary_10_1016_j_mce_2007_07_006 crossref_primary_10_1262_jrd_11_035A crossref_primary_10_3389_fmolb_2016_00082 crossref_primary_10_1111_j_1541_0420_2008_01180_x crossref_primary_10_1016_j_cell_2009_04_056 crossref_primary_10_1186_2040_2392_4_14 crossref_primary_10_1038_nature06634 crossref_primary_10_1186_1471_2105_9_453 crossref_primary_10_1091_mbc_e09_08_0707 crossref_primary_10_1007_s00335_021_09869_1 crossref_primary_10_1371_journal_pgen_1002277 crossref_primary_10_1093_nar_gku1377 crossref_primary_10_1186_gb_2007_8_5_r82 crossref_primary_10_1186_1471_2164_12_134 crossref_primary_10_1186_gb_2007_8_5_r81 crossref_primary_10_1016_j_ajog_2014_01_045 crossref_primary_10_1634_stemcells_2007_0964 crossref_primary_10_1101_gad_1819509 crossref_primary_10_1101_gr_093047_109 crossref_primary_10_1128_MCB_01464_10 crossref_primary_10_3389_fmolb_2016_00074 crossref_primary_10_1186_gb_2014_15_4_r62 crossref_primary_10_1038_s41380_021_01079_0 crossref_primary_10_4161_chim_1_1_12439 crossref_primary_10_1016_j_chest_2016_02_648 crossref_primary_10_1093_hmg_ddp152 crossref_primary_10_1073_pnas_0711758105 crossref_primary_10_1093_bib_bbs078 crossref_primary_10_1371_journal_pgen_1000489 crossref_primary_10_1371_journal_pgen_1003753 crossref_primary_10_1089_cmb_2010_0020 crossref_primary_10_1093_bioinformatics_btn571 crossref_primary_10_1158_0008_5472_CAN_09_0919 crossref_primary_10_1074_jbc_M110_165704 crossref_primary_10_1016_j_lfs_2010_02_001 crossref_primary_10_1093_bioinformatics_btn570 crossref_primary_10_1128_MCB_00781_10 crossref_primary_10_1089_cmb_2007_0141 crossref_primary_10_1038_nrc2591 crossref_primary_10_1371_journal_pgen_1003195 crossref_primary_10_1101_gr_6006107 crossref_primary_10_1186_1471_2164_12_313 crossref_primary_10_1016_j_bbrc_2010_07_132 crossref_primary_10_1210_me_2008_0100 crossref_primary_10_1371_journal_pone_0021667 crossref_primary_10_1101_gad_2029711 crossref_primary_10_1186_1471_2105_11_275 crossref_primary_10_1128_mBio_01714_14 crossref_primary_10_1016_j_cell_2010_05_028 crossref_primary_10_1128_MCB_00419_10 crossref_primary_10_1126_scitranslmed_3008326 crossref_primary_10_1038_s41598_020_71336_9 crossref_primary_10_1038_nbt1279 crossref_primary_10_1101_gr_084582_108 crossref_primary_10_1534_g3_113_007534 crossref_primary_10_1371_journal_pone_0006960 crossref_primary_10_1093_bioinformatics_btm592 crossref_primary_10_1093_nar_gkq012 crossref_primary_10_1186_gb_2010_11_11_140 crossref_primary_10_1371_journal_pcbi_1000201 crossref_primary_10_1073_pnas_0902573106 crossref_primary_10_1007_s11464_011_0125_x crossref_primary_10_1186_1756_8935_3_6 crossref_primary_10_1093_bioinformatics_btp626 crossref_primary_10_1371_journal_pgen_1006331 crossref_primary_10_1093_nar_gkp282 crossref_primary_10_1128_MCB_00821_10 crossref_primary_10_1073_pnas_1011134107 crossref_primary_10_1128_JB_00961_12 crossref_primary_10_1534_genetics_112_146647 