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Journal articles on the topic 'Bioinformatics tools'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 June 2025

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1

Lomberk, Gwen. "Bioinformatics tools." Pancreatology 5, no. 4-5 (2005): 314–15. http://dx.doi.org/10.1159/000086531.

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Moreews, François, Olivier Sallou, Hervé Ménager, et al. "BioShaDock: a community driven bioinformatics shared Docker-based tools registry." F1000Research 4 (December 14, 2015): 1443. http://dx.doi.org/10.12688/f1000research.7536.1.

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Linux container technologies, as represented by Docker, provide an alternative to complex and time-consuming installation processes needed for scientific software. The ease of deployment and the process isolation they enable, as well as the reproducibility they permit across environments and versions, are among the qualities that make them interesting candidates for the construction of bioinformatic infrastructures, at any scale from single workstations to high throughput computing architectures. The Docker Hub is a public registry which can be used to distribute bioinformatic software as Docker images. However, its lack of curation and its genericity make it difficult for a bioinformatics user to find the most appropriate images needed. BioShaDock is a bioinformatics-focused Docker registry, which provides a local and fully controlled environment to build and publish bioinformatic software as portable Docker images. It provides a number of improvements over the base Docker registry on authentication and permissions management, that enable its integration in existing bioinformatic infrastructures such as computing platforms. The metadata associated with the registered images are domain-centric, including for instance concepts defined in the EDAM ontology, a shared and structured vocabulary of commonly used terms in bioinformatics. The registry also includes user defined tags to facilitate its discovery, as well as a link to the tool description in the ELIXIR registry if it already exists. If it does not, the BioShaDock registry will synchronize with the registry to create a new description in the Elixir registry, based on the BioShaDock entry metadata. This link will help users get more information on the tool such as its EDAM operations, input and output types. This allows integration with the ELIXIR Tools and Data Services Registry, thus providing the appropriate visibility of such images to the bioinformatics community.
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3

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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Zhang, Xiaorong. "Teaching Botany Using Bioinformatics Tools." Creative Education 10, no. 10 (2019): 2137–46. http://dx.doi.org/10.4236/ce.2019.1010155.

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Cantó‐Pastor, Alex, G. Alex Mason, Siobhan M. Brady, and Nicholas J. Provart. "Arabidopsis bioinformatics: tools and strategies." Plant Journal 108, no. 6 (2021): 1585–96. http://dx.doi.org/10.1111/tpj.15547.

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6

Skuse, Gary R., and Chunguang Du. "Bioinformatics Tools for Plant Genomics." International Journal of Plant Genomics 2008 (June 11, 2008): 1–2. http://dx.doi.org/10.1155/2008/910474.

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7

Arndt, Timothy. "Visual software tools for bioinformatics." Journal of Visual Languages & Computing 19, no. 2 (2008): 291–301. http://dx.doi.org/10.1016/j.jvlc.2007.06.001.

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8

Margolis, David J., Nandita Mitra, Bradley Wubbenhorst, and Katherine L. Nathanson. "Filaggrin sequencing and bioinformatics tools." Archives of Dermatological Research 312, no. 2 (2019): 155–58. http://dx.doi.org/10.1007/s00403-019-01956-3.

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Arora, Pankaj Kumar, and Wenxin Shi. "Tools of bioinformatics in biodegradation." Reviews in Environmental Science and Bio/Technology 9, no. 3 (2010): 211–13. http://dx.doi.org/10.1007/s11157-010-9211-x.

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10

Lomberk, Gwen. "Educational Websites — Bioinformatics Tools II." Pancreatology 9, no. 1-2 (2009): 4–5. http://dx.doi.org/10.1159/000178767.

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11

Iwakiri, Junichi, Michiaki Hamada, and Kiyoshi Asai. "Bioinformatics tools for lncRNA research." Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms 1859, no. 1 (2016): 23–30. http://dx.doi.org/10.1016/j.bbagrm.2015.07.014.

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12

Caccia, Dario, Matteo Dugo, Maurizio Callari, and Italia Bongarzone. "Bioinformatics tools for secretome analysis." Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics 1834, no. 11 (2013): 2442–53. http://dx.doi.org/10.1016/j.bbapap.2013.01.039.

