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Journal articles on the topic 'Bioinformatics (biochemistry)'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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1

Ibba, Michael. "Biochemistry and bioinformatics: when worlds collide." Trends in Biotechnology 20, no. 2 (2002): 53–54. http://dx.doi.org/10.1016/s0167-7799(01)01928-x.

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2

Ibba, Michael. "Biochemistry and bioinformatics: when worlds collide." Trends in Biochemical Sciences 27, no. 2 (2002): 64. http://dx.doi.org/10.1016/s0968-0004(01)02043-6.

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3

Craveiro Sarmento, Aquiles Sales, Lázaro Batista de Azevedo Medeiros, Lucymara Fassarella Agnez-Lima, Josivan Gomes Lima, and Julliane Tamara Araújo de Melo Campos. "Exploring Seipin: From Biochemistry to Bioinformatics Predictions." International Journal of Cell Biology 2018 (September 19, 2018): 1–21. http://dx.doi.org/10.1155/2018/5207608.

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Seipin is a nonenzymatic protein encoded by the BSCL2 gene. It is involved in lipodystrophy and seipinopathy diseases. Named in 2001, all seipin functions are still far from being understood. Therefore, we reviewed much of the research, trying to find a pattern that could explain commonly observed features of seipin expression disorders. Likewise, this review shows how this protein seems to have tissue-specific functions. In an integrative view, we conclude by proposing a theoretical model to explain how seipin might be involved in the triacylglycerol synthesis pathway.
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4

Sansom, Clare. "Bioinformatics in China." Biochemist 33, no. 5 (2011): 39–40. http://dx.doi.org/10.1042/bio03305039.

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The Cyberbiochemist theme for this special issue of The Biochemist devoted to China hardly needs stating: an overview, such as is possible in the space available, of bioinformatics – and genomics – research in the country that is the most populous on earth and that boasts the fastest growing economy of any major nation.
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5

Peterson, Julian A. "A microcomputer network for biochemistry." Bioinformatics 1, no. 1 (1985): 3–5. http://dx.doi.org/10.1093/bioinformatics/1.1.3.

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6

Sansom, Clare. "The European Bioinformatics Institute: an overview." Biochemist 36, no. 4 (2014): 39–40. http://dx.doi.org/10.1042/bio03604039.

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Almost exactly 20 years ago, in September 1994, the European Bioinformatics Institute (EBI) opened its doors for the first time. Since then, this institute, established on the Wellcome Trust Genome Campus at Hinxton near Cambridge, has become one of the world's foremost bioinformatics centres. All biochemists who need to analyse molecular data, i.e. very nearly all biochemists, are bound to have used some of its services, although they may not have realized it.
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7

Moore, Jason H. "Bioinformatics." Journal of Cellular Physiology 213, no. 2 (2007): 365–69. http://dx.doi.org/10.1002/jcp.21218.

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8

Feig, Andrew L., and Evelyn Jabri. "Incorporation of bioinformatics exercises into the undergraduate biochemistry curriculum." Biochemistry and Molecular Biology Education 30, no. 4 (2002): 224–31. http://dx.doi.org/10.1002/bmb.2002.494030040093.

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9

de Bono, Stephanie. "Bioinformatics boost." Trends in Biochemical Sciences 26, no. 7 (2001): 413. http://dx.doi.org/10.1016/s0968-0004(01)01914-4.

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10

Martz, Eric. "Structural bioinformatics." Biochemistry and Molecular Biology Education 31, no. 5 (2003): 370–71. http://dx.doi.org/10.1002/bmb.2003.494031059996.

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11

Liang, Xin, Wen Zhu, Zhibin Lv, and Quan Zou. "Molecular Computing and Bioinformatics." Molecules 24, no. 13 (2019): 2358. http://dx.doi.org/10.3390/molecules24132358.

