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Journal articles on the topic 'Agricultural Bioinformatics'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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1

AHOUSE, JEREMY C. "Bioinformatics–A Middle Way." BioScience 50, no. 3 (2000): 264. http://dx.doi.org/10.1641/0006-3568(2000)050[0264:bamw]2.3.co;2.

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2

Elwess, Nancy L., Sandra M. Latourelle, and Olivia Cauthorn. "Visualising ‘junk’ DNAthrough bioinformatics." Journal of Biological Education 39, no. 2 (2005): 76–80. http://dx.doi.org/10.1080/00219266.2005.9655966.

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3

Dalpech, Roger. "Bioinformatics and school biology." Journal of Biological Education 40, no. 4 (2006): 147–48. http://dx.doi.org/10.1080/00219266.2006.9656035.

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4

Bromham, Lindell. "Putting the ‘bio’ into bioinformatics." Biology Letters 5, no. 3 (2009): 391–93. http://dx.doi.org/10.1098/rsbl.2009.0227.

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Bioinformatic analyses have grown rapidly in sophistication and efficiency to accommodate the vast increase in available data. One of the major challenges has been to incorporate the growing appreciation of the complexity of molecular evolution into new analytical methods. As the reliance on molecular data in biology and medicine increases, we need to be confident that these methods adequately reflect the underlying processes of genome change. This special issue focuses on the way that patterns and processes of molecular evolution are influenced by features of populations of whole organisms, such as selection pressure, population size and life history. The advantage of this approach to molecular evolution is that it views genomic change not simply as a biochemical or stochastic process, but as the result of a complex series of interactions that shape the kinds of genomic changes that can and do happen.
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5

Bloom, Mark. "Biology insilico: The Bioinformatics Revolution." American Biology Teacher 63, no. 6 (2001): 400–407. http://dx.doi.org/10.1662/0002-7685(2001)063[0397:bistbr]2.0.co;2.

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6

Ali, Muhammad Muddassir, Muhammad Hamid, Muhammad Saleem, et al. "Status of Bioinformatics Education in South Asia: Past and Present." BioMed Research International 2021 (April 26, 2021): 1–9. http://dx.doi.org/10.1155/2021/5568262.

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Bioinformatics education has been a hot topic in South Asia, and the interest in this education peaks with the start of the 21st century. The governments of South Asian countries had a systematic effort for bioinformatics. They developed the infrastructures to provide maximum facility to the scientific community to gain maximum output in this field. This article renders bioinformatics, measures, and its importance of implementation in South Asia with proper ways of improving bioinformatics education flaws. It also addresses the problems faced in South Asia and proposes some recommendations regarding bioinformatics education. The information regarding bioinformatics education and institutes was collected from different existing research papers, databases, and surveys. The information was then confirmed by visiting each institution’s website, while problems and solutions displayed in the article are mostly in line with South Asian bioinformatics conferences and institutions’ objectives. Among South Asian countries, India and Pakistan have developed infrastructure and education regarding bioinformatics rapidly as compared to other countries, whereas Bangladesh, Sri Lanka, and Nepal are still in a progressing phase in this field. To advance in a different sector, the bioinformatics industry has to be revolutionized, and it will contribute to strengthening the pharmaceutical, agricultural, and molecular sectors in South Asia. To advance in bioinformatics, universities’ infrastructure needs to be on a par with the current international standards, which will produce well-trained professionals with skills in multiple fields like biotechnology, mathematics, statistics, and computer science. The bioinformatics industry has revolutionized and strengthened the pharmaceutical, agricultural, and molecular sectors in South Asia, and it will serve as the standard of education increases in the South Asian countries. A framework for developing a centralized database is suggested after the literature review to collect and store the information on the current status of South Asian bioinformatics education. This will be named as the South Asian Bioinformatics Education Database (SABE). This will provide comprehensive information regarding the bioinformatics in South Asian countries by the country name, the experts of this field, and the university name to explore the top-ranked outputs relevant to queries.
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Bloom, Mark. "Biology in silico: The Bioinformatics Revolution." American Biology Teacher 63, no. 6 (2001): 397–403. http://dx.doi.org/10.2307/4451145.