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13

Blekherman, Grigoriy, Reinhard Laubenbacher, Diego F. Cortes, et al. "Bioinformatics tools for cancer metabolomics." Metabolomics 7, no. 3 (2011): 329–43. http://dx.doi.org/10.1007/s11306-010-0270-3.

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14

Mullany, Lila E., Roger K. Wolff, and Martha L. Slattery. "Effectiveness and Usability of Bioinformatics Tools to Analyze Pathways Associated with miRNA Expression." Cancer Informatics 14 (January 2015): CIN.S32716. http://dx.doi.org/10.4137/cin.s32716.

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MiRNAs are small, nonprotein-coding RNA molecules involved in gene regulation. While bioinformatics help guide miRNA research, it is less clear how they perform when studying biological pathways. We used 13 criteria to evaluate effectiveness and usability of existing bioinformatics tools. We evaluated the performance of six bioinformatics tools with a cluster of 12 differentially expressed miRNAs in colorectal tumors and three additional sets of 12 miRNAs that are not part of a known cluster. MiRPath performed the best of all the tools in linking miRNAs, with 92% of all miRNAs linked as well as the highest based on our established criteria followed by Ingenuity (58% linked). Other tools, including Empirical Gene Ontology, miRó, miRMaid, and PhenomiR, were limited by their lack of available tutorials, lack of flexibility and interpretability, and/or difficulty using the tool. In summary, we observed a lack of standardization across bioinformatic tools and a general lack of specificity in terms of pathways identified between groups of miRNAs. Hopefully, this evaluation will help guide the development of new tools.
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Mazola, Yuliet, Glay Chinea, and Alexis Musacchio. "Integrating Bioinformatics Tools to Handle Glycosylation." PLoS Computational Biology 7, no. 12 (2011): e1002285. http://dx.doi.org/10.1371/journal.pcbi.1002285.

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16

Fernandez-Pozo, N., A. Gómez-Ollé, A. Bullones, L. A. Mueller, and J. I. Hormaza. "MangoBase: bioinformatics tools for mango research." Acta Horticulturae, no. 1415 (January 2025): 229–36. https://doi.org/10.17660/actahortic.2025.1415.27.

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17

Debes, Jose D., and Raul Urrutia. "Bioinformatics tools to understand human diseases." Surgery 135, no. 6 (2004): 579–85. http://dx.doi.org/10.1016/j.surg.2003.11.010.

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18

Prilusky, Jaime, and Joel L. Sussman. "Guest Editorial: Databases and Bioinformatics Tools." Israel Journal of Chemistry 53, no. 3-4 (2013): 143. http://dx.doi.org/10.1002/ijch.201310004.

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19

Vitorino, Rui. "Special Issue: “Bioinformatics and Omics Tools”." International Journal of Molecular Sciences 24, no. 14 (2023): 11625. http://dx.doi.org/10.3390/ijms241411625.

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With the rapid introduction of high-throughput omics approaches such as genomics, transcriptomics, proteomics and metabolomics, the generation of large amounts of data has become a fundamental aspect of modern biological research [...]
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20

Luna Buitrago, Diana, Ruth C. Lovering, and Andrea Caporali. "Insights into Online microRNA Bioinformatics Tools." Non-Coding RNA 9, no. 2 (2023): 18. http://dx.doi.org/10.3390/ncrna9020018.

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MicroRNAs (miRNAs) are members of the small non-coding RNA family regulating gene expression at the post-transcriptional level. MiRNAs have been found to have critical roles in various biological and pathological processes. Research in this field has significantly progressed, with increased recognition of the importance of miRNA regulation. As a result of the vast data and information available regarding miRNAs, numerous online tools have emerged to address various biological questions related to their function and influence across essential cellular processes. This review includes a brief introduction to available resources for an investigation covering aspects such as miRNA sequences, target prediction/validation, miRNAs associated with disease, pathway analysis and genetic variants within miRNAs.
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21

Esteban, David J., Melissa Da Silva, and Chris Upton. "New bioinformatics tools for viral genome analyses at Viral Bioinformatics – Canada." Pharmacogenomics 6, no. 3 (2005): 271–80. http://dx.doi.org/10.1517/14622416.6.3.271.

