Science Reviews - Biology, 2024, 3(2), 13-21 Martina Elena Tarozzi
Next Generation Sequencing Technologies, Bioinfor-
matics and Artificial Intelligence: A Shared Timeline
Martina Elena Tarozzi
Indipendent researcher. Florence, Tuscany, Italy
References
1. Watson JD, Crick FHC. Molecular Structure of Nucleic Acids: A Structure for Deoxyribose Nucleic
Acid. Nat 1953 1714356 [Internet]. 1953 Apr 25 [cited 2021 Jul 6];171(4356):7378. Available from:
https://www.nature.com/articles/171737a0
2. Sanger F, Nicklen S, Coulson AR. DNA sequencing with chain-terminating inhibitors. Proc Natl
Acad Sci U S A [Internet]. 1977 [cited 2021 Jul 6];74(12):5463. Available from:
/pmc/articles/PMC431765/?report=abstract
3. Mullis K, Faloona F, Scharf S, Saiki R, Horn G, Erlich H. Specific enzymatic amplification of DNA
in vitro: The polymerase chain reaction. Cold Spring Harb Symp Quant Biol. 1986;51(1):26373.
4. JC V, MD A, EW M, PW L, RJ M, GG S, et al. The sequence of the human genome. Science
[Internet]. 2001 Feb 16 [cited 2021 Jul 6];291(5507):130451. Available from:
https://pubmed.ncbi.nlm.nih.gov/11181995/
5. Heather J, Chain B, Heather JM, Chain B. The Sequence of Sequencers: The History of Sequencing
DNA Genomics The sequence of sequencers: The history of sequencing DNA. Genomics [Internet].
2015;107(1):18. Available from: http://dx.doi.org/10.1016/j.ygeno.2015.11.003
6. Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D, Gerstein M, et al. The Transcriptional
Landscape of the Yeast Genome Defined by RNA Sequencing. Science [Internet]. 2008 Jun 6 [cited
2021 Jul 19];320(5881):1344. Available from: /pmc/articles/PMC2951732/
7. SJ E, WB B, L L, PS S. Gene discovery and annotation using LCM-454 transcriptome sequencing.
Genome Res [Internet]. 2007 Jan [cited 2021 Jul 6];17(1):6973. Available from:
https://pubmed.ncbi.nlm.nih.gov/17095711/
8. JFriedman N, Rando OJ. Epigenomics and the structure of the living genome. Genome Res.
2015;25(10):148290.
9. Wang D, Bodovitz S. Single cell analysis: the new frontier in ‘Omics.’ Trends Biotechnol [Internet].
2010 Jun [cited 2021 Jul 6];28(6):281. Available from: /pmc/articles/PMC2876223/
10. McCarthy J, Minsky ML, Rochester N, Shannon CE. A proposal for the Dartmouth summer
research project on artificial intelligence [Internet]. Vol. 27, AI Magazine. 2006 [cited 2022 Oct 12]. p.
124. Available from: http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html
11. LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, et al. Backpropagation
applied to digit recognition [Internet]. Vol. 1, Neural computation. 1989. p. 54151. Available from:
https://www.ics.uci.edu/~welling/teaching/273ASpring09/lecun-89e.pdf
12. Lecun Y, Bottou E, Bengio Y, Haffner P. Gradient-Based Learning Applied to Document
Recognition. 1998.
13. Rosenblatt F. The perceptron: A probabilistic model for information storage and organization in
the brain. Psychol Rev. 1958;65(6):386408.
14. Boser BE, Guyon IM, Vapnik VN. Training algorithm for optimal margin classifiers. Proc Fifth
Annu ACM Work Comput Learn Theory. 1992;14452.
Science Reviews - Biology, 2024, 3(2), 13-21 Martina Elena Tarozzi
15. Muir P, Li S, Lou S, Wang D, Spakowicz DJ, Salichos L, et al. The real cost of sequencing: Scaling
computation to keep pace with data generation. Genome Biol [Internet]. 2016 Mar 23 [cited 2023 Apr
28];17(1):19. Available from: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-
016-0917-0
16. Lister R, O’Malley RC, Tonti-Filippini J, Gregory BD, Berry CC, Millar AH, et al. Highly
Integrated Single-Base Resolution Maps of the Epigenome in Arabidopsis. Cell [Internet]. 2008 May
2 [cited 2021 Jul 19];133(3):523. Available from: /pmc/articles/PMC2723732/
17. U N, Z W, K W, C S, D R, M G, et al. The transcriptional landscape of the yeast genome defined
by RNA sequencing. Science [Internet]. 2008 Jun 6 [cited 2021 Jul 6];320(5881):13449. Available from:
https://pubmed.ncbi.nlm.nih.gov/18451266/
18. Mehmood A, Laiho A, Venäläinen MS, McGlinchey AJ, Wang N, Elo LL. Systematic evaluation
of differential splicing tools for RNA-seq studies. Brief Bioinform [Internet]. 2020 Dec 1 [cited 2021
Jul 20];21(6):205265. Available from: https://academic.oup.com/bib/article/21/6/2052/5648232
19. Castel SE, Levy-Moonshine A, Mohammadi P, Banks E, Lappalainen T. Tools and best practices
for data processing in allelic expression analysis. Genome Biol [Internet]. 2015;16(1):113. Available
from: http://dx.doi.org/10.1186/s13059-015-0762-6
20. Hutchins AP, Poulain S, Fujii H, Miranda-Saavedra D. Discovery and characterization of new
transcripts from RNA-seq data in mouse CD4+ T cells. Genomics. 2012 Nov 1;100(5):30313.
