Journal articles: 'Bioinformatic'

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Bottomley, S. "Bioinformatics: guide for evaluating bioinformatic software." Drug Discovery Today 4, no. 5 (May 1, 1999): 240–43. http://dx.doi.org/10.1016/s1359-6446(99)01352-5.

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Moreews, François, Olivier Sallou, Hervé Ménager, Yvan Le bras, Cyril Monjeaud, Christophe Blanchet, and Olivier Collin. "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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Kim, Min Cheol, Jaclyn M. Winter, Reiko Cullum, Alexander J. Smith, and William Fenical. "Expanding the Utility of Bioinformatic Data for the Full Stereostructural Assignments of Marinolides A and B, 24- and 26-Membered Macrolactones Produced by a Chemically Exceptional Marine-Derived Bacterium." Marine Drugs 21, no. 6 (June 20, 2023): 367. http://dx.doi.org/10.3390/md21060367.

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Marinolides A and B, two new 24- and 26-membered bacterial macrolactones, were isolated from the marine-derived actinobacterium AJS-327 and their stereostructures initially assigned by bioinformatic data analysis. Macrolactones typically possess complex stereochemistry, the assignments of which have been one of the most difficult undertakings in natural products chemistry, and in most cases, the use of X-ray diffraction methods and total synthesis have been the major methods of assigning their absolute configurations. More recently, however, it has become apparent that the integration of bioinformatic data is growing in utility to assign absolute configurations. Genome mining and bioinformatic analysis identified the 97 kb mld biosynthetic cluster harboring seven type I polyketide synthases. A detailed bioinformatic investigation of the ketoreductase and enoylreductase domains within the multimodular polyketide synthases, coupled with NMR and X-ray diffraction data, allowed for the absolute configurations of marinolides A and B to be determined. While using bioinformatics to assign the relative and absolute configurations of natural products has high potential, this method must be coupled with full NMR-based analysis to both confirm bioinformatic assignments as well as any additional modifications that occur during biosynthesis.

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Brazas, M. D., J. T. Yamada, and B. F. F. Ouellette. "Evolution in bioinformatic resources: 2009 update on the Bioinformatics Links Directory." Nucleic Acids Research 37, Web Server (June 15, 2009): W3—W5. http://dx.doi.org/10.1093/nar/gkp531.

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SoRelle, Jeffrey A., Megan Wachsmann, and Brandi L. Cantarel. "Assembling and Validating Bioinformatic Pipelines for Next-Generation Sequencing Clinical Assays." Archives of Pathology & Laboratory Medicine 144, no. 9 (February 11, 2020): 1118–30. http://dx.doi.org/10.5858/arpa.2019-0476-ra.

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Context.— Clinical next-generation sequencing (NGS) is being rapidly adopted, but analysis and interpretation of large data sets prompt new challenges for a clinical laboratory setting. Clinical NGS results rely heavily on the bioinformatics pipeline for identifying genetic variation in complex samples. The choice of bioinformatics algorithms, genome assembly, and genetic annotation databases are important for determining genetic alterations associated with disease. The analysis methods are often tuned to the assay to maximize accuracy. Once a pipeline has been developed, it must be validated to determine accuracy and reproducibility for samples similar to real-world cases. In silico proficiency testing or institutional data exchange will ensure consistency among clinical laboratories. Objective.— To provide molecular pathologists a step-by-step guide to bioinformatics analysis and validation design in order to navigate the regulatory and validation standards of implementing a bioinformatic pipeline as a part of a new clinical NGS assay. Data Sources.— This guide uses published studies on genomic analysis, bioinformatics methods, and methods comparison studies to inform the reader on what resources, including open source software tools and databases, are available for genetic variant detection and interpretation. Conclusions.— This review covers 4 key concepts: (1) bioinformatic analysis design for detecting genetic variation, (2) the resources for assessing genetic effects, (3) analysis validation assessment experiments and data sets, including a diverse set of samples to mimic real-world challenges that assess accuracy and reproducibility, and (4) if concordance between clinical laboratories will be improved by proficiency testing designed to test bioinformatic pipelines.

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García-García, Natalia, Javier Tamames, and Fernando Puente-Sánchez. "M&Ms: a versatile software for building microbial mock communities." Bioinformatics 38, no. 7 (January 12, 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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Hsu, Pei-Chun, Evaristus Nwulia, and Akira Sawa. "Using Bioinformatic Tools." American Journal of Psychiatry 166, no. 8 (August 2009): 854. http://dx.doi.org/10.1176/appi.ajp.2009.09060908.

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Chen, Runsheng. "On Bioinformatic Resources." Genomics, Proteomics & Bioinformatics 13, no. 1 (February 2015): 1–3. http://dx.doi.org/10.1016/j.gpb.2015.02.002.

