The Essential Genome of Escherichia coli K-12

Transposon-directed insertion site sequencing (TraDIS) is a high-throughput method coupling transposon mutagenesis with short-fragment DNA sequencing. It is commonly used to identify essential genes. Single gene deletion libraries are considered the gold standard for identifying essential genes. Cur...

Full description

Saved in:
Bibliographic Details
Published inmBio Vol. 9; no. 1
Main Authors Goodall, Emily C. A., Robinson, Ashley, Johnston, Iain G., Jabbari, Sara, Turner, Keith A., Cunningham, Adam F., Lund, Peter A., Cole, Jeffrey A., Henderson, Ian R.
Format Journal Article
LanguageEnglish
Published United States American Society for Microbiology 20.02.2018
Subjects
Online AccessGet full text

Cover

Loading…
Abstract Transposon-directed insertion site sequencing (TraDIS) is a high-throughput method coupling transposon mutagenesis with short-fragment DNA sequencing. It is commonly used to identify essential genes. Single gene deletion libraries are considered the gold standard for identifying essential genes. Currently, the TraDIS method has not been benchmarked against such libraries, and therefore, it remains unclear whether the two methodologies are comparable. To address this, a high-density transposon library was constructed in Escherichia coli K-12. Essential genes predicted from sequencing of this library were compared to existing essential gene databases. To decrease false-positive identification of essential genes, statistical data analysis included corrections for both gene length and genome length. Through this analysis, new essential genes and genes previously incorrectly designated essential were identified. We show that manual analysis of TraDIS data reveals novel features that would not have been detected by statistical analysis alone. Examples include short essential regions within genes, orientation-dependent effects, and fine-resolution identification of genome and protein features. Recognition of these insertion profiles in transposon mutagenesis data sets will assist genome annotation of less well characterized genomes and provides new insights into bacterial physiology and biochemistry. IMPORTANCE Incentives to define lists of genes that are essential for bacterial survival include the identification of potential targets for antibacterial drug development, genes required for rapid growth for exploitation in biotechnology, and discovery of new biochemical pathways. To identify essential genes in Escherichia coli , we constructed a transposon mutant library of unprecedented density. Initial automated analysis of the resulting data revealed many discrepancies compared to the literature. We now report more extensive statistical analysis supported by both literature searches and detailed inspection of high-density TraDIS sequencing data for each putative essential gene for the E. coli model laboratory organism. This paper is important because it provides a better understanding of the essential genes of E. coli , reveals the limitations of relying on automated analysis alone, and provides a new standard for the analysis of TraDIS data. Incentives to define lists of genes that are essential for bacterial survival include the identification of potential targets for antibacterial drug development, genes required for rapid growth for exploitation in biotechnology, and discovery of new biochemical pathways. To identify essential genes in Escherichia coli , we constructed a transposon mutant library of unprecedented density. Initial automated analysis of the resulting data revealed many discrepancies compared to the literature. We now report more extensive statistical analysis supported by both literature searches and detailed inspection of high-density TraDIS sequencing data for each putative essential gene for the E. coli model laboratory organism. This paper is important because it provides a better understanding of the essential genes of E. coli , reveals the limitations of relying on automated analysis alone, and provides a new standard for the analysis of TraDIS data.
AbstractList Transposon-directed insertion site sequencing (TraDIS) is a high-throughput method coupling transposon mutagenesis with short-fragment DNA sequencing. It is commonly used to identify essential genes. Single gene deletion libraries are considered the gold standard for identifying essential genes. Currently, the TraDIS method has not been benchmarked against such libraries, and therefore, it remains unclear whether the two methodologies are comparable. To address this, a high-density transposon library was constructed in Escherichia coli K-12. Essential genes predicted from sequencing of this library were compared to existing essential gene databases. To decrease false-positive identification of essential genes, statistical data analysis included corrections for both gene length and genome length. Through this analysis, new essential genes and genes previously incorrectly designated essential were identified. We show that manual analysis of TraDIS data reveals novel features that would not have been detected by statistical analysis alone. Examples include short essential regions within genes, orientation-dependent effects, and fine-resolution identification of genome and protein features. Recognition of these insertion profiles in transposon mutagenesis data sets will assist genome annotation of less well characterized genomes and provides new insights into bacterial physiology and biochemistry. IMPORTANCE Incentives to define lists of genes that are essential for bacterial survival include the identification of potential targets for antibacterial drug development, genes required for rapid growth for exploitation in biotechnology, and discovery of new biochemical pathways. To identify essential genes in Escherichia coli , we constructed a transposon mutant library of unprecedented density. Initial automated analysis of the resulting data revealed many discrepancies compared to the literature. We now report more extensive statistical analysis supported by both literature searches and detailed inspection of high-density TraDIS sequencing data for each putative essential gene for the E. coli model laboratory organism. This paper is important because it provides a better understanding of the essential genes of E. coli , reveals the limitations of relying on automated analysis alone, and provides a new standard for the analysis of TraDIS data. Incentives to define lists of genes that are essential for bacterial survival include the identification of potential targets for antibacterial drug development, genes required for rapid growth for exploitation in biotechnology, and discovery of new biochemical pathways. To identify essential genes in Escherichia coli , we constructed a transposon mutant library of unprecedented density. Initial automated analysis of the resulting data revealed many discrepancies compared to the literature. We now report more extensive statistical analysis supported by both literature searches and detailed inspection of high-density TraDIS sequencing data for each putative essential gene for the E. coli model laboratory organism. This paper is important because it provides a better understanding of the essential genes of E. coli , reveals the limitations of relying on automated analysis alone, and provides a new standard for the analysis of TraDIS data.