crossref_primary_10_1073_pnas_1003822107 crossref_primary_10_1101_gr_074070_107 crossref_primary_10_1038_ni_1721 crossref_primary_10_1093_nar_gkn515 crossref_primary_10_1186_1471_2164_9_S1_S20 crossref_primary_10_1371_journal_pgen_1000225 crossref_primary_10_1186_1471_2105_10_204 crossref_primary_10_1371_journal_pgen_1000224 crossref_primary_10_1111_j_1365_2443_2010_01404_x crossref_primary_10_1186_gb_2011_12_2_r11 crossref_primary_10_1007_s11010_012_1269_z crossref_primary_10_1104_pp_109_150185 crossref_primary_10_1371_journal_pone_0014686 crossref_primary_10_1210_en_2007_1096 crossref_primary_10_1002_dvg_20534 crossref_primary_10_1186_1471_2407_11_30 crossref_primary_10_1016_j_molcel_2011_03_026 crossref_primary_10_1016_j_bbrc_2010_01_089 crossref_primary_10_1016_j_molcel_2009_09_011 crossref_primary_10_1016_j_ccr_2008_10_001 crossref_primary_10_1016_j_ajhg_2010_02_008 crossref_primary_10_1261_rna_1319309 crossref_primary_10_1016_j_ygeno_2012_08_003 crossref_primary_10_1038_ijo_2015_38 crossref_primary_10_1038_ncomms2259 crossref_primary_10_1371_journal_pcbi_1000227 crossref_primary_10_1016_j_leukres_2010_10_032 crossref_primary_10_1016_j_leukres_2012_09_014 crossref_primary_10_1038_nbt_1508 crossref_primary_10_1038_nbt_1505 crossref_primary_10_1186_s13041_021_00894_4 crossref_primary_10_1101_gad_211011_112 crossref_primary_10_1016_j_ymeth_2012_03_022 crossref_primary_10_1186_1471_2105_8_221 crossref_primary_10_1101_pdb_top110 crossref_primary_10_1101_gr_110601_110 crossref_primary_10_1124_mol_107_039099 crossref_primary_10_1016_j_ygeno_2010_02_009 crossref_primary_10_1093_bioinformatics_bts142 crossref_primary_10_1186_1471_2105_10_305 crossref_primary_10_1093_bioinformatics_btm043 crossref_primary_10_1258_ebm_2012_011258 crossref_primary_10_1093_biostatistics_kxq055 crossref_primary_10_1128_MCB_02224_07 crossref_primary_10_1016_j_molcel_2007_06_039 crossref_primary_10_1371_journal_pone_0154836 crossref_primary_10_1038_ng_2416 crossref_primary_10_1093_bioinformatics_btm167 crossref_primary_10_1093_nar_gkq041 crossref_primary_10_1158_0008_5472_CAN_09_4407 crossref_primary_10_1038_nmeth_1985 crossref_primary_10_1038_nature07483 crossref_primary_10_4049_jimmunol_1000082 crossref_primary_10_1371_journal_pone_0093873 crossref_primary_10_1038_tp_2011_26 crossref_primary_10_1177_0962280209351924 crossref_primary_10_1016_j_devcel_2010_03_012 crossref_primary_10_1016_j_molcel_2007_04_015 crossref_primary_10_1101_gr_120162_110 crossref_primary_10_1371_journal_pgen_1000396 crossref_primary_10_1186_1471_2172_13_58 crossref_primary_10_1128_MCB_00020_09 crossref_primary_10_1186_gb_2010_11_5_r50 crossref_primary_10_1093_nar_gkv500 crossref_primary_10_1073_pnas_1012674107 crossref_primary_10_1073_pnas_1707957114 crossref_primary_10_1093_biostatistics_kxr013 crossref_primary_10_1186_gb_2009_10_12_r142 crossref_primary_10_1371_journal_pone_0015069 crossref_primary_10_4161_epi_6_4_14876 