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8

MUSANTE, SUSAN. "Using Bioinformatics in the Undergraduate Classroom." BioScience 54, no. 7 (2004): 625. http://dx.doi.org/10.1641/0006-3568(2004)054[0625:ubituc]2.0.co;2.

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9

Bourne, Philip E. "Is “bioinformatics” dead?" PLOS Biology 19, no. 3 (2021): e3001165. http://dx.doi.org/10.1371/journal.pbio.3001165.

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10

Khan, Fasiha F., Kaleem Ahmad, Aleem Ahmed, and Shujjah Haider. "APPLICATIONS OF BIOTECHNOLOGY IN AGRICULTURE- REVIEW ARTICLE." World Journal of Biology and Biotechnology 2, no. 1 (2017): 139. http://dx.doi.org/10.33865/wjb.002.01.0013.

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Agricultural biotechnology plays a key role in research tools that scientists use to understand and manipulate the genetic makeup of organisms for use in agriculture: crops, livestock, forestry and fisheries. Biotechnology has vast application than genetic engineering; it also includes genomics and bioinformatics, markers-assisted selection, micropropagation, tissue culture, cloning, artificial insemination, embryo transfer and other technologies. However, genetic engineering, mainly in crop sector, is the area in which biotechnology is most directly affecting agriculture in developing countries and in which the most vital public concerns and policy issues have arisen. Therefore, this review report tries to touches all the aspect of biotechnology in the field of agriculture.
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11

WALTERS, CHRISTINA. "EXPLORING EVOLUTION, ECOLOGY, BREEDING, BIOINFORMATICS, AND BIOPRESERVATION." BioScience 53, no. 5 (2003): 524. http://dx.doi.org/10.1641/0006-3568(2003)053[0524:eeebba]2.0.co;2.

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12

Tao, Lin, Bohua Wang, Yafen Zhong, et al. "Database and Bioinformatics Studies of Probiotics." Journal of Agricultural and Food Chemistry 65, no. 35 (2017): 7599–606. http://dx.doi.org/10.1021/acs.jafc.7b01815.

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13

Bacusmo, Jo Marie, Julie Bokor, Kathy Savage, and Valérie de Crécy-Lagard. "Identifying Pathogenic Islands through Genome Comparison." American Biology Teacher 81, no. 8 (2019): 577–81. http://dx.doi.org/10.1525/abt.2019.81.8.577.

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Bioinformatics, the study of biological data using various computational techniques, is a very important aspect of biology, and its integration would greatly benefit current high school curricula. However, because most bioinformatics tools have not been readily accessible until recently, most high school instructors were not exposed to them during their formative years. We describe a bioinformatics-based module that introduces the application of genome comparison in the identification of “pathogenic islands.” The module also introduces foundational concepts of horizontal gene transfer and the genetic basis of virulence, with a special focus on antibiotic resistance – a theme teachers and students alike can easily connect and relate to. The module takes students on a journey: from conceptualizing the perfect pathogen, to an immersive experience of being a pathogen, and finally the experience of being a research scientist identifying drug-resistant genes and other virulence factors using the bioinformatics tool of genome comparison.
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14

Ladics, Gregory S., Robert F. Cressman, Corinne Herouet-Guicheney, et al. "Bioinformatics and the allergy assessment of agricultural biotechnology products: Industry practices and recommendations." Regulatory Toxicology and Pharmacology 60, no. 1 (2011): 46–53. http://dx.doi.org/10.1016/j.yrtph.2011.02.004.

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15

Zhong, Yang, Xiaoyan Zhang, Jian Ma, and Liang Zhang. "Rapid development of bioinformatics education in China." Journal of Biological Education 37, no. 2 (2003): 75–78. http://dx.doi.org/10.1080/00219266.2003.9655855.

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16

Nehm, Ross H., and Ann F. Budd. "Missing “Links” in Bioinformatics Education: Expanding Students' Conceptions of Bioinformatics Using a Biodiversity Database of Living & Fossil Reef Corals." American Biology Teacher 68, no. 7 (2006): e91-e97. http://dx.doi.org/10.1662/0002-7685(2006)68[91:mlibee]2.0.co;2.