Transposon-directed insertion site sequencing (TraDIS) is a high-throughput method coupling transposon mutagenesis with short-fragment DNA sequencing. It is commonly used to identify essential genes. Single gene deletion libraries are considered the gold standard for identifying essential genes. Currently, the TraDIS method has not been benchmarked against such libraries, and therefore, it remains unclear whether the two methodologies are comparable. To address this, a high-density transposon library was constructed in Escherichia coli K-12. Essential genes predicted from sequencing of this library were compared to existing essential gene databases. To decrease false-positive identification of essential genes, statistical data analysis included corrections for both gene length and genome length. Through this analysis, new essential genes and genes previously incorrectly designated essential were identified. We show that manual analysis of TraDIS data reveals novel features that would not have been detected by statistical analysis alone. Examples include short essential regions within genes, orientation-dependent effects, and fine-resolution identification of genome and protein features. Recognition of these insertion profiles in transposon mutagenesis data sets will assist genome annotation of less well characterized genomes and provides new insights into bacterial physiology and biochemistry.IMPORTANCE Incentives to define lists of genes that are essential for bacterial survival include the identification of potential targets for antibacterial drug development, genes required for rapid growth for exploitation in biotechnology, and discovery of new biochemical pathways. To identify essential genes in Escherichia coli, we constructed a transposon mutant library of unprecedented density. Initial automated analysis of the resulting data revealed many discrepancies compared to the literature. We now report more extensive statistical analysis supported by both literature searches and detailed inspection of high-density TraDIS sequencing data for each putative essential gene for the E. coli model laboratory organism. This paper is important because it provides a better understanding of the essential genes of E. coli, reveals the limitations of relying on automated analysis alone, and provides a new standard for the analysis of TraDIS data.Transposon-directed insertion site sequencing (TraDIS) is a high-throughput method coupling transposon mutagenesis with short-fragment DNA sequencing. It is commonly used to identify essential genes. Single gene deletion libraries are considered the gold standard for identifying essential genes. Currently, the TraDIS method has not been benchmarked against such libraries, and therefore, it remains unclear whether the two methodologies are comparable. To address this, a high-density transposon library was constructed in Escherichia coli K-12. Essential genes predicted from sequencing of this library were compared to existing essential gene databases. To decrease false-positive identification of essential genes, statistical data analysis included corrections for both gene length and genome length. Through this analysis, new essential genes and genes previously incorrectly designated essential were identified. We show that manual analysis of TraDIS data reveals novel features that would not have been detected by statistical analysis alone. Examples include short essential regions within genes, orientation-dependent effects, and fine-resolution identification of genome and protein features. Recognition of these insertion profiles in transposon mutagenesis data sets will assist genome annotation of less well characterized genomes and provides new insights into bacterial physiology and biochemistry.IMPORTANCE Incentives to define lists of genes that are essential for bacterial survival include the identification of potential targets for antibacterial drug development, genes required for rapid growth for exploitation in biotechnology, and discovery of new biochemical pathways. To identify essential genes in Escherichia coli, we constructed a transposon mutant library of unprecedented density. Initial automated analysis of the resulting data revealed many discrepancies compared to the literature. We now report more extensive statistical analysis supported by both literature searches and detailed inspection of high-density TraDIS sequencing data for each putative essential gene for the E. coli model laboratory organism. This paper is important because it provides a better understanding of the essential genes of E. coli, reveals the limitations of relying on automated analysis alone, and provides a new standard for the analysis of TraDIS data.
Transposon-directed insertion site sequencing (TraDIS) is a high-throughput method coupling transposon mutagenesis with short-fragment DNA sequencing. It is commonly used to identify essential genes. Single gene deletion libraries are considered the gold standard for identifying essential genes. Currently, the TraDIS method has not been benchmarked against such libraries, and therefore, it remains unclear whether the two methodologies are comparable. To address this, a high-density transposon library was constructed in K-12. Essential genes predicted from sequencing of this library were compared to existing essential gene databases. To decrease false-positive identification of essential genes, statistical data analysis included corrections for both gene length and genome length. Through this analysis, new essential genes and genes previously incorrectly designated essential were identified. We show that manual analysis of TraDIS data reveals novel features that would not have been detected by statistical analysis alone. Examples include short essential regions within genes, orientation-dependent effects, and fine-resolution identification of genome and protein features. Recognition of these insertion profiles in transposon mutagenesis data sets will assist genome annotation of less well characterized genomes and provides new insights into bacterial physiology and biochemistry. Incentives to define lists of genes that are essential for bacterial survival include the identification of potential targets for antibacterial drug development, genes required for rapid growth for exploitation in biotechnology, and discovery of new biochemical pathways. To identify essential genes in , we constructed a transposon mutant library of unprecedented density. Initial automated analysis of the resulting data revealed many discrepancies compared to the literature. We now report more extensive statistical analysis supported by both literature searches and detailed inspection of high-density TraDIS sequencing data for each putative essential gene for the model laboratory organism. This paper is important because it provides a better understanding of the essential genes of , reveals the limitations of relying on automated analysis alone, and provides a new standard for the analysis of TraDIS data.
