• Informatics has assisted evolutionary biologists by enabling researchers to: • trace the evolution of a large number of organisms by measuring changes in their DNA, rather
    than through physical taxonomy or physiological observations alone, • compare entire genomes, which permits the study of more complex evolutionary events, such as gene duplication, horizontal gene transfer, and the prediction of factors important
    in bacterial speciation, • build complex computational population genetics models to predict the outcome of the system over time[27] • track and share information on an increasingly large number of species and organisms Future work endeavours
    to reconstruct the now more complex tree of life.

  • Bioinformatics is very much involved in making sense of protein microarray and HT MS data; the former approach faces similar problems as with microarrays targeted at mRNA,
    the latter involves the problem of matching large amounts of mass data against predicted masses from protein sequence databases, and the complicated statistical analysis of samples where multiple, but incomplete peptides from each protein
    are detected.

  • Expression data can be used to infer gene regulation: one might compare microarray data from a wide variety of states of an organism to form hypotheses about the genes involved
    in each state.

  • Although biological networks can be constructed from a single type of molecule or entity (such as genes), network biology often attempts to integrate many different data types,
    such as proteins, small molecules, gene expression data, and others, which are all connected physically, functionally, or both.

  • Most current genome annotation systems work similarly, but the programs available for analysis of genomic DNA, such as the GeneMark program trained and used to find protein-coding
    genes in Haemophilus influenzae, are constantly changing and improving.

  • Major research efforts in the field include sequence alignment, gene finding, genome assembly, drug design, drug discovery, protein structure alignment, protein structure
    prediction, prediction of gene expression and protein–protein interactions, genome-wide association studies, the modeling of evolution and cell division/mitosis.

  • For example: • Abbreviation recognition – identify the long-form and abbreviation of biological terms • Named-entity recognition – recognizing biological terms such as gene
    names • Protein–protein interaction – identify which proteins interact with which proteins from text The area of research draws from statistics and computational linguistics.

  • Bioinformaticians continue to produce specialized automated systems to manage the sheer volume of sequence data produced, and they create new algorithms and software to compare
    the sequencing results to the growing collection of human genome sequences and germline polymorphisms.

  • The area of research within computer science that uses genetic algorithms is sometimes confused with computational evolutionary biology, but the two areas are not necessarily

  • Therefore, the field of bioinformatics has evolved such that the most pressing task now involves the analysis and interpretation of various types of data.

  • The so-called shotgun sequencing technique (which was used, for example, by The Institute for Genomic Research (TIGR) to sequence the first bacterial genome, Haemophilus influenzae)[24]
    generates the sequences of many thousands of small DNA fragments (ranging from 35 to 900 nucleotides long, depending on the sequencing technology).

  • All of these techniques are extremely noise-prone and/or subject to bias in the biological measurement, and a major research area in computational biology involves developing
    statistical tools to separate signal from noise in high-throughput gene expression studies.

  • The so-called ENCODE project is a collaborative data collection of the functional elements of the human genome that uses next-generation DNA-sequencing technologies and genomic
    tiling arrays, technologies able to automatically generate large amounts of data at a dramatically reduced per-base cost but with the same accuracy (base call error) and fidelity (assembly error).

  • Some examples are: • high-throughput and high-fidelity quantification and sub-cellular localization (high-content screening, cytohistopathology, Bioimage informatics) • morphometrics
    • clinical image analysis and visualization • determining the real-time air-flow patterns in breathing lungs of living animals • quantifying occlusion size in real-time imagery from the development of and recovery during arterial injury •
    making behavioral observations from extended video recordings of laboratory animals • infrared measurements for metabolic activity determination • inferring clone overlaps in DNA mapping, e.g.

  • [38] Another type of data that requires novel informatics development is the analysis of lesions found to be recurrent among many tumors.

  • Analysis of cellular organization Several approaches have been developed to analyze the location of organelles, genes, proteins, and other components within cells.