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17

Hamada, Michiaki, Hisanori Kiryu, Wataru Iwasaki, and Kiyoshi Asai. "Generalized Centroid Estimators in Bioinformatics." PLoS ONE 6, no. 2 (2011): e16450. http://dx.doi.org/10.1371/journal.pone.0016450.

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18

Wefer, Stephen H. "Name That Gene: An Authentic Classroom Activity Incorporating Bioinformatics." American Biology Teacher 65, no. 8 (2003): 610–13. http://dx.doi.org/10.2307/4451571.

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19

Anderson, Nadja, and Margaret Wilch. "Online Instruction – Bioinformatics Lesson for a COVID-19 Vaccine." American Biology Teacher 83, no. 7 (2021): 464–71. http://dx.doi.org/10.1525/abt.2021.83.7.464.

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In the spring of 2020, remote learning was implemented in schools throughout the world due to the pandemic of SARS CoV-2, the novel coronavirus that causes the disease COVID-19. Thrust into online instruction, many science teachers scrambled during this transition, and classes were severely hampered by a lack of hands-on investigations involving critical thinking and problem-solving skills. In response to a need for online experimentation, bioinformatics lessons centered around SARS-CoV-2 were developed. This article presents a multipart bioinformatics lesson that allows students to (1) compare spike protein sequences from the database portal NCBI Virus, to investigate whether this protein would be a good target for a vaccine against COVID-19; and (2) create phylogenetic trees and demonstrate evolutionary relatedness of human coronaviruses. This lesson allows for instruction in molecular biology, virology, immunology, bioinformatics, and phylogenetics, as well as analysis of scientific data. It is appropriate for high school AP Biology and biotechnology courses and can be taught entirely online.
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20

Jones, David T., Michael J. E. Sternberg, and Janet M. Thornton. "Introduction. Bioinformatics: from molecules to systems." Philosophical Transactions of the Royal Society B: Biological Sciences 361, no. 1467 (2005): 389–91. http://dx.doi.org/10.1098/rstb.2005.1811.

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21

Obom, Kristina M., and Patrick J. Cummings. "Comparison of Online and Onsite Bioinformatics Instruction for a Fully Online Bioinformatics Master’s Program." Journal of Microbiology & Biology Education 8, no. 1 (2007): 22–27. http://dx.doi.org/10.1128/jmbe.8.1.22-27.2007.

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The completely online Master of Science in Bioinformatics program differs from the onsite program only in the mode of content delivery. Analysis of student satisfaction indicates no statistically significant difference between most online and onsite student responses, however, online and onsite students do differ significantly in their responses to a few questions on the course evaluation queries. Analysis of student exam performance using three assessments indicates that there was no significant difference in grades earned by students in online and onsite courses. These results suggest that our model for online bioinformatics education provides students with a rigorous course of study that is comparable to onsite course instruction and possibly provides a more rigorous course load and more opportunities for participation.
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22

Bystroff, Christopher. "Bioinformatics: Methods and Protocols. Stephen Misener , Stephen A. Krawetz." Quarterly Review of Biology 75, no. 4 (2000): 450. http://dx.doi.org/10.1086/393644.

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23

Zhang, Qian, Yeqi Liu, Chuanyang Gong, Yingyi Chen, and Huihui Yu. "Applications of Deep Learning for Dense Scenes Analysis in Agriculture: A Review." Sensors 20, no. 5 (2020): 1520. http://dx.doi.org/10.3390/s20051520.