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22

Mbareche, Hamza, Nathan Dumont-Leblond, Guillaume J. Bilodeau, and Caroline Duchaine. "An Overview of Bioinformatics Tools for DNA Meta-Barcoding Analysis of Microbial Communities of Bioaerosols: Digest for Microbiologists." Life 10, no. 9 (2020): 185. http://dx.doi.org/10.3390/life10090185.

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High-throughput DNA sequencing (HTS) has changed our understanding of the microbial composition present in a wide range of environments. Applying HTS methods to air samples from different environments allows the identification and quantification (relative abundance) of the microorganisms present and gives a better understanding of human exposure to indoor and outdoor bioaerosols. To make full use of the avalanche of information made available by these sequences, repeated measurements must be taken, community composition described, error estimates made, correlations of microbiota with covariates (variables) must be examined, and increasingly sophisticated statistical tests must be conducted, all by using bioinformatics tools. Knowing which analysis to conduct and which tools to apply remains confusing for bioaerosol scientists, as a litany of tools and data resources are now available for characterizing microbial communities. The goal of this review paper is to offer a guided tour through the bioinformatics tools that are useful in studying the microbial ecology of bioaerosols. This work explains microbial ecology features like alpha and beta diversity, multivariate analyses, differential abundances, taxonomic analyses, visualization tools and statistical tests using bioinformatics tools for bioaerosol scientists new to the field. It illustrates and promotes the use of selected bioinformatic tools in the study of bioaerosols and serves as a good source for learning the “dos and don’ts” involved in conducting a precise microbial ecology study.
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23

Lazarus, R., A. Kaspi, and M. Ziemann. "Creating reusable tools from scripts: the Galaxy Tool Factory." Bioinformatics 28, no. 23 (2012): 3139–40. http://dx.doi.org/10.1093/bioinformatics/bts573.

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24

García-García, Natalia, Javier Tamames, and Fernando Puente-Sánchez. "M&Ms: a versatile software for building microbial mock communities." Bioinformatics 38, no. 7 (2022): 2057–59. http://dx.doi.org/10.1093/bioinformatics/btab882.

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Abstract Summary Advances in sequencing technologies have triggered the development of many bioinformatic tools aimed to analyze 16S rDNA sequencing data. As these tools need to be tested, it is important to simulate datasets that resemble samples from different environments. Here, we introduce M&Ms, a user-friendly open-source bioinformatic tool to produce different 16S rDNA datasets from reference sequences, based on pragmatic ecological parameters. It creates sequence libraries for ‘in silico’ microbial communities with user-controlled richness, evenness, microdiversity and source environment. M&Ms allows the user to generate simple to complex read datasets based on real parameters that can be used in developing bioinformatic software or in benchmarking current tools. Availability and implementation The source code of M&Ms is freely available at https://github.com/ggnatalia/MMs (GPL-3.0 License). Supplementary information Supplementary data are available at Bioinformatics online.
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25

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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26

Yan, Qing. "Bioinformatics Databases and Tools in Virology Research: An Overview." In Silico Biology: Journal of Biological Systems Modeling and Multi-Scale Simulation 8, no. 2 (2008): 71–85. https://doi.org/10.3233/isb-00345.

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Viruses are major factors of human infectious diseases. Understanding of the structure-function correlation in viruses is important for the identification of potential anti-viral inhibitors and vaccine targets. In virology research, virus-related databases and bioinformatic analysis tools are essential for discerning relationships within complex datasets about viruses and host-virus interactions. Bioinformatic analyses on viruses include the identification of open reading frames, gene prediction, homology searching, sequence alignment, and motif and epitope recognition. The predictions of features such as transmembrane domains, glycosylation sites, and protein secondary and tertiary structure are important for analyzing the structure-function relationship of proteins encoded in viral genomes. Biochemical pathway analysis can help elucidate information at the biological systems level. Microarray analysis provides methods for high throughput screening and gene expression profiling. Virus-related bioinformatics databases include those concerned with viral sequences, taxonomy, homologous protein families, structures, or dedicated to specific viruses such as influenza and herpes simplex virus (HSV). This review provides a guide and overview of computational programs for these analyses as a resource for genomics and proteomics studies in virology research. These resources are useful for understanding viral diseases, as well as for the design and development of anti-viral agents.
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27

Pereira, Rute, Jorge Oliveira, and Mário Sousa. "Bioinformatics and Computational Tools for Next-Generation Sequencing Analysis in Clinical Genetics." Journal of Clinical Medicine 9, no. 1 (2020): 132. http://dx.doi.org/10.3390/jcm9010132.

