Open Access
Issue
Med Sci (Paris)
Volume 42, Number 6-7, Juin-Juillet 2026
Page(s) 621 - 629
Section Repères
DOI https://doi.org/10.1051/medsci/2026105
Published online 17 juillet 2026
  1. Edman P, Begg G. A protein sequenator. In : Liébecq C, ed. European Journal of Biochemistry. Berlin, Heidelberg : Springer Berlin Heidelberg, 1967 : 80–91. [Google Scholar]
  2. Edman P. A method for the determination of amino acid sequence in peptides. Arch Biochem 1949 ; 22 : 475. [Google Scholar]
  3. Dayhoff MO, Ledley RS. COMPROTEIN, a computer program to aid primary protein structure determination. AFIPS (Fall)1, 1962 : 262. [Google Scholar]
  4. Goodman M, Moore GW. Phylogeny of hemoglobin. Syst Zool 1973 ; 22: 508. [Google Scholar]
  5. Gauthier J, Vincent AT, Charette SJ, et al. A brief history of bioinformatics. Brief Bioinform 2019 ; 20 : 1981–96. [Google Scholar]
  6. Needleman SB, Wunsch CD. A general method applicable to the search for similarities in the amino acid sequence of two proteins. J Mol Bio 1970 ; 48 : 443–53. [Google Scholar]
  7. Higgins DG, Sharp PM. CLUSTAL: a package for performing multiple sequence alignment on a microcomputer. Gene 1988 ; 73 : 237–44. [Google Scholar]
  8. O’Farrell PH. High resolution two-dimensional electrophoresis of proteins. J Biol Chem 1975 ; 250 : 4007–21. [Google Scholar]
  9. Watson JD, Crick FHC. Molecular structure of nucleic acids: a structure for deoxyribose nucleic acid. Nature 1953 ; 171 : 737–8. [Google Scholar]
  10. Kahn A. L’hélice de la vie. Med Sci (Paris) 2003 ; 19 : 491–5. [Google Scholar]
  11. Crick FHC. The origin of the genetic code. J Mol Biol 1968 ; 38 : 367–9. [Google Scholar]
  12. Sanger F, Nicklen S, Coulson AR. DNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci U.S.A. 1977 ; 74 : 5463–7. [Google Scholar]
  13. Sanger F, Air GM, Barrell BG, et al. Nucleotide sequence of bacteriophage φX174 DNA. Nature 1977 ; 265 : 687–95. [Google Scholar]
  14. Christiansen T, Wall L, Orwant J. Programming Perl: Unmatched power for text processing and scripting. O’Reilly Media Inc, 4th edition. 2012 ; 1184 p. [Google Scholar]
  15. Kernighan BW, Ritchie DM. The C programming language. 28. print. Englewood Cliffs, NJ : Prentice-Hall, 1991 : 228 p. [Google Scholar]
  16. Stoesser G, Sterk P, Tuli MA, et al. The EMBL Nucleotide Sequence Database. Nucleic Acids Res 1997 ; 25 : 7–13. [Google Scholar]
  17. Bairoch A, Apweiler R. The SWISS-PROT protein sequence data bank and its supplement TrEMBL. Nucleic Acids Res 1997 ; 25 : 31–6. [Google Scholar]
  18. Camacho C, Coulouris G, Avagyan V, et al. BLAST+: architecture and applications. BMC Bioinformatics 2009 ; 10 : 421. [Google Scholar]
  19. Benson DA, Boguski MS, Lipman DJ, et al. GenBank. Nucleic Acids Res 1997 ; 25 : 1–6. [Google Scholar]
  20. Karsch-Mizrachi I, Takagi T, Cochrane G, et al. The international nucleotide sequence database collaboration. Nucleic Acids Res 2018 ; 46 : D48–51. [Google Scholar]
  21. Margulies M, Egholm M, Altman WE, et al. Genome sequencing in microfabricated high-density picolitre reactors. Nature 2005 ; 437 : 376–80. [Google Scholar]
  22. Goodwin S, McPherson JD, McCombie WR. Coming of age: ten years of next-generation sequencing technologies. Nat Rev Genet 2016 ; 17 : 333–51. [Google Scholar]
  23. Reuter JA, Spacek DV, Snyder MP. High-throughput sequencing technologies. Mol Cell 2015 ; 58 : 586–97. [Google Scholar]