crossref_primary_10_1101_gr_7080508 crossref_primary_10_1093_bioinformatics_btp425 crossref_primary_10_1016_j_ydbio_2017_10_018 crossref_primary_10_1186_1471_2156_9_27 crossref_primary_10_1093_toxsci_kfq115 crossref_primary_10_1093_molehr_gav051 crossref_primary_10_1016_j_cell_2009_12_003 crossref_primary_10_1038_ng1901 crossref_primary_10_1523_JNEUROSCI_3714_13_2014 crossref_primary_10_1128_MCB_00799_08 crossref_primary_10_1371_journal_pone_0004159 crossref_primary_10_1073_pnas_0901676106 crossref_primary_10_1371_journal_pone_0057326 crossref_primary_10_1128_MCB_00918_13 crossref_primary_10_1016_j_ccr_2009_12_045 crossref_primary_10_1111_j_1541_0420_2009_01298_x crossref_primary_10_1038_nature06929 crossref_primary_10_1101_gr_112755_110 crossref_primary_10_1111_j_1365_313X_2010_04236_x crossref_primary_10_1186_1471_2105_14_188 crossref_primary_10_1371_journal_pone_0120296 crossref_primary_10_1016_j_gdata_2015_05_004 crossref_primary_10_1186_1741_7007_7_35 crossref_primary_10_1371_journal_pone_0017912 crossref_primary_10_1214_09_AOAS248 crossref_primary_10_1016_j_ymeth_2009_02_024 crossref_primary_10_1038_ncomms5719 crossref_primary_10_1101_gr_084970_108 crossref_primary_10_1186_1752_0509_5_67 crossref_primary_10_1371_journal_pone_0004721 crossref_primary_10_1038_ni_3257 crossref_primary_10_1101_gr_108217_110 crossref_primary_10_1073_pnas_1203028109 crossref_primary_10_1073_pnas_0910586106 crossref_primary_10_1186_1471_2105_11_207 crossref_primary_10_1523_JNEUROSCI_1314_10_2010 crossref_primary_10_4028_www_scientific_net_AMM_380_384_2569 crossref_primary_10_1038_ng_3635 crossref_primary_10_1007_s00439_011_0993_x crossref_primary_10_1371_journal_pgen_1000814 crossref_primary_10_1128_JVI_01549_12 crossref_primary_10_1093_molbev_msm061 |
| Cites_doi | 10.1093/nar/27.2.573 10.1093/nar/gkg283 10.1016/j.cell.2005.05.008 10.1093/bioinformatics/19.2.185 10.1093/bioinformatics/bti593 10.1101/gr.187101 10.1038/nature02800 10.1038/35054095 10.1126/science.290.5500.2306 10.1038/ng569 10.1016/S0092-8674(04)00127-8 10.1093/bioinformatics/bti1046 |
| ContentType | Journal Article |
| Copyright | Copyright 2006 National Academy of Sciences of the United States of America Copyright National Academy of Sciences Aug 15, 2006 2006 by The National Academy of Sciences of the USA 2006 |
| Copyright_xml | – notice: Copyright 2006 National Academy of Sciences of the United States of America – notice: Copyright National Academy of Sciences Aug 15, 2006 – notice: 2006 by The National Academy of Sciences of the USA 2006 |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7QG 7QL 7QP 7QR 7SN 7SS 7T5 7TK 7TM 7TO 7U9 8FD C1K FR3 H94 M7N P64 RC3 7QO 7X8 5PM ADTOC UNPAY |