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Mshvidobadze, Tinatin. "Bioinformatics as Emerging Tool and Pipeline Frameworks." Science Progress and Research 1, no. 4 (October 23, 2021): 411–15. http://dx.doi.org/10.52152/spr/2021.162.

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In this article, we will discuss the areas of origin of bioinformatics in the human health care system. Due to the growing network of biological information databases such as human genomes, transcriptomics and proteomics, bioinformatics has become the approach of choosing forensic sciences. High-throughput bioinformatic analyses increasingly rely on pipeline frameworks to process sequence and metadata. Here we survey and compare the design philosophies of several current pipeline frameworks.

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Chen, Ray, Hon Wong, and Brendan Burns. "New Approaches to Detect Biosynthetic Gene Clusters in the Environment." Medicines 6, no. 1 (February 25, 2019): 32. http://dx.doi.org/10.3390/medicines6010032.

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Microorganisms in the environment can produce a diverse range of secondary metabolites (SM), which are also known as natural products. Bioactive SMs have been crucial in the development of antibiotics and can also act as useful compounds in the biotechnology industry. These natural products are encoded by an extensive range of biosynthetic gene clusters (BGCs). The developments in omics technologies and bioinformatic tools are contributing to a paradigm shift from traditional culturing and screening methods to bioinformatic tools and genomics to uncover BGCs that were previously unknown or transcriptionally silent. Natural product discovery using bioinformatics and omics workflow in the environment has demonstrated an extensive distribution of BGCs in various environments, such as soil, aquatic ecosystems and host microbiome environments. Computational tools provide a feasible and culture-independent route to find new secondary metabolites where traditional approaches cannot. This review will highlight some of the advances in the approaches, primarily bioinformatic, in identifying new BGCs, especially in environments where microorganisms are rarely cultured. This has allowed us to tap into the huge potential of microbial dark matter.

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Theil, Sebastien, and Etienne Rifa. "rANOMALY: AmplicoN wOrkflow for Microbial community AnaLYsis." F1000Research 10 (January 7, 2021): 7. http://dx.doi.org/10.12688/f1000research.27268.1.

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Bioinformatic tools for marker gene sequencing data analysis are continuously and rapidly evolving, thus integrating most recent techniques and tools is challenging. We present an R package for data analysis of 16S and ITS amplicons based sequencing. This workflow is based on several R functions and performs automatic treatments from fastq sequence files to diversity and differential analysis with statistical validation. The main purpose of this package is to automate bioinformatic analysis, ensure reproducibility between projects, and to be flexible enough to quickly integrate new bioinformatic tools or statistical methods. rANOMALY is an easy to install and customizable R package, that uses amplicon sequence variants (ASV) level for microbial community characterization. It integrates all assets of the latest bioinformatics methods, such as better sequence tracking, decontamination from control samples, use of multiple reference databases for taxonomic annotation, all main ecological analysis for which we propose advanced statistical tests, and a cross-validated differential analysis by four different methods. Our package produces ready to publish figures, and all of its outputs are made to be integrated in Rmarkdown code to produce automated reports.

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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 (June 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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Baloch, Iftekhar Ahmed. "Bioinformatic Hunting of MicroRNAs." Pure and Applied Biology 3, no. 2 (June 6, 2014): 72–80. http://dx.doi.org/10.19045/bspab.2014.32004.

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Jossinet, Fabrice, Thomas E. Ludwig, and Eric Westhof. "RNA structure: bioinformatic analysis." Current Opinion in Microbiology 10, no. 3 (June 2007): 279–85. http://dx.doi.org/10.1016/j.mib.2007.05.010.

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Andreini, Claudia, Ivano Bertini, and Antonio Rosato. "Metalloproteomes: A Bioinformatic Approach." Accounts of Chemical Research 42, no. 10 (October 20, 2009): 1471–79. http://dx.doi.org/10.1021/ar900015x.

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Tabassum Khan, Nida. "The Emerging Role of Bioinformatics in Biotechnology." Journal of Biotechnology and Biomedical Science 1, no. 3 (August 7, 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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Спринджук, Матвей Владимирович, Василий Иванович Берник, Николай Иванович Калоша, and Балтын Батгэрел. "AUTOMATION AND MATHEMATICAL APPARATUS FOR THE ANALYSIS OF GENOMICS DATA." СИСТЕМНЫЙ АНАЛИЗ И УПРАВЛЕНИЕ В БИОМЕДИЦИНСКИХ СИСТЕМАХ, no. 4 (December 14, 2022): 129–39. http://dx.doi.org/10.36622/vstu.2022.21.4.018.