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Molecular computing and bioinformatics are two important interdisciplinary sciences that study molecules and computers. Molecular computing is a branch of computing that uses DNA, biochemistry, and molecular biology hardware, instead of traditional silicon-based computer technologies. Research and development in this area concerns theory, experiments, and applications of molecular computing. The core advantage of molecular computing is its potential to pack vastly more circuitry onto a microchip than silicon will ever be capable of—and to do it cheaply. Molecules are only a few nanometers in size, making it possible to manufacture chips that contain billions—even trillions—of switches and components. To develop molecular computers, computer scientists must draw on expertise in subjects not usually associated with their field, including organic chemistry, molecular biology, bioengineering, and smart materials. Bioinformatics works on the contrary; bioinformatics researchers develop novel algorithms or software tools for computing or predicting the molecular structure or function. Molecular computing and bioinformatics pay attention to the same object, and have close relationships, but work toward different orientations.
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12

Voolstra, C. "Microarray Bioinformatics." Briefings in Functional Genomics and Proteomics 3, no. 3 (2004): 289–90. http://dx.doi.org/10.1093/bfgp/3.3.289.

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13

Gauthier, Louis, Rémicia Di Franco, and Adrian W. R. Serohijos. "SodaPop: a forward simulation suite for the evolutionary dynamics of asexual populations on protein fitness landscapes." Bioinformatics 35, no. 20 (2019): 4053–62. http://dx.doi.org/10.1093/bioinformatics/btz175.

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Abstract Motivation Protein evolution is determined by forces at multiple levels of biological organization. Random mutations have an immediate effect on the biophysical properties, structure and function of proteins. These same mutations also affect the fitness of the organism. However, the evolutionary fate of mutations, whether they succeed to fixation or are purged, also depends on population size and dynamics. There is an emerging interest, both theoretically and experimentally, to integrate these two factors in protein evolution. Although there are several tools available for simulating protein evolution, most of them focus on either the biophysical or the population-level determinants, but not both. Hence, there is a need for a publicly available computational tool to explore both the effects of protein biophysics and population dynamics on protein evolution. Results To address this need, we developed SodaPop, a computational suite to simulate protein evolution in the context of the population dynamics of asexual populations. SodaPop accepts as input several fitness landscapes based on protein biochemistry or other user-defined fitness functions. The user can also provide as input experimental fitness landscapes derived from deep mutational scanning approaches or theoretical landscapes derived from physical force field estimates. Here, we demonstrate the broad utility of SodaPop with different applications describing the interplay of selection for protein properties and population dynamics. SodaPop is designed such that population geneticists can explore the influence of protein biochemistry on patterns of genetic variation, and that biochemists and biophysicists can explore the role of population size and demography on protein evolution. Availability and implementation Source code and binaries are freely available at https://github.com/louisgt/SodaPop under the GNU GPLv3 license. The software is implemented in C++ and supported on Linux, Mac OS/X and Windows. Supplementary information Supplementary data are available at Bioinformatics online.
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14

Kaminski, Naftali. "Bioinformatics." American Journal of Respiratory Cell and Molecular Biology 23, no. 6 (2000): 705–11. http://dx.doi.org/10.1165/ajrcmb.23.6.4291.

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15

Sansom, Clare. "A bioinformatics rookie's guide to DNA methylation." Biochemist 32, no. 5 (2010): 44–45. http://dx.doi.org/10.1042/bio03205044.

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With this issue of The Biochemist focusing on epigenetics, Cyberbiochemist has chosen to explore one particular type of epigenetic change, DNA methylation, and the bioinformatics tools that are used to analyse patterns of methylation in DNA.
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16

Thornton, Janet, Graham Cameron, and Cath Brooksbank. "The European Bioinformatics Institute: Leading the bioinformatics revolution." Biochemist 26, no. 4 (2004): 33–38. http://dx.doi.org/10.1042/bio02604033.

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Life without databases is almost inconceivable to today's researchers in the biomolecular sciences -- the world of biomolecules is freely available on the Internet, along with a powerful set of tools for analysing the data. Many of the world's most widely used data resources are hosted and developed at the European Molecular Biology Laboratory (EMBL)'s European Bioinformatics Institute (EBI), often in collaboration with partners throughout the world. The EBI is also a thriving research centre.
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17

Santesmasses, Didac, Marco Mariotti, and Vadim N. Gladyshev. "Bioinformatics of Selenoproteins." Antioxidants & Redox Signaling 33, no. 7 (2020): 525–36. http://dx.doi.org/10.1089/ars.2020.8044.

