Journal articles: 'Bioinformatics analysis'

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Irham, Lalu Muhammad, Danang Prasetyaning Amukti, Wirawan Adikusuma, et al. "Applied of bioinformatics in drug discovery and drug development: Bioinformatic analysis 1996-2024." BIO Web of Conferences 148 (2024): 01003. https://doi.org/10.1051/bioconf/202414801003.

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Drug discovery and drug development were two complex process to find new drugs. Advance science of medicine after human genome project were established accelerating the development of new field called bionformatics. Currently, bioinformatics integrated multidisciplinary studies including molecular biology, mathematics and information engineering. This study utilized the Biblioshiny and VosViewer databases as well as the Scopus database to evalute the study related to the bioinformatics in Drug Discovery and Drug Development. Our study were analyzed the scopus data which were retrieved from 1996-2024. We highlighted that 1581 research articles which were publised in 701 journals. Our findings showed that the annual grow up of the research related study was increased annually with the peak of study in 2023. Besides, top five most relevant sources of study was PlosOne (32 documents), international journal of molecular sciences (30 documents), BMS Bioinformatics (29 documents), Bioinfromatics (24 documents), and Frontiers in Genetics (19 documents). In conclusion, through the integration of the use of Vosviewer, biblioshiny and Scopus database software, our findings show a positive trend regarding research on the application of bioinformatics in drug discovery and drug development.

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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 (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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Parmen, Adibah, MOHD NOOR MAT ISA, FARAH FADWA BENBELGACEM, Hamzah Mohd Salleh, and Ibrahim Ali Noorbatcha. "COMPARATIVE METAGENOMICS ANALYSIS OF PALM OIL MILL EFFLUENT (POME) USING THREE DIFFERENT BIOINFORMATICS PIPELINES." IIUM Engineering Journal 20, no. 1 (2019): 1–11. http://dx.doi.org/10.31436/iiumej.v20i1.909.

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ABSTRACT: The substantial cost reduction and massive production of next-generation sequencing (NGS) data have contributed to the progress in the rapid growth of metagenomics. However, production of the massive amount of data by NGS has revealed the challenges in handling the existing bioinformatics tools related to metagenomics. Therefore, in this research we have investigated an equal set of DNA metagenomics data from palm oil mill effluent (POME) sample using three different freeware bioinformatics pipelines’ websites of metagenomics RAST server (MG-RAST), Integrated Microbial Genomes with Microbiome Samples (IMG/M) and European Bioinformatics Institute (EBI) Metagenomics, in term of the taxonomic assignment and functional analysis. We found that MG-RAST is the quickest among these three pipelines. However, in term of analysis of results, IMG/M provides more variety of phylum with wider percent identities for taxonomical assignment and IMG/M provides the highest carbohydrates, amino acids, lipids, and coenzymes transport and metabolism functional annotation beside the highest in total number of glycoside hydrolase enzymes. Next, in identifying the conserved domain and family involved, EBI Metagenomics would be much more appropriate. All the three bioinformatics pipelines have their own specialties and can be used alternately or at the same time based on the user’s functional preference. ABSTRAK: Pengurangan kos dalam skala besar dan pengeluaran data ‘next-generation sequencing’ (NGS) secara besar-besaran telah menyumbang kepada pertumbuhan pesat metagenomik. Walau bagaimanapun, pengeluaran data dalam skala yang besar oleh NGS telah menimbulkan cabaran dalam mengendalikan alat-alat bioinformatika yang sedia ada berkaitan dengan metagenomik. Justeru itu, dalam kajian ini, kami telah menyiasat satu set data metagenomik DNA yang sama dari sampel effluen kilang minyak sawit dengan menggunakan tiga laman web bioinformatik percuma iaitu dari laman web ‘metagenomics RAST server’ (MG-RAST), ‘Integrated Microbial Genomes with Microbiome Samples’ (IMG/M) dan ‘European Bioinformatics Institute’ (EBI) Metagenomics dari segi taksonomi dan analisis fungsi. Kami mendapati bahawa MG-RAST ialah yang paling cepat di antara ketiga-tiga ‘pipeline’, tetapi mengikut keputusan analisa, IMG/M mengeluarkan maklumat philum yang lebih pelbagai bersama peratus identiti yang lebih luas berbanding yang lain untuk pembahagian taksonomi dan IMG/M juga mempunyai bacaan tertinggi dalam hampir semua anotasi fungsional karbohidrat, amino asid, lipid, dan koenzima pengangkutan dan metabolisma malah juga paling tinggi dalam jumlah enzim hidrolase glikosida. Kemudian, untuk mengenal pasti ‘domain’ terpelihara dan keluarga yang terlibat, EBI metagenomics lebih bersesuaian. Ketiga-tiga saluran ‘bioinformatics pipeline’ mempunyai keistimewaan mereka yang tersendiri dan boleh digunakan bersilih ganti dalam masa yang sama berdasarkan pilihan fungsi penggun.