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Рассматриваются вопросы терминологии экспертных систем, таксономии, классификации и обобщения описания современного математического и кибернетического аппарата автоматизированных систем, предназначенных для анализа биоинформации геномной природы. Обсуждаются ключевые алгоритмы биоинформатики, вычислительной биологии, геномики и медицинской кибернетики, задачи анализа обработки информации и основные функции биоинформационных программных систем и вычислительных комплексов, методологические основы современных систем для автоматизированного анализа биоинформационных геномных данных. Авторы уделяют особое внимание результатам развития прикладной математики, искусственного интеллекта, машинного обучения и математического моделирования для медицинских технологий, обзору программных инструментов для создания эпидемиологических моделей с целью изучения закономерностей распространения опасных инфекций, приоритетным вопросам анализа биоинформационных данных нового высокопатогенного для человека и животных коронавируса. Статья содержит иллюстрации, листинг кода и формулы, которые можно использовать для лекций и презентаций. Научный обзор представляет интерес как для начинающих исследователей междисциплинарных наук, которым необходимы навыки анализа информации, так и для опытных ученых и практического программирования для медицины и биологии. Авторы подчеркивают необходимость развития отечественных независимых информационных технологий для медицины и биологии, для вирусологии, в частности The questions of terminology of expert systems, taxonomy and classification and summary of the description of the modern mathematical and cybernetic apparatus of automated systems designed for the analysis of bioinformation of genomic nature are described. The key algorithms of bioinformatics, computational biology, genomics and medical cybernetics, the problems of information processing analysis and the principal functions of bioinformatic computational systems, the methodological foundations of modern software designed for automated analysis of bioinformatic genomic data are discussed. The authors pay special attention to the results of the development of applied mathematics, artificial intelligence, machine learning and mathematical modeling for medical technologies, to the survey of software tools for developing and implementing epidemiological models in order to study the patterns of the transmission of dangerous infections, priority issues in the analysis of bioinformatic data of the new highly pathogenic coronavirus, which is extremely dangerous for humans and animals. The article contains illustrations, code listing and formulas which can be used for lectures and presentations. The scientific review is of interest both for novice researchers in interdisciplinary sciences who need information analysis skills, and for experienced scientists and practical programming for medicine and biology. The authors emphasize the need for the development of independent information technologies for medicine and biology, for virology in particular

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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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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 (January 3, 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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Droit, Arnaud, Guy G. Poirier, and Joanna M. Hunter. "Experimental and bioinformatic approaches for interrogating protein–protein interactions to determine protein function." Journal of Molecular Endocrinology 34, no. 2 (April 2005): 263–80. http://dx.doi.org/10.1677/jme.1.01693.

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An ambitious goal of proteomics is to elucidate the structure, interactions and functions of all proteins within cells and organisms. One strategy to determine protein function is to identify the protein–protein interactions. The increasing use of high-throughput and large-scale bioinformatics-based studies has generated a massive amount of data stored in a number of different databases. A challenge for bioinformatics is to explore this disparate data and to uncover biologically relevant interactions and pathways. In parallel, there is clearly a need for the development of approaches that can predict novel protein–protein interaction networks in silico. Here, we present an overview of different experimental and bioinformatic methods to elucidate protein–protein interactions.

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Reiner, Benjamin C., Glenn A. Doyle, Andrew E. Weller, Rachel N. Levinson, Esin Namoglu, Alicia Pigeon, Emilie Dávila Perea, et al. "Restriction Enzyme Based Enriched L1Hs Sequencing (REBELseq): A Scalable Technique for Detection of Ta Subfamily L1Hs in the Human Genome." G3: Genes|Genomes|Genetics 10, no. 5 (March 4, 2020): 1647–55. http://dx.doi.org/10.1534/g3.119.400613.

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Long interspersed element-1 retrotransposons (LINE-1 or L1) are ∼6 kb mobile DNA elements implicated in the origins of many Mendelian and complex diseases. The actively retrotransposing L1s are mostly limited to the L1 human specific (L1Hs) transcriptional active (Ta) subfamily. In this manuscript, we present REBELseq as a method for the construction of Ta subfamily L1Hs-enriched next-generation sequencing libraries and bioinformatic identification. REBELseq was performed on DNA isolated from NeuN+ neuronal nuclei from postmortem brain samples of 177 individuals and empirically-driven bioinformatic and experimental cutoffs were established. Putative L1Hs insertions passing bioinformatics cutoffs were experimentally validated. REBELseq reliably identified both known and novel Ta subfamily L1Hs insertions distributed throughout the genome. Differences in the proportion of individuals possessing a given reference or non-reference retrotransposon insertion were identified. We conclude that REBELseq is an unbiased, whole genome approach to the amplification and detection of Ta subfamily L1Hs retrotransposons.