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Deep Learning (DL) is the state-of-the-art machine learning technology, which shows superior performance in computer vision, bioinformatics, natural language processing, and other areas. Especially as a modern image processing technology, DL has been successfully applied in various tasks, such as object detection, semantic segmentation, and scene analysis. However, with the increase of dense scenes in reality, due to severe occlusions, and small size of objects, the analysis of dense scenes becomes particularly challenging. To overcome these problems, DL recently has been increasingly applied to dense scenes and has begun to be used in dense agricultural scenes. The purpose of this review is to explore the applications of DL for dense scenes analysis in agriculture. In order to better elaborate the topic, we first describe the types of dense scenes in agriculture, as well as the challenges. Next, we introduce various popular deep neural networks used in these dense scenes. Then, the applications of these structures in various agricultural tasks are comprehensively introduced in this review, including recognition and classification, detection, counting and yield estimation. Finally, the surveyed DL applications, limitations and the future work for analysis of dense images in agriculture are summarized.
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24

Martins, Ana, Maria João Fonseca, and Fernando Tavares. "Mining the Genome: Using Bioinformatics Tools in the Classroom to Support Student Discovery of Genes." American Biology Teacher 80, no. 8 (2018): 619–24. http://dx.doi.org/10.1525/abt.2018.80.8.619.

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Adapting research-driven routines to the classroom context can promote innovative and motivational learning environments. Using a case-study approach, we propose a set of bioinformatics-based activities supported by a tutorial video aiming to identify genes and disclosing their genomic context in different species. The rationale is to strengthen teachers’ competencies to introduce bioinformatics resources and tools (e.g., NCBI, ORFinder, BLAST, and MaGe) in their teaching practices. By doing so, teachers will ultimately enhance students’ understanding of how genomic data mining and comparative genomics are instrumental for biological research.
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25

Liu, Wei, Dong Li, YunPing Zhu, and FuChu He. "Bioinformatics analyses for signal transduction networks." Science in China Series C: Life Sciences 51, no. 11 (2008): 994–1002. http://dx.doi.org/10.1007/s11427-008-0134-5.

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26

Childers, Anna K., Scott M. Geib, Sheina B. Sim, et al. "The USDA-ARS Ag100Pest Initiative: High-Quality Genome Assemblies for Agricultural Pest Arthropod Research." Insects 12, no. 7 (2021): 626. http://dx.doi.org/10.3390/insects12070626.

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The phylum Arthropoda includes species crucial for ecosystem stability, soil health, crop production, and others that present obstacles to crop and animal agriculture. The United States Department of Agriculture’s Agricultural Research Service initiated the Ag100Pest Initiative to generate reference genome assemblies of arthropods that are (or may become) pests to agricultural production and global food security. We describe the project goals, process, status, and future. The first three years of the project were focused on species selection, specimen collection, and the construction of lab and bioinformatics pipelines for the efficient production of assemblies at scale. Contig-level assemblies of 47 species are presented, all of which were generated from single specimens. Lessons learned and optimizations leading to the current pipeline are discussed. The project name implies a target of 100 species, but the efficiencies gained during the project have supported an expansion of the original goal and a total of 158 species are currently in the pipeline. We anticipate that the processes described in the paper will help other arthropod research groups or other consortia considering genome assembly at scale.
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27

Alaie, Adrienne, Virginia Teller, and Wei-gang Qiu. "A Bioinformatics Module for Use in an Introductory Biology Laboratory." American Biology Teacher 74, no. 5 (2012): 318–22. http://dx.doi.org/10.1525/abt.2012.74.5.6.

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Since biomedical science has become increasingly data-intensive, acquisition of computational and quantitative skills by science students has become more important. For non-science students, an introduction to biomedical databases and their applications promotes the development of a scientifically literate population. Because typical college introductory biology laboratories do not include experiences of this type, we present a bioinformatics module that can easily be included in a 90-minute session of a biology course for both majors and non-majors. Students completing this computational, inquiry-based module observed the value of computer-assisted analysis. The module gave students an understanding of how to read files in a biological database (GenBank) and how to use a software tool (BLAST) to mine the database.
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28

Eisenberg, David, Edward Marcotte, Andrew D. McLachlan, and Matteo Pellegrini. "Bioinformatic challenges for the next decade(s)." Philosophical Transactions of the Royal Society B: Biological Sciences 361, no. 1467 (2006): 525–27. http://dx.doi.org/10.1098/rstb.2005.1797.