Transposon-directed insertion site sequencing (TraDIS) is a high-throughput method coupling transposon mutagenesis with short-fragment DNA sequencing. It is commonly used to identify essential genes. Single gene deletion libraries are considered the gold standard for identifying essential genes. Currently, the TraDIS method has not been benchmarked against such libraries, and therefore, it remains unclear whether the two methodologies are comparable. To address this, a high-density transposon library was constructed in Escherichia coli K-12. Essential genes predicted from sequencing of this library were compared to existing essential gene databases. To decrease false-positive identification of essential genes, statistical data analysis included corrections for both gene length and genome length. Through this analysis, new essential genes and genes previously incorrectly designated essential were identified. We show that manual analysis of TraDIS data reveals novel features that would not have been detected by statistical analysis alone. Examples include short essential regions within genes, orientation-dependent effects, and fine-resolution identification of genome and protein features. Recognition of these insertion profiles in transposon mutagenesis data sets will assist genome annotation of less well characterized genomes and provides new insights into bacterial physiology and biochemistry. Incentives to define lists of genes that are essential for bacterial survival include the identification of potential targets for antibacterial drug development, genes required for rapid growth for exploitation in biotechnology, and discovery of new biochemical pathways. To identify essential genes in Escherichia coli , we constructed a transposon mutant library of unprecedented density. Initial automated analysis of the resulting data revealed many discrepancies compared to the literature. We now report more extensive statistical analysis supported by both literature searches and detailed inspection of high-density TraDIS sequencing data for each putative essential gene for the E. coli model laboratory organism. This paper is important because it provides a better understanding of the essential genes of E. coli , reveals the limitations of relying on automated analysis alone, and provides a new standard for the analysis of TraDIS data.
Author Johnston, Iain G.
Henderson, Ian R.
Goodall, Emily C. A.
Turner, Keith A.
Cunningham, Adam F.
Cole, Jeffrey A.
Jabbari, Sara
Robinson, Ashley
Lund, Peter A.
Author_xml – sequence: 1
  givenname: Emily C. A.
  surname: Goodall
  fullname: Goodall, Emily C. A.
  organization: Institute of Microbiology and Infection, University of Birmingham, Birmingham, United Kingdom
– sequence: 2
  givenname: Ashley
  surname: Robinson
  fullname: Robinson, Ashley
  organization: Institute of Microbiology and Infection, University of Birmingham, Birmingham, United Kingdom
– sequence: 3
  givenname: Iain G.
  surname: Johnston
  fullname: Johnston, Iain G.
  organization: Institute of Microbiology and Infection, University of Birmingham, Birmingham, United Kingdom
– sequence: 4
  givenname: Sara
  surname: Jabbari
  fullname: Jabbari, Sara
  organization: Institute of Microbiology and Infection, University of Birmingham, Birmingham, United Kingdom
– sequence: 5
  givenname: Keith A.
  surname: Turner
  fullname: Turner, Keith A.
  organization: Discuva Ltd., Cambridge, United Kingdom
– sequence: 6
  givenname: Adam F.
  orcidid: 0000-0003-0248-964X
  surname: Cunningham
  fullname: Cunningham, Adam F.
  organization: Institute of Microbiology and Infection, University of Birmingham, Birmingham, United Kingdom
– sequence: 7
  givenname: Peter A.
  surname: Lund
  fullname: Lund, Peter A.
  organization: Institute of Microbiology and Infection, University of Birmingham, Birmingham, United Kingdom
– sequence: 8
  givenname: Jeffrey A.
  surname: Cole
  fullname: Cole, Jeffrey A.
  organization: Institute of Microbiology and Infection, University of Birmingham, Birmingham, United Kingdom
– sequence: 9
  givenname: Ian R.
  surname: Henderson
  fullname: Henderson, Ian R.
  organization: Institute of Microbiology and Infection, University of Birmingham, Birmingham, United Kingdom
BackLink https://www.ncbi.nlm.nih.gov/pubmed/29463657$$D View this record in MEDLINE/PubMed
BookMark eNp1kc1LxDAQxYMorh979Co9eolmJpumuQi6-IWCFz2HbHbqRtpGm67gf2_r6qKCc8kw-eW9IW-XbTaxIcYOQBwDYHFSn4d4LFCYnIPeYDsISnCtADaHPgeOgGbExik9i76khEKKbTZCM8llrvQO4w8Lyi5SoqYLrsquqIk1ZbHsZ35BbfCL4DIfq5DdcsB9tlW6KtH469xjj5cXD9Nrfnd_dTM9u-NeFrrjE62Mz11hSCiaG6SJcRJFjkgalEZSSko1L0B4M5flrNBeSwMzgzMjSoNyj52udF-Ws5rmvt-udZV9aUPt2ncbXbC_b5qwsE_xzaoCQRSTXuDoS6CNr0tKna1D8lRVrqG4TBaF0IAoNfTo4U-vtcn3H_UAXwG-jSm1VK4REHaIwQ4x2M8YLAy8_MP70LkuxGHVUP3z6gOGboio
CitedBy_id crossref_primary_10_1038_s41564_020_00839_y
crossref_primary_10_1128_MMBR_00077_19