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Mróz, Jakub, Magdalena Pelc, Karolina Mitusińska, Joanna Chorostowska-Wynimko, and Aleksandra Jezela-Stanek. "Computational Tools to Assist in Analyzing Effects of the SERPINA1 Gene Variation on Alpha-1 Antitrypsin (AAT)." Genes 15, no. 3 (March 6, 2024): 340. http://dx.doi.org/10.3390/genes15030340.

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In the rapidly advancing field of bioinformatics, the development and application of computational tools to predict the effects of single nucleotide variants (SNVs) are shedding light on the molecular mechanisms underlying disorders. Also, they hold promise for guiding therapeutic interventions and personalized medicine strategies in the future. A comprehensive understanding of the impact of SNVs in the SERPINA1 gene on alpha-1 antitrypsin (AAT) protein structure and function requires integrating bioinformatic approaches. Here, we provide a guide for clinicians to navigate through the field of computational analyses which can be applied to describe a novel genetic variant. Predicting the clinical significance of SERPINA1 variation allows clinicians to tailor treatment options for individuals with alpha-1 antitrypsin deficiency (AATD) and related conditions, ultimately improving the patient’s outcome and quality of life. This paper explores the various bioinformatic methodologies and cutting-edge approaches dedicated to the assessment of molecular variants of genes and their product proteins using SERPINA1 and AAT as an example.

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Zok, Tomasz. "BioCommons: a robust java library for RNA structural bioinformatics." Bioinformatics 37, no. 17 (February 3, 2021): 2766–67. http://dx.doi.org/10.1093/bioinformatics/btab069.

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Abstract Motivation Biomolecular structures come in multiple representations and diverse data formats. Their incompatibility with the requirements of data analysis programs significantly hinders the analytics and the creation of new structure-oriented bioinformatic tools. Therefore, the need for robust libraries of data processing functions is still growing. Results BioCommons is an open-source, Java library for structural bioinformatics. It contains many functions working with the 2D and 3D structures of biomolecules, with a particular emphasis on RNA. Availability and implementation The library is available in Maven Central Repository and its source code is hosted on GitHub: https://github.com/tzok/BioCommons Supplementary information Supplementary data are available at Bioinformatics online.

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OUYANG, Chun-Lei, Jia-Dong GAO, Yu-Kun REN, Liang XIAO, Qian-Qian WANG, Yu-Feng GUO, Bin-Xin CAI, and Li-Ming ZHANG. "Bioinformatic Analysis on Jellyfish Hematoxin." Chinese Journal of Natural Medicines 7, no. 2 (June 22, 2009): 145–49. http://dx.doi.org/10.3724/sp.j.1009.2009.00145.

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Petrossian, Tanya, and Steven Clarke. "Bioinformatic identification of novel methyltransferases." Epigenomics 1, no. 1 (October 2009): 163–75. http://dx.doi.org/10.2217/epi.09.3.

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Schmidt, Andreas, Ignasi Forne, and Axel Imhof. "Bioinformatic analysis of proteomics data." BMC Systems Biology 8, Suppl 2 (2014): S3. http://dx.doi.org/10.1186/1752-0509-8-s2-s3.

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Reker, Daniel, and Lars Malmström. "Bioinformatic Challenges in Targeted Proteomics." Journal of Proteome Research 11, no. 9 (August 23, 2012): 4393–402. http://dx.doi.org/10.1021/pr300276f.

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Efromovich, Sam. "Multiwavelets: Theory and Bioinformatic Applications." Communications in Statistics - Theory and Methods 38, no. 16-17 (August 20, 2009): 2829–42. http://dx.doi.org/10.1080/03610920902947170.

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Botzer, Alon, Yoram Finkelstein, Ehud Grossman, John Moult, and Ron Unger. "Iatrogenic hypertension: a bioinformatic analysis." Pharmacogenomics Journal 19, no. 4 (November 5, 2018): 337–46. http://dx.doi.org/10.1038/s41397-018-0062-0.

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Song, Catharine, Aseem Kumar, and Mazen Saleh. "Bioinformatic Comparison of Bacterial Secretomes." Genomics, Proteomics & Bioinformatics 7, no. 1-2 (June 2009): 37–46. http://dx.doi.org/10.1016/s1672-0229(08)60031-5.

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Akhtar, Most Mauluda, Luigina Micolucci, Md Soriful Islam, Fabiola Olivieri, and Antonio Domenico Procopio. "Bioinformatic tools for microRNA dissection." Nucleic Acids Research 44, no. 1 (November 17, 2015): 24–44. http://dx.doi.org/10.1093/nar/gkv1221.

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Eiden, L. E. "A Two-Way Bioinformatic Street." Science 306, no. 5701 (November 26, 2004): 1437. http://dx.doi.org/10.1126/science.1107196.