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The science of bioinformatics has developed in the wake of methods to determine the sequences of the informational macromolecules—DNAs, RNAs and proteins. But in a wider sense, the biological world depends in its every process on the transmission of information, and hence bioinformatics is the fundamental core of biology. We here give a consideration of some of the key problems of bioinformatics in the coming decade, and perhaps longer.
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29

Bammann, Karin. "An Introduction to Bioinformatics Algorithms." Biometrics 62, no. 2 (2006): 626. http://dx.doi.org/10.1111/j.1541-0420.2006.00589_2.x.

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30

Joppich, Markus, and Ralf Zimmer. "From command-line bioinformatics to bioGUI." PeerJ 7 (November 21, 2019): e8111. http://dx.doi.org/10.7717/peerj.8111.

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Bioinformatics is a highly interdisciplinary field providing (bioinformatics) applications for scientists from many disciplines. Installing and starting applications on the command-line (CL) is inconvenient and/or inefficient for many scientists. Nonetheless, most methods are implemented with a command-line interface only. Providing a graphical user interface (GUI) for bioinformatics applications is one step toward routinely making CL-only applications available to more scientists and, thus, toward a more effective interdisciplinary work. With our bioGUI framework we address two main problems of using CL bioinformatics applications: First, many tools work on UNIX-systems only, while many scientists use Microsoft Windows. Second, scientists refrain from using CL tools which, however, could well support them in their research. With bioGUI install modules and templates, installing and using CL tools is made possible for most scientists—even on Windows, due to bioGUI’s support for Windows Subsystem for Linux. In addition, bioGUI templates can easily be created, making the bioGUI framework highly rewarding for developers. From the bioGUI repository it is possible to download, install and use bioinformatics tools with just a few clicks.
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31

Astwood, James, Richard E. Goodman, Andre Silvanovich, et al. "A bioinformatics approach to the assessment of the allergenicity of foods produced through agricultural biotechnology." Journal of Allergy and Clinical Immunology 109, no. 1 (2002): S180. http://dx.doi.org/10.1016/s0091-6749(02)81669-4.

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32

Zhang, Jianzhi. "EVOLUTION FOR BIOINFORMATICIANS AND BIOINFORMATICS FOR EVOLUTIONISTS." Evolution 59, no. 10 (2005): 2281–83. http://dx.doi.org/10.1111/j.0014-3820.2005.tb00937.x.

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33

Jiricny, Josef. "DNA Repair: Bioinformatics Helps Reverse Methylation Damage." Current Biology 12, no. 24 (2002): R846—R848. http://dx.doi.org/10.1016/s0960-9822(02)01350-7.

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34

Zhang, Jianzhi. "EVOLUTION FOR BIOINFORMATICIANS AND BIOINFORMATICS FOR EVOLUTIONISTS1." Evolution 59, no. 10 (2005): 2281. http://dx.doi.org/10.1554/br05-9.1.

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35

Al-Banna, Luma, Monther T. Sadder, Hamzeh A. Lafi, Ahmed A. M. Dawabah, and Saleh N. Al-Nadhari. "Bioinformatics analysis of ubiquitin expression protein gene from Heterodera latipons." Saudi Journal of Biological Sciences 26, no. 7 (2019): 1463–67. http://dx.doi.org/10.1016/j.sjbs.2018.06.005.

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36

Ksepka, Daniel T., Michael J. Benton, Matthew T. Carrano, et al. "Synthesizing and databasing fossil calibrations: divergence dating and beyond." Biology Letters 7, no. 6 (2011): 801–3. http://dx.doi.org/10.1098/rsbl.2011.0356.

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Divergence dating studies, which combine temporal data from the fossil record with branch length data from molecular phylogenetic trees, represent a rapidly expanding approach to understanding the history of life. National Evolutionary Synthesis Center hosted the first Fossil Calibrations Working Group (3–6 March, 2011, Durham, NC, USA), bringing together palaeontologists, molecular evolutionists and bioinformatics experts to present perspectives from disciplines that generate, model and use fossil calibration data. Presentations and discussions focused on channels for interdisciplinary collaboration, best practices for justifying, reporting and using fossil calibrations and roadblocks to synthesis of palaeontological and molecular data. Bioinformatics solutions were proposed, with the primary objective being a new database for vetted fossil calibrations with linkages to existing resources, targeted for a 2012 launch.
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37

Repchevsky, Dmitry, and Josep Ll Gelpi. "BioSWR – Semantic Web Services Registry for Bioinformatics." PLoS ONE 9, no. 9 (2014): e107889. http://dx.doi.org/10.1371/journal.pone.0107889.