| DOI | 10.1073/pnas.0601180103 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Animal Behavior Abstracts Bacteriology Abstracts (Microbiology B) Calcium & Calcified Tissue Abstracts Chemoreception Abstracts Ecology Abstracts Entomology Abstracts (Full archive) Immunology Abstracts Neurosciences Abstracts Nucleic Acids Abstracts Oncogenes and Growth Factors Abstracts Virology and AIDS Abstracts Technology Research Database Environmental Sciences and Pollution Management Engineering Research Database AIDS and Cancer Research Abstracts Algology Mycology and Protozoology Abstracts (Microbiology C) Biotechnology and BioEngineering Abstracts Genetics Abstracts Biotechnology Research Abstracts 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) Virology and AIDS Abstracts Oncogenes and Growth Factors Abstracts Technology Research Database Nucleic Acids Abstracts Ecology Abstracts Neurosciences Abstracts Biotechnology and BioEngineering Abstracts Environmental Sciences and Pollution Management Entomology Abstracts Genetics Abstracts Animal Behavior Abstracts Bacteriology Abstracts (Microbiology B) Algology Mycology and Protozoology Abstracts (Microbiology C) AIDS and Cancer Research Abstracts Chemoreception Abstracts Immunology Abstracts Engineering Research Database Calcium & Calcified Tissue Abstracts Biotechnology Research Abstracts MEDLINE - Academic |
| DatabaseTitleList | Engineering Research Database Virology and AIDS Abstracts CrossRef 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: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Sciences (General) |
| EISSN | 1091-6490 |
| EndPage | 12462 |
| ExternalDocumentID | oai:pubmedcentral.nih.gov:1567901 PMC1567901 1150974571 16895995 10_1073_pnas_0601180103 103_33_12457 30050586 |
| Genre | Research Support, Non-U.S. Gov't Evaluation Study Journal Article Research Support, N.I.H., Extramural Feature |
| GrantInformation_xml | – fundername: NIDDK NIH HHS grantid: T90 DK070078 – fundername: NHGRI NIH HHS grantid: R01 HG004069 – fundername: NIDDK NIH HHS grantid: T90 DK070078-01 |
| GroupedDBID | --- -DZ -~X .55 .GJ 0R~ 123 29P 2AX 2FS 2WC 3O- 4.4 53G 5RE 5VS 85S AACGO AAFWJ AANCE AAYJJ ABBHK ABOCM ABPLY ABPPZ ABTLG ABXSQ ABZEH ACGOD ACHIC ACIWK ACNCT ACPRK ADQXQ ADULT ADXHL AENEX AEUPB AEXZC AFFNX AFOSN AFRAH ALMA_UNASSIGNED_HOLDINGS AQVQM AS~ BKOMP CS3 D0L DCCCD DIK DU5 E3Z EBS EJD F5P FRP GX1 H13 HH5 HQ3 HTVGU HYE IPSME JAAYA JBMMH JENOY JHFFW JKQEH JLS JLXEF JPM JSG JST KQ8 L7B LU7 MVM N9A N~3 O9- OK1 P-O PNE PQQKQ R.V RHI RNA RNS RPM RXW SA0 SJN TAE TN5 UKR W8F WH7 WHG WOQ WOW X7M XSW Y6R YBH YKV YSK ZCA ZCG ~02 ~KM - 02 0R 1AW 55 AAPBV ABFLS ABPTK ADACO ADZLD AJYGW AS ASUFR DNJUQ DOOOF DWIUU DZ F20 GJ JSODD KM PQEST RHF VQA X XFK XHC ZA5 AAYXX CITATION CGR CUY CVF ECM EIF NPM 7QG 7QL 7QP 7QR 7SN 7SS 7T5 7TK 7TM 7TO 7U9 8FD C1K FR3 H94 M7N P64 RC3 7QO 7X8 5PM 692 6TJ 79B ACKIV ADTOC AFHIN AFQQW HGD NEJ NHB UNPAY VOH |
| ID | FETCH-LOGICAL-c596t-8a4d4826d245f1f256d551783f92fc7bae571bb0dc5e9476876c40e65a03430c3 |
| IEDL.DBID | UNPAY |
| ISSN | 0027-8424 1091-6490 |