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Clinical genetics has an important role in the healthcare system to provide a definitive diagnosis for many rare syndromes. It also can have an influence over genetics prevention, disease prognosis and assisting the selection of the best options of care/treatment for patients. Next-generation sequencing (NGS) has transformed clinical genetics making possible to analyze hundreds of genes at an unprecedented speed and at a lower price when comparing to conventional Sanger sequencing. Despite the growing literature concerning NGS in a clinical setting, this review aims to fill the gap that exists among (bio)informaticians, molecular geneticists and clinicians, by presenting a general overview of the NGS technology and workflow. First, we will review the current NGS platforms, focusing on the two main platforms Illumina and Ion Torrent, and discussing the major strong points and weaknesses intrinsic to each platform. Next, the NGS analytical bioinformatic pipelines are dissected, giving some emphasis to the algorithms commonly used to generate process data and to analyze sequence variants. Finally, the main challenges around NGS bioinformatics are placed in perspective for future developments. Even with the huge achievements made in NGS technology and bioinformatics, further improvements in bioinformatic algorithms are still required to deal with complex and genetically heterogeneous disorders.
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28

Najeb Mohammed, Bafreen. "A Review: Genetics Algorithms in Bioinformatics Tools." ICONTECH INTERNATIONAL JOURNAL 5, no. 1 (2021): 16–25. http://dx.doi.org/10.46291/icontechvol5iss1pp16-25.

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Bioinformaticians study biological questions by analyzing molecular data with various programs and tools. Today, bioinformatics is used in large number of fields such as microbial genome applications, biotechnology, waste cleanup, Gene therapy, fingerprint and eye detection. The field of bioinformatics, is one of the most prominent areas that our need is increasing, and the demand for it is increasing day by day. Where dealing with this vital and biological information using advanced computer technologies to generate useful information and new discoveries. For this reason, vital bioinformatics is one of the domains that combines both interested and programming at the same time. It provides you with resources for self-learning, the most important information in the field of vital information, and asked questions of those wishing to learn this field. The term bioinformatics was first used in 1968 by Margret Dayhoff, which is a pioneer in this field, but its definition appeared for the first time in 1978. This science arose and developed in conjunction with the emergence and development of computers. It is also referred to as "computational biology."
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Tabassum Khan, Nida. "The Emerging Role of Bioinformatics in Biotechnology." Journal of Biotechnology and Biomedical Science 1, no. 3 (2018): 13–24. http://dx.doi.org/10.14302/issn.2576-6694.jbbs-18-2173.

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Bioinformatic tools is widely used to manage the enormous genomic and proteomic data involving DNA/protein sequences management, drug designing, homology modelling, motif/domain prediction ,docking, annotation and dynamic simulation etc. Bioinformatics offers a wide range of applications in numerous disciplines such as genomics. Proteomics, comparative genomics, nutrigenomics, microbial genome, biodefense, forensics etc. Thus it offers promising future to accelerate scientific research in biotechnology
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30

Khan, Johra, and Rajeev K. Singla. "Bioinformatics Tools for Pharmaceutical Drug Product Development." Indo Global Journal of Pharmaceutical Sciences 12, no. 12 (2022): 281–94. http://dx.doi.org/10.35652/igjps.2022.12037.

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Drug discovery and production is a long and expensive process which starts with target identification followed by validation of targets to lead optimization, taking years to develop a drug which sometime false to reach marked resulting in loss of time, effort, and huge amount of money. Bioinformatics tools are becoming more and more important in drug product development. Repurposing large amount of data needs to be exploited and generated fromgenomics, epigenetics, cistromic, proteomics, transcriptomics, ribosomal profiling, and genomic based studies of drug targets. Bioinformatics analysis and data mining are effective tools to explore big series of biological and biomedical data, however theadvance tools are often found difficult to understand making their use limited to difficult to access by the researchers working in drug discovery. In this review we focused on systematically presenting the different tools used for drug target identification and product development. The tools are broadly classified according to disease based computational tools, gene based tools, and web based tools and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) study for drug repurposing. The focus was on the basic principle of these tools functioning, uses and limitations in drug target identification, validation, data analysis, comparison with other similar tools in target analysis.©2022Caproslaxy Media. All rights reserved.
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31

Nwabueze, Ekwonwune, and E. Charles. "Impact of Bioinformatics Tools in Genomic Biomedicine." British Journal of Applied Science & Technology 19, no. 3 (2017): 1–8. http://dx.doi.org/10.9734/bjast/2017/30404.