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Upadhyay, Apoorva, Andrey A. Kovalev, Elena A. Zhuravleva, et al. "A Review of Basic Bioinformatic Techniques for Microbial Community Analysis in an Anaerobic Digester." Fermentation 9, no. 1 (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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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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Kibirev, Ya A., A. V. Kuznetsovskiy, S. G. Isupov, and I. V. Darmov. "Modern Bioinformatics Solutions Used for Genetic Data Analysis." Journal of NBC Protection Corps 7, no. 4 (2024): 366–83. http://dx.doi.org/10.35825/2587-5728-2023-7-4-366-383.

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Effective counteraction to biological threats, both natural and man-made, requires the availability of means and methods for rapid and reliable microorganism identification and a comprehensive study of their basic biological properties. Over the past decade, the arsenal of domestic microbiologists has been supplemented by numerous methods for analyzing the genomes of pathogens, primarily based on nucleic acid sequencing. The purpose of this work is to provide the reader with information about capabilities of modern technical and methodological arsenal used for in-depth molecular genetic study of microorganisms, including bioinformatics solutions used for the genetic data analysis. The source base for this research is English-language scientific literature available via the Internet, bioinformation software documentation. The research method is an analysis of scientific sources from the general to the specific. We considered the features of sequencing platforms, the main stages of genetic information analysis, current bioinformation utilities, their interaction and organization into a single workflow. Results and discussion. The performance of modern genetic analyzers allows for complete decoding of the bacterial genome within one day, including the time required to prepare the sample for research. The key factor that largely determines the effectiveness of the genetic analysis methods used is the competent use of the necessary bioinformatics software utilities. Standard stages of primary genetic data analysis are assessment of the quality control, data preprocessing, mapping to a reference genome or de novo genome assembly, genome annotation, typing and identification of significant genetic determinants (resistance to antibacterial drugs, pathogenicity factors, etc.), phylogenetic analysis. For each stage bioinformation utilities have been developed, differing in implemented analysis algorithms. Conclusion. Open source utilities that do not require access to remote resources for their operation are of greatest interest due to activities specifics of NBC protection corps units.

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Lee, Young-Mee, Ji-Hae Lee, and Hyeon S. Son. "Bioinformatics analysis for drug repositioning." Korean Journal of Public Health 53, no. 2 (2016): 19–27. http://dx.doi.org/10.17262/kjph.2016.09.53.2.19.

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Woo, Patrick C. Y., Yi Huang, Susanna K. P. Lau, and Kwok-Yung Yuen. "Coronavirus Genomics and Bioinformatics Analysis." Viruses 2, no. 8 (2010): 1804–20. http://dx.doi.org/10.3390/v2081803.

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Moore, B., G. Fan, and K. Eilbeck. "SOBA: sequence ontology bioinformatics analysis." Nucleic Acids Research 38, Web Server (2010): W161—W164. http://dx.doi.org/10.1093/nar/gkq426.

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10

Goeta, B. "Bioinformatics-Sequence and Genome Analysis." Briefings in Bioinformatics 3, no. 1 (2002): 101–3. http://dx.doi.org/10.1093/bib/3.1.101.

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Romero, P. "Bioinformatics: Sequence and Genome Analysis." Briefings in Bioinformatics 5, no. 4 (2004): 393–96. http://dx.doi.org/10.1093/bib/5.4.393-a.

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Lee, C. "Bioinformatics analysis of alternative splicing." Briefings in Bioinformatics 6, no. 1 (2005): 23–33. http://dx.doi.org/10.1093/bib/6.1.23.

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Stelkic, Ana, Dragana Mitic Potkrajac, Branka Rakic, Allan Price, and Gordana Apic. "Bioinformatics analysis of nanomaterials toxicity." Toxicology Letters 229 (September 2014): S197. http://dx.doi.org/10.1016/j.toxlet.2014.06.666.

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Bolchini, Davide, Anthony Finkelstein, Vito Perrone, and Sylvia Nagl. "Better bioinformatics through usability analysis." Bioinformatics 25, no. 3 (2008): 406–12. http://dx.doi.org/10.1093/bioinformatics/btn633.