| IngestDate | Sun Oct 26 04:08:20 EDT 2025 Tue Sep 30 16:42:13 EDT 2025 Fri Sep 05 14:27:09 EDT 2025 Mon Oct 06 17:59:07 EDT 2025 Mon Jun 30 08:19:37 EDT 2025 Fri May 30 11:00:38 EDT 2025 Thu Apr 24 23:10:06 EDT 2025 Wed Oct 01 04:31:07 EDT 2025 Wed Nov 11 00:29:51 EST 2020 Thu May 30 08:49:40 EDT 2019 Thu May 29 08:42:47 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 33 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c596t-8a4d4826d245f1f256d551783f92fc7bae571bb0dc5e9476876c40e65a03430c3 |
| Notes | SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23 ObjectType-Undefined-1 ObjectType-Feature-3 Edited by Michael S. Waterman, University of Southern California, Los Angeles, CA, and approved June 18, 2006 Author contributions: C.A.M., M.B., and X.S.L. designed research; W.E.J., W.L., and J.S.C. performed research; W.L., C.A.M., and R.G. contributed new reagents/analytic tools; W.E.J. and W.L. analyzed data; and W.E.J. and X.S.L. wrote the paper. W.E.J., W.L., and C.A.M. contributed equally to this work. |
| OpenAccessLink | https://proxy.k.utb.cz/login?url=http://doi.org/10.1073/pnas.0601180103 |
| PMID | 16895995 |
| PQID | 201386116 |
| PQPubID | 42026 |
| PageCount | 6 |
| ParticipantIDs | proquest_journals_201386116 proquest_miscellaneous_19312961 crossref_primary_10_1073_pnas_0601180103 jstor_primary_30050586 unpaywall_primary_10_1073_pnas_0601180103 proquest_miscellaneous_68757595 pubmed_primary_16895995 pnas_primary_103_33_12457 pnas_primary_103_33_12457_fulltext crossref_citationtrail_10_1073_pnas_0601180103 pubmedcentral_primary_oai_pubmedcentral_nih_gov_1567901 |
| ProviderPackageCode | RNA PNE CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2006-08-15 |
| PublicationDateYYYYMMDD | 2006-08-15 |
| PublicationDate_xml | – month: 08 year: 2006 text: 2006-08-15 day: 15 |
| PublicationDecade | 2000 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: Washington |
| PublicationTitle | Proceedings of the National Academy of Sciences - PNAS |
| PublicationTitleAlternate | Proc Natl Acad Sci U S A |
| PublicationYear | 2006 |
| Publisher | National Academy of Sciences National Acad Sciences |
| Publisher_xml | – name: National Academy of Sciences – name: National Acad Sciences |
| References | e_1_3_4_3_2 e_1_3_4_2_2 e_1_3_4_1_2 e_1_3_4_9_2 e_1_3_4_8_2 e_1_3_4_6_2 Wu Z. (e_1_3_4_12_2) 2003 e_1_3_4_5_2 e_1_3_4_4_2 e_1_3_4_11_2 e_1_3_4_10_2 e_1_3_4_13_2 e_1_3_4_14_2 Keles S. (e_1_3_4_7_2) 2004 |
| References_xml | – volume-title: A Model Based Background Adjustment for Oligonucleotide Expression Arrays: Technical Report year: 2003 ident: e_1_3_4_12_2 – ident: e_1_3_4_13_2 doi: 10.1093/nar/27.2.573 – ident: e_1_3_4_11_2 doi: 10.1093/nar/gkg283 – ident: e_1_3_4_9_2 doi: 10.1016/j.cell.2005.05.008 – ident: e_1_3_4_10_2 doi: 10.1093/bioinformatics/19.2.185 – ident: e_1_3_4_8_2 doi: 10.1093/bioinformatics/bti593 – ident: e_1_3_4_14_2 doi: 10.1101/gr.187101 – volume-title: Multiple Testing Methods For ChIP-Chip High Density Oligonucleotide Array Data year: 2004 ident: e_1_3_4_7_2 – ident: e_1_3_4_4_2 doi: 10.1038/nature02800 – ident: e_1_3_4_1_2 doi: 10.1038/35054095 – ident: e_1_3_4_3_2 doi: 10.1126/science.290.5500.2306 – ident: e_1_3_4_2_2 doi: 10.1038/ng569 – ident: e_1_3_4_5_2 doi: 10.1016/S0092-8674(04)00127-8 – ident: e_1_3_4_6_2 doi: 10.1093/bioinformatics/bti1046 |