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Jayne McKnight, Amy, and Alexander Peter Maxwell. "Bioinformatic Resources For Diabetic Nephropathy." Journal of Bioinformatics And Diabetes 1, no. 1 (September 15, 2013): 11–18. http://dx.doi.org/10.14302/issn.2374-9431.jbd-13-226.

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Ueno, Yoshiyuki, Koji Fukushima, Yu Nakagome, Osamu Kido, and Tooru Shimosegawa. "Bioinformatic approach for cholangiocyte pathophysiology." Hepatology Research 37, s3 (October 2007): S444—S448. http://dx.doi.org/10.1111/j.1872-034x.2007.00233.x.

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Jacq, N., C. Blanchet, C. Combet, E. Cornillot, L. Duret, K. Kurata, H. Nakamura, T. Silvestre, and V. Breton. "Grid as a bioinformatic tool." Parallel Computing 30, no. 9-10 (September 2004): 1093–107. http://dx.doi.org/10.1016/j.parco.2004.07.013.

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OUYANG, Chun-Lei, Jia-Dong GAO, Yu-Kun REN, Liang XIAO, Qian-Qian WANG, Yu-Feng GUO, Bin-Xin CAI, and Li-Ming ZHANG. "Bioinformatic Analysis on Jellyfish Hematoxin." Chinese Journal of Natural Medicines 7, no. 2 (March 2009): 145–49. http://dx.doi.org/10.1016/s1875-5364(09)60050-9.

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Melero, Juan L., Sergi Andrades, Lluís Arola, and Antoni Romeu. "Deciphering psoriasis. A bioinformatic approach." Journal of Dermatological Science 89, no. 2 (February 2018): 120–26. http://dx.doi.org/10.1016/j.jdermsci.2017.11.010.

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LEUNG, Anthony K. L., Jens S. ANDERSEN, Matthias MANN, and Angus I. LAMOND. "Bioinformatic analysis of the nucleolus." Biochemical Journal 376, no. 3 (December 15, 2003): 553–69. http://dx.doi.org/10.1042/bj20031169.

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The nucleolus is a plurifunctional, nuclear organelle, which is responsible for ribosome biogenesis and many other functions in eukaryotes, including RNA processing, viral replication and tumour suppression. Our knowledge of the human nucleolar proteome has been expanded dramatically by the two recent MS studies on isolated nucleoli from HeLa cells [Andersen, Lyon, Fox, Leung, Lam, Steen, Mann and Lamond (2002) Curr. Biol. 12, 1–11; Scherl, Coute, Deon, Calle, Kindbeiter, Sanchez, Greco, Hochstrasser and Diaz (2002) Mol. Biol. Cell 13, 4100–4109]. Nearly 400 proteins were identified within the nucleolar proteome so far in humans. Approx. 12% of the identified proteins were previously shown to be nucleolar in human cells and, as expected, nearly all of the known housekeeping proteins required for ribosome biogenesis were identified in these analyses. Surprisingly, approx. 30% represented either novel or uncharacterized proteins. This review focuses on how to apply the derived knowledge of this newly recognized nucleolar proteome, such as their amino acid/peptide composition and their homologies across species, to explore the function and dynamics of the nucleolus, and suggests ways to identify, in silico, possible functions of the novel/uncharacterized proteins and potential interaction networks within the human nucleolus, or between the nucleolus and other nuclear organelles, by drawing resources from the public domain.

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Xiao, Guohua, Xinyu Zhang, and Qiang Gao. "Bioinformatic Approaches for Fungal Omics." BioMed Research International 2017 (2017): 1. http://dx.doi.org/10.1155/2017/7270485.

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Aladağ, Ahmet Emre, Cesim Erten, and Melih Sözdinler. "Reliability-Oriented bioinformatic networks visualization." Bioinformatics 27, no. 11 (April 9, 2011): 1583–84. http://dx.doi.org/10.1093/bioinformatics/btr178.

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Upadhyay, Apoorva, Andrey A. Kovalev, Elena A. Zhuravleva, Dmitriy A. Kovalev, Yuriy V. Litti, Shyam Kumar Masakapalli, Nidhi Pareek, and Vivekanand Vivekanand. "A Review of Basic Bioinformatic Techniques for Microbial Community Analysis in an Anaerobic Digester." Fermentation 9, no. 1 (January 12, 2023): 62. http://dx.doi.org/10.3390/fermentation9010062.