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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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Ji, Fei, and Ruslan I. Sadreyev. "RNA-seq: Basic Bioinformatics Analysis." Current Protocols in Molecular Biology 124, no. 1 (2018): e68. http://dx.doi.org/10.1002/cpmb.68.

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B. Veena, B. Veena. "Bioinformatics Analysis of Mirna Data for Potential Biomarker Discovery." International Journal of Scientific Research 2, no. 8 (2012): 45–47. http://dx.doi.org/10.15373/22778179/aug2013/16.

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Chen, Yian A., Jonas S. Almeida, and Lien‐siang Chou. "Whale song analyses using bioinformatics sequence analysis approaches." Journal of the Acoustical Society of America 117, no. 4 (2005): 2470. http://dx.doi.org/10.1121/1.4787459.

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Ponting, C. P. "A coming of age for bioinformatics: Bioinformatics: Sequence and Genome Analysis." Journal of Cell Science 116, no. 1 (2003): 6–7. http://dx.doi.org/10.1242/jcs.00197.

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Zheng, Fu, Yang Hui, Wei Xixiang, et al. "Retinopathy of Prematurity and Bioinformatics Analysis: Bibliometric Studies and Visual Analysis by Cite Space." Journal of Clinical Pediatrics and Child Care Research 5, no. 2 (2024): 01–06. http://dx.doi.org/10.33140/jcpccr.05.02.01.

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Objective: The study aims to explore the hotspots and frontiers of retinopathy of prematurity (ROP) and bioinformatics analysis by reviewing the current status by Cite Space. Methods: The Web of Science database (WoS) was searched from ROP to 2010. Cite Space is used to generate network maps about the collaboration between authors, countries and institutions and to reveal hotspots and frontiers of both. Results: 98 studies related to ROP and various bioinformatics analyses were retrieved from WoS. North-Eastern University and Massachusetts General Hospital are the major countries and institutions. Hot topics focus on the interaction between the two, and possible new diagnostic and control measures. Conclusion: based on the results of the Cite Space study, scholars suggest that the deepening of the authors, positive cooperation between countries and institutions, mainly committed to research including artificial intelligence deep learning and the mutual recognition of biological information, especially through vascularization, additional lesions, classification and gene expression, this may mean that the future may be rapid and accurate diagnosis of ROP, especially invasive retinopathy of premature maturity (A-ROP).

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Johnson, Andrew D. "Bioinformatics for Geneticists: A Bioinformatics Primer for the Analysis of Genetic Data." Circulation: Cardiovascular Genetics 1, no. 2 (2008): 153. http://dx.doi.org/10.1161/circgenetics.108.829358.

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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 (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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Bellgard, Matthew, Adam Hunter, and William Kenworthy. "Microarray analysis using bioinformatics analysis audit trails (BAATs)." Comptes Rendus Biologies 326, no. 10-11 (2003): 1083–87. http://dx.doi.org/10.1016/j.crvi.2003.09.005.

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Pant, Dhruv Chander, and O. P. Gupta. "Performance Analysis of Parallelized Bioinformatics Applications." Asian Journal of Computer Science and Technology 7, no. 2 (2018): 70–74. http://dx.doi.org/10.51983/ajcst-2018.7.2.1881.

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The main challenges bioinformatics applications facing today are to manage, analyze and process a huge volume of genome data. This type of analysis and processing is very difficult using general purpose computer systems. So the need of distributed computing, cloud computing and high performance computing in bioinformatics applications arises. Now distributed computers, cloud computers and multi-core processors are available at very low cost to deal with bulk amount of genome data. Along with these technological developments in distributed computing, many efforts are being done by the scientists and bioinformaticians to parallelize and implement the algorithms to take the maximum advantage of the additional computational power. In this paper a few bioinformatics algorithms have been discussed. The parallelized implementations of these algorithms have been explained. The performance of these parallelized algorithms has been also analyzed. It has been also observed that in parallel implementations of the various bioinformatics algorithms, impact of communication subsystems with respect to the job sizes should also be analyzed.

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Sari, Indah Juwita, Eli Setiawati, Rihadatul Aisy, and Alpha Bayoh Jr. "Analysis of Teacher Readiness to Implement Bioinformatics to Biology Learning in Senior High School." Cybersecurity and Innovative Technology Journal 2, no. 2 (2024): 104–11. https://doi.org/10.53889/citj.v2i2.454.