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32

Huang, Jian, Beibei Ru, and Ping Dai. "Bioinformatics Resources and Tools for Phage Display." Molecules 16, no. 1 (2011): 694–709. http://dx.doi.org/10.3390/molecules16010694.

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33

Prabha, Ratna, D. P. Singh, and Anil Rai. "BioInfoKnowledgeBase: Comprehensive Information Resource for Bioinformatics Tools." American Journal of Bioinformatics 4, no. 2 (2015): 28–33. http://dx.doi.org/10.3844/ajbsp.2015.28.33.

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34

de Azevedo Junior, Walter Filgueira, Raquel Dias, Luis Fernando Macedo Timmers, Ivani Pauli, Rafael Caceres, and Milena Pereira Soares. "Bioinformatics Tools for Screening of Antiparasitic Drugs." Current Drug Targets 10, no. 3 (2009): 232–39. http://dx.doi.org/10.2174/138945009787581122.

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35

Lee, Michael. "bit: a multipurpose collection of bioinformatics tools." F1000Research 11 (January 31, 2022): 122. http://dx.doi.org/10.12688/f1000research.79530.1.

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bit is a collection of small scripts and programs that facilitate many common tasks in bioinformatics. It operates in a Unix-like command-line environment and is comprised of bash and python code. bit is openly available on GitHub, archived with Zenodo, and is conda installable. The package is useful for users who want to do things such as manipulate fasta files, calculate GC content, quickly summarize nucleotide assemblies, easily download assemblies from NCBI just based on accessions, pull amino-acid sequences from GenBank files, calculate Shannon uncertainty for columns in multiple sequence alignments, and more. The source code is hosted on GitHub: github.com/AstrobioMike/bit
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36

ORTON, R. J., Q. GU, J. HUGHES, et al. "Bioinformatics tools for analysing viral genomic data." Revue Scientifique et Technique de l'OIE 35, no. 1 (2016): 271–85. http://dx.doi.org/10.20506/rst.35.1.2432.

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37

Sirangelo, Tiziana Maria, and Grazia Calabro. "Soil Metagegomics: Approaches, Bioinformatics Tools and Applications." Scholars Journal of Agriculture and Veterinary Sciences 7, no. 6 (2020): 125–32. http://dx.doi.org/10.36347/sjavs.2020.v07i06.003.

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38

Brusic, Vladimir, and Darren R. Flower. "Bioinformatics tools for identifying T-cell epitopes." Drug Discovery Today: BIOSILICO 2, no. 1 (2004): 18–23. http://dx.doi.org/10.1016/s1741-8364(04)02374-1.

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39

Romano, P., R. Giugno, and A. Pulvirenti. "Tools and collaborative environments for bioinformatics research." Briefings in Bioinformatics 12, no. 6 (2011): 549–61. http://dx.doi.org/10.1093/bib/bbr055.

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40

Garg, Priyanka, and Pankaj Jaiswal. "Databases and bioinformatics tools for rice research." Current Plant Biology 7-8 (November 2016): 39–52. http://dx.doi.org/10.1016/j.cpb.2016.12.006.

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41

Lambrix, P., M. Habbouche, and M. Perez. "Evaluation of ontology development tools for bioinformatics." Bioinformatics 19, no. 12 (2003): 1564–71. http://dx.doi.org/10.1093/bioinformatics/btg194.

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42

Holloway, Eric. "Tutorial: Bioinformatics Basics." Communications of the Blyth Institute 2, no. 2 (2020): 35–38. http://dx.doi.org/10.33014/issn.2640-5652.2.2.holloway.1.

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 Bioinformatics can appear to be a daunting field, since it combines the complex science of biology with the complex theory of computer science. However, the basics are surprisingly simple. This tutorial gives a glimpse of the tools and techniques needed to get started in the field.
 
