CoChat
Sign Up
Cited by 18Genomics and Phylogenetic StudiesPneumonia and Respiratory InfectionsBacterial Genetics and Biotechnology

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

PneumoBrowse 2: an integrated visual platform for curated genome annotation and multiomics data analysis of Streptococcus pneumoniae

Open access
Janssen, Axel B, Gibson, Paddy S, Bravo, Afonso M, de Bakker, Vincent, Slager, Jelle, Veening, Jan-Willem·2025·Nucleic Acids Research
DOI 10.1093/nar/gkae923PMC PMC11701578

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

Abstract

Streptococcus pneumoniae is an opportunistic human pathogen responsible for high morbidity and mortality rates. Extensive genome sequencing revealed its large pangenome, serotype diversity, and provided insight into genome dynamics. However, functional genome analysis has lagged behind, as that requires detailed and time-consuming manual curation of genome annotations and integration of genomic and phenotypic data. To remedy this, PneumoBrowse was presented in 2018, a user-friendly interactive online platform, which provided the detailed annotation of the S. pneumoniae D39V genome, alongside transcriptomic data. Since 2018, many new studies on S. pneumoniae genome biology and protein functioning have been performed. Here, we present PneumoBrowse 2 (https://veeninglab.com/pneumobrowse), fully rebuilt in JBrowse 2. We updated annotations for transcribed and transcriptional regulatory features in the D39V genome. We added genome-wide data tracks for high-resolution chromosome conformation capture (Hi-C) data, chromatin immunoprecipitation coupled to high-throughput sequencing (ChIP-Seq), ribosome profiling, CRISPRi-seq gene essentiality data and more. Additionally, we included 18 phylogenetically diverse S. pneumoniae genomes and their annotations. By providing easy access to diverse high-quality genome annotations and links to other databases (including UniProt and AlphaFold), PneumoBrowse 2 will further accelerate research and development into preventive and treatment strategies, through increased understanding of the pneumococcal genome.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/nar/gkae923

