Journal articles: 'Open Bioinformation'

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Pavithra, Senkuttavan. "Challenges and other linked features in promoting open access to bioinformation literature over about 2 decades." Bioinformation 18, no. 6 (2022): 525–30. http://dx.doi.org/10.6026/97320630018525.

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Open access to known literature is critical for creating a harmonious society across continents on planet earth. However, this objective is not simple. Therefore, it is of interest to document the challenges and linked features in promoting open access to bioinformation literature over about 2 decades.

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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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Jin Su Kim, Gyu Ho Choi, and Sung Bum Pan. "A Study on ECG-based Biometrics using Open Source Hardware." Research Briefs on Information and Communication Technology Evolution 3 (November 15, 2017): 165–71. http://dx.doi.org/10.56801/rebicte.v3i.56.

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Biometric technology uses unique physical features as bioinformation. Electrocardiogram(ECG) isa signal recording potential changes related to heartbeats in the form of voltage, and each individualhas unique ECG signals in terms of heart location and size thereof. Therefore, they are used to diagnosediseases and examine personal identity. The ECG device can be made compact in comparisonwith other biosignal measurement instruments and the signals measured in locations easily accessible.This paper aims to implement a personal identification system based on ECG signals. Noiseremoval and signal split is conducted as a pre-processing process by using the Band Pass Filter(BPF),Median Filter(MF) and R-Peak. Features are extracted by using the morphological method from thetime domain. Euclidean Distance(ED) is used to analyze the verification data and registration data. Inthe experiment result, For 18 verification data with the lowest EER 1.67%, driving was 113.4 seconds.

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Xue, Yongbiao, Yiming Bao, Zhang Zhang, et al. "Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2022." Nucleic Acids Research 50, no. D1 (2021): D27—D38. http://dx.doi.org/10.1093/nar/gkab951.

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Abstract The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support global research in both academia and industry. With the explosively accumulated multi-omics data at ever-faster rates, CNCB-NGDC is constantly scaling up and updating its core database resources through big data archive, curation, integration and analysis. In the past year, efforts have been made to synthesize the growing data and knowledge, particularly in single-cell omics and precision medicine research, and a series of resources have been newly developed, updated and enhanced. Moreover, CNCB-NGDC has continued to daily update SARS-CoV-2 genome sequences, variants, haplotypes and literature. Particularly, OpenLB, an open library of bioscience, has been established by providing easy and open access to a substantial number of abstract texts from PubMed, bioRxiv and medRxiv. In addition, Database Commons is significantly updated by cataloguing a full list of global databases, and BLAST tools are newly deployed to provide online sequence search services. All these resources along with their services are publicly accessible at https://ngdc.cncb.ac.cn.

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Okido, Toshihisa, Yuichi Kodama, Jun Mashima, Takehide Kosuge, Takatomo Fujisawa, and Osamu Ogasawara. "DNA Data Bank of Japan (DDBJ) update report 2021." Nucleic Acids Research 50, no. D1 (2021): D102—D105. http://dx.doi.org/10.1093/nar/gkab995.

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Abstract The Bioinformation and DDBJ (DNA Data Bank of Japan) Center (DDBJ Center; https://www.ddbj.nig.ac.jp) operates archival databases that collect nucleotide sequences, study and sample information, and distribute them without access restriction to progress life science research as a member of the International Nucleotide Sequence Database Collaboration (INSDC), in collaboration with the National Center for Biotechnology Information (NCBI) and the European Bioinformatics Institute. Besides the INSDC databases, the DDBJ Center also provides the Genomic Expression Archive for functional genomics data and the Japanese Genotype-phenotype Archive for human data requiring controlled access. Additionally, the DDBJ Center started a new public repository, MetaboBank, for experimental raw data and metadata from metabolomics research in October 2020. In response to the COVID-19 pandemic, the DDBJ Center openly shares SARS-CoV-2 genome sequences in collaboration with Shizuoka Prefecture and Keio University. The operation of DDBJ is based on the National Institute of Genetics (NIG) supercomputer, which is open for large-scale sequence data analysis for life science researchers. This paper reports recent updates on the archival databases and the services of DDBJ.

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Zhu, Yan-Fang, Hong-Hong Fan, Da-Hui Li, et al. "MOLECULAR CLONING, BIOINFORMATION ANALYSIS AND EXPRESSION OF THE STRICTOSIDINE SYNTHASE IN Dendrobium officinale." Acta Scientiarum Polonorum Hortorum Cultus 19, no. 3 (2020): 111–24. http://dx.doi.org/10.24326/asphc.2020.3.10.