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This study aimed to analyze teachers' readiness in applying bioinformatics to biology learning in senior high schools. Bioinformatics is a combination of computer science, statistics, and biology to analyze and interpret complex biological data. The application of bioinformatics into learning can foster students' realization of the real-life commitment of bioinformatics, enhance their understanding, and expand their curiosity about bioinformatics. The application of bioinformatics into biology learning needs to be supported by teacher readiness in terms of understanding, skills and factors that influence teacher readiness in applying bioinformatics. This research used a case study method with a qualitative descriptive approach to analyze the readiness of biology teachers from several senior high schools in Banten province in implementing bioinformatics into biology learning. The results of this study show that most teachers understand and realize the importance of bioinformatics in learning, on the other hand some teachers also feel not fully confident and able to apply bioinformatics into learning effectively. Although most teachers have adequate access to technology, teachers are not familiar with bioinformatics concepts and software such as BLAST and NCBI. Therefore, to fulfill the aspect of teachers' understanding and skills in applying bioinformatics to learning, focused training is needed that discusses the basic concepts of bioinformatics, the use of bioinformatics software, and its application in learning to improve teacher competence and create interactive and relevant biology learning.

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Gasparovica-Asīte, M., and L. Aleksejeva. "Classification Methodology for Bioinformatics Data Analysis." Automatic Control and Computer Sciences 53, no. 1 (2019): 28–38. http://dx.doi.org/10.3103/s0146411619010073.

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Heng, Hu, Huang Guoqiang, Shi Jin, Zhang Fengli, and Zhang Dabing. "Bioinformatics analysis for Piezo in rice." Reproduction and Breeding 1, no. 2 (2021): 108–13. http://dx.doi.org/10.1016/j.repbre.2021.07.001.

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Zhao, Bin. "Bioinformatics analysis of myocardial immune progression." Gazette of Medical Sciences 1, no. 5 (2020): 13–46. http://dx.doi.org/10.46766/thegms.bioinfo.20091203.

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Boyce, K., and A. Kriete. "Automated Tissue Analysis – a Bioinformatics Perspective." Methods of Information in Medicine 44, no. 01 (2005): 32–37. http://dx.doi.org/10.1055/s-0038-1633920.

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Summary Objectives: Recent progress in automated tissue analysis (tissomics) provides reproducible phenotypical characterization of histological specimens. We introduce informatics tools to cluster and correlate quantitative tissue profiles with gene expression data. The great potential of synergies between tissue analysis and bioinformatics and its perspectives in medical research and computational diagnostics are discussed. Methods: Key enablers in microscopic imaging and machine vision are reviewed to perform a high-throughput tissue analysis. Methodologies are described and results are demonstrated that support a combined analysis of tissue with gene expression profiles whereby the consideration of individual responses is key. Results: Comprehensive histomorphometric profiles, extracted using machine vision, provide information regarding the components and heterogeneity of a tissue in a reproducible format amenable to data mining and analysis. Tissue quantitative information can be placed in synergetic context with bioinformatics data, such as gene expression profiles, for a more comprehensive stratification of individual responses. From a bioinformatics point of view tissue data are co-variants that support the identification of candidate genes relevant in tissue injury or disease. Conclusions: Progress in automated analytics enables the generation of quantitative data about tissue previously limited to visual histopathology. Such reproducible data sets can be statistically correlated and clustered throughout the continuum of bioinformatics. The combined approach supports a system-wide view of biology and has a potential to accelerate developments for a personalized computational diagnosis.

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Viti, Federica, Ivan Merelli, Andrea Calabria, et al. "Ontology-based resources for bioinformatics analysis." International Journal of Metadata, Semantics and Ontologies 6, no. 1 (2011): 35. http://dx.doi.org/10.1504/ijmso.2011.042488.

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李, 敬. "Genomic Bioinformatics Analysis of Cordyceps militaris." Advances in Microbiology 09, no. 02 (2020): 51–59. http://dx.doi.org/10.12677/amb.2020.92009.

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Dutta, Mainak, Elavarasan Subramani, Khushman Taunk, et al. "Mass spectrometry and bioinformatics analysis data." Data in Brief 2 (March 2015): 21–25. http://dx.doi.org/10.1016/j.dib.2014.11.002.

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Saadat, Fatemeh, Houshang Alizadeh, and Seyed Hadi Razavi. "A Bioinformatics Analysis of Plant Caleosins." Iranian Journal of Mathematical Sciences and Informatics 18, no. 2 (2023): 95–105. http://dx.doi.org/10.61186/ijmsi.18.2.95.