Cites

7 of 7 references verified0 citation errors · 0 not checkable

Statistics

Figures

Graphical Abstract
Graphical Abstract CC BY · PubMed Central
Figure 1. Genome-wide data tracks for D39V in PneumoBrowse 2. (A) A genome-wide data track for the normalized intensities of ribosome profiling under control conditions has been added for D39V (14). The data track includes the score for the positive strand (upwards, red), and the negative strand (downwards, blue). (B) Genome-wide data tracks for methylated bases in the chromosome of D39V and GC content. The annotations of the methylated sites indicate the type and strand-specificity of the methylated bases. A track for the calculation of GC content (using customizable settings) is provided through the NucContent plugin.
Figure 1. Genome-wide data tracks for D39V in PneumoBrowse 2. (A) A genome-wide data track for the normalized intensities of ribosome profiling under control conditions has been added for D39V (14). The data track includes the score for the positive strand (upwards, red), and the negative strand (downwards, blue). (B) Genome-wide data tracks for methylated bases in the chromosome of D39V and GC content. The annotations of the methylated sites indicate the type and strand-specificity of the methylated bases. A track for the calculation of GC content (using customizable settings) is provided through the NucContent plugin.CC BY · PubMed Central
Figure 2. Tracks with ChIP-Seq data for chromosomal protein binding and Hi-C conformation capture data in PneumoBrowse 2. (A) Genome-wide data tracks representing ChIP-Seq results for HU and RpoB binding to the chromosome. The plotted values indicate the ratio of the signal between the immunoprecipitated and the control experiments at a resolution of 50 bp. MACS2-called peaks in the RpoB- and HU-binding data indicate regions of enriched binding (15). (B) Hi-C chromosome conformation capture data of the first 500,000 nucleotides, with a resolution of 1000 bp, showing the interactions this part of the chromosome on a diagonal line, with interactions marked in red (15).
Figure 2. Tracks with ChIP-Seq data for chromosomal protein binding and Hi-C conformation capture data in PneumoBrowse 2. (A) Genome-wide data tracks representing ChIP-Seq results for HU and RpoB binding to the chromosome. The plotted values indicate the ratio of the signal between the immunoprecipitated and the control experiments at a resolution of 50 bp. MACS2-called peaks in the RpoB- and HU-binding data indicate regions of enriched binding (15). (B) Hi-C chromosome conformation capture data of the first 500,000 nucleotides, with a resolution of 1000 bp, showing the interactions this part of the chromosome on a diagonal line, with interactions marked in red (15).CC BY · PubMed Central
Figure 3. Position of sgRNAs binding sites and detailed information of annotated features for the D39V genome in PneumoBrowse 2. (A) A genome-wide data track displaying the position of the sgRNAs included in the CRISPRi-Seq library for D39V (12,13). The positions of the 20-nucleotide long sgRNA spacers are on the non-template strand of the targeted coding feature. (B) Overview of the information available for each coding feature in the D39V annotation: the information for galU (SPV_1919) is shown. Information for each coding sequence includes links to external resources (AlphaFold, PneumoWiki, UniProt, amongst others), gene essentiality data, and expression data (12) (de Bakker and Veening, data to be published). (C) The predicted AlphaFold structure of GalU that is available through the AlphaFold and UniProt links in PneumoBrowse, is shown.
Figure 3. Position of sgRNAs binding sites and detailed information of annotated features for the D39V genome in PneumoBrowse 2. (A) A genome-wide data track displaying the position of the sgRNAs included in the CRISPRi-Seq library for D39V (12,13). The positions of the 20-nucleotide long sgRNA spacers are on the non-template strand of the targeted coding feature. (B) Overview of the information available for each coding feature in the D39V annotation: the information for galU (SPV_1919) is shown. Information for each coding sequence includes links to external resources (AlphaFold, PneumoWiki, UniProt, amongst others), gene essentiality data, and expression data (12) (de Bakker and Veening, data to be published). (C) The predicted AlphaFold structure of GalU that is available through the AlphaFold and UniProt links in PneumoBrowse, is shown.CC BY · PubMed Central
Figure 4. Addition of 18 phylogenetically diverse pneumococcal genomes besides D39V to PneumoBrowse 2. (A) A mid-point-rooted maximum likelihood phylogenetic tree based on the core genome alignment of the genome sequences presented here, and genomes representing the phylogenetic diversity within the S. pneumoniae species, based on a recombination-corrected core genome alignment (42). Genomes included in PneumoBrowse 2 are highlighted in orange boldface text. (B) For the 18 genomes besides D39V, tracks describing the following are available: the reference sequence; genomic annotation; predicted Rho-independent terminator sequences; predicted binding sites for ComE, ComX and ParB; and methylated genomic bases (not available for SN7572 and R1501). A track for the calculation (using customizable settings) of GC content is provided through the NucContent plugin. (C) Genome wide alignments between all genomes included in PneumoBrowse 2 are available through the data track from each individual genome panel, or through the linear synteny and dotplot alignment views panels. The linear synteny view and dotplot view of the alignment between D39V and DP1322 show the large inversion previously identified in D39V (5).
Figure 4. Addition of 18 phylogenetically diverse pneumococcal genomes besides D39V to PneumoBrowse 2. (A) A mid-point-rooted maximum likelihood phylogenetic tree based on the core genome alignment of the genome sequences presented here, and genomes representing the phylogenetic diversity within the S. pneumoniae species, based on a recombination-corrected core genome alignment (42). Genomes included in PneumoBrowse 2 are highlighted in orange boldface text. (B) For the 18 genomes besides D39V, tracks describing the following are available: the reference sequence; genomic annotation; predicted Rho-independent terminator sequences; predicted binding sites for ComE, ComX and ParB; and methylated genomic bases (not available for SN7572 and R1501). A track for the calculation (using customizable settings) of GC content is provided through the NucContent plugin. (C) Genome wide alignments between all genomes included in PneumoBrowse 2 are available through the data track from each individual genome panel, or through the linear synteny and dotplot alignment views panels. The linear synteny view and dotplot view of the alignment between D39V and DP1322 show the large inversion previously identified in D39V (5).CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 78