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18

Goldberg, R. N., Y. B. Tewari, and T. N. Bhat. "Thermodynamics of enzyme-catalyzed reactions--a database for quantitative biochemistry." Bioinformatics 20, no. 16 (2004): 2874–77. http://dx.doi.org/10.1093/bioinformatics/bth314.

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19

Holzhütter, Hermann Georg, and Alfredo Colosimo. "SIMFIT: a microcomputer software-toolkit for modelistic studies in biochemistry." Bioinformatics 6, no. 1 (1990): 23–28. http://dx.doi.org/10.1093/bioinformatics/6.1.23.

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20

Cai, Yudong, Tao Huang, Lei Chen, and Bin Niu. "Application of Systems Biology and Bioinformatics Methods in Biochemistry and Biomedicine." BioMed Research International 2013 (2013): 1–2. http://dx.doi.org/10.1155/2013/651968.

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21

Vuong, Phu, Drew Bennion, Jeremy Mantei, Danielle Frost, and Rajeev Misra. "Analysis of YfgL and YaeT Interactions through Bioinformatics, Mutagenesis, and Biochemistry." Journal of Bacteriology 190, no. 5 (2007): 1507–17. http://dx.doi.org/10.1128/jb.01477-07.

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ABSTRACT In Escherichia coli, YaeT, together with four lipoproteins, YfgL, YfiO, NlpB, and SmpA, forms a complex that is essential for β-barrel outer membrane protein biogenesis. Data suggest that YfgL and YfiO make direct but independent physical contacts with YaeT. Whereas the YaeT-YfiO interaction needs NlpB and SmpA for complex stabilization, the YaeT-YfgL interaction does not. Using bioinformatics, genetics, and biochemical approaches, we have identified three residues, L173, L175, and R176, in the mature YfgL protein that are critical for both function and interactions with YaeT. A single substitution at any of these sites produces no phenotypic defect, but two or three simultaneous alterations produce mild or yfgL-null phenotypes, respectively. Interestingly, biochemical data show that all YfgL variants, including those with single substitutions, have weakened in vivo YaeT-YfgL interaction. These defects are not due to mislocalization or low steady-state levels of YfgL. Cysteine-directed cross-linking data show that the region encompassing L173, L175, and R176 makes direct contact with YaeT. Using the same genetic and biochemical strategies, it was found that altering residues D227 and D229 in another region of YfgL from E221 to D229 resulted in defective YaeT bindings. In contrast, mutational analysis of conserved residues V319 to H328 of YfgL shows that they are important for YfgL biogenesis but not YfgL-YaeT interactions. The five YfgL mutants defective in YaeT associations and the yfgL background were used to show that SurA binds to YaeT (or another complex member) without going through YfgL.
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22

Greene, Casey S., Jie Tan, Matthew Ung, Jason H. Moore, and Chao Cheng. "Big Data Bioinformatics." Journal of Cellular Physiology 229, no. 12 (2014): 1896–900. http://dx.doi.org/10.1002/jcp.24662.

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23

Lopes, Robson da Silva, Nathalia Maria Resende, Adenilda Cristina Honorio-França, and Eduardo Luzía França. "Application of Bioinformatics in Chronobiology Research." Scientific World Journal 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/153839.

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Bioinformatics and other well-established sciences, such as molecular biology, genetics, and biochemistry, provide a scientific approach for the analysis of data generated through “omics” projects that may be used in studies of chronobiology. The results of studies that apply these techniques demonstrate how they significantly aided the understanding of chronobiology. However, bioinformatics tools alone cannot eliminate the need for an understanding of the field of research or the data to be considered, nor can such tools replace analysts and researchers. It is often necessary to conduct an evaluation of the results of a data mining effort to determine the degree of reliability. To this end, familiarity with the field of investigation is necessary. It is evident that the knowledge that has been accumulated through chronobiology and the use of tools derived from bioinformatics has contributed to the recognition and understanding of the patterns and biological rhythms found in living organisms. The current work aims to develop new and important applications in the near future through chronobiology research.
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24

Koch, Ina, Miguel Andrade-Navarro, Marcel H. Schulz, and Kathi Zarnack. "Bioinformatics in theory and application – highlights of the 36th German Conference on Bioinformatics." Biological Chemistry 402, no. 8 (2021): 869–70. http://dx.doi.org/10.1515/hsz-2021-0298.