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Fernández-de-Bobadilla, Miguel D., Alba Talavera-Rodríguez, Lucía Chacón, Fernando Baquero, Teresa M. Coque, and Val F. Lanza. "PATO: Pangenome Analysis Toolkit." Bioinformatics 37, no. 23 (2021): 4564–66. http://dx.doi.org/10.1093/bioinformatics/btab697.

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Abstract Motivation We present the Pangenome Analysis Toolkit (PATO) designed to simultaneously analyze thousands of genomes using a desktop computer. The tool performs common tasks of pangenome analysis such as core-genome definition and accessory genome properties and includes new features that help characterize population structure, annotate pathogenic features and create gene sharedness networks. PATO has been developed in R to integrate with the large set of tools available for genetic, phylogenetic and statistical analysis in this environment. Results PATO can perform the most demanding bioinformatic analyses in minutes with an accuracy comparable to state-of-the-art software but 20–30× times faster. PATO also integrates all the necessary functions for the complete analysis of the most common objectives in microbiology studies. Finally, PATO includes the necessary tools for visualizing the results and can be integrated with other analytical packages available in R. Availabilityand implementation The source code for PATO is freely available at https://github.com/irycisBioinfo/PATO under the GPLv3 license. Supplementary information Supplementary data are available at Bioinformatics online.

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Bischof, Jocelyn M., and Rachel Wevrick. "Genome-wide analysis of gene transcription in the hypothalamus." Physiological Genomics 22, no. 2 (2005): 191–96. http://dx.doi.org/10.1152/physiolgenomics.00071.2005.

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As the genomic regions containing loci predisposing to obesity-related traits are mapped in human population screens and mouse genetic studies, identification of susceptibility genes will increasingly be facilitated by bioinformatic methods. We hypothesized that candidate genes can be prioritized by their expression levels in tissues of central importance in obesity. Our objective was to develop a combined bioinformatics and molecular paradigm to identify novel genes as candidates for murine or human obesity genetic modifiers based on their differential expression patterns in the hypothalamus compared with other murine tissues. We used bioinformatics tools to search publicly available gene expression databases using criteria designed to identify novel genes differentially expressed in the hypothalamus. We used RNA methods to determine their expression sites and levels of expression in the hypothalamus of the murine brain. We identified the chromosomal location of the novel genes in mice and in humans and compared these locations with those of genetic loci predisposing to obesity-related traits. We developed a search strategy that correctly identified a set of genes known to be important in hypothalamic function as well as a candidate gene for Prader-Willi syndrome that was not previously identified as differentially expressed in the hypothalamus. Using this same strategy, we identified and characterized a set of 11 genes not previously known to be differentially expressed in the murine hypothalamus. Our results demonstrate the feasibility of combined bioinformatics and molecular approaches to the identification of genes that are candidates for obesity-related disorders in humans and mice.

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Khatun, Most Sharmin, and Afrin Jahan. "Bioinformatics Analysis of Toxicity and Functional Properties of Plant-Derived Bioactive Proteins." Control Systems and Optimization Letters 2, no. 2 (2024): 241–47. http://dx.doi.org/10.59247/csol.v2i2.112.

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The main objective of this review is to discuss the biological activities of plants and their potential for therapeutic use, as well as to highlight the many kinds of bioactive proteins. Plant-derived bioactive proteins are essential because of their many functional qualities and health advantages in a variety of domains, including nutrition, medicine, and agriculture. Plant-derived bioactive proteins have attracted a lot of attention because of their potential as medicines and health advantages. To improve comprehension and application, this study uses bioinformatic tools to present a thorough analysis of the toxicity and functional properties of these proteins. We examine the variety of bioactive proteins originating from plants, emphasizing their functions in anti-inflammatory, anti-cancer, and antibacterial properties. We evaluate these proteins' structural characteristics, binding affinities, and processes of interaction with target molecules using sophisticated bioinformatics technologies. A particular focus is on assessing possible toxicity, using in silico predictive algorithms to detect side effects and guarantee safety in medicinal applications. We also go over how to anticipate the functional characteristics of novel bioactive proteins by integrating proteomic and genomic data. There are many tools such as BLAST, Clustal Omega, Inter Pro Scan for the analysis of bioinformatic data have been reviewed here. This study emphasizes how important bioinformatics is to understand the safety and therapeutic potential of bioactive proteins generated from plants, which opens the door to their optimal application in nutrition and medicine.