  1. Troeger, 2018, Estimates of the global, regional, and national morbidity, mortality, and aetiologies of lower respiratory infections in 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016, Lancet Infect. Dis., 18, 1191, 10.1016/S1473-3099(18)30310-4 · DOI
  2. Weiser, 2018, Streptococcus pneumoniae: transmission, colonization and invasion, Nat. Rev. Micro., 16, 355, 10.1038/s41579-018-0001-8 · DOI
  3. GBD 2021 Lower Respiratory Infections and Antimicrobial Resistance Collaborators, 2024, Global, regional, and national incidence and mortality burden of non-COVID-19 lower respiratory infections and aetiologies, 1990 –2021: a systematic analysis from the Global Burden of Disease Study 2021, Lancet Infect. Dis., 3099, 974
  4. Avery, 1944, Studies on the chemical nature of the substance inducing transformation of pneumococcal types: induction of transformation by a desoxyribonucleic acid fraction isolated from pneumococcus type III, J. Exp. Med., 79, 2, 10.1084/jem.79.2.137 · DOI · Paper page
  5. Slager, 2018, Deep genome annotation of the opportunistic human pathogen Streptococcus pneumoniae D39, Nucleic Acids Res., 46, 9971
  6. Aprianto, 2018, High-resolution analysis of the pneumococcal transcriptome under a wide range of infection-relevant conditions, Nucleic Acids Res., 46, 9990
  7. Slager, 2019, Refining the pneumococcal competence regulon by RNA sequencing, J. Bacteriol., 201, e0078-18, 10.1128/JB.00780-18 · DOI
  8. Aprianto, 2016, Time-resolved dual RNA-seq reveals extensive rewiring of lung epithelial and pneumococcal transcriptomes during early infection, Genome Biol., 17, 198, 10.1186/s13059-016-1054-5 · DOI
  9. Sinha, 2019, Redefining the small regulatory RNA transcriptome in Streptococcus pneumoniae serotype 2 strain D39, J. Bacteriol., 201, e00764-18, 10.1128/JB.00764-18 · DOI
  10. Tsui, 2010, Identification and characterization of noncoding small RNAs in Streptococcus pneumoniae serotype 2 strain D39, J. Bacteriol., 192, 264, 10.1128/JB.01204-09 · DOI
  11. Ng, 2003, Transcriptional regulation and signature patterns revealed by microarray analyses of Streptococcus pneumoniae R6 challenged with sublethal concentrations of translation inhibitors, J. Bacteriol., 185, 359, 10.1128/JB.185.1.359-370.2003 · DOI
  12. Liu, 2021, Exploration of bacterial bottlenecks and Streptococcus pneumoniae pathogenesis by CRISPRi-Seq, Cell Host Microbe, 29, 107, 10.1016/j.chom.2020.10.001 · DOI
  13. Liu, 2017, High-throughput CRISPRi phenotyping identifies new essential genes in Streptococcus pneumoniae, Mol. Syst. Biol., 13, 931, 10.15252/msb.20167449 · DOI
  14. Laczkovich, 2022, Discovery of unannotated small open reading frames in Streptococcus pneumoniae D39 involved in quorum sensing and virulence using ribosome profiling, mBio, 13, e0124722, 10.1128/mbio.01247-22 · DOI
  15. Diesh, 2023, JBrowse 2: a modular genome browser with views of synteny and structural variation, Genome Biol., 24, 74, 10.1186/s13059-023-02914-z · DOI
  16. Tettelin, 2001, Complete genome sequence of a virulent isolate of Streptococcus pneumoniae, Science (1979), 293, 498
  17. Vijayakumar, 1986, Cloning and physical characterization of chromosomal conjugative elements in streptococci, J. Bacteriol., 166, 972, 10.1128/jb.166.3.972-977.1986 · DOI
  18. Dang-Van, 1978, Chloramphenicol resistance in Streptococcus pneumoniae: enzymatic acetylation and possible plasmid linkage, Antimicrob. Agents Chemother., 13, 577, 10.1128/AAC.13.4.577 · DOI
  19. Collins, 2015, First human challenge testing of a pneumococcal vaccine: double-blind randomized controlled trial, Am. J. Respir. Crit. Care Med., 192, 853, 10.1164/rccm.201503-0542OC · DOI
  20. Dula, 2023, Effect of 13-valent pneumococcal conjugate vaccine on experimental carriage of Streptococcus pneumoniae serotype 6B in Blantyre, Malawi: a randomised controlled trial and controlled human infection study, Lancet Microbe., 4, e683, 10.1016/S2666-5247(23)00178-7 · DOI
  21. Ali, 2021, Extracellular pneumococcal serine proteases affect nasopharyngeal colonization, Front. Cell. Infect. Microbiol., 10, 613467, 10.3389/fcimb.2020.613467 · DOI
  22. Gallay, 2021, CcrZ is a pneumococcal spatiotemporal cell cycle regulator that interacts with FtsZ and controls DNA replication by modulating the activity of DnaA, Nat. Microbiol., 6, 1175, 10.1038/s41564-021-00949-1 · DOI
  23. Domenech, 2018, Antibiotic-induced cell chaining triggers pneumococcal competence by reshaping quorum sensing to autocrine-like signaling, Cell Rep., 25, 2390, 10.1016/j.celrep.2018.11.007 · DOI
  24. Clark, 2012, Characterization of DNA methyltransferase specificities using single-molecule, real-time DNA sequencing, Nucleic Acids Res., 40, e29, 10.1093/nar/gkr1146 · DOI