  • For example, there are methods to locate a gene within a sequence, to predict protein structure and/or function, and to cluster protein sequences into families of related

  • Bioinformatics includes biological studies that use computer programming as part of their methodology, as well as specific analysis “pipelines” that are repeatedly used, particularly
    in the field of genomics.

  • The data is often found to contain considerable variability, or noise, and thus Hidden Markov model and change-point analysis methods are being developed to infer real copy
    number changes.

  • This could create a more flexible process for classifying types of cancer by analysis of cancer driven mutations in the genome.

  • Two important principles can be used in the analysis of cancer genomes bioinformatically pertaining to the identification of mutations in the exome.

  • In a less formal way, bioinformatics also tries to understand the organizational principles within nucleic acid and protein sequences, called proteomics.

  • One can then apply clustering algorithms to that expression data to determine which genes are co-expressed.

  • It is divided in two parts- The Core genome: Set of genes common to all the genomes under study (These are often housekeeping genes vital for survival) and The Dispensable/Flexible
    Genome: Set of genes not present in all but one or some genomes under study.

  • In fact, most gene function prediction methods focus on protein sequences as they are more informative and more feature-rich.

  • Another aspect of structural bioinformatics include the use of protein structures for Virtual Screening models such as Quantitative Structure-Activity Relationship models
    and proteochemometric models (PCM).

  • For complex genomes, the most successful methods use a combination of ab initio gene prediction and sequence comparison with expressed sequence databases and other organisms.

  • [33] Genome-wide association studies are a useful approach to pinpoint the mutations responsible for such complex diseases.

  • Goals To study how normal cellular activities are altered in different disease states, the biological data must be combined to form a comprehensive picture of these activities.

  • At a more integrative level, it helps analyze and catalogue the biological pathways and networks that are an important part of systems biology.

  • It is these intergenomic maps that make it possible to trace the evolutionary processes responsible for the divergence of two genomes.

  • [41][42] Nuclear organization of chromatin[edit] Main article: Nuclear organization Data from high-throughput chromosome conformation capture experiments, such as Hi-C (experiment)
    and ChIA-PET, can provide information on the spatial proximity of DNA loci.

  • [14] Computers became essential in molecular biology when protein sequences became available after Frederick Sanger determined the sequence of insulin in the early 1950s.

  • Massive sequencing efforts are used to identify previously unknown point mutations in a variety of genes in cancer.

  • [15] She compiled one of the first protein sequence databases, initially published as books[16] and pioneered methods of sequence alignment and molecular evolution.

  • Nevertheless, half of the predicted proteins in a new genome sequence tend to have no obvious function.

  • [44] AlphaFold, during the 14th Critical Assessment of protein Structure Prediction (CASP14) computational protein structure prediction software competition, became the first
    contender ever to deliver prediction submissions with accuracy competitive with experimental structures in a majority of cases and greatly outperforming all other prediction software methods up to that point.

  • [30] Genetics of disease[edit] Main article: Genome-wide association studies With the advent of next-generation sequencing we are obtaining enough sequence data to map the
    genes of complex diseases including infertility,[31] breast cancer[32] or Alzheimer’s disease.

  • A variety of methods have been developed to tackle the protein–protein docking problem, though it seems that there is still much work to be done in this field.

  • [36] Analysis of mutations in cancer[edit] Main article: Oncogenomics In cancer, the genomes of affected cells are rearranged in complex or even unpredictable ways.

  • In the genomic branch of bioinformatics, homology is used to predict the function of a gene: if the sequence of gene A, whose function is known, is homologous to the sequence
    of gene B, whose function is unknown, one could infer that B may share A’s function.

  • Gene function prediction[edit] While genome annotation is primarily based on sequence similarity (and thus homology), other properties of sequences can be used to predict
    the function of genes.

  • In a technique called homology modeling, this information is used to predict the structure of a protein once the structure of a homologous protein is known.

  • This is relevant as the location of these components affects the events within a cell and thus helps us to predict the behavior of biological systems.