crossref_primary_10_1016_j_celrep_2023_113517
crossref_primary_10_1128_AEM_00543_20
crossref_primary_10_1038_s41598_022_07557_x
crossref_primary_10_1128_mbio_01225_22
crossref_primary_10_1016_j_celrep_2021_109413
crossref_primary_10_1128_mBio_02430_19
crossref_primary_10_1186_s12864_023_09266_9
crossref_primary_10_1128_jb_00264_22
crossref_primary_10_1093_bioinformatics_btac376
crossref_primary_10_3389_fcimb_2020_592906
crossref_primary_10_1021_acssynbio_3c00239
crossref_primary_10_1007_s12010_023_04807_0
crossref_primary_10_1093_jac_dkaa204
crossref_primary_10_1093_nar_gkae174
crossref_primary_10_1111_nyas_14280
crossref_primary_10_3389_fmicb_2021_803307
crossref_primary_10_1016_j_csbj_2020_03_021
crossref_primary_10_3389_fbioe_2023_1296132
crossref_primary_10_1128_aem_00956_23
crossref_primary_10_1016_j_crmeth_2023_100693
crossref_primary_10_1126_science_adj8543
crossref_primary_10_1016_j_ymben_2025_02_011
crossref_primary_10_1103_PhysRevE_103_062412
crossref_primary_10_3390_antiox11102053
crossref_primary_10_1038_s41467_024_52816_2
crossref_primary_10_1002_bit_28958
crossref_primary_10_1038_s41592_019_0629_y
crossref_primary_10_1128_msystems_00387_24
crossref_primary_10_1016_j_jmb_2019_05_004
crossref_primary_10_1042_BCJ20220062
crossref_primary_10_1038_s41598_019_43587_8
crossref_primary_10_1073_pnas_2211197119
crossref_primary_10_1128_mbio_03467_21
crossref_primary_10_1128_spectrum_02162_22
crossref_primary_10_1080_15476286_2020_1813411
crossref_primary_10_1016_j_celrep_2020_107927
crossref_primary_10_1016_j_jhazmat_2024_133849
crossref_primary_10_1128_AAC_01771_19
crossref_primary_10_1093_molbev_msae185
crossref_primary_10_1128_jb_00184_23
crossref_primary_10_1093_g3journal_jkac295
crossref_primary_10_1073_pnas_2001507117
crossref_primary_10_1128_spectrum_00833_22
crossref_primary_10_1042_BSR20231227
crossref_primary_10_3390_microorganisms9030592
crossref_primary_10_1038_s41598_024_57537_6
crossref_primary_10_1093_bioinformatics_btab508
crossref_primary_10_1093_molbev_msac069
crossref_primary_10_1007_s00203_022_03245_6
crossref_primary_10_1093_femsre_fuac005
crossref_primary_10_1074_jbc_RA120_012611
crossref_primary_10_7554_eLife_88971_3
crossref_primary_10_1038_s44320_024_00017_w
crossref_primary_10_1186_s12934_022_01746_z
crossref_primary_10_7554_eLife_60482
crossref_primary_10_1146_annurev_micro_020518_115822
crossref_primary_10_3389_fcimb_2022_1062682
crossref_primary_10_7554_eLife_88971
crossref_primary_10_1128_msystems_00282_20
crossref_primary_10_1111_mmi_14882
crossref_primary_10_1186_s12866_021_02184_4
crossref_primary_10_1021_acscentsci_0c01621
crossref_primary_10_1080_21541264_2021_1981713
crossref_primary_10_1186_s13062_023_00362_0
crossref_primary_10_7554_eLife_97465_3
crossref_primary_10_1074_jbc_RA119_012161
crossref_primary_10_1128_msystems_00896_22
crossref_primary_10_3389_fmicb_2021_755801
crossref_primary_10_1016_j_crmeth_2024_100697
crossref_primary_10_1016_j_gene_2021_145890
crossref_primary_10_3390_ijms25052957
crossref_primary_10_1021_acs_jafc_4c12842
crossref_primary_10_1093_nargab_lqae110
crossref_primary_10_1128_aem_00480_22
crossref_primary_10_1038_s41586_023_05824_z
crossref_primary_10_3389_fmicb_2022_858983
crossref_primary_10_1016_j_critrevonc_2023_104088
crossref_primary_10_1016_j_micres_2022_127202
crossref_primary_10_1038_s41467_020_19235_5
crossref_primary_10_1094_MPMI_07_22_0152_R
crossref_primary_10_1016_j_foodres_2022_112280
crossref_primary_10_1186_s12864_021_07448_x
crossref_primary_10_1126_sciadv_abf5851
crossref_primary_10_1093_g3journal_jkac235
crossref_primary_10_1111_omi_12256
crossref_primary_10_1128_mbio_01798_24
crossref_primary_10_1038_s41586_019_1192_5
crossref_primary_10_1093_nar_gkad234
crossref_primary_10_1128_mBio_00836_21
crossref_primary_10_1016_j_virol_2024_110169
crossref_primary_10_1038_s41586_024_08124_2
crossref_primary_10_1038_s41467_018_04899_x
crossref_primary_10_1128_mBio_02259_20
crossref_primary_10_15252_msb_202311596
crossref_primary_10_1093_nar_gkaa204
crossref_primary_10_3390_antibiotics10060632
crossref_primary_10_1099_mgen_0_000650
crossref_primary_10_1111_lam_13423
crossref_primary_10_1016_j_jgar_2022_01_026
crossref_primary_10_1016_j_tifs_2021_06_032
crossref_primary_10_1080_07388551_2023_2208285
crossref_primary_10_1101_gr_254391_119
crossref_primary_10_1128_jb_00268_22
crossref_primary_10_1128_JB_00432_20
crossref_primary_10_1128_spectrum_02043_22
crossref_primary_10_1099_mic_0_001385
crossref_primary_10_1021_acsomega_4c08427
crossref_primary_10_1146_annurev_genet_112618_043838
crossref_primary_10_3389_fmicb_2018_01059
crossref_primary_10_1093_nar_gkaa294
crossref_primary_10_1128_mBio_02846_21
crossref_primary_10_1016_j_ijmm_2020_151395
crossref_primary_10_1038_s41576_020_0244_x
crossref_primary_10_1186_s12866_023_02835_8
crossref_primary_10_1186_s13059_021_02344_9
crossref_primary_10_1099_mgen_0_000546