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Chukamnerd, Arnon, Kongpop Jeenkeawpiam, Sarunyou Chusri, Rattanaruji Pomwised, Kamonnut Singkhamanan, and Komwit Surachat. "BacSeq: A User-Friendly Automated Pipeline for Whole-Genome Sequence Analysis of Bacterial Genomes." Microorganisms 11, no. 7 (2023): 1769. http://dx.doi.org/10.3390/microorganisms11071769.

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Whole-genome sequencing (WGS) of bacterial pathogens is widely conducted in microbiological, medical, and clinical research to explore genetic insights that could impact clinical treatment and molecular epidemiology. However, analyzing WGS data of bacteria can pose challenges for microbiologists, clinicians, and researchers, as it requires the application of several bioinformatics pipelines to extract genetic information from raw data. In this paper, we present BacSeq, an automated bioinformatic pipeline for the analysis of next-generation sequencing data of bacterial genomes. BacSeq enables the assembly, annotation, and identification of crucial genes responsible for multidrug resistance, virulence factors, and plasmids. Additionally, the pipeline integrates comparative analysis among isolates, offering phylogenetic tree analysis and identification of single-nucleotide polymorphisms (SNPs). To facilitate easy analysis in a single step and support the processing of multiple isolates, BacSeq provides a graphical user interface (GUI) based on the JAVA platform. It is designed to cater to users without extensive bioinformatics skills.

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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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Ben Ali, Ferid, Denis Mustafov, Maria Braoudaki, Sola Adeleke, and Iosif Mporas. "Identification of a New Lung Cancer Biomarker Signature Using Data Mining and Preliminary In Vitro Validation." BioMedInformatics 5, no. 2 (2025): 32. https://doi.org/10.3390/biomedinformatics5020032.

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Background: Lung adenocarcinoma is one of the major subtype of non-Small Cell Lung Cancer and biomarkers are essential to be identified for early diagnosis. The study aims to find in silico and preliminary in vitro analysis of potential biomarkers for lung adenocarcinoma. Methods: Bioinformatics analysis in parallel to data mining analysis was performed on microarray data with lung adenocarcinoma samples to identify potent gene biomarkers associated with lung cancer type. Afterwards, these genes were then validated in vitro using RT-qPCR analysis in cancerous (Calu-3) and non-cancerous (MRC-5) cell lines. Moreover, these genes were used in machine learning-based analysis to classify lung adenocarcinoma samples from controls. The analysis includes three experiments—the bioinformatic (in silico), in vitro, and machine learning analyses. Results: The three experiments identified four genes, namely, SLC15A1, GPR123 (ADGRA1), KCNAB2, and KNDC1, as key biomarkers and the most relevant gene features for distinguishing lung adenocarcinoma from control. Conclusions: This study identifies four biomarkers associated with lung adenocarcinoma through bioinformatics, in vitro and machine learning analyses. These four genes shows strong potential for further investigation in clinical research.

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Budhadev, Harshita. "Bioinformatics Analysis of α-Amylase Three-Dimensional Structure in Aspergillus oryzae". International Journal of Research Publication and Reviews 4, № 9 (2023): 868–74. http://dx.doi.org/10.55248/gengpi.4.923.52600.

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Kulsum#, Umay, Chitra Patankar#, and Debasis Biswas. "MOSMAP: Mosquito metagenome analysis pipeline." Bioinformation 21, no. 2 (2025): 110–12. https://doi.org/10.6026/973206300210110.

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MosMAP is a bioinformatics pipeline designed for mosquito metagenome analysis. MosMAP automates essential processes like quality control, taxonomic classification, species abundance estimation and visualization by integrating tools such as Trimgalore, Kraken2, Bracken and Krona into a user-friendly workflow. Each of these tools is integrated to ensure a smooth and efficient workflow from raw data to interpretable results. The pipeline simplifies complex bioinformatics tasks, making them accessible to researchers with limited computational expertise. MosMAP demonstrated high concordance with standard bioinformatics workflows such as Kraken and Bracken in terms of read retention, taxonomic accuracy and abundance estimation when applied to metagenomes of mosquito collected in Bhopal, India. This accessible pipeline promotes the simplification of meta-genomics, supporting research in microbiology, ecology and vector-borne diseases.

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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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Li, Huixing, Yan Xue, and Xiancai Zeng. "Investigation of data mining technique and artificial intelligence algorithm in microflora bioinformatics." E3S Web of Conferences 267 (2021): 01040. http://dx.doi.org/10.1051/e3sconf/202126701040.