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38

Shanahan, Hugh P., Anne M. Owen, and Andrew P. Harrison. "Bioinformatics on the Cloud Computing Platform Azure." PLoS ONE 9, no. 7 (2014): e102642. http://dx.doi.org/10.1371/journal.pone.0102642.

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39

MABEE, PAULA M. "Integrating Evolution and Development: The Need for Bioinformatics in Evo-Devo." BioScience 56, no. 4 (2006): 301. http://dx.doi.org/10.1641/0006-3568(2006)56[301:ieadtn]2.0.co;2.

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40

Hamernik, Debora L., and David L. Adelson. "USDA Stakeholder Workshop on Animal Bioinformatics: Summary and Recommendations." Comparative and Functional Genomics 4, no. 2 (2003): 271–74. http://dx.doi.org/10.1002/cfg.266.

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An electronic workshop was conducted on 4 November–13 December 2002 to discuss current issues and needs in animal bioinformatics. The electronic (e-mail listserver) format was chosen to provide a relatively speedy process that is broad in scope, cost-efficient and easily accessible to all participants. Approximately 40 panelists with diverse species and discipline expertise communicated through the panel e-mail listserver. The panel included scientists from academia, industry and government, in the USA, Australia and the UK. A second ‘stakeholder’ e-mail listserver was used to obtain input from a broad audience with general interests in animal genomics. The objectives of the electronic workshop were: (a) to define priorities for animal genome database development; and (b) to recommend ways in which the USDA could provide leadership in the area of animal genome database development. E-mail messages from panelists and stakeholders are archived at http://genome.cvm.umn.edu/bioinfo/. Priorities defined for animal genome database development included: (a) data repository; (b) tools for genome analysis; (c) annotation; (d) practical application of genomic data; and (e) a biological framework for DNA sequence. A stable source of funding, such as the USDA Agricultural Research Service (ARS), was recommended to support maintenance of data repositories and data curation. Continued support for competitive grants programs within the USDA Cooperative State Research, Education and Extension Service (CSREES) was recommended for tool development and hypothesis-driven research projects in genome analysis. Additional stakeholder input will be required to continuously refine priorities and maximize the use of limited resources for animal bioinformatics within the USDA.
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41

Tucker, Allan, and Daniel Duplisea. "Bioinformatics tools in predictive ecology: applications to fisheries." Philosophical Transactions of the Royal Society B: Biological Sciences 367, no. 1586 (2012): 279–90. http://dx.doi.org/10.1098/rstb.2011.0184.

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There has been a huge effort in the advancement of analytical techniques for molecular biological data over the past decade. This has led to many novel algorithms that are specialized to deal with data associated with biological phenomena, such as gene expression and protein interactions. In contrast, ecological data analysis has remained focused to some degree on off-the-shelf statistical techniques though this is starting to change with the adoption of state-of-the-art methods, where few assumptions can be made about the data and a more explorative approach is required, for example, through the use of Bayesian networks. In this paper, some novel bioinformatics tools for microarray data are discussed along with their ‘crossover potential’ with an application to fisheries data. In particular, a focus is made on the development of models that identify functionally equivalent species in different fish communities with the aim of predicting functional collapse.
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42

Pascar, Jane, and Christopher H. Chandler. "A bioinformatics approach to identifyingWolbachiainfections in arthropods." PeerJ 6 (September 3, 2018): e5486. http://dx.doi.org/10.7717/peerj.5486.