  24. Montel F. Séquençage de l’ADN par nanopores: Résultats et perspectives. Med Sci (Paris) 2018 ; 34 : 161–5. [Google Scholar]
  25. Whitehouse CM, Dreyer RN, Yamashita M, et al. Electrospray interface for liquid chromatographs and mass spectrometers. Anal Chem 1985 ; 57 : 675–9. [Google Scholar]
  26. Aebersold R, Mann M. Mass spectrometry-based proteomics. Nature 2003 ; 422: 198–207. [Google Scholar]
  27. Leinonen R, Sugawara H, Shumway M, et al. The sequence read archive. Nucleic Acids Res 2011 ; 39 : D19–21. [Google Scholar]
  28. Leinonen R, Akhtar R, Birney E, et al. The european nucleotide archive. Nucleic Acids Res 2011 ; 39 : D28–31. [Google Scholar]
  29. Wilkinson MD, Dumontier M, Aalbersberg IjJ, et al. The FAIR guiding principles for scientific data management and stewardship. Sci Data 2016 ; 3 : 160018. [CrossRef] [PubMed] [Google Scholar]
  30. Di Tommaso P, Chatzou M, Floden EW, et al. Nextflow enables reproducible computational workflows. Nat Biotechnol 2017 ; 35 : 316–9. [Google Scholar]
  31. Mölder F, Jablonski KP, Letcher B, et al. Sustainable data analysis with Snakemake. F1000Res 2021 ; 10 : 33. [Google Scholar]
  32. Merkel D. Docker: lightweight Linux containers for consistent development and deployment. Linux J 2014 ; 2. [Google Scholar]
  33. Kurtzer GM, Sochat V, Bauer MW. Singularity: scientific containers for mobility of compute. PLoS ONE 2017 ; 12: e0177459. [Google Scholar]
  34. Taly A, Verger A. Prédiction de structures biomoléculaires complexes par AlphaFold 3. Med Sci (Paris) 2024 ; 40 : 725–7. [Google Scholar]
  35. Jordan B. AlphaFold : un pas essentiel vers la fonction des protéines : Chroniques génomiques. Med Sci (Paris) 2021 ; 37 : 197–200. [Google Scholar]
  36. Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature 2021 ; 596 : 583–9. [CrossRef] [PubMed] [Google Scholar]
  37. Varadi M, Anyango S, Deshpande M, et al. AlphaFold protein structure database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res 2022; 50 : D439–44. [Google Scholar]
  38. Aguéro-Pizzolo S, Bettler E, Gouet P. Prix Nobel de chimie 2024: David Baker, Demis Hassabis et John M. Jumper - La révolution de l’intelligence artificielle en biologie structurale. Med Sci (Paris) 2025 ; 41 : 367–73. [Google Scholar]
  39. International human genome sequencing consortium. Finishing the euchromatic sequence of the human genome. Nature 2004 ; 431 : 931–45. [Google Scholar]
  40. Ozaki K, Ohnishi Y, Iida A, et al. Functional SNPs in the lymphotoxin-α gene that are associated with susceptibility to myocardial infarction. Nat Genet 2002; 32: 650–4. [Google Scholar]
  41. Mahajan A, Taliun D, Thurner M, et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat Genet 2018 ; 50 : 1505–13. [Google Scholar]
  42. Schizophrenia working group of the psychiatric genomics consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 2014 ; 511 : 421–7. [Google Scholar]
  43. Yengo L, Vedantam S, Marouli E, et al. A saturated map of common genetic variants associated with human height. Nature 2022; 610 : 704–12. [Google Scholar]
  44. Moreau K, Clemenceau A, Le Moing V, et al. Human genetic susceptibility to native valve staphylococcus aureus endocarditis in patients with S. aureus bacteremia: genome-wide association study. Front Microbiol 2018 ; 9. [Google Scholar]