  25. Wick, 2017, Unicycler: resolving bacterial genome assemblies from short and long sequencing reads, PLoS Comput. Biol., 13, e1005595, 10.1371/journal.pcbi.1005595 · DOI
  26. Seemann, 2014, Prokka: rapid prokaryotic genome annotation, Bioinformatics, 30, 2068, 10.1093/bioinformatics/btu153 · DOI
  27. Hyatt, 2010, Prodigal: prokaryotic gene recognition and translation initiation site identification, BMC Bioinf., 11, 119, 10.1186/1471-2105-11-119 · DOI
  28. Laslett, 2004, ARAGORN, a program to detect tRNA genes and tmRNA genes in nucleotide sequences, Nucleic Acids Res., 32, 11, 10.1093/nar/gkh152 · DOI
  29. Kolbe, 2011, Fast filtering for RNA homology search, Bioinformatics, 27, 3102, 10.1093/bioinformatics/btr545 · DOI
  30. Bland, 2007, CRISPR Recognition Tool (CRT): a tool for automatic detection of clustered regularly interspaced palindromic repeats, BMC Bioinf., 8, 209, 10.1186/1471-2105-8-209 · DOI
  31. Finn, 2011, HMMER web server: interactive sequence similarity searching, Nucleic Acids Res., 39, W29, 10.1093/nar/gkr367 · DOI · Paper page
  32. Kingsford, 2007, Rapid, accurate, computational discovery of Rho-independent transcription terminators illuminates their relationship to DNA uptake, Genome Biol., 8, R22, 10.1186/gb-2007-8-2-r22 · DOI
  33. Grant, 2011, FIMO: scanning for occurrences of a given motif, Bioinformatics, 27, 1017, 10.1093/bioinformatics/btr064 · DOI · Paper page
  34. Bailey, 2015, The MEME suite, Nucleic Acids Res., 43, W39, 10.1093/nar/gkv416 · DOI
  35. Huerta-Cepas, 2019, EggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses, Nucleic Acids Res., 47, D309, 10.1093/nar/gky1085 · DOI
  36. Cantalapiedra, 2021, eggNOG-mapper v2: functional annotation, orthology assignments, and domain prediction at the metagenomic scale, Mol. Biol. Evol., 38, 5825, 10.1093/molbev/msab293 · DOI
  37. Ashburner, 2000, Gene ontology: tool for the unification of biology, Nat. Genet., 25, 25, 10.1038/75556 · DOI · Paper page
  38. The Gene Ontology Consortium, 2023, The Gene Ontology knowledgebase in 2023, Genetics, 224, iyad031, 10.1093/genetics/iyad031 · DOI
  39. Darling, 2010, progrssiveMauve: multiple genome alignment with gene gain, loss and rearrangement, PLoS One, 5, e11147, 10.1371/journal.pone.0011147 · DOI · Paper page
  40. Antic, 2017, Gene acquisition by a distinct Pphyletic group within Streptococcus pneumoniae promotes adhesion to the ocular epithelium, mSphere, 2, e00213-17, 10.1128/mSphere.00213-17 · DOI
  41. Croucher, 2015, Rapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences using Gubbins, Nucleic Acids Res., 43, e15, 10.1093/nar/gku1196 · DOI
  42. Price, 2009, FastTree: computing large minimum evolution trees with profiles instead of a distance matrix, Mol. Biol. Evol., 26, 1641, 10.1093/molbev/msp077 · DOI · Paper page
  43. Kille, 2023, Minmers are a generalization of minimizers that enable unbiased local Jaccard estimation, Bioinformatics, 39, btad512, 10.1093/bioinformatics/btad512 · DOI
  44. Buels, 2016, JBrowse: a dynamic web platform for genome visualization and analysis, Genome Biol., 17, 66, 10.1186/s13059-016-0924-1 · DOI
  45. Mercy, 2019, RocS drives chromosome segregation and nucleoid protection in Streptococcus pneumoniae, Nat. Microbiol., 4, 1661, 10.1038/s41564-019-0472-z · DOI
  46. Hu, 2023, A novel aquaporin subfamily imports oxygen and contributes to pneumococcal virulence by controlling the production and release of virulence factors, mBio, 12, e01309
  47. Wang, 2020, Molecular determinants of substrate selectivity of a pneumococcal Rgg-regulated peptidase-containing ABC transporter, mBio, 11, e02502-19, 10.1128/mBio.02502-19 · DOI
  48. Hu, 2023, Identification and characterization of negative regulators of Rgg1518 quorum sensing in Streptococcus pneumoniae, J. Bacteriol., 205, e0008723, 10.1128/jb.00087-23 · DOI
  49. The UniProt Consortium, 2021, UniProt: the universal protein knowledgebase in 2021, Nucleic Acids Res., 49, D480, 10.1093/nar/gkaa1100 · DOI · Paper page
  50. Zhi, 2018, Rgg-Shp regulators are important for pneumococcal colonization and invasion through their effect on mannose utilization and capsule synthesis, Sci. Rep., 8, 6369, 10.1038/s41598-018-24910-1 · DOI
  51. Cuevas, 2017, A novel streptococcal cell–cell communication peptide promotes pneumococcal virulence and biofilm formation, Mol. Microbiol., 105, 554, 10.1111/mmi.13721 · DOI
  52. Halfmann, 2007, Identification of the genes directly controlled by the response regulator CiaR in Streptococcus pneumoniae: five out of 15 promoters drive expression of small non-coding RNAs, Mol. Microbiol., 66, 110, 10.1111/j.1365-2958.2007.05900.x · DOI
  53. Manso, 2014, A random six-phase switch regulates pneumococcal virulence via global epigenetic changes, Nat. Commun., 5, 5055, 10.1038/ncomms6055 · DOI