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Biogas production involves various types of intricate microbial populations in an anaerobic digester (AD). To understand the anaerobic digestion system better, a broad-based study must be conducted on the microbial population. Deep understanding of the complete metagenomics including microbial structure, functional gene form, similarity/differences, and relationships between metabolic pathways and product formation, could aid in optimization and enhancement of AD processes. With advancements in technologies for metagenomic sequencing, for example, next generation sequencing and high-throughput sequencing, have revolutionized the study of microbial dynamics in anaerobic digestion. This review includes a brief introduction to the basic process of metagenomics research and includes a detailed summary of the various bioinformatics approaches, viz., total investigation of data obtained from microbial communities using bioinformatics methods to expose metagenomics characterization. This includes (1) methods of DNA isolation and sequencing, (2) investigation of anaerobic microbial communities using bioinformatics techniques, (3) application of the analysis of anaerobic microbial community and biogas production, and (4) restriction and prediction of bioinformatics analysis on microbial metagenomics. The review has been concluded, giving a summarized insight into bioinformatic tools and also promoting the future prospects of integrating humungous data with artificial intelligence and neural network software.

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Kutnjak, Denis, Lucie Tamisier, Ian Adams, Neil Boonham, Thierry Candresse, Michela Chiumenti, Kris De Jonghe, et al. "A Primer on the Analysis of High-Throughput Sequencing Data for Detection of Plant Viruses." Microorganisms 9, no. 4 (April 14, 2021): 841. http://dx.doi.org/10.3390/microorganisms9040841.

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High-throughput sequencing (HTS) technologies have become indispensable tools assisting plant virus diagnostics and research thanks to their ability to detect any plant virus in a sample without prior knowledge. As HTS technologies are heavily relying on bioinformatics analysis of the huge amount of generated sequences, it is of utmost importance that researchers can rely on efficient and reliable bioinformatic tools and can understand the principles, advantages, and disadvantages of the tools used. Here, we present a critical overview of the steps involved in HTS as employed for plant virus detection and virome characterization. We start from sample preparation and nucleic acid extraction as appropriate to the chosen HTS strategy, which is followed by basic data analysis requirements, an extensive overview of the in-depth data processing options, and taxonomic classification of viral sequences detected. By presenting the bioinformatic tools and a detailed overview of the consecutive steps that can be used to implement a well-structured HTS data analysis in an easy and accessible way, this paper is targeted at both beginners and expert scientists engaging in HTS plant virome projects.

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Ascenso, Rita M. T. "Bioinformatic tools help molecular characterization of Perkinsus olseni differentially expressed genes." Journal of Integrative Bioinformatics 8, no. 3 (December 1, 2011): 130–40. http://dx.doi.org/10.1515/jib-2011-179.

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Abstract In the 80ies, in Southern Europe and in particular in Ria Formosa there was an episode of heavy mortality of the economically relevant clam Ruditapes (R.) decussatus associated with a debilitating disease (Perkinsosis) caused by Perkinsus olseni. This protozoan parasite was poorly known concerning its’ differential transcriptome in response to its host, R. decussatus. This laboratory available protozoan system was used to identify parasite genes related to host interaction. Beyond the application of molecular biology technologies and methodologies, only the help of Bioinformatics tools allowed to analyze the results of the study. The strategy started with SSH technique, allowing the identification of parasite up-regulated genes in response to its natural host, then a macroarray was constructed and hybridized to characterize the parasite genes expression when exposed to bivalves hemolymph from permissive host (R. decussatus), resistant host (R. philippinarum) and non permissive bivalve (Donax trunculus) that cohabit in the same or adjacent habitats in Southern Portugal. Genes and respective peptides full molecular characterization depended on several Bioinformatic tools application. Also a new Bioinformatic tool was developed.

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LEONARD WHYE KIT LIM and HUNG HUI CHUNG. "Analysis of Seven Human Respiratory Coronavirus (CoV) S Proteins from a Bioinformatics Approach." Borneo Journal of Resource Science and Technology 13, no. 2 (December 25, 2023): 103–10. http://dx.doi.org/10.33736/bjrst.5853.2023.

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The coronavirus disease 2019 (COVID-19) has caused a huge pandemic repercussion across the globe and it is mainly contributed by the human severe acute respiratory syndrome coronavirus (SARS-CoV-2). There are seven human respiratory coronaviruses identified to date, namely HCoV-229E, HCoV-NL63, HCoV-OC43, HCoV-HKU1, MERS-CoV, SARS-CoV and SARS-CoV-2. A recently published bioinformatic human CoV comparison only covered four human CoV. Therefore, in this study, a bioinformatics approach-based analyses route was taken to dissect the S proteins of all the available (seven) human respiratory coronaviruses publicly available in the GenBank database. The antigenic epitope amount is postulated to be the most accurate bioindicator among all in determining the severity of a particular human respiratory coronavirus. Other powerful bioinformatic indicators are global similarity index, maximum likelihood phylogenetic analysis as well as domain analysis. The data generated in this study can be channelled to the vaccine and antiviral drug development to combat the current and future spread of the human respiratory coronaviruses.