  • These new methods and software allow bioinformaticians to sequence many cancer genomes quickly and affordably.

  • [48] Main articles: Protein–protein interaction prediction and interactome Tens of thousands of three-dimensional protein structures have been determined by X-ray crystallography
    and protein nuclear magnetic resonance spectroscopy (protein NMR) and a central question in structural bioinformatics is whether it is practical to predict possible protein–protein interactions only based on these 3D shapes, without performing
    protein–protein interaction experiments.

  • Bioinformatics (/ˌbaɪ.oʊˌɪnfərˈmætɪks/) is an interdisciplinary field that develops methods and software tools for understanding biological data, in particular when the data
    sets are large and complex.

  • Common activities in bioinformatics include mapping and analyzing DNA and protein sequences, aligning DNA and protein sequences to compare them, and creating and viewing 3-D
    models of protein structures.

  • Many of these studies are based on the detection of sequence homology to assign sequences to protein families.

  • See also: sequence analysis, sequence mining, sequence profiling tool, and sequence motif Genome annotation[edit] Main article: Gene prediction In the context of genomics,
    annotation is the process of marking the genes and other biological features in a DNA sequence.

  • In the structural branch of bioinformatics, homology is used to determine which parts of a protein are important in structure formation and interaction with other proteins.

  • These studies illustrated that well known features, such as the coding segments and the triplet code, are revealed in straightforward statistical analyses and were thus proof
    of the concept that bioinformatics would be insightful.

  • Analyzing biological data to produce meaningful information involves writing and running software programs that use algorithms from graph theory, artificial intelligence,
    soft computing, data mining, image processing, and computer simulation.

  • Nucleotide-level annotation also allows the integration of genome sequence with other genetic and physical maps of the genome.

  • With the growing amount of data, it long ago became impractical to analyze DNA sequences manually.

  • [40] Analysis of regulation[edit] Gene regulation is the complex orchestration of events by which a signal, potentially an extracellular signal such as a hormone, eventually
    leads to an increase or decrease in the activity of one or more proteins.

  • Often, such identification is made with the aim to better understand the genetic basis of disease, unique adaptations, desirable properties (esp.

  • Algorithms have been developed for base calling for the various experimental approaches to DNA sequencing.

  • High-throughput image analysis[edit] Computational technologies are used to accelerate or fully automate the processing, quantification and analysis of large amounts of high-information-content
    biomedical imagery.

  • Over the past few decades, rapid developments in genomic and other molecular research technologies and developments in information technologies have combined to produce a
    tremendous amount of information related to molecular biology.

  • Bioinformatics is the name given to these mathematical and computing approaches used to glean understanding of biological processes.

  • For example, gene expression can be regulated by nearby elements in the genome.