  54. Dame, 2020, Chromosome organization in bacteria: mechanistic insights into genome structure and function, Nat. Rev. Genet., 21, 227, 10.1038/s41576-019-0185-4 · DOI
  55. Guo, 2012, Prediction of genomic islands in three bacterial pathogens of pneumonia, Int. J. Mol. Sci., 13, 3134, 10.3390/ijms13033134 · DOI
  56. Varadi, 2022, AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models, Nucleic Acids Res., 50, D439, 10.1093/nar/gkab1061 · DOI · Paper page
  57. Szklarczyk, 2023, The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest, Nucleic Acids Res., 51, D638, 10.1093/nar/gkac1000 · DOI · Paper page
  58. Price, 2017, PaperBLAST: text mining papers for Information about homologs, Msystems, 2, e00039, 10.1128/mSystems.00039-17 · DOI
  59. Lanie, 2007, Genome sequence of Avery's virulent serotype 2 strain D39 of Streptococcus pneumoniae and comparison with that of unencapsulated laboratory strain R6, J. Bacteriol., 189, 38, 10.1128/JB.01148-06 · DOI
  60. Sanchez, 2010, The pneumococcal serine-rich repeat protein is an intraspecies bacterial adhesin that promotes bacterial aggregation in vivo and in biofilms, PLoS Pathog., 6, 33, 10.1371/journal.ppat.1001044 · DOI
  61. Håvarstein, 1995, An unmodified heptadecapeptide pheromone induces competence for genetic transformation in Streptococcus pneumoniae, Proc. Natl. Acad. Sci. USA, 92, 11140, 10.1073/pnas.92.24.11140 · DOI
  62. Achaz, 2003, Associations between inverted repeats and the structural evolution of bacterial genomes, Genetics, 164, 1279, 10.1093/genetics/164.4.1279 · DOI
  63. Baptista, 2018, Assembly of highly repetitive genomes using short reads: the genome of discrete typing unit III Trypanosoma cruzi strain 231, Microb. Genom., 4, e000156
  64. Jensch, 2010, PavB is a surface-exposed adhesin of Streptococcus pneumoniae contributing to nasopharyngeal colonization and airways infections, Mol. Microbiol., 77, 22, 10.1111/j.1365-2958.2010.07189.x · DOI
  65. Schoch, 2020, NCBI Taxonomy: a comprehensive update on curation, resources and tools, Database, 2020, baaa062, 10.1093/database/baaa062 · DOI · Paper page
  66. Herson, 2024, The Genome Explorer genome browser, Msystems, 9, e00267-24, 10.1128/msystems.00267-24 · DOI
  67. Sayers, 2022, Database resources of the national center for biotechnology information, Nucleic Acids Res., 50, D20, 10.1093/nar/gkab1112 · DOI · Paper page
  68. Katz, 2022, The Sequence Read Archive: a decade more of explosive growth, Nucleic Acids Res., 50, D387, 10.1093/nar/gkab1053 · DOI · Paper page
  69. Gibson, 2022, The acquisition of clinically relevant amoxicillin resistance in Streptococcus pneumoniae requires ordered horizontal gene transfer of four loci, PLoS Pathog., 18, e101727, 10.1371/journal.ppat.1010727 · DOI
  70. Gherardi, 2000, Major related sets of antibiotic-resistant pneumococci in the United States as determined by pulsed-field gel electrophoresis and pbp1a-pbp2b-pbp2x-dhf restriction profiles, J. Infect. Dis., 181, 216, 10.1086/315194 · DOI
  71. Browall, 2014, Intraclonal variations among streptococcus pneumoniae isolates influence the likelihood of invasive disease in children, J. Infect. Dis., 209, 377, 10.1093/infdis/jit481 · DOI
  72. Sá-Leão, 2022, Carriage of internationally spread clones of Streptococcus pneumoniae with unusual drug resistance patterns in children attending day care centers in Lisbon, Portugal, J. Infect. Dis., 182, 1153, 10.1086/315813 · DOI
  73. Andersson, 1983, Identification of an active disaccharide unit of a glycoconjugate receptor for pneumococci attaching to human pharyngeal epithelial cells, J. Exp. Med., 158, 559, 10.1084/jem.158.2.559 · DOI
  74. McGee, 2001, Nomenclature of major antimicrobial-resistant clones of Streptococcus pneumoniae defined by the pneumococcal molecular epidemiology network, J. Clin. Microbiol., 39, 2565, 10.1128/JCM.39.7.2565-2571.2001 · DOI
  75. Enright, 1998, A multilocus sequence typing scheme for Streptococcus pneumoniae?: identification of clones associated with serious invasive disease, Microbiology, 144, 3049, 10.1099/00221287-144-11-3049 · DOI
  76. Henriques Normark, 2001, Dynamics of penicillin-susceptible clones in invasive pneumococcal disease, J. Infect. Dis., 184, 861, 10.1086/323339 · DOI
  77. Dagkessamanskaia, 2004, Interconnection of competence, stress and CiaR regulons in Streptococcus pneumoniae?: competence triggers stationary phase autolysis of ciaR mutant cells, Mol. Microbiol., 51, 1071, 10.1111/j.1365-2958.2003.03892.x · DOI
  78. Tamayo, 1999, Dissemination of a chloramphenicol-and tetracycline-resistant but penicillin-susceptible invasive clone of serotype 5 Streptococcus pneumoniae in Colombia, J. Clin. Microbiol., 37, 2337, 10.1128/JCM.37.7.2337-2342.1999 · DOI