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25

Grisham, William, Natalie A. Schottler, Joanne Valli-Marill, Lisa Beck, and Jackson Beatty. "Teaching Bioinformatics and Neuroinformatics by Using Free Web-based Tools." CBE—Life Sciences Education 9, no. 2 (2010): 98–107. http://dx.doi.org/10.1187/cbe.09-11-0079.

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This completely computer-based module's purpose is to introduce students to bioinformatics resources. We present an easy-to-adopt module that weaves together several important bioinformatic tools so students can grasp how these tools are used in answering research questions. Students integrate information gathered from websites dealing with anatomy (Mouse Brain Library), quantitative trait locus analysis (WebQTL from GeneNetwork), bioinformatics and gene expression analyses (University of California, Santa Cruz Genome Browser, National Center for Biotechnology Information's Entrez Gene, and the Allen Brain Atlas), and information resources (PubMed). Instructors can use these various websites in concert to teach genetics from the phenotypic level to the molecular level, aspects of neuroanatomy and histology, statistics, quantitative trait locus analysis, and molecular biology (including in situ hybridization and microarray analysis), and to introduce bioinformatic resources. Students use these resources to discover 1) the region(s) of chromosome(s) influencing the phenotypic trait, 2) a list of candidate genes—narrowed by expression data, 3) the in situ pattern of a given gene in the region of interest, 4) the nucleotide sequence of the candidate gene, and 5) articles describing the gene. Teaching materials such as a detailed student/instructor's manual, PowerPoints, sample exams, and links to free Web resources can be found at http://mdcune.psych.ucla.edu/modules/bioinformatics .
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26

Sansom, Clare. "Which Bioinformatics MSc?" Biochemist 34, no. 4 (2012): 40–41. http://dx.doi.org/10.1042/bio03404040.

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27

Lebo, Matthew S., Limin Hao, Chiao-Feng Lin, and Arti Singh. "Bioinformatics in Clinical Genomic Sequencing." Clinics in Laboratory Medicine 40, no. 2 (2020): 163–87. http://dx.doi.org/10.1016/j.cll.2020.02.003.

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28

Jamison, D. C. "Open Bioinformatics." Bioinformatics 19, no. 6 (2003): 679–80. http://dx.doi.org/10.1093/bioinformatics/btg214.

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29

Brown, Stuart M. "Bioinformatics Becomes Respectable." BioTechniques 34, no. 6 (2003): 1124–27. http://dx.doi.org/10.2144/brown346.

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30

Likić, Vladimir A., Malcolm J. McConville, Trevor Lithgow, and Antony Bacic. "Systems Biology: The Next Frontier for Bioinformatics." Advances in Bioinformatics 2010 (February 9, 2010): 1–10. http://dx.doi.org/10.1155/2010/268925.

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Biochemical systems biology augments more traditional disciplines, such as genomics, biochemistry and molecular biology, by championing (i) mathematical and computational modeling; (ii) the application of traditional engineering practices in the analysis of biochemical systems; and in the past decade increasingly (iii) the use of near-comprehensive data sets derived from ‘omics platform technologies, in particular “downstream” technologies relative to genome sequencing, including transcriptomics, proteomics and metabolomics. The future progress in understanding biological principles will increasingly depend on the development of temporal and spatial analytical techniques that will provide high-resolution data for systems analyses. To date, particularly successful were strategies involving (a) quantitative measurements of cellular components at the mRNA, protein and metabolite levels, as well as in vivo metabolic reaction rates, (b) development of mathematical models that integrate biochemical knowledge with the information generated by high-throughput experiments, and (c) applications to microbial organisms. The inevitable role bioinformatics plays in modern systems biology puts mathematical and computational sciences as an equal partner to analytical and experimental biology. Furthermore, mathematical and computational models are expected to become increasingly prevalent representations of our knowledge about specific biochemical systems.
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31

Alkhnbashi, Omer S., Tobias Meier, Alexander Mitrofanov, Rolf Backofen, and Björn Voß. "CRISPR-Cas bioinformatics." Methods 172 (February 2020): 3–11. http://dx.doi.org/10.1016/j.ymeth.2019.07.013.