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The enzyme strictosidine synthase (STR, EC: 4.3.3.2) plays a key role in the biosynthetic pathway of terpenoid indole alkaloid (TIA). It catalyzes the condensation of the tryptamine and secologanin to form 3α(S)-strictosidine, which is the common precursor of all TIAs. In this paper, a STR gene designated as DoSTR (GenBank: KX068707) was first cloned and characterized from Dendrobium officinale with rapid amplified cDNA ends method (RACE). DoSTR has a length of 1380bp with 1179bp open reading frame encoding 392 amino acids. BlastP analyses showed that its amino acid sequence was classified into Str_synth superfamily. qRT-PCR showed that DoSTR was expressed in all tissues tested, with a significantly higher level in flower and the lowest in stem. Four different treatments with MeJA, SA, ABA and AgNO3, respectively, could induce the DoSTR expression to a different extent. And the effect of MeJA was the most obvious and transcript level of DoSTR induced by MeJA was 20.7 times greater than that of control at 48 hours after treatment. Furthermore, it was found that DoSTR was localized in vacuole through transient expression in tobacco. The characterization and expression of DoSTR can help in further studying the role of DoSTR in the biosynthesis of TIAs in D. officinale. This study may throw light on the alkaloid biosynthesis pathway of D. officinale.

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Lee, Byungwook, Seungwoo Hwang, Pan-Gyu Kim, et al. "Introduction of the Korea BioData Station (K-BDS) for sharing biological data." Genomics & Informatics 21, no. 1 (2023): e12. http://dx.doi.org/10.5808/gi.22073.

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A wave of new technologies has created opportunities for the cost-effective generation of high-throughput profiles of biological systems, foreshadowing a "data-driven science" era. The large variety of data available from biological research is also a rich resource that can be used for innovative endeavors. However, we are facing considerable challenges in big data deposition, integration, and translation due to the complexity of biological data and its production at unprecedented exponential rates. To address these problems, in 2020, the Korean government officially announced a national strategy to collect and manage the biological data produced through national R&D fund allocations and provide the collected data to researchers. To this end, the Korea Bioinformation Center (KOBIC) developed a new biological data repository, the Korea BioData Station (K-BDS), for sharing data from individual researchers and research programs to create a data-driven biological study environment. The K-BDS is dedicated to providing free open access to a suite of featured data resources in support of worldwide activities in both academia and industry.

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

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Gao, Xiangqian, Guiyan Yang, Dapei Li, et al. "Potential Role of WIP Family Genes in Drought Stress Response in Rubus idaeus." Agriculture 14, no. 11 (2024): 2047. http://dx.doi.org/10.3390/agriculture14112047.

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Rubus idaeus is one of the primary cultivated species of raspberries, renowned for its appealing color, distinctive flavor and numerous health benefits. WIP proteins, which contain three conserved amino acids (W: Tryptophan, I: Isoleucine, P: Proline) and four zinc finger motifs in a highly conserved C-terminal region, are members of the A1d subgroup of C2H2 zinc finger proteins. Drought is one of the main limiting factors of plant growth and development, which restricts the cultivation and utilization of raspberry in northwest China. In this study, to obtain candidate genes for drought resistance, we identified key related genes, RiWIPs, from R. idaeus and analyzed their bioinformation and tissue stress response expression to drought. We found that there are three RiWIPs in R. idaeus and they are located on chromosomes 3, 4 and 6 of R. idaeus, respectively. The open reading frames (ORFs) of the RiWIPs ranged from 870 to 1056 bp in length, encoding 289 to 372 amino acid residues. The proteins were highly conserved and feature diverse conserved motifs. The promoters of the RiWIPs contained abundant cis-elements related to growth, development and stress response. Tissue-specific expression analysis revealed that the RiWIPs were expressed in the leaves, stems and roots of both drought-susceptible and drought-tolerant cultivars, except for RiWIP2, which was only expressed in the roots of the drought-tolerant one. Under drought stress, the transcriptional activity of the RiWIPs was increased to different degrees with specificity in the leaves, stems and roots. Our study demonstrated the role of WIP genes in raspberry drought response and provided a marker gene, RiWIP2, for drought resistance and candidate genes for subsequent drought-resistant breeding of R. idaeus.

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Vlagioiu, Constantin, Vlad Vuta, Florica Barbuceanu, Gabriel Predoi, and Nicolae Tudor. "Open-source bioinformatics software." Journal of Biotechnology 256 (August 2017): S53. http://dx.doi.org/10.1016/j.jbiotec.2017.06.979.

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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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Gilmanov, Khamid, Vladislav Semipyatniy, Alana Bigaeva, Ramil Vafin та Svetlana Turovskaya. "New determination method for the ratio of the relative proportions of ϰ-casein alleles in milk powder". Food Processing: Techniques and Technology 50, № 3 (2020): 525–35. http://dx.doi.org/10.21603/2074-9414-2020-3-525-535.