Works Cited

[‘1. Lesk AM (26 July 2013). “Bioinformatics”. Encyclopaedia Britannica.
2. ^ Jump up to:a b Sim AY, Minary P, Levitt M (June 2012). “Modeling nucleic acids”. Current Opinion in Structural Biology. 22 (3): 273–8. doi:10.1016/ PMC
4028509. PMID 22538125.
3. ^ Dawson WK, Maciejczyk M, Jankowska EJ, Bujnicki JM (July 2016). “Coarse-grained modeling of RNA 3D structure”. Methods. 103: 138–56. doi:10.1016/j.ymeth.2016.04.026. PMID 27125734.
4. ^ Kmiecik S, Gront D, Kolinski
M, Wieteska L, Dawid AE, Kolinski A (July 2016). “Coarse-Grained Protein Models and Their Applications”. Chemical Reviews. 116 (14): 7898–936. doi:10.1021/acs.chemrev.6b00163. PMID 27333362.
5. ^ Wong KC (2016). Computational Biology and Bioinformatics:
Gene Regulation. CRC Press/Taylor & Francis Group. ISBN 9781498724975.
6. ^ Joyce AP, Zhang C, Bradley P, Havranek JJ (January 2015). “Structure-based modeling of protein: DNA specificity”. Briefings in Functional Genomics. 14 (1): 39–49. doi:10.1093/bfgp/elu044.
PMC 4366589. PMID 25414269.
7. ^ Spiga E, Degiacomi MT, Dal Peraro M (2014). “New Strategies for Integrative Dynamic Modeling of Macromolecular Assembly”. In Karabencheva-Christova T (ed.). Biomolecular Modelling and Simulations. Advances in Protein
Chemistry and Structural Biology. Vol. 96. Academic Press. pp. 77–111. doi:10.1016/bs.apcsb.2014.06.008. ISBN 9780128000137. PMID 25443955.
8. ^ Ciemny M, Kurcinski M, Kamel K, Kolinski A, Alam N, Schueler-Furman O, Kmiecik S (August 2018). “Protein-peptide
docking: opportunities and challenges”. Drug Discovery Today. 23 (8): 1530–1537. doi:10.1016/j.drudis.2018.05.006. PMID 29733895.
9. ^ Ouzounis, C. A.; Valencia, A. (2003). “Early bioinformatics: the birth of a discipline—a personal view”. Bioinformatics.
19 (17): 2176–2190. doi:10.1093/bioinformatics/btg309. PMID 14630646.
10. ^ Jump up to:a b Hogeweg P (March 2011). Searls DB (ed.). “The roots of bioinformatics in theoretical biology”. PLOS Computational Biology. 7 (3): e1002021. Bibcode:2011PLSCB…7E2021H.
doi:10.1371/journal.pcbi.1002021. PMC 3068925. PMID 21483479.
11. ^ Hesper B, Hogeweg P (1970). “Bio-informatica: een werkconcept”. Kameleon. 1 (6): 28–29.
12. ^ Hesper B, Hogeweg P (2021). “Bio-informatics: a working concept. A translation of
“Bio-informatica: een werkconcept” by B. Hesper and P. Hogeweg”. arXiv:2111.11832v1 [q-bio.OT].
13. ^ Hogeweg P (1978). “Simulating the growth of cellular forms”. Simulation. 31 (3): 90–96. doi:10.1177/003754977803100305. S2CID 61206099.
14. ^
Colby B (2022). “Whole Genome Sequencing Cost”.
15. ^ Moody G (2004). Digital Code of Life: How Bioinformatics is Revolutionizing Science, Medicine, and Business. ISBN 978-0-471-32788-2.
16. ^ Dayhoff, M.O. (1966) Atlas of protein
sequence and structure. National Biomedical Research Foundation, 215 pp.
17. ^ Eck RV, Dayhoff MO (April 1966). “Evolution of the structure of ferredoxin based on living relics of primitive amino Acid sequences”. Science. 152 (3720): 363–6. Bibcode:1966Sci…152..363E.
doi:10.1126/science.152.3720.363. PMID 17775169. S2CID 23208558.
18. ^ Johnson G, Wu TT (January 2000). “Kabat database and its applications: 30 years after the first variability plot”. Nucleic Acids Research. 28 (1): 214–8. doi:10.1093/nar/28.1.214.
PMC 102431. PMID 10592229.