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32

Fuller, Jonathan C., Pierre Khoueiry, Holger Dinkel, et al. "Biggest challenges in bioinformatics." EMBO reports 14, no. 4 (2013): 302–4. http://dx.doi.org/10.1038/embor.2013.34.

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33

Edwards, Y. J. K. "Bioinformatics and Functional Genomics." Briefings in Functional Genomics and Proteomics 3, no. 2 (2004): 187–90. http://dx.doi.org/10.1093/bfgp/3.2.187.

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34

Franck, M. "Glycosylation — Bioinformatics and modelling." Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology 150, no. 3 (2008): S166. http://dx.doi.org/10.1016/j.cbpa.2008.04.435.

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35

Connor, Ramsey F., and Rachel L. Roper. "Unique SARS-CoV protein nsp1: bioinformatics, biochemistry and potential effects on virulence." Trends in Microbiology 15, no. 2 (2007): 51–53. http://dx.doi.org/10.1016/j.tim.2006.12.005.

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36

Cai, Yudong, Tao Huang, Lei Chen, and Bing Niu. "Application of Systems Biology and Bioinformatics Methods in Biochemistry and Biomedicine 2014." BioMed Research International 2015 (2015): 1–2. http://dx.doi.org/10.1155/2015/568607.

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37

Schuurman, Nadine, and Agnieszka Leszczynski. "Ontologies for Bioinformatics." Bioinformatics and Biology Insights 2 (January 2008): BBI.S451. http://dx.doi.org/10.4137/bbi.s451.

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The past twenty years have witnessed an explosion of biological data in diverse database formats governed by heterogeneous infrastructures. Not only are semantics (attribute terms) different in meaning across databases, but their organization varies widely. Ontologies are a concept imported from computing science to describe different conceptual frameworks that guide the collection, organization and publication of biological data. An ontology is similar to a paradigm but has very strict implications for formatting and meaning in a computational context. The use of ontologies is a means of communicating and resolving semantic and organizational differences between biological databases in order to enhance their integration. The purpose of interoperability (or sharing between divergent storage and semantic protocols) is to allow scientists from around the world to share and communicate with each other. This paper describes the rapid accumulation of biological data, its various organizational structures, and the role that ontologies play in interoperability.
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38

Parker, D. Stott, Michael M. Gorlick, and Christopher J. Lee. "Evolving from Bioinformatics in-the-Small to Bioinformatics in-the-Large." OMICS: A Journal of Integrative Biology 7, no. 1 (2003): 37–48. http://dx.doi.org/10.1089/153623103322006580.

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39

Robison, Wade. "Bioinformatics and Privacy." Ethics in Biology, Engineering and Medicine 1, no. 1 (2010): 9–17. http://dx.doi.org/10.1615/ethicsbiologyengmed.v1.i1.30.

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40

Kopec, Klaus O., Vikram Alva, and Andrei N. Lupas. "Bioinformatics of the TULIP domain superfamily." Biochemical Society Transactions 39, no. 4 (2011): 1033–38. http://dx.doi.org/10.1042/bst0391033.

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Proteins of the BPI (bactericidal/permeability-increasing protein)-like family contain either one or two tandem copies of a fold that usually provides a tubular cavity for the binding of lipids. Bioinformatic analyses show that, in addition to its known members, which include BPI, LBP [LPS (lipopolysaccharide)-binding protein)], CETP (cholesteryl ester-transfer protein), PLTP (phospholipid-transfer protein) and PLUNC (palate, lung and nasal epithelium clone) protein, this family also includes other, more divergent groups containing hypothetical proteins from fungi, nematodes and deep-branching unicellular eukaryotes. More distantly, BPI-like proteins are related to a family of arthropod proteins that includes hormone-binding proteins (Takeout-like; previously described to adopt a BPI-like fold), allergens and several groups of uncharacterized proteins. At even greater evolutionary distance, BPI-like proteins are homologous with the SMP (synaptotagmin-like, mitochondrial and lipid-binding protein) domains, which are found in proteins associated with eukaryotic membrane processes. In particular, SMP domain-containing proteins of yeast form the ERMES [ER (endoplasmic reticulum)-mitochondria encounter structure], required for efficient phospholipid exchange between these organelles. This suggests that SMP domains themselves bind lipids and mediate their exchange between heterologous membranes. The most distant group of homologues we detected consists of uncharacterized animal proteins annotated as TM (transmembrane) 24. We propose to group these families together into one superfamily that we term as the TULIP (tubular lipid-binding) domain superfamily.
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41