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Wolbachiais the most widespread endosymbiont, infecting >20% of arthropod species, and capable of drastically manipulating the host’s reproductive mechanisms. Conventionally, diagnosis has relied on PCR amplification; however, PCR is not always a reliable diagnostic technique due to primer specificity, strain diversity, degree of infection and/or tissue sampled. Here, we look for evidence ofWolbachiainfection across a wide array of arthropod species using a bioinformatic approach to detect theWolbachiagenesftsZ, wsp,and thegroEoperon in next-generation sequencing samples available through the NCBI Sequence Read Archive. For samples showing signs of infection, we attempted to assemble entireWolbachiagenomes, and in order to better understand the relationships between hosts and symbionts, phylogenies were constructed using the assembled gene sequences. Out of the 34 species with positively identified infections, eight species of arthropod had not previously been recorded to harborWolbachiainfection. All putative infections cluster with known representative strains belonging to supergroup A or B, which are known to only infect arthropods. This study presents an efficient bioinformatic approach for post-sequencing diagnosis and analysis ofWolbachiainfection in arthropods.
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43

Bedő, Justin. "BioShake: a Haskell EDSL for bioinformatics workflows." PeerJ 7 (July 9, 2019): e7223. http://dx.doi.org/10.7717/peerj.7223.

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Typical bioinformatics analyses comprise of long running computational workflows. An important part of reproducible research is the management and execution of these workflows to allow robust execution and to minimise errors. BioShake is an embedded domain specific language in Haskell for specifying and executing computational workflows for bioinformatics that significantly reduces the possibility of errors occurring. Unlike other workflow frameworks, BioShake raises many properties to the type level allowing the correctness of a workflow to be statically checked during compilation, catching errors before any lengthy execution process. BioShake builds on the Shake build tool to provide robust dependency tracking, parallel execution, reporting, and resumption capabilities. Finally, BioShake abstracts execution so that jobs can either be executed directly or submitted to a cluster. BioShake is available at http://github.com/PapenfussLab/bioshake.
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Simi, Manuele, and Fabien Campagne. "Composable languages for bioinformatics: the NYoSh experiment." PeerJ 2 (January 2, 2014): e241. http://dx.doi.org/10.7717/peerj.241.

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45

Molenberghs, Geert. "Biometry, Biometrics, Biostatistics, Bioinformatics, ... , Bio-X." Biometrics 61, no. 1 (2005): 1–9. http://dx.doi.org/10.1111/j.0006-341x.2005.040831.x.

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46

Marsili, Stefania, Ailone Tichon, Deepali Kundnani, and Francesca Storici. "Gene Co-Expression Analysis of Human RNASEH2A Reveals Functional Networks Associated with DNA Replication, DNA Damage Response, and Cell Cycle Regulation." Biology 10, no. 3 (2021): 221. http://dx.doi.org/10.3390/biology10030221.

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Ribonuclease (RNase) H2 is a key enzyme for the removal of RNA found in DNA-RNA hybrids, playing a fundamental role in biological processes such as DNA replication, telomere maintenance, and DNA damage repair. RNase H2 is a trimer composed of three subunits, RNASEH2A being the catalytic subunit. RNASEH2A expression levels have been shown to be upregulated in transformed and cancer cells. In this study, we used a bioinformatics approach to identify RNASEH2A co-expressed genes in different human tissues to underscore biological processes associated with RNASEH2A expression. Our analysis shows functional networks for RNASEH2A involvement such as DNA replication and DNA damage response and a novel putative functional network of cell cycle regulation. Further bioinformatics investigation showed increased gene expression in different types of actively cycling cells and tissues, particularly in several cancers, supporting a biological role for RNASEH2A but not for the other two subunits of RNase H2 in cell proliferation. Mass spectrometry analysis of RNASEH2A-bound proteins identified players functioning in cell cycle regulation. Additional bioinformatic analysis showed that RNASEH2A correlates with cancer progression and cell cycle related genes in Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Atlas (TCGA) Pan Cancer datasets and supported our mass spectrometry findings.
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47

Wilson Sayres, Melissa A., Charles Hauser, Michael Sierk, et al. "Bioinformatics core competencies for undergraduate life sciences education." PLOS ONE 13, no. 6 (2018): e0196878. http://dx.doi.org/10.1371/journal.pone.0196878.