21. Haas BJ, Dobin A, Stransky N, Li B, Yang X, Tickle T, et al. STAR-Fusion: Fast and Accurate Fusion
Transcript Detection from RNA-Seq. bioRxiv [Internet]. 2017 Mar 24 [cited 2021 Jul 20];120295.
Available from: https://www.biorxiv.org/content/10.1101/120295v1
22. Van Dijk EL, Jaszczyszyn Y, Thermes C. Library preparation methods for next-generation
sequencing: Tone down the bias. Exp Cell Res. 2014 Mar 10;322(1):1220.
23. Mehrmohamadi M, Sepehri MH, Nazer N, Norouzi MR. A Comparative Overview of Epigenomic
Profiling Methods. Front Cell Dev Biol. 2021;9(July):114.
24. Landt SG, Marinov GK, Kundaje A, Kheradpour P, Pauli F, Batzoglou S, et al. ChIP-seq guidelines
and practices of the ENCODE and modENCODE consortia. Genome Res [Internet]. 2012 Sep 1 [cited
2022 Oct 27];22(9):181331. Available from: https://genome.cshlp.org/content/22/9/1813.full
25. Buenrostro JD, Wu B, Chang HY, Greenleaf WJ. ATAC-seq: A Method for Assaying Chromatin
Accessibility Genome-Wide. Curr Protoc Mol Biol [Internet]. 2015 [cited 2022 Oct 27];109:21.29.1.
Available from: /pmc/articles/PMC4374986/
26. Lieberman-Aiden E, Van Berkum NL, Williams L, Imakaev M, Ragoczy T, Telling A, et al.
Comprehensive mapping of long-range interactions reveals folding principles of the human
genome. Science (80- ) [Internet]. 2009 Oct 9 [cited 2021 Jul 5];326(5950):28993. Available from:
/pmc/articles/PMC2858594
27. Bentley DR, Balasubramanian S, Swerdlow HP, Smith GP, Milton J, Brown CG, et al. Accurate
Whole Human Genome Sequencing using Reversible Terminator Chemistry. Nature [Internet]. 2008
Nov 11 [cited 2022 Oct 18];456(7218):53. Available from: /pmc/articles/PMC2581791/
28. Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, et al. Standards and guidelines for the
interpretation of sequence variants: A joint consensus recommendation of the American College of
Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med
[Internet]. 2015 May 8 [cited 2021 Mar 31]; 17(5):40524. Available from:
/pmc/articles/PMC4544753/
29. Alonso CM, Llop M, Sargas C, Pedrola L, Panadero J, Hervás D, et al. Clinical Utility of a Next-
Generation Sequencing Panel for Acute Myeloid Leukemia Diagnostics. J Mol Diagn [Internet]. 2019
Mar 1 [cited 2022 Oct 13];21(2):22840. Available from: https://pubmed.ncbi.nlm.nih.gov/30576870/
Science Reviews - Biology, 2024, 3(2), 13-21 Martina Elena Tarozzi
30. Di Resta C, Galbiati S, Carrera P, Ferrari M. Next-generation sequencing approach for the
diagnosis of human diseases: open challenges and new opportunities. EJIFCC [Internet]. 2018 Apr 1
[cited 2022 Oct 13];29(1):4. Available from: /pmc/articles/PMC5949614/
31. Bartoletti-Stella A, Tarozzi M, Mengozzi G, Asirelli F, Brancaleoni L, Mometto N, et al. Dementia-
related genetic variants in an Italian population of early-onset Alzheimer’s disease. Front Aging
Neurosci. 2022;14(September):113.
32. Köster J, Rahmann S. Snakemake-a scalable bioinformatics workflow engine. Bioinformatics.
2012.
33. DI Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables
reproducible computational workflows. Nat Biotechnol 2017 354 [Internet]. 2017 Apr 11 [cited 2023
Mar 2];35(4):3169. Available from: https://www.nature.com/articles/nbt.3820
34. Andrews, Simon, Krueger, Felix , Segonds-Pichon, Anne , Biggins, Laura , Krueger, Christel ,
Wingett S. FastQC [Internet]. Babraham, UK. 2010. Available from:
http://www.bioinformatics.babraham.ac.uk/projects/fastqc
35. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data.