Vaudel, Marc. "The EuPA Bioinformatics Community (EuBIC) initiative." EuPA Open Proteomics 11 (June 2016): 34. http://dx.doi.org/10.1016/j.euprot.2016.03.009.

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42

Oliver, Gavin R., Steven N. Hart, and Eric W. Klee. "Bioinformatics for Clinical Next Generation Sequencing." Clinical Chemistry 61, no. 1 (2015): 124–35. http://dx.doi.org/10.1373/clinchem.2014.224360.

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Abstract BACKGROUND Next generation sequencing (NGS)-based assays continue to redefine the field of genetic testing. Owing to the complexity of the data, bioinformatics has become a necessary component in any laboratory implementing a clinical NGS test. CONTENT The computational components of an NGS-based work flow can be conceptualized as primary, secondary, and tertiary analytics. Each of these components addresses a necessary step in the transformation of raw data into clinically actionable knowledge. Understanding the basic concepts of these analysis steps is important in assessing and addressing the informatics needs of a molecular diagnostics laboratory. Equally critical is a familiarity with the regulatory requirements addressing the bioinformatics analyses. These and other topics are covered in this review article. SUMMARY Bioinformatics has become an important component in clinical laboratories generating, analyzing, maintaining, and interpreting data from molecular genetics testing. Given the rapid adoption of NGS-based clinical testing, service providers must develop informatics work flows that adhere to the rigor of clinical laboratory standards, yet are flexible to changes as the chemistry and software for analyzing sequencing data mature.
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43

Nakahara, Taku, Shin-Ichiro Nishimura, and Tsuyoshi Shirai. "Current Aspects of Carbohydrate Structural Bioinformatics." Current Chemical Biology 1, no. 3 (2007): 265–70. http://dx.doi.org/10.2174/187231307781662189.

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McDonald, Shawn, Michael Dudek, and Kal Ramnarayan. "Bioinformatics of Botulin Neurotoxin Structures." Current Proteomics 1, no. 2 (2004): 145–66. http://dx.doi.org/10.2174/1570164043379334.

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45

Fung, Eric T., Scot R. Weinberger, Ed Gavin, and Fujun Zhang. "Bioinformatics approaches in clinical proteomics." Expert Review of Proteomics 2, no. 6 (2005): 847–62. http://dx.doi.org/10.1586/14789450.2.6.847.

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46

Cooper, Scott. "Integrating bioinformatics into undergraduate courses." Biochemistry and Molecular Biology Education 29, no. 4 (2001): 167–68. http://dx.doi.org/10.1111/j.1539-3429.2001.tb00110.x.

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47

Rinaldi, Andrea. "A transatlantic bridge for bioinformatics." Trends in Biochemical Sciences 26, no. 2 (2001): 92. http://dx.doi.org/10.1016/s0968-0004(01)01796-0.

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48

Zhang, Xiaorong. "UsingArabidopsisgenetic sequences to teach bioinformatics." Biochemistry and Molecular Biology Education 37, no. 1 (2009): 16–23. http://dx.doi.org/10.1002/bmb.20250.

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Tolvanen, Martti, and Mauno Vihinen. "Virtual bioinformatics distance learning suite." Biochemistry and Molecular Biology Education 32, no. 3 (2004): 156–60. http://dx.doi.org/10.1002/bmb.2004.494032030336.

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Wu, Fang-Xiang, Habtom Ressom, and Michael J. Dunn. "Focus on Bioinformatics in Proteomics." PROTEOMICS 13, no. 2 (2013): 219–20. http://dx.doi.org/10.1002/pmic.201370022.

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