Metadata

Authors
Janssen, A. B., Gibson, P. S., Bravo, A. M., de Bakker, V., Slager, J., & Veening, J.-W.
Affiliations
  • University of Lausanne (CH)
  • University Medical Center Groningen (NL)
Corresponding author
Veening, Jan-Willem
ORCID
Venue
Nucleic Acids Research
Publisher
Oxford University Press (OUP)
Published
2024-10-22
Volume
53
Issue
D1
Pages
D839-D851
ISSN
  • 0305-1048 (ISSN-L)
  • 1362-4962
DOI
10.1093/nar/gkae923
PMC
PMC11701578
OpenAlex
W4403646540
Cited by
18
References
7
Citations by year
2026: 8 · 2025: 10
FWCI
2.44
Type
journal article
Language
en
Open access
Licence
Locations
  • Nucleic Acids Research (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • SERVAL (Université de Lausanne) (submittedVersion, cc-by)
  • IRIS (submittedVersion, cc-by)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by)
Funders
  • Swiss National Science Foundation (TMPFP3_210 202, 310 030_192 517, 310 030_200, 51NF40_180 541 792)
  • Faculty of Biology and Medicine of the University of Lausanne
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Health Sciences › Medicine › Epidemiology › Pneumonia and Respiratory Infections
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Bacterial Genetics and Biotechnology
Keywords
Streptococcus pneumoniae, genome annotation, multi-omics integration, JBrowse, Hi-C, ChIP-seq, ribosome profiling, gene essentiality, transcriptional regulation, pan-genome, serotype diversity
MeSH terms
Multiomics, Humans, Software, Streptococcus pneumoniae, Genome, Bacterial, Genomics, Databases, Genetic, Molecular Sequence Annotation

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review