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Thota, Lalitha Saroja, and Allam Appa Rao. "Identification of Biomarkers for Obesity associated with Diabetes using Sequence Mining Techniques." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 10, no. 4 (August 15, 2013): 1510–21. http://dx.doi.org/10.24297/ijct.v10i4.3251.

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The advancements in the field of information technology are moving ahead in the discipline of medicine empowering the researchers with superior tools. By taking the advantage of Information Technology, today's researcher successfully navigate the flood of data and many diabetic complications can be overcome. Biomarker plays very major role in disease detection at early stages of its stages and also helpful in knowing the state of treatment and how body is acting or responding to the medication. The dramatic rise in obesity-associated diabetes resulted in an alarming increase in the incidence and prevalence of obesity an important complication of diabetes. The twin epidemic of diabetes and obesity pose daunting challenges worldwide. Differences among individuals in their susceptibility to both these conditions probably reflect their genetic constitutions. Predicting obesity associated diabetes is both useful and important because the number of obese patients is increasing while its main cause cannot yet be defined. Bioinformatics, a truly multidisciplinary science, aims to bring the benefits of computer technologies to bear in understanding the biology of life itself. The dramatic improvements in genomic and bioinformatic resources are accelerating the pace of gene discovery for many medical diseases. It is tempting to speculate the key susceptible genes/proteins biomarker that bridges diabetes mellitus and obesity. The emergence of post-genomic technologies has led to the development of strategies aimed at identifying specific and sensitive biomarkers from the thousands of molecules present in a tissue or biological fluid. In this regard, we evaluated the role of several genes/proteins that are believed to be involved in the evolution of obesity associated diabetes by employing a sequence mining technique, multiple sequence alignment using ClustalW tool and constructed a phylogram tree using functional protein sequences extracted from NCBI. Phylogram was constructed using Neighbor-Joining Algorithm a bioinformatic tool. Our bioinformatic analysis reports a biomarker, resistin gene as ominous link with obesity associated diabetes. This bioinformatic study will be useful for future studies towards therapeutic inventions of obesity associated type 2 diabetes.

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Yuan, Shixing. "Mitigating the Off-target Effects in CRISPR/Cas9-mediated Genetic Editing with Bioinformatic Technologies." Transactions on Materials, Biotechnology and Life Sciences 3 (March 24, 2024): 318–26. http://dx.doi.org/10.62051/dpgwbz03.

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The biological and clinical fields have recognized the CRISPR/Cas9 (clustered regularly interspaced short palindromic repeat/CRISPR associated protein 9) system as a precise and efficient tool for editing the genome. Despite its merits, the system poses crucial challenges, notably the off-target effects which could lead to unintended mutations - a substantial impediment for clinical applications that may potentially compromise the validity of research and the safety of therapeutic applications. Bioinformatics plays a pivotal role in mitigating this risk. Utilizing more refined bioinformatic tools and algorithms, researchers can reduce off-target mutations remarkably. These instruments, powered by machine learning and computational modelling, are able to predict off-target effects and provide aid for the design of more efficient sgRNA. Despite these advancements, it remains crucial to continue to focus on the improvement and assessment of such bioinformatics strategies. This review aims to holistically explore the mechanism and applications of CRISPR/Cas9 genome editing, its off-target effects, and the consequent impacts, along with the potential of bioinformatics techniques to identify off-target risks and facilitate sgRNA design. This review will also incorporate a clinical trial on HIV-1 treatment as a case study to highlight the potential of bioinformatics in devising solutions to mitigate the potential off-target effects of CRISPR/Cas9-mediated genetic editing.

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Hynst, Jakub, Veronika Navrkalova, Karol Pal, and Sarka Pospisilova. "Bioinformatic strategies for the analysis of genomic aberrations detected by targeted NGS panels with clinical application." PeerJ 9 (March 31, 2021): e10897. http://dx.doi.org/10.7717/peerj.10897.

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Molecular profiling of tumor samples has acquired importance in cancer research, but currently also plays an important role in the clinical management of cancer patients. Rapid identification of genomic aberrations improves diagnosis, prognosis and effective therapy selection. This can be attributed mainly to the development of next-generation sequencing (NGS) methods, especially targeted DNA panels. Such panels enable a relatively inexpensive and rapid analysis of various aberrations with clinical impact specific to particular diagnoses. In this review, we discuss the experimental approaches and bioinformatic strategies available for the development of an NGS panel for a reliable analysis of selected biomarkers. Compliance with defined analytical steps is crucial to ensure accurate and reproducible results. In addition, a careful validation procedure has to be performed before the application of NGS targeted assays in routine clinical practice. With more focus on bioinformatics, we emphasize the need for thorough pipeline validation and management in relation to the particular experimental setting as an integral part of the NGS method establishment. A robust and reproducible bioinformatic analysis running on powerful machines is essential for proper detection of genomic variants in clinical settings since distinguishing between experimental noise and real biological variants is fundamental. This review summarizes state-of-the-art bioinformatic solutions for careful detection of the SNV/Indels and CNVs for targeted sequencing resulting in translation of sequencing data into clinically relevant information. Finally, we share our experience with the development of a custom targeted NGS panel for an integrated analysis of biomarkers in lymphoproliferative disorders.