crossref_primary_10_1128_spectrum_01338_23
crossref_primary_10_1615_IntJMedMushrooms_2023047722
crossref_primary_10_3390_genes12010053
crossref_primary_10_1016_j_micres_2020_126500
crossref_primary_10_1111_nyas_14991
crossref_primary_10_3390_microorganisms8010003
crossref_primary_10_1128_mSystems_00491_20
crossref_primary_10_7554_eLife_97465
crossref_primary_10_1186_s12918_018_0653_z
crossref_primary_10_3389_fmicb_2019_01670
crossref_primary_10_1038_s41598_022_05028_x
crossref_primary_10_1038_s41598_024_54169_8
crossref_primary_10_1101_gr_276747_122
crossref_primary_10_1016_j_mex_2020_101143
crossref_primary_10_7554_eLife_94919
crossref_primary_10_7554_eLife_55308
crossref_primary_10_1038_s41598_023_32525_4
crossref_primary_10_12688_f1000research_51873_1
crossref_primary_10_1039_D0ME00001A
crossref_primary_10_1093_nar_gkac362
crossref_primary_10_1093_nar_gkad692
crossref_primary_10_1099_mic_0_001491
crossref_primary_10_12688_f1000research_51873_2
crossref_primary_10_1093_nar_gkab757
crossref_primary_10_3390_microorganisms10020423
crossref_primary_10_3390_genes15050590
crossref_primary_10_1016_j_ebiom_2023_104439
crossref_primary_10_1038_s41540_024_00426_5
crossref_primary_10_1126_science_add1417
crossref_primary_10_3389_fmicb_2024_1527113
crossref_primary_10_1093_nar_gkae745
crossref_primary_10_1073_pnas_1900570116
crossref_primary_10_1016_j_cell_2025_02_010
crossref_primary_10_1128_spectrum_00195_22
crossref_primary_10_1016_j_tibtech_2024_02_008
crossref_primary_10_1099_mgen_0_000554
crossref_primary_10_1016_j_celrep_2021_109635
crossref_primary_10_1038_s41598_019_54196_w
crossref_primary_10_1128_spectrum_01662_21
crossref_primary_10_7717_peerj_6233
crossref_primary_10_1007_s12275_021_1130_8
crossref_primary_10_1128_JB_00698_20
crossref_primary_10_1080_21505594_2022_2158708
crossref_primary_10_1016_j_bbrc_2025_151546
crossref_primary_10_1016_j_jbc_2023_103003
crossref_primary_10_1111_mmi_14609
crossref_primary_10_1126_sciadv_ado3095
crossref_primary_10_1099_mgen_0_000719
crossref_primary_10_3390_microorganisms7100409
crossref_primary_10_1002_bit_27443
crossref_primary_10_1002_bit_28098
crossref_primary_10_1128_mBio_01129_21
crossref_primary_10_1186_s12864_021_08223_8
crossref_primary_10_1099_mgen_0_001017
crossref_primary_10_1038_s41587_020_00745_y
crossref_primary_10_1111_mpp_12754
crossref_primary_10_1128_IAI_00758_19
crossref_primary_10_7554_eLife_62614
crossref_primary_10_1093_molbev_msab329
crossref_primary_10_3389_frabi_2022_957942
crossref_primary_10_1007_s12275_022_2425_0
crossref_primary_10_1111_mmi_14677
crossref_primary_10_1128_mSphere_00031_19
crossref_primary_10_15252_msb_202211081
crossref_primary_10_1128_msystems_00813_21
crossref_primary_10_1093_nar_gkaa320
crossref_primary_10_1038_s41598_022_15997_8
crossref_primary_10_1016_j_copbio_2020_09_010
Cites_doi 10.1111/j.1365-2958.2007.05915.x
10.1073/pnas.0903229106
10.1038/msb.2009.92
10.1080/2159256X.2017.1313805
10.1111/mmi.13437
10.1128/jb.178.4.1154-1161.1996
10.1111/j.1365-2958.2008.06495.x
10.1006/geno.1997.4995
10.1371/journal.pone.0126070
10.1038/340245a0
10.1073/pnas.0404907101
10.1093/nar/gks1235
10.1046/j.1365-2958.2003.03658.x
10.1038/msb.2011.58
10.1111/j.1365-2958.2010.07412.x
10.7554/eLife.19042
10.1073/pnas.1117884109
10.1016/S0021-9258(18)49924-3
10.1111/j.1574-6968.1992.tb05378.x
10.1046/j.1365-2958.2002.02726.x
10.1371/journal.pone.0043012
10.1126/science.1109730
10.1093/bioinformatics/btp324
10.1007/978-1-59745-321-9_24
10.1016/j.chom.2015.03.003
10.1073/pnas.95.10.5752
10.1016/j.resmic.2005.11.014
10.1093/bioinformatics/16.10.944
10.1371/journal.ppat.1002946
10.1371/journal.pgen.1003834
10.1073/pnas.0906627106
10.1046/j.1365-2958.2001.02475.x
10.1128/JB.180.5.1296-1304.1998
10.1038/msb4100050
10.1101/gad.379506
10.1093/bioinformatics/btu170
10.1186/1471-2164-9-488
10.1016/j.chom.2009.08.003
10.1128/jb.178.4.1146-1153.1996
10.1186/1471-2105-14-303
10.1128/JB.185.10.3244-3248.2003
10.1128/IAI.01423-15
10.1046/j.1365-2958.1998.00986.x
10.1101/gad.13.18.2449
10.1093/bioinformatics/btp352
10.1093/nar/gkj405
10.1093/nar/gkt1274
10.1111/mmi.12686
10.1016/j.cell.2014.11.045
10.1093/genetics/162.4.1513
10.1126/science.276.5311.431
10.1093/bioinformatics/btq033
10.1038/nbt919
10.1093/emboj/20.15.4253
10.1534/genetics.107.081836
10.1038/nmeth.1377
10.1016/S0014-5793(00)01820-2
10.1186/s12866-016-0818-0
10.1042/BJ20110912
10.1371/journal.pgen.1004719
10.1128/JB.01713-06
10.1016/j.resmic.2011.03.007
10.1093/emboj/cdf362
10.1073/pnas.120163297
10.1101/gr.097097.109
10.1371/journal.ppat.1002788
10.1128/mBio.01200-16
10.1073/pnas.1220225110
10.1371/journal.pgen.1006114
10.1038/nmeth932
10.1046/j.1365-2958.1998.00958.x
10.1099/00221287-142-9-2429
10.1007/978-1-59745-321-9_26
10.1128/JB.134.3.1141-1156.1978
10.1128/JB.180.17.4621-4627.1998
ContentType Journal Article
Copyright Copyright © 2018 Goodall et al.