 
 
 
 
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Paul, Piby, Vimala Antonydhason, Judy Gopal, Steve W. Haga, Nazim Hasan, and Jae-Wook Oh. "Bioinformatics for Renal and Urinary Proteomics: Call for Aggrandization." International Journal of Molecular Sciences 21, no. 3 (2020): 961. http://dx.doi.org/10.3390/ijms21030961.

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The clinical sampling of urine is noninvasive and unrestricted, whereby huge volumes can be easily obtained. This makes urine a valuable resource for the diagnoses of diseases. Urinary and renal proteomics have resulted in considerable progress in kidney-based disease diagnosis through biomarker discovery and treatment. This review summarizes the bioinformatics tools available for this area of proteomics and the milestones reached using these tools in clinical research. The scant research publications and the even more limited bioinformatic tool options available for urinary and renal proteomics are highlighted in this review. The need for more attention and input from bioinformaticians is highlighted, so that progressive achievements and releases can be made. With just a handful of existing tools for renal and urinary proteomic research available, this review identifies a gap worth targeting by protein chemists and bioinformaticians. The probable causes for the lack of enthusiasm in this area are also speculated upon in this review. This is the first review that consolidates the bioinformatics applications specifically for renal and urinary proteomics.
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Anashkina, Anastasia A., Elena Y. Leberfarb, and Yuriy L. Orlov. "Recent Trends in Cancer Genomics and Bioinformatics Tools Development." International Journal of Molecular Sciences 22, no. 22 (2021): 12146. http://dx.doi.org/10.3390/ijms222212146.

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We overview recent research trends in cancer genomics, bioinformatics tools development and medical genetics, based on results discussed in papers collections “Medical Genetics, Genomics and Bioinformatics” (https://www [...]
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S. Atheena, Milagi Pandian, Murugan Rashika, Manoj Kumar N. Sri, N. Aparna, and Sakthi M. Kriya. "Utilizing bioinformatics tools for analyzing high-throughput data in biomedical research." i-manager’s Journal on Future Engineering and Technology 19, no. 3 (2024): 33. http://dx.doi.org/10.26634/jfet.19.3.20561.

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Bioinformatics has become crucial in biomedical research, enabling the processing of massive volumes of high- throughput data generated by various omics technologies. This work investigates the use of bioinformatics tools to process, analyze, and interpret omics data, including genomics, transcriptomics, proteomics, and metabolomics. It provides an overview of widely used bioinformatics methodologies and algorithms for data preparation, quality control, differential expression analysis, pathway analysis, and functional annotation. The study also highlights current trends and challenges in bioinformatics, such as integrating multi-omics data and developing machine learning algorithms for predictive modeling. This work aims to encourage academics to utilize bioinformatics methods to gain insights into complex biological systems and enhance our understanding of human health and disease.
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KMS, Rana, Ahammad K, and Salam MA. "Bioinformatics: scope and challenges in aquaculture research of Bangladesh- a review." International Journal of Agricultural Research, Innovation and Technology 10, no. 2 (2020): 137–45. https://doi.org/10.3329/ijarit.v10i2.51587.

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Bioinformatics is one of the ongoing trends of biological research integrating gene based information and computational technology to produce new knowledge. It works to synthesize complex biological information from multiomics data (results of high throughput technologies) by employing a number of bioinformatics tools (software). User convenience and availability are the determining factors of these tools being widely used in bioinformatics research. BLAST<strong>,&nbsp;</strong>FASTA (FAST-All), EMBOSS, ClustalW, RasMol and Protein Explorer, Cn3D, Swiss PDB viewer, Hex, Vega, Bioeditor etc. are commonly operated bioinformatics software tools in fisheries and aquaculture research. By default, these software tools mine and analyze a vast biological data set using the available databases. However, aquaculture scientists can use bioinformatics for genomic data manipulation, genome annotation and expression profiling, molecular folding, modeling, and design as well as generating biological network and system biology. Therefore, they can contribute in specified fields of aquaculture such as disease diagnosis and aquatic health management, fish nutritional aspects and culture-able strain development. Although having huge prospects, Bangladesh is still in infancy of applying bioinformatics in aquaculture research with limited resources. Research council at national level should be formed to bring all the enthusiastic scientists and skilled manpower under a single umbrella and facilitate to contribute in a collaborative platform. Besides, fully-fledged bioinformatics degree should be launched at University levels to produce knowledgeable and trained work force for future research. This review was attempted to shed light on bioinformatics, as young integrated field of bio-computational research, and its significance in aquaculture research of Bangladesh.
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47

Stankov, Karmen. "Bioinformatic tools for cancer geneticists." Archive of Oncology 13, no. 2 (2005): 69–75. http://dx.doi.org/10.2298/aoo0502069s.