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Bioinformatics has gradually received widespread attention and has shown the characteristics of a large amount of calculation and high complexity. Therefore, it is required to adopt computer algorithms in bioinformatics to improve the efficiency of bioinformatics processing problems. Big data and artificial intelligence technologies have the characteristics of supporting bioinformatics and have achieved certain results in the field of bioinformatics. Introduced the application basis of big data and artificial intelligence in bioinformatics, analyzed data collection, preprocessing, data storage and management, data analysis, and mining technology. Furthermore, typical applications in bioinformatics are discussed in terms of gene expression data analysis, genome sequence information analysis, biological sequence difference and similarity analysis, genetic data analysis, and protein structure and function prediction. Finally, the bottlenecks and challenges in the application of big data and artificial intelligence in bioinformatics are discussed, and the application prospects of related technologies in bioinformatics have prospected.

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Thi Nhung, Doan, and Bui Van Ngoc. "Bioinformatic approaches for analysis of coral-associated bacteria using R programming language." Vietnam Journal of Biotechnology 18, no. 4 (2021): 733–43. http://dx.doi.org/10.15625/1811-4989/18/4/15320.

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Recent advances in metagenomics and bioinformatics allow the robust analysis of the composition and abundance of microbial communities, functional genes, and their metabolic pathways. So far, there has been a variety of computational/statistical tools or software for analyzing microbiome, the common problems that occurred in its implementation are, however, the lack of synchronization and compatibility of output/input data formats between such software. To overcome these challenges, in this study context, we aim to apply the DADA2 pipeline (written in R programming language) instead of using a set of different bioinformatics tools to create our own workflow for microbial community analysis in a continuous and synchronous manner. For the first effort, we tried to investigate the composition and abundance of coral-associated bacteria using their 16S rRNA gene amplicon sequences. The workflow or framework includes the following steps: data processing, sequence clustering, taxonomic assignment, and data visualization. Moreover, we also like to catch readers’ attention to the information about bacterial communities living in the ocean as most marine microorganisms are unculturable, especially residing in coral reefs, namely, bacteria are associated with the coral Acropora tenuis in this case. The outcomes obtained in this study suggest that the DADA2 pipeline written in R programming language is one of the potential bioinformatics approaches in the context of microbiome analysis other than using various software. Besides, our modifications for the workflow execution help researchers to illustrate metagenomic data more easily and systematically, elucidate the composition, abundance, diversity, and relationship between microorganism communities as well as to develop other bioinformatic tools more effectively.

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Zok, Tomasz. "BioCommons: a robust java library for RNA structural bioinformatics." Bioinformatics 37, no. 17 (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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Chen, Yang, En-Min Li, and Li-Yan Xu. "Guide to Metabolomics Analysis: A Bioinformatics Workflow." Metabolites 12, no. 4 (2022): 357. http://dx.doi.org/10.3390/metabo12040357.

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Metabolomics is an emerging field that quantifies numerous metabolites systematically. The key purpose of metabolomics is to identify the metabolites corresponding to each biological phenotype, and then provide an analysis of the mechanisms involved. Although metabolomics is important to understand the involved biological phenomena, the approach’s ability to obtain an exhaustive description of the processes is limited. Thus, an analysis-integrated metabolomics, transcriptomics, proteomics, and other omics approach is recommended. Such integration of different omics data requires specialized statistical and bioinformatics software. This review focuses on the steps involved in metabolomics research and summarizes several main tools for metabolomics analyses. We also outline the most abnormal metabolic pathways in several cancers and diseases, and discuss the importance of multi-omics integration algorithms. Overall, our goal is to summarize the current metabolomics analysis workflow and its main analysis software to provide useful insights for researchers to establish a preferable pipeline of metabolomics or multi-omics analysis.

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Zhang, Yinxi. "Analysis of the application of bioinformatics in the medicine." Theoretical and Natural Science 29, no. 1 (2024): 82–86. http://dx.doi.org/10.54254/2753-8818/29/20240751.

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As humans develop, science is also rapidly evolving, and biological science and medical support are vital for humans need. The development of biology has been particularly important. It stretches from the initial understanding of plants and animals to human biology and micro molecular biology, from macro understanding of life to micro molecular biology. Although it has accumulated a lot of knowledge about biology, it still has many unknowns about this giant and precise system. However, in recent years, a new interdisciplinary and emerging discipline has greatly opened up peoples understanding about biological information-bioinformatics. The most fundamental research target of bioinformatics is different sequences. The most important is the amino acid sequence of proteins and the base sequence of DNA. To study sequences and their constituent components, bioinformatics has another major research target - the biological database. This involves analysing the structure, connotation, and function of biological information through basic protein and nucleic acid sequences. Therefore, bioinformatics has made tremendous applications in the medical field. This research mainly analyzes and discusses the advantages and disadvantages of the application of bioinformatics in the medical field.