Copyright © 2018 Goodall et al. 2018 Goodall et al.
Copyright_xml – notice: Copyright © 2018 Goodall et al.
– notice: Copyright © 2018 Goodall et al. 2018 Goodall et al.
DBID AAYXX
CITATION
CGR
CUY
CVF
ECM
EIF
NPM
7X8
5PM
DOI 10.1128/mBio.02096-17
DatabaseName CrossRef
Medline
MEDLINE
MEDLINE (Ovid)
MEDLINE
MEDLINE
PubMed
MEDLINE - Academic
PubMed Central (Full Participant titles)
DatabaseTitle CrossRef
MEDLINE
Medline Complete
MEDLINE with Full Text
PubMed
MEDLINE (Ovid)
MEDLINE - Academic
DatabaseTitleList 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
DeliveryMethod fulltext_linktorsrc
Discipline Biology
DocumentTitleAlternate The Essential Genome of E. coli K-12
EISSN 2150-7511
ExternalDocumentID PMC5821084
29463657
10_1128_mBio_02096_17
Genre Research Support, Non-U.S. Gov't
Journal Article
GrantInformation_xml – fundername: Biotechnology and Biological Sciences Research Council
– fundername: Medical Research Council
  grantid: G1002093
– fundername: ;
  grantid: MIBTP
– fundername: ;
  grantid: Elite PhD
– fundername: ;
  grantid: Birmingham Fellow
GroupedDBID ---
0R~
53G
5VS
AAFWJ
AAGFI
AAUOK
AAYXX
ADBBV
ADRAZ
AENEX
AFPKN
ALMA_UNASSIGNED_HOLDINGS
AOIJS
BAWUL
BCNDV
BTFSW
CITATION
DIK
E3Z
EBS
EJD
FRP
GROUPED_DOAJ
GX1
H13
HYE
HZ~
KQ8
M48
O5R
O5S
O9-
OK1
P2P
PGMZT
RHI
RNS
RPM
RSF
CGR
CUY
CVF
ECM
EIF
NPM
7X8
5PM
ID FETCH-LOGICAL-c387t-4759c6a89e05ed92e49a320622e71572e55335d810c9d3fb87c7391b92b90f923
IEDL.DBID M48
ISSN 2161-2129
2150-7511
IngestDate Thu Aug 21 18:24:09 EDT 2025
Fri Jul 11 10:55:48 EDT 2025
Sat May 31 02:08:42 EDT 2025
Thu Apr 24 22:55:07 EDT 2025
Tue Jul 01 01:52:36 EDT 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 1
Keywords TraDIS
genomics
tn-seq
Escherichia coli
Language English
License Copyright © 2018 Goodall et al.
This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c387t-4759c6a89e05ed92e49a320622e71572e55335d810c9d3fb87c7391b92b90f923
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
E.C.A.G. and A.R. contributed equally to this work.
ORCID 0000-0003-0248-964X
OpenAccessLink http://journals.scholarsportal.info/openUrl.xqy?doi=10.1128/mBio.02096-17
PMID 29463657
PQID 2007122371
PQPubID 23479
ParticipantIDs pubmedcentral_primary_oai_pubmedcentral_nih_gov_5821084
proquest_miscellaneous_2007122371
pubmed_primary_29463657
crossref_primary_10_1128_mBio_02096_17
crossref_citationtrail_10_1128_mBio_02096_17
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate 20180220
PublicationDateYYYYMMDD 2018-02-20
PublicationDate_xml – month: 2
  year: 2018
  text: 20180220
  day: 20
PublicationDecade 2010
PublicationPlace United States
PublicationPlace_xml – name: United States
– name: 1752 N St., N.W., Washington, DC
PublicationTitle mBio
PublicationTitleAlternate mBio
PublicationYear 2018
Publisher American Society for Microbiology
Publisher_xml – name: American Society for Microbiology
References e_1_3_2_26_2
e_1_3_2_49_2
e_1_3_2_28_2
e_1_3_2_41_2
e_1_3_2_64_2
e_1_3_2_20_2
e_1_3_2_43_2
e_1_3_2_62_2
e_1_3_2_22_2
e_1_3_2_45_2
e_1_3_2_68_2
e_1_3_2_24_2
e_1_3_2_47_2
e_1_3_2_66_2
e_1_3_2_60_2
e_1_3_2_9_2
e_1_3_2_16_2
e_1_3_2_37_2
e_1_3_2_7_2
e_1_3_2_18_2
e_1_3_2_39_2
e_1_3_2_54_2
e_1_3_2_75_2
e_1_3_2_10_2
e_1_3_2_31_2
e_1_3_2_52_2
e_1_3_2_73_2
e_1_3_2_5_2
e_1_3_2_12_2
e_1_3_2_33_2
e_1_3_2_58_2
e_1_3_2_3_2
e_1_3_2_14_2
e_1_3_2_35_2
e_1_3_2_56_2
e_1_3_2_50_2
e_1_3_2_71_2