19. ^ Erickson JW, Altman GG (1979). “A Search for Patterns in the Nucleotide Sequence of the MS2 Genome”. Journal of Mathematical Biology. 7 (3): 219–230. doi:10.1007/BF00275725. S2CID 85199492.
20. ^ Shulman MJ, Steinberg
CM, Westmoreland N (February 1981). “The coding function of nucleotide sequences can be discerned by statistical analysis”. Journal of Theoretical Biology. 88 (3): 409–20. Bibcode:1981JThBi..88..409S. doi:10.1016/0022-5193(81)90274-5. PMID 6456380.
21. ^
Xiong J (2006). Essential Bioinformatics. Cambridge, United Kingdom: Cambridge University Press. pp. 4. ISBN 978-0-511-16815-4 – via Internet Archive.
22. ^ Sanger F, Air GM, Barrell BG, Brown NL, Coulson AR, Fiddes CA, et al. (February 1977). “Nucleotide
sequence of bacteriophage phi X174 DNA”. Nature. 265 (5596): 687–95. Bibcode:1977Natur.265..687S. doi:10.1038/265687a0. PMID 870828. S2CID 4206886.
23. ^ Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL (January 2008). “GenBank”. Nucleic
Acids Research. 36 (Database issue): D25-30. doi:10.1093/nar/gkm929. PMC 2238942. PMID 18073190.
24. ^ Jump up to:a b c Fleischmann RD, Adams MD, White O, Clayton RA, Kirkness EF, Kerlavage AR, et al. (July 1995). “Whole-genome random sequencing
and assembly of Haemophilus influenzae Rd”. Science. 269 (5223): 496–512. Bibcode:1995Sci…269..496F. doi:10.1126/science.7542800. PMID 7542800.
25. ^ Stein, Lincoln (2001). “Genome annotation: from sequence to biology”. Nature. 2 (7): 493–503.
doi:10.1038/35080529. PMID 11433356. S2CID 12044602.
26. ^ Erdin S, Lisewski AM, Lichtarge O (April 2011). “Protein function prediction: towards integration of similarity metrics”. Current Opinion in Structural Biology. 21 (2): 180–8. doi:10.1016/
PMC 3120633. PMID 21353529.
27. ^ Carvajal-Rodríguez A (March 2010). “Simulation of genes and genomes forward in time”. Current Genomics. 11 (1): 58–61. doi:10.2174/138920210790218007. PMC 2851118. PMID 20808525.
28. ^ Brown TA (2002). “Mutation,
Repair and Recombination”. Genomes (2nd ed.). Manchester (UK): Oxford.
29. ^ Carter NP, Fiegler H, Piper J (October 2002). “Comparative analysis of comparative genomic hybridization microarray technologies: report of a workshop sponsored by the
Wellcome Trust”. Cytometry. 49 (2): 43–8. doi:10.1002/cyto.10153. PMID 12357458.
30. ^ Chaudhari NM, Gupta VK, Dutta C (April 2016). “BPGA- an ultra-fast pan-genome analysis pipeline”. Scientific Reports. 6: 24373. Bibcode:2016NatSR…624373C. doi:10.1038/srep24373.
PMC 4829868. PMID 27071527.
31. ^ Aston KI (May 2014). “Genetic susceptibility to male infertility: news from genome-wide association studies”. Andrology. 2 (3): 315–21. doi:10.1111/j.2047-2927.2014.00188.x. PMID 24574159. S2CID 206007180.
32. ^
Véron A, Blein S, Cox DG (2014). “Genome-wide association studies and the clinic: a focus on breast cancer”. Biomarkers in Medicine. 8 (2): 287–96. doi:10.2217/bmm.13.121. PMID 24521025.
33. ^ Tosto G, Reitz C (October 2013). “Genome-wide association
studies in Alzheimer’s disease: a review”. Current Neurology and Neuroscience Reports. 13 (10): 381. doi:10.1007/s11910-013-0381-0. PMC 3809844. PMID 23954969.
34. ^ Londin E, Yadav P, Surrey S, Kricka LJ, Fortina P (2013). “Use of linkage analysis,
genome-wide association studies, and next-generation sequencing in the identification of disease-causing mutations”. Pharmacogenomics. Methods in Molecular Biology. Vol. 1015. pp. 127–46. doi:10.1007/978-1-62703-435-7_8. ISBN 978-1-62703-434-0. PMID