  45. Weber RE, Fuchs S, Layer F, et al. Genome-wide association studies for the detection of genetic variants associated with daptomycin and ceftaroline resistance in Staphylococcus aureus. Front Microbiol 2021 ; 12: 639660. [Google Scholar]
  46. Young BC, Wu C-H, Charlesworth J, et al. Antimicrobial resistance determinants are associated with Staphylococcus aureus bacteraemia and adaptation to the healthcare environment: a bacterial genome-wide association study. Microb Genom 2021 ; 7 : 000700. [Google Scholar]
  47. Nagalakshmi U, Wang Z, Waern K, et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 2008 ; 320 : 1344–9. [Google Scholar]
  48. Mortazavi A, Williams BA, McCue K, et al. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 2008 ; 5 : 621–8. [Google Scholar]
  49. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 2009 ; 10 : 57–63. [Google Scholar]
  50. Pan Q, Shai O, Lee LJ, et al. Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat Genet 2008 ; 40 : 1413–5. [Google Scholar]
  51. Sultan M, Schulz MH, Richard H, et al. A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science 2008 ; 321 : 956–60. [Google Scholar]
  52. Cabili MN, Trapnell C, Goff L, et al. Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses. Genes Dev 2011 ; 25 : 1915–27. [Google Scholar]
  53. Chen X, Huang Y, Huang L, et al. A brain cell atlas integrating single-cell transcriptomes across human brain regions. Nat Med 2024 ; 30 : 2679–91. [Google Scholar]
  54. Wang Y, Li R, Tong R, et al. Integrating single-cell RNA and T cell/B cell receptor sequencing with mass cytometry reveals dynamic trajectories of human peripheral immune cells from birth to old age. Nat Immunol 2025 ; 26 : 308–22. [Google Scholar]
  55. Wilk AJ, Rustagi A, Zhao NQ, et al. A single-cell atlas of the peripheral immune response in patients with severe COVID-19. Nat Med 2020 ; 26 : 1070–6. [Google Scholar]
  56. Ren X, Wen W, Fan X, et al. COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas. Cell 2021 ; 184 : 1895–913. [Google Scholar]
  57. La Manno G, Soldatov R, Zeisel A, et al. RNA velocity of single cells. Nature 2018 ; 560 : 494–8. [Google Scholar]
  58. Kim M-S, Pinto SM, Getnet D, et al. A draft map of the human proteome. Nature 2014 ; 509 : 575–81. [Google Scholar]
  59. Wilhelm M, Schlegl J, Hahne H, et al. Mass-spectrometry-based draft of the human proteome. Nature 2014 ; 509 : 582–7. [Google Scholar]
  60. Krug K, Jaehnig EJ, Satpathy S, et al. Proteogenomic landscape of breast cancer tumorigenesis and targeted therapy. Cell 2020 ; 183 : 1436–56. [Google Scholar]
  61. McDermott JE, Arshad OA, Petyuk VA, et al. Proteogenomic characterization of ovarian HGSC implicates mitotic kinases, replication stress in observed chromosomal instability. Cell Rep Med 2020 ; 1 : 100004. [Google Scholar]
  62. Sarkar S, Murphy MA, Dammer EB, et al. Comparative proteomic analysis highlights metabolic dysfunction in α-synucleinopathy. NPJ Parkinsons Dis 2020 ; 6 : 40. [Google Scholar]
  63. Yarbro JM, Shrestha HK, Wang Z, et al. Proteomic landscape of Alzheimer’s disease: emerging technologies, advances and insights (2021 – 2025). Mol Neurodegeneration 2025 ; 20 : 83. [Google Scholar]
  64. Reigada I, San-Martin-Galindo P, Gilbert-Girard S, et al. Surfaceome and Eexoproteome dynamics in dual-species Pseudomonas aeruginosa and Staphylococcus aureus Biofilms. Front Microbiol 2021 ; 12: 672975. [Google Scholar]

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