e_1_3_2_27_2
e_1_3_2_48_2
e_1_3_2_29_2
e_1_3_2_40_2
e_1_3_2_65_2
e_1_3_2_21_2
e_1_3_2_42_2
e_1_3_2_63_2
e_1_3_2_23_2
e_1_3_2_44_2
e_1_3_2_69_2
e_1_3_2_25_2
e_1_3_2_46_2
e_1_3_2_67_2
e_1_3_2_61_2
e_1_3_2_15_2
e_1_3_2_38_2
e_1_3_2_8_2
e_1_3_2_17_2
e_1_3_2_59_2
e_1_3_2_6_2
e_1_3_2_19_2
e_1_3_2_30_2
e_1_3_2_53_2
e_1_3_2_76_2
e_1_3_2_32_2
e_1_3_2_51_2
e_1_3_2_74_2
e_1_3_2_11_2
e_1_3_2_34_2
e_1_3_2_57_2
e_1_3_2_4_2
e_1_3_2_13_2
e_1_3_2_36_2
e_1_3_2_55_2
e_1_3_2_2_2
e_1_3_2_72_2
e_1_3_2_70_2
References_xml – ident: e_1_3_2_18_2
  doi: 10.1111/j.1365-2958.2007.05915.x
– ident: e_1_3_2_42_2
  doi: 10.1073/pnas.0903229106
– ident: e_1_3_2_35_2
  doi: 10.1038/msb.2009.92
– ident: e_1_3_2_12_2
  doi: 10.1080/2159256X.2017.1313805
– ident: e_1_3_2_15_2
  doi: 10.1111/mmi.13437
– ident: e_1_3_2_40_2
  doi: 10.1128/jb.178.4.1154-1161.1996
– ident: e_1_3_2_53_2
  doi: 10.1111/j.1365-2958.2008.06495.x
– ident: e_1_3_2_68_2
  doi: 10.1006/geno.1997.4995
– ident: e_1_3_2_20_2
  doi: 10.1371/journal.pone.0126070
– ident: e_1_3_2_61_2
  doi: 10.1038/340245a0
– ident: e_1_3_2_27_2
  doi: 10.1073/pnas.0404907101
– ident: e_1_3_2_74_2
  doi: 10.1093/nar/gks1235
– ident: e_1_3_2_50_2
  doi: 10.1046/j.1365-2958.2003.03658.x
– ident: e_1_3_2_9_2
  doi: 10.1038/msb.2011.58
– ident: e_1_3_2_22_2
  doi: 10.1111/j.1365-2958.2010.07412.x
– ident: e_1_3_2_43_2
  doi: 10.7554/eLife.19042
– ident: e_1_3_2_4_2
  doi: 10.1073/pnas.1117884109
– ident: e_1_3_2_36_2
  doi: 10.1016/S0021-9258(18)49924-3
– ident: e_1_3_2_33_2
  doi: 10.1111/j.1574-6968.1992.tb05378.x
– ident: e_1_3_2_37_2
  doi: 10.1046/j.1365-2958.2002.02726.x
– ident: e_1_3_2_30_2
  doi: 10.1371/journal.pone.0043012
– ident: e_1_3_2_56_2
  doi: 10.1126/science.1109730
– ident: e_1_3_2_76_2
  doi: 10.1093/bioinformatics/btp324
– ident: e_1_3_2_17_2
  doi: 10.1007/978-1-59745-321-9_24
– ident: e_1_3_2_57_2
  doi: 10.1016/j.chom.2015.03.003
– ident: e_1_3_2_62_2
  doi: 10.1073/pnas.95.10.5752
– ident: e_1_3_2_65_2
  doi: 10.1016/j.resmic.2005.11.014
– ident: e_1_3_2_73_2
  doi: 10.1093/bioinformatics/16.10.944
– ident: e_1_3_2_32_2
  doi: 10.1371/journal.ppat.1002946
– ident: e_1_3_2_10_2
  doi: 10.1371/journal.pgen.1003834
– ident: e_1_3_2_7_2
  doi: 10.1073/pnas.0906627106
– ident: e_1_3_2_39_2
  doi: 10.1046/j.1365-2958.2001.02475.x
– ident: e_1_3_2_24_2
  doi: 10.1128/JB.180.5.1296-1304.1998
– ident: e_1_3_2_2_2
  doi: 10.1038/msb4100050
– ident: e_1_3_2_45_2
  doi: 10.1101/gad.379506
– ident: e_1_3_2_69_2
  doi: 10.1093/bioinformatics/btu170
– ident: e_1_3_2_75_2
  doi: 10.1186/1471-2164-9-488
– ident: e_1_3_2_8_2
  doi: 10.1016/j.chom.2009.08.003
– ident: e_1_3_2_41_2
  doi: 10.1128/jb.178.4.1146-1153.1996
– ident: e_1_3_2_28_2
  doi: 10.1186/1471-2105-14-303
– ident: e_1_3_2_46_2
  doi: 10.1128/JB.185.10.3244-3248.2003
– ident: e_1_3_2_14_2
  doi: 10.1128/IAI.01423-15
– ident: e_1_3_2_21_2
  doi: 10.1046/j.1365-2958.1998.00986.x
– ident: e_1_3_2_38_2
  doi: 10.1101/gad.13.18.2449
– ident: e_1_3_2_71_2
  doi: 10.1093/bioinformatics/btp352
– ident: e_1_3_2_52_2
  doi: 10.1093/nar/gkj405
– ident: e_1_3_2_70_2
  doi: 10.1093/nar/gkt1274
– ident: e_1_3_2_19_2
  doi: 10.1111/mmi.12686
– ident: e_1_3_2_59_2
  doi: 10.1016/j.cell.2014.11.045
– ident: e_1_3_2_58_2
  doi: 10.1093/genetics/162.4.1513
– ident: e_1_3_2_60_2
  doi: 10.1126/science.276.5311.431
– ident: e_1_3_2_72_2