35. ^ Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA (June 2009). “Potential etiologic and functional implications of genome-wide association loci for human diseases and traits”. Proceedings of the National
Academy of Sciences of the United States of America. 106 (23): 9362–7. Bibcode:2009PNAS..106.9362H. doi:10.1073/pnas.0903103106. PMC 2687147. PMID 19474294.
36. ^ Hall LO (2010). “Finding the right genes for disease and prognosis prediction”. 2010
International Conference on System Science and Engineering. System Science and Engineering (ICSSE),2010 International Conference. pp. 1–2. doi:10.1109/ICSSE.2010.5551766. ISBN 978-1-4244-6472-2. S2CID 21622726.
37. ^ Vazquez M, de la Torre V, Valencia
A (27 December 2012). “Chapter 14: Cancer genome analysis”. PLOS Computational Biology. 8 (12): e1002824. Bibcode:2012PLSCB…8E2824V. doi:10.1371/journal.pcbi.1002824. PMC 3531315. PMID 23300415.
38. ^ Hye-Jung EC, Jaswinder K, Martin K, Samuel
AA, Marco AM (2014). “Second-Generation Sequencing for Cancer Genome Analysis”. In Dellaire G, Berman JN, Arceci RJ (eds.). Cancer Genomics. Boston (US): Academic Press. pp. 13–30. doi:10.1016/B978-0-12-396967-5.00002-5. ISBN 9780123969675.
39. ^
Grau J, Ben-Gal I, Posch S, Grosse I (July 2006). “VOMBAT: prediction of transcription factor binding sites using variable order Bayesian trees”. Nucleic Acids Research. 34 (Web Server issue): W529-33. doi:10.1093/nar/gkl212. PMC 1538886. PMID 16845064.
40. ^
“The Human Protein Atlas”. Retrieved 2 October 2017.
41. ^ “The human cell”. Retrieved 2 October 2017.
42. ^ Thul PJ, Åkesson L, Wiking M, Mahdessian D, Geladaki A, Ait Blal H, et al. (May 2017). “A
subcellular map of the human proteome”. Science. 356 (6340): eaal3321. doi:10.1126/science.aal3321. PMID 28495876. S2CID 10744558.
43. ^ Ay F, Noble WS (September 2015). “Analysis methods for studying the 3D architecture of the genome”. Genome Biology.
16 (1): 183. doi:10.1186/s13059-015-0745-7. PMC 4556012. PMID 26328929.
44. ^ “AlphaFold”. Retrieved 10 October 2022.
45. ^ Jumper, John; Evans, Richard; Pritzel, Alexander; Green, Tim; Figurnov, Michael; Ronneberger, Olaf; Tunyasuvunakool,
Kathryn; Bates, Russ; Žídek, Augustin; Potapenko, Anna; Bridgland, Alex; Meyer, Clemens; Kohl, Simon A. A.; Ballard, Andrew J.; Cowie, Andrew (August 2021). “Highly accurate protein structure prediction with AlphaFold”. Nature. 596 (7873): 583–589.
doi:10.1038/s41586-021-03819-2. ISSN 1476-4687.
46. ^ “AlphaFold Protein Structure Database”. Retrieved 10 October 2022.
47. ^ Hoy JA, Robinson H, Trent JT, Kakar S, Smagghe BJ, Hargrove MS (August 2007). “Plant hemoglobins:
a molecular fossil record for the evolution of oxygen transport”. Journal of Molecular Biology. 371 (1): 168–79. doi:10.1016/j.jmb.2007.05.029. PMID 17560601.
48. ^ Titz B, Rajagopala SV, Goll J, Häuser R, McKevitt MT, Palzkill T, Uetz P (May 2008).
Hall N (ed.). “The binary protein interactome of Treponema pallidum–the syphilis spirochete”. PLOS ONE. 3 (5): e2292. Bibcode:2008PLoSO…3.2292T. doi:10.1371/journal.pone.0002292. PMC 2386257. PMID 18509523.
49. ^ Jump up to:a b “Open Bioinformatics
Foundation: About us”. Official website. Open Bioinformatics Foundation. Retrieved 10 May 2011.
50. ^ “Open Bioinformatics Foundation: BOSC”. Official website. Open Bioinformatics Foundation. Retrieved 10 May 2011.
51. ^ Brohée S, Barriot R, Moreau