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Hillje, Roman, Pier Giuseppe Pelicci, and Lucilla Luzi. "Cerebro: interactive visualization of scRNA-seq data." Bioinformatics 36, no. 7 (November 25, 2019): 2311–13. http://dx.doi.org/10.1093/bioinformatics/btz877.

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Abstract Despite the growing availability of sophisticated bioinformatic methods for the analysis of single-cell RNA-seq data, few tools exist that allow biologists without extensive bioinformatic expertise to directly visualize and interact with their own data and results. Here, we present Cerebro (cell report browser), a Shiny- and Electron-based standalone desktop application for macOS and Windows which allows investigation and inspection of pre-processed single-cell transcriptomics data without requiring bioinformatic experience of the user. Through an interactive and intuitive graphical interface, users can (i) explore similarities and heterogeneity between samples and cell clusters in two-dimensional or three-dimensional projections such as t-SNE or UMAP, (ii) display the expression level of single genes or gene sets of interest, (iii) browse tables of most expressed genes and marker genes for each sample and cluster and (iv) display trajectories calculated with Monocle 2. We provide three examples prepared from publicly available datasets to show how Cerebro can be used and which are its capabilities. Through a focus on flexibility and direct access to data and results, we think Cerebro offers a collaborative framework for bioinformaticians and experimental biologists that facilitates effective interaction to shorten the gap between analysis and interpretation of the data. Availability and implementation The Cerebro application, additional documentation, and example datasets are available at https://github.com/romanhaa/Cerebro. Similarly, the cerebroApp R package is available at https://github.com/romanhaa/cerebroApp. All components are released under the MIT License. Supplementary information Supplementary data are available at Bioinformatics online.

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Quan, Yuan, Zhong-Yi Wang, Min Xiong, Zheng-Tao Xiao, and Hong-Yu Zhang. "Dissecting Traditional Chinese Medicines by Omics and Bioinformatics." Natural Product Communications 9, no. 9 (September 2014): 1934578X1400900. http://dx.doi.org/10.1177/1934578x1400900942.

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Traditional Chinese medicines (TCM) are a rich source of potential leads for drug development. However, there are fundamental differences between traditional Chinese medical concepts and modern pharmacology, which greatly hinder the modern development of TCM. To address this challenge, new techniques associated with genomics, transcriptomics, proteomics, metabolomics and bioinformatics have been used to dissect the pharmacological mechanisms of TCM. This review article provides an overview of the current research in this area, and illustrates the potential of omic and bioinformatic methods in TCM-based drug discovery.

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Garrido, Oswgladys, and Elizabeth Ferrer. "Characterization of Three cDNAs Obtained by Spliced Leader-PCR Screening of a Taenia solium cDNA Library." Uniciencia 36, no. 1 (June 1, 2022): 1–10. http://dx.doi.org/10.15359/ru.36-1.31.

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Cysticerci or metacestodes of Taenia solium cause cysticercosis, being the neurocysticercosis (NCC) the main pathology. The characterization of genes is essential for the knowledge of parasite biology, the understanding of the parasite-host relationship, and the identification of possible targets for diagnosis, treatment, and protection. The objective of this work was the molecular and bioinformatic characterization of three news cDNAs (TsTF10, TsAAP8, and TsrGAP8), obtained by spliced leader-PCR screening of a Taenia solium cDNA library. The sequences of the three cDNA were compared with the sequences in the nucleic acid and protein databases (GenBank, EMBL) and analyzed by bioinformatic programs (CDD-Search of the National Center for Biotechnology Information, Interpro of the European Bioinformatics Institute, Motif scan and Expert Protein Analysis System of the Proteomics Server from Swiss Institute of Bioinformatics). Considering the high identities with similar molecules of related helminths (Taenia asiatica, Echinococcus granulosus, Echinococcus multilocularis, and Hymenolepis diminuta) and the functional domains found, the TsTF10, TsAAP8, and TsrGAP8 genes of Taenia could act as a nuclear transcription factor gamma, a putative vacuolar ATPase membrane sector associated protein and a Rho GTPase activating protein, respectively. Although few B epitopes could be predicted in the sequences, it could be relevant to evaluate them as possible candidates for diagnosis and protection in cysticercosis. The characterization and analysis of these sequences and the prediction of their possible usefulness as antigens, vaccines, or therapeutic targets contribute to the designing and planning of future studies.

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Journal articles on the topic 'Bioinformatic'

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