Bioinformatics [Internet]. 2014 Aug 1 [cited 2021 Nov 17];30(15):211420. Available from:
https://academic.oup.com/bioinformatics/article/30/15/2114/2390096 metanalysis of machine
and deep learning-based CRISPR gRNA design tools. RNA Biol. 2020; 17:13
36. Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics
[Internet]. 2018 Sep 1 [cited 2023 Jan 10];34(17):i88490. Available from:
https://academic.oup.com/bioinformatics/article/34/17/i884/5093234
37. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMB-
net.journal [Internet]. 2011 May 2 [cited 2023 Jan 10];17(1):102. Available from: https://journal.emb-
net.org/index.php/embnetjournal/article/view/200/479
38. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioin-
formatics. 2009;
39. Ho DSW, Schierding W, Wake M, Saffery R, O’Sullivan J. Machine learning SNP based prediction
for precision medicine. Front Genet. 2019;10(MAR):267.
40. Dias R, Torkamani A. Artificial intelligence in clinical and genomic diagnostics. Genome Med.
2019;11(1):112.
41. Álvarez-Machancoses Ó, Galiana EJD, Cernea A, de la Viña JF, Fernández-Martínez JL. On the
role of artificial intelligence in genomics to enhance precision medicine. Pharmgenomics Pers Med.
2020;13:10519.
42. Poplin R, Chang PC, Alexander D, Schwartz S, Colthurst T, Ku A, et al. A universal snp and small-
indel variant caller using deep neural networks. Nat Biotechnol. 2018;36(10):983.
43. Khazeeva G, Sablauskas K, van der Sanden B, Steyaert W, Kwint M, Rots D, et al. DeNovoCNN:
a deep learning approach to de novo variant calling in next generation sequencing data. Nucleic Ac-
ids Res. 2022;50(17):e97.
44. Ramachandran A, Lumetta SS, Klee EW, Chen D. HELLO: improved neural network architectures
and methodologies for small variant calling. BMC Bioinformatics [Internet]. 2021;22(1):131. Availa-
ble from: https://doi.org/10.1186/s12859-021-04311-4
45. Capriotti E, Calabrese R, Fariselli P, Martelli PL, Altman RB, Casadio R. WS-SNPs&GO: a web
server for predicting the deleterious effect of human protein variants using functional annotation.
BMC Genomics [Internet]. 2013;14 Suppl 3(Suppl 3):S6. Available from: http://www.biomedcen-
tral.com/1471-2164/14/S3/S6
Science Reviews - Biology, 2024, 3(2), 13-21 Martina Elena Tarozzi
46. Adzhubei I, Jordan DM, Sunyaev SR. Predicting Functional Effect of Human Missense Mutations
Using PolyPhen-2. Curr Protoc Hum Genet [Internet]. 2013 [cited 2021
47. Raimondi D, Tanyalcin I, FertCrossed JSD, Gazzo A, Orlando G, Lenaerts T, et al. DEOGEN2:
Prediction and interactive visualization of single amino acid variant deleteriousness in human pro-
teins. Nucleic Acids Res. 2017;45(W1):W2016.
48. Riesselman AJ, Ingraham JB, Marks DS. Deep generative models of genetic variation capture the
effects of mutations. Nat Methods [Internet]. 2018;15(10):81622. Available from:
http://dx.doi.org/10.1038/s41592-018-0138-4
49. Livesey BJ, Marsh JA. Using deep mutational scanning to benchmark variant effect predictors and
identify disease mutations. Mol Syst Biol [Internet]. 2020 Jul 1 [cited 2022 Oct 26];16(7):e9380. Availa-
ble from: https://onlinelibrary.wiley.com/doi/full/10.15252/msb.20199380
50. Sundaram L, Gao H, Padigepati SR, McRae JF, Li Y, Kosmicki JA, et al. Predicting the clinical
impact of human mutation with deep neural networks. Nat Genet 2018 508 [Internet]. 2018 Jul 23
[cited 2022 Oct 27];50(8):116170. Available from: https://www.nature.com/articles/s41588-018-
0167-z
51. Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, et al. A survey
of best practices for RNA-seq data analysis. Genome Biol. 2016;17(1):119.
52. Tarozzi M, Bartoletti-Stella A, Dall’Olio D, Matteuzzi T, Baiardi S, Parchi P, et al. Identification of
recurrent genetic patterns from targeted sequencing panels with advanced data science: a case-study
on sporadic and genetic neurodegenerative diseases. BMC Med Genomics 2022 151 [Internet]. 2022
Feb 10 [cited 2022 Feb 25];15(1):112. Available from: https://bmcmedgenomics.biomedcen-
tral.com/articles/10.1186/s12920-022-01173-4
53. Saelens W, Cannoodt R, Todorov H, Saeys Y. A comparison of single-cell trajectory inference
methods. Nat Biotechnol [Internet]. 2019;37(5):54754. Available from:
http://dx.doi.org/10.1038/s41587-019-0071-9