Y (September 2010). “Biological knowledge bases using Wikis: combining the flexibility of Wikis with the structure of databases”. Bioinformatics. 26 (17): 2210–1. doi:10.1093/bioinformatics/btq348. PMID 20591906.
52. ^ Nisbet R, Elder IV J, Miner
G (2009). “Bioinformatics”. Handbook of Statistical Analysis and Data Mining Applications. Academic Press. p. 328. ISBN 978-0080912035.
53. ^ Office of the Commissioner. “Advancing Regulatory Science – Sept. 24–25, 2014 Public Workshop: Next Generation
Sequencing Standards”. Retrieved 30 November 2017.
54. ^ Simonyan V, Goecks J, Mazumder R (2017). “Biocompute Objects-A Step towards Evaluation and Validation of Biomedical Scientific Computations”. PDA Journal of Pharmaceutical Science
and Technology. 71 (2): 136–146. doi:10.5731/pdajpst.2016.006734. PMC 5510742. PMID 27974626.
55. ^ Office of the Commissioner. “Advancing Regulatory Science – Community-based development of HTS standards for validating data and computation and
encouraging interoperability”. Retrieved 30 November 2017.
56. ^ Alterovitz G, Dean D, Goble C, Crusoe MR, Soiland-Reyes S, Bell A, et al. (December 2018). “Enabling precision medicine via standard communication of HTS provenance, analysis,
and results”. PLOS Biology. 16 (12): e3000099. doi:10.1371/journal.pbio.3000099. PMC 6338479. PMID 30596645.
57. ^ BioCompute Object (BCO) project is a collaborative and community-driven framework to standardize HTS computational data. 1. BCO Specification
Document: user manual for understanding and creating B., biocompute-objects, 3 September 2017
58. ^ Campbell, A. Malcolm (1 June 2003). “Public Access for Teaching Genomics, Proteomics, and Bioinformatics”. Cell Biology Education. 2 (2): 98–111.
doi:10.1187/cbe.03-02-0007. PMC 162192. PMID 12888845.
59. ^ Arenas, Miguel (September 2021). “General considerations for online teaching practices in bioinformatics in the time of COVID ‐19”. Biochemistry and Molecular Biology Education. 49 (5):
683–684. doi:10.1002/bmb.21558. ISSN 1470-8175. PMC 8426940. PMID 34231941.
60. ^ Barker D, Ferrier DE, Holland PW, Mitchell JB, Plaisier H, Ritchie MG, Smart SD (August 2013). “4273π: bioinformatics education on low cost ARM hardware”. BMC Bioinformatics.
14: 243. doi:10.1186/1471-2105-14-243. PMC 3751261. PMID 23937194.
61. ^ Barker D, Alderson RG, McDonagh JL, Plaisier H, Comrie MM, Duncan L, et al. (2015). “University-level practical activities in bioinformatics benefit voluntary groups of pupils
in the last 2 years of school”. International Journal of STEM Education. 2 (17). doi:10.1186/s40594-015-0030-z.
62. ^ McDonagh JL, Barker D, Alderson RG (2016). “Bringing computational science to the public”. SpringerPlus. 5 (259): 259. doi:10.1186/s40064-016-1856-7.
PMC 4775721. PMID 27006868.
63. ^ Robson JF, Barker D (October 2015). “Comparison of the protein-coding gene content of Chlamydia trachomatis and Protochlamydia amoebophila using a Raspberry Pi computer”. BMC Research Notes. 8 (561): 561. doi:10.1186/s13104-015-1476-2.
PMC 4604092. PMID 26462790.
64. ^ Wreggelsworth KM, Barker D (October 2015). “A comparison of the protein-coding genomes of two green sulphur bacteria, Chlorobium tepidum TLS and Pelodictyon phaeoclathratiforme BU-1”. BMC Research Notes. 8 (565):
565. doi:10.1186/s13104-015-1535-8. PMC 4606965. PMID 26467441.
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