| SSID | ssj0009580 |
| Score | 2.3962147 |
| Snippet | We propose a fast and powerful analysis algorithm, titled Modelbased Analysis of Tiling-arrays (MAT), to reliably detect regions enriched by transcription... We propose a fast and powerful analysis algorithm, titled Model-based Analysis of Tiling-arrays (MAT), to reliably detect regions enriched by transcription... |
| SourceID | unpaywall pubmedcentral proquest pubmed crossref pnas jstor |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 12457 |
| SubjectTerms | Algorithms Antibodies Behavior modeling Biological Sciences Chromatin Chromosomes Data analysis DNA probes Field programmable gate arrays Genetic algorithms Genomes Genomics Humans Internet Modeling Models, Theoretical Nucleotides Oligonucleotide Array Sequence Analysis - instrumentation Oligonucleotide Array Sequence Analysis - methods Physical Sciences Receptors, Estrogen - genetics Receptors, Estrogen - metabolism Software Standardization Statistical variance Tiling Transcription Factors - genetics Transcription Factors - metabolism |
| Title | Model-Based Analysis of Tiling-Arrays for ChIP-Chip |
| URI | https://www.jstor.org/stable/30050586 http://www.pnas.org/content/103/33/12457.abstract https://www.ncbi.nlm.nih.gov/pubmed/16895995 https://www.proquest.com/docview/201386116 https://www.proquest.com/docview/19312961 https://www.proquest.com/docview/68757595 https://pubmed.ncbi.nlm.nih.gov/PMC1567901 http://doi.org/10.1073/pnas.0601180103 |
| UnpaywallVersion | submittedVersion |
| Volume | 103 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVFSB databaseName: Free Full-Text Journals in Chemistry customDbUrl: eissn: 1091-6490 dateEnd: 20250502 omitProxy: true ssIdentifier: ssj0009580 issn: 0027-8424 databaseCode: HH5 dateStart: 19150101 isFulltext: true titleUrlDefault: http://abc-chemistry.org/ providerName: ABC ChemistRy – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 1091-6490 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0009580 issn: 0027-8424 databaseCode: KQ8 dateStart: 19150101 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 1091-6490 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0009580 issn: 0027-8424 databaseCode: KQ8 dateStart: 19150115 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries – providerCode: PRVBFR databaseName: Free Medical Journals customDbUrl: eissn: 1091-6490 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0009580 issn: 0027-8424 databaseCode: DIK