  doi: 10.1093/bioinformatics/btq033
– ident: e_1_3_2_13_2
  doi: 10.1038/nbt919
– ident: e_1_3_2_44_2
  doi: 10.1093/emboj/20.15.4253
– ident: e_1_3_2_55_2
  doi: 10.1534/genetics.107.081836
– ident: e_1_3_2_6_2
  doi: 10.1038/nmeth.1377
– ident: e_1_3_2_25_2
  doi: 10.1016/S0014-5793(00)01820-2
– ident: e_1_3_2_29_2
  doi: 10.1186/s12866-016-0818-0
– ident: e_1_3_2_34_2
  doi: 10.1042/BJ20110912
– ident: e_1_3_2_51_2
  doi: 10.1371/journal.pgen.1004719
– ident: e_1_3_2_48_2
  doi: 10.1128/JB.01713-06
– ident: e_1_3_2_64_2
  doi: 10.1016/j.resmic.2011.03.007
– ident: e_1_3_2_47_2
  doi: 10.1093/emboj/cdf362
– ident: e_1_3_2_66_2
  doi: 10.1073/pnas.120163297
– ident: e_1_3_2_5_2
  doi: 10.1101/gr.097097.109
– ident: e_1_3_2_16_2
  doi: 10.1371/journal.ppat.1002788
– ident: e_1_3_2_11_2
  doi: 10.1128/mBio.01200-16
– ident: e_1_3_2_31_2
  doi: 10.1073/pnas.1220225110
– ident: e_1_3_2_49_2
  doi: 10.1371/journal.pgen.1006114
– ident: e_1_3_2_63_2
  doi: 10.1038/nmeth932
– ident: e_1_3_2_26_2
  doi: 10.1046/j.1365-2958.1998.00958.x
– ident: e_1_3_2_54_2
  doi: 10.1099/00221287-142-9-2429
– ident: e_1_3_2_3_2
  doi: 10.1007/978-1-59745-321-9_26
– ident: e_1_3_2_67_2
  doi: 10.1128/JB.134.3.1141-1156.1978
– ident: e_1_3_2_23_2
  doi: 10.1128/JB.180.17.4621-4627.1998
SSID ssj0000331830
Score 2.5838587
Snippet Transposon-directed insertion site sequencing (TraDIS) is a high-throughput method coupling transposon mutagenesis with short-fragment DNA sequencing. It is...
SourceID pubmedcentral
proquest
pubmed
crossref
SourceType Open Access Repository
Aggregation Database
Index Database
Enrichment Source
SubjectTerms Computational Biology
DNA Transposable Elements
Escherichia coli K12 - genetics
Escherichia coli K12 - growth & development
Genes, Essential
Genome, Bacterial
Molecular Biology - methods
Mutagenesis, Insertional
Sequence Analysis, DNA
Title The Essential Genome of Escherichia coli K-12
URI https://www.ncbi.nlm.nih.gov/pubmed/29463657
https://www.proquest.com/docview/2007122371
https://pubmed.ncbi.nlm.nih.gov/PMC5821084
Volume 9
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1LS8NAEF60IngR39ZHiSCe3JrdPHb3IKJiW5R6stBbSDYbLLSJ9gH23zuTpNVWBS85bGaXMDPZ_SaZ-YaQ81BELNEQ5GgTQoAiY4dK14lpIhX4l_F9LbHAuf3stzruY9frflEKlQoc_RraYT-pzrBf_3if3sALf10UwMirwV0vqwPsUT5lYpWswaEksJlBu0T6-absoPPiFxcOGIfChq1mjJvLKyyeUD9g53L25LfjqLFFNkscad0Wht8mKybdIetFZ8npLqFgfuthhJVF4GBW06TZwFhZAmNopR5mOFvgAz3riTK-RzqNh5f7Fi07I1DtSDGmSNKn_VAqY3smVty4KnS47XNuBPMENx6gOC-WzNYqdpJICi0cxSLFI2UngOn2SSXNUnNIrISZhEnfCXXkup5h0sZEEh-G3QTmiyq5nGkj0CVtOHav6Ad5-MBlgMoLcuUFDMQv5uJvBV_GX4JnM9UG4NH4myJMTTYZYWNMwQC1CFYlB4Wq50txhQRnHswWC0aYCyBb9uKdtPeas2ZjRbAt3aP_PuAx2QBwJPPydfuEVMbDiTkFADKOanngDtdml9VyN_sE0qrYEQ
linkProvider Scholars Portal
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=The+Essential+Genome+of+Escherichia+coli+K-12&rft.jtitle=mBio&rft.au=Goodall%2C+Emily+C.+A.&rft.au=Robinson%2C+Ashley&rft.au=Johnston%2C+Iain+G.&rft.au=Jabbari%2C+Sara&rft.date=2018-02-20&rft.issn=2161-2129&rft.eissn=2150-7511&rft.volume=9&rft.issue=1&rft_id=info:doi/10.1128%2FmBio.02096-17&rft.externalDBID=n%2Fa&rft.externalDocID=10_1128_mBio_02096_17
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2161-2129&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2161-2129&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2161-2129&client=summon