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48

Tang, You, and Xiaolei Liu. "G2P: a Genome-Wide-Association-Study simulation tool for genotype simulation, phenotype simulation and power evaluation." Bioinformatics 35, no. 19 (2019): 3852–54. http://dx.doi.org/10.1093/bioinformatics/btz126.

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Abstract Motivation Plenty of Genome-Wide-Association-Study (GWAS) methods have been developed for mapping genetic markers that associated with human diseases and agricultural economic traits. Computer simulation is a nice tool to test the performances of various GWAS methods under certain scenarios. Existing tools are either inefficient in terms of computation and memory efficiency or inconvenient to use to simulate big, realistic genotype data and phenotype data to evaluate available GWAS methods. Results Here, we present a GWAS simulation tool named G2P that can be used to simulate genotype data, phenotype data and perform power evaluation of GWAS methods. G2P is a user-friendly tool with all functions is provided in both graphical user interface and pipeline manners and it is available for Windows, Mac and Linux environments. Furthermore, G2P achieves maximum efficiency in terms of both memory usage and simulation speed; with G2P, the simulation of genotype data that includes 1 000 000 samples and 2 000 000 markers can be accomplished in 5 h. Availability and implementation The G2P software, user manual, and example datasets are freely available at GitHub: https://github.com/XiaoleiLiuBio/G2P. Supplementary information Supplementary data are available at Bioinformatics online.
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Peros, Iván Gabriel, Carolina Susana Cerrudo, Marcela Gabriela Pilloff, Mariano Nicolás Belaich, Mario Enrique Lozano, and Pablo Daniel Ghiringhelli. "Advances in the Bioinformatics Knowledge of mRNA Polyadenylation in Baculovirus Genes." Viruses 12, no. 12 (2020): 1395. http://dx.doi.org/10.3390/v12121395.

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Baculoviruses are a group of insect viruses with large circular dsDNA genomes exploited in numerous biotechnological applications, such as the biological control of agricultural pests, the expression of recombinant proteins or the gene delivery of therapeutic sequences in mammals, among others. Their genomes encode between 80 and 200 proteins, of which 38 are shared by all reported species. Thanks to multi-omic studies, there is remarkable information about the baculoviral proteome and the temporality in the virus gene expression. This allows some functional elements of the genome to be very well described, such as promoters and open reading frames. However, less information is available about the transcription termination signals and, consequently, there are still imprecisions about what are the limits of the transcriptional units present in the baculovirus genomes and how is the processing of the 3′ end of viral mRNA. Regarding to this, in this review we provide an update about the characteristics of DNA signals involved in this process and we contribute to their correct prediction through an exhaustive analysis that involves bibliography information, data mining, RNA structure and a comprehensive study of the core gene 3′ ends from 180 baculovirus genomes.
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Giassa, Ilektra-Chara, and Panagiotis Alexiou. "Bioinformatics and Machine Learning Approaches to Understand the Regulation of Mobile Genetic Elements." Biology 10, no. 9 (2021): 896. http://dx.doi.org/10.3390/biology10090896.

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Transposable elements (TEs, or mobile genetic elements, MGEs) are ubiquitous genetic elements that make up a substantial proportion of the genome of many species. The recent growing interest in understanding the evolution and function of TEs has revealed that TEs play a dual role in genome evolution, development, disease, and drug resistance. Cells regulate TE expression against uncontrolled activity that can lead to developmental defects and disease, using multiple strategies, such as DNA chemical modification, small RNA (sRNA) silencing, chromatin modification, as well as sequence-specific repressors. Advancements in bioinformatics and machine learning approaches are increasingly contributing to the analysis of the regulation mechanisms. A plethora of tools and machine learning approaches have been developed for prediction, annotation, and expression profiling of sRNAs, for methylation analysis of TEs, as well as for genome-wide methylation analysis through bisulfite sequencing data. In this review, we provide a guided overview of the bioinformatic and machine learning state of the art of fields closely associated with TE regulation and function.
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