dateStart: 19150101 isFulltext: true titleUrlDefault: http://www.freemedicaljournals.com providerName: Flying Publisher – providerCode: PRVFQY databaseName: GFMER Free Medical Journals customDbUrl: eissn: 1091-6490 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0009580 issn: 0027-8424 databaseCode: GX1 dateStart: 0 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: 1091-6490 dateEnd: 20250502 omitProxy: true ssIdentifier: ssj0009580 issn: 0027-8424 databaseCode: RPM dateStart: 19150101 isFulltext: true titleUrlDefault: https://www.ncbi.nlm.nih.gov/pmc/ providerName: National Library of Medicine |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fb9MwED6x7gFegAGDMBgR4mF7cEniX8njNLENHqY9UKk8WY4dqxVVWjWp0PjrOSdpSlWqiWefreR8l_suPn8H8IlFziWRyYilmmOCklmieR4RGRnhA5Kwpqm2uBU3I_ZtzMebAtmt43tJPy9KXQ09ZUic-oYEB3AoOGLuARyObu8ufrT1G_iZZW33Wgx-RLAsWnP47K6wFX7aCkRPa4pC_4KYu5WSj1flQt__0rPZX2Ho6hlcry_ztNUnP4erOh-a37vcjg-84XN42iHR8KI1nSN4VJQv4Kjz9So86wipz18C9Q3TZsQHPBvqjsUknLuwnvrL7EQvl_q-ChH-hpeTr3fETKaLVzC6-vL98oZ0zRaIQY3WJNXMMsw1bMK4ix0iIYtgSqbUZYkzMtcFl3GeR9bwImOYpEhhWFQIriPKaGToMQzKeVm8gTBlLklcqhEpWpYUsS5wrtC4qGUaVw5guN4DZTomct8QY6aaE3FJlVeK2iglgLN-wqIl4dgvetxsai9Hm159qQggaEQ386miVCG84TKAj3vHlOuKcAI4WduH6vy8Uok_6BVxjMt_6EfRQf2piy6L-apSiJARU4l4v4TwTQV4hnp53Vrb5kFEmnlGuADklh32Ap4cfHuknE4aknDMyyVivQDOe4t9SHdv_0P2BJ60P6NSEvN3MKiXq-I9wrM6P4WD63F82nnoH8G5L8E |
| linkProvider | Unpaywall |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3fb9MwED5B9wAvsAGDsA0ixMP24C6JfyWP07QxeJj2QKXxZDl2rFZUadWkQuOv55ykKVWpJp59tpLzXe67-PwdwGcWOZdEJiOWao4JSmaJ5nlEZGSED0jCmqba4lbcjNi3e36_LpDdOL6X9Hxe6mroKUPi1DckeAp7giPmHsDe6Pbu4kdbv4GfWdZ2r8XgRwTLohWHz_YKG-GnrUD0tKYo9C-IuV0p-WxZzvXDLz2d_hWGrl_Cl9Vlnrb65OdwWedD83ub2_GRN9yHFx0SDS9a0zmAJ0X5Cg46X6_C046Q-uw1UN8wbUp8wLOh7lhMwpkL64m_zE70YqEfqhDhb3g5_npHzHgyfwOj66vvlzeka7ZADGq0JqlmlmGuYRPGXewQCVkEUzKlLkuckbkuuIzzPLKGFxnDJEUKw6JCcB1RRiNDD2FQzsriHYQpc0niUo1I0bKkiHWBc4XGRS3TuHIAw9UeKNMxkfuGGFPVnIhLqrxS1FopAZz2E-YtCcdu0cNmU3s52vTqS0UAQSO6nk8VpQrhDZcBfNo5plxXhBPA0co-VOfnlUr8Qa-IY1z-Yz-KDupPXXRZzJaVQoSMmErEuyWEbyrAM9TL29ba1g8i0swzwgUgN-ywF_Dk4Jsj5WTckIRjXi4R6wVw1lvsY7p7_x-yR_C8_RmVkpgfw6BeLIsThGd1_qHzzT-o7C7Q |
| 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=Model-based+analysis+of+tiling-arrays+for+ChIP-chip&rft.jtitle=Proceedings+of+the+National+Academy+of+Sciences+-+PNAS&rft.au=W.+Evan+Johnson&rft.au=Wei+Li&rft.au=Clifford+A.+Meyer&rft.au=Raphael+Gottardo&rft.date=2006-08-15&rft.pub=National+Acad+Sciences&rft.issn=0027-8424&rft.eissn=1091-6490&rft.volume=103&rft.issue=33&rft.spage=12457&rft_id=info:doi/10.1073%2Fpnas.0601180103&rft_id=info%3Apmid%2F16895995&rft.externalDBID=n%2Fa&rft.externalDocID=103_33_12457 |
| thumbnail_m | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.pnas.org%2Fcontent%2F103%2F33.cover.gif |
| thumbnail_s | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.pnas.org%2Fcontent%2F103%2F33.cover.gif |