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Early detection is essential for the control and prevention of many diseases, particularly cancer, which is the reason why the need for new disease markers with improved sensitivity and specificity continues to grow. Utilization of sophisticated bioinformatic tools enables the increased specificity and a relatively large quantity of high quality assays for any gene of interest. Understanding the molecular characteristics of diseases, such as cancer and the detection of mutations or changes in gene expression patterns that occur as a result of the disease, will bring researchers one step closer to achieving the predictive power needed for the development of new therapies, the design of clinical trials, and specific patient treatment planning. Genetic screening is one of the fastest moving areas of medical science, particularly in oncology, and as more genes are cloned, and more disease-associated mutations discovered, the workload is set to increase considerably with the utilization of bioinformatics tools used in integration and analysis of genomic, proteomic and metabolomic profiles of cancer. .
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Dr., Subhan Shahid Dr. Erum Naseem Ahmed Dr. Dujanah Siddique Bhatti. "TOOLS FOR TRACKING ANTIBIOTIC RESISTANCE." INDO AMERICAN JOURNAL OF PHARMACEUTICAL SCIENCES 05, no. 05 (2018): 4270–73. https://doi.org/10.5281/zenodo.1254075.

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Objective: Antibiotic resistance is amongst leading problems in pharmaceutical and medicinal science. The resistive genes are imposed a pressure by antibiotics which cause excessive genetic material exchange by resulting malfunctioning. Microbial population has also been terminated by undue antibiotic uses. Therefore, the problem needs a solution to introduce new tools to measure accurate resistance level. Bioinformatic and pharmaceutical technologies can revolutionize the industry. Patients and Methods: The molecular study was conducted to determine protein natures, structures and functioning in subjected individuals. The biological modeling of living cell system and proteins enables to discover effective drug strategies. This helped to contest the expanding antibiotic resistance problem. Results: The present study analyzed several types of data that included nucleotide and protein structures and sequences. Conclusion: The results of protein analysis indicated that the accurate drug treatment is much effective and computational modeling can help to determine antibiotic resistance levels. Keywords: Antibiotic resistance, microbial population, bioinformatics, pharmaceuticals, biological modeling, drug, proteins
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Shevtsov, V., A. Ismailova, U. Aitimova, and O. Khapilina. "APPLICATION OF INFORMATION SYSTEMS AND TOOLS IN BIOINFORMATICS." Scientific Journal of Astana IT University, no. 9 (March 30, 2022): 14–21. http://dx.doi.org/10.37943/aitu.2022.59.49.002.

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Abstract. The pace at which scientific data is produced and disseminated has never been ashigh as it is currently. Modern sequencing technologies make it possible to obtain the genomeof a specific organism in a few days, and the genome of a bacterial organism in less than a day,and therefore researchers from the field of life science are faced with a huge amount of datathat needs to be analyzed. In this connection, various fields of science are converging with eachother, giving rise to new disciplines. So, bioinformatics is one of these fields, it is a scientificdiscipline that has been actively developing over the past decades and uses IT tools andmethods to solve problems related to the study of biological processes. In particular, a crucialrole in the field of bioinformatics is played by the development of new algorithms, tools andthe creation of new databases, as well as the integration of extremely large amounts of data.The rapid development of bioinformatics has made it possible to conduct modern biologicalresearch. Bioinformatics can help a biologist to extract valuable information from biologicaldata by using tools to process them. Despite the fact that bioinformatics is a relatively newdiscipline, various web and computer tools already exist, most of which are freely available.This is a review article that provides an exhaustive overview of some of the tools for biologicalanalysis available to a biologist, as well as describes the key role of information systems in thisinterdisciplinary field.
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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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