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Ramírez-Martínez, Carla Monserrat, Luis Fernando Jacinto-Alemán, Luis Pablo Cruz-Hervert, Javier Portilla-Robertson, and Elba Rosa Leyva-Huerta. "Bioinformatic Analysis for Mucoepidermoid and Adenoid Cystic Carcinoma of Therapeutic Targets." Vaccines 10, no. 9 (2022): 1557. http://dx.doi.org/10.3390/vaccines10091557.

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Salivary gland neoplasms are a heterogeneous neoplasm group, including mucoepidermoid carcinoma (MECa), adenoid cystic carcinoma (AdCC), and many others. Objective: We aimed to identify new critical genes of MECa and AdCC using bioinformatics analysis. Methods: Gene expression profile of GSE153283 was analyzed by the GEO2R online tool to use the DAVID software for their subsequent enrichment. Protein–protein interactions (PPI) were visualized using String. Cytoscape with MCODE plugin followed by Kaplan–Meier online for overall survival analysis were performed. Results: 97 upregulated genes were identified for MECa and 86 for AdCC. PPI analysis revealed 22 genes for MECa and 63 for AdCC that were validated by Kaplan–Meier that showed FN1 and SPP1 for MECa, and EGF and ERBB2 for AdCC as more significant candidate genes for each neoplasm. Conclusion: With bioinformatics methods, we identify upregulated genes in MECa and AdCC. The resulting candidate genes as possible therapeutic targets were FN1, SPP1, EGF, and ERBB2, and all those genes had been tested as a target in other neoplasm kinds but not salivary gland neoplasm. The bioinformatic evidence is a solid strategy to select them for more extensive research with clinical impact.

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Zhao, Xin-mei, Yuan-Bin Li, Peng Sun, Ya-di Pu, Meng-jie shan, and Yuan-meng Zhang. "Bioinformatics analysis of key biomarkers for retinoblastoma." Journal of International Medical Research 49, no. 6 (2021): 030006052110222. http://dx.doi.org/10.1177/03000605211022210.

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Objective To identify key genes involved in occurrence and development of retinoblastoma. Methods The microarray dataset, GSE5222, was downloaded from the gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) between unilateral and bilateral retinoblastoma were identified and functional enrichment analysis performed. The protein–protein interaction (PPI) network was constructed and analysed by STRING and Cytoscape. Results DEGs were mainly associated with activation of cysteine-type endopeptidase activity involved in apoptotic process and small molecule catabolic process. Seven genes (WAS, GNB3, PTGER1, TACR1, GPR143, NPFF and CDKN2A) were identified as HUB genes. Conclusion Our research provides more understanding of the mechanisms of the disease at a molecular level and may help in the identification of novel biomarkers for retinoblastoma.

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Ding, Hongjun, Yanju Li, Yanlong Zhang, et al. "Bioinformatics analysis of Myelin Transcription Factor 1." Technology and Health Care 29 (March 25, 2021): 441–53. http://dx.doi.org/10.3233/thc-218042.

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BACKGROUND AND OBJECTIVE: We aimed to further study the role of Myelin Transcription Factor 1(MyT1) in tumor and other diseases and epigenetic regulation, and better understand the regulatory mechanism of MyT1. METHODS: Using bioinformatics analysis, the structure and function of MyT1sequence were predicted and analyzed using bioinformatics analysis, and providing a theoretical basis for further experimental verification and understanding the regulatory mechanism of MyT1. The first, second and third-level structures of MyT1 were predicted and analyzed by bioinformatics analysis tools. RESULTS: MyT1 is found to be an unstable hydrophilic protein, rather than a secretory protein, with no signal peptide or trans-membrane domain; total amino acids located on the surface of the cell membrane. It contains seven zinc finger domains structurally. At sub-cellular level, MyT1 is localized in the nucleus. The phosphorylation site mainly exists in serine, and its secondary structure is mainly composed of random coils and alpha helices; the three-dimensional structure is analyzed by modeling. CONCLUSIONS: In this study, the structure and function of MyT1 protein were predicted, thereby providing a basis for subsequent expression analysis and functional research; it laid the foundation for further investigation of the molecular mechanism involved in the development of diseases.

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Journal articles on the topic 'Bioinformatics analysis'

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