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Introduction. High-quality dairy products depend on the properties of raw materials. Milk is the basic raw material for all dairy products. Its quality is subject to requirements of biological safety, and its physicochemical and technological properties comply with strict indicators. ϰ-casein gene is a basic milk protein. Its allelic variants affect milk productivity, as well as its composition and technological properties, e.g. cheeseability and heat resistance. The research objective was to develop a new method for determining the ratio of the relative proportions of selection-significant kappa-casein gene alleles in bulk cow’s milk powder by means of molecular genetics and bioinformation system. The method can help to test raw materials for further processing rationality. Study objects and methods. The research included a genetic analysis of samples of bulk whole milk powder, bulk powdered skimmed milk, and bulk raw milk. The developed method involved DNA extraction, combined PCR-RFLP technique, electrophoretic results, and analysis detection of information data using new mathematical algorithms and software. Results and discussion. The ratio of the relative proportions of the kappa-casein gene alleles in milk powder was determined using primer sets JK5 and JK3, as well as restriction endonuclease HinfI for PCR-RFLP analysis. The experiment showed satisfactory reproducibility and interpretation of the obtained data. The program provided a ratio calculation of the kappa-casein gene alleles relative proportions in the studied milk powder samples, expressed as a percentage of the A allele proportion with additional indication of the absolute and relative errors. It also placed the information block of the generated numerical indicators into the percentage scale. Conclusion. The developed methodology was implemented by a set of laboratory procedures. The new specialized program “Calculation of the relative proportions ratio of ϰ-casein alleles in bulk milk” is on open access. It provides a correct and prompt data interpretation, generated during the analysis of dry milk raw materials of bulk origin.

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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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Saxby, C. "The Bioinformatics Open Access option." Bioinformatics 21, no. 22 (2005): 4071–72. http://dx.doi.org/10.1093/bioinformatics/bti707.

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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 (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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Konagaya, Akihiko, Fumikazu Konishi, Mariko Hatakeyama, and Kenji Satou. "The superstructure toward open bioinformatics grid." New Generation Computing 22, no. 2 (2004): 167–76. http://dx.doi.org/10.1007/bf03040955.

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Harris, Nomi L., Peter J. A. Cock, Brad Chapman, et al. "The 2017 Bioinformatics Open Source Conference (BOSC)." F1000Research 6 (October 19, 2017): 1858. http://dx.doi.org/10.12688/f1000research.12929.1.

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The Bioinformatics Open Source Conference (BOSC) is a meeting organized by the Open Bioinformatics Foundation (OBF), a non-profit group dedicated to promoting the practice and philosophy of Open Source software development and Open Science within the biological research community. The 18th annual BOSC (http://www.open-bio.org/wiki/BOSC_2017) took place in Prague, Czech Republic in July 2017. The conference brought together nearly 250 bioinformatics researchers, developers and users of open source software to interact and share ideas about standards, bioinformatics software development, open and reproducible science, and this year’s theme, open data. As in previous years, the conference was preceded by a two-day collaborative coding event open to the bioinformatics community, called the OBF Codefest.

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Harris, Nomi L., Peter J. A. Cock, Brad Chapman, et al. "The 2016 Bioinformatics Open Source Conference (BOSC)." F1000Research 5 (October 6, 2016): 2464. http://dx.doi.org/10.12688/f1000research.9663.1.

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Message from the ISCB: The Bioinformatics Open Source Conference (BOSC) is a yearly meeting organized by the Open Bioinformatics Foundation (OBF), a non-profit group dedicated to promoting the practice and philosophy of Open Source software development and Open Science within the biological research community. BOSC has been run since 2000 as a two-day Special Interest Group (SIG) before the annual ISMB conference. The 17th annual BOSC (http://www.open-bio.org/wiki/BOSC_2016) took place in Orlando, Florida in July 2016. As in previous years, the conference was preceded by a two-day collaborative coding event open to the bioinformatics community. The conference brought together nearly 100 bioinformatics researchers, developers and users of open source software to interact and share ideas about standards, bioinformatics software development, and open and reproducible science.

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Holland, R. C. G., T. A. Down, M. Pocock, et al. "BioJava: an open-source framework for bioinformatics." Bioinformatics 24, no. 18 (2008): 2096–97. http://dx.doi.org/10.1093/bioinformatics/btn397.

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Harris, N. L., P. J. A. Cock, B. A. Chapman, J. Goecks, H. R. Hotz, and H. Lapp. "The Bioinformatics Open Source Conference (BOSC) 2013." Bioinformatics 31, no. 2 (2014): 299–300. http://dx.doi.org/10.1093/bioinformatics/btu413.

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de Hoon, M. J. L., S. Imoto, J. Nolan, and S. Miyano. "Open source clustering software." Bioinformatics 20, no. 9 (2004): 1453–54. http://dx.doi.org/10.1093/bioinformatics/bth078.

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Lee, Michael. "Happy Belly Bioinformatics: an open-source resource dedicated to helping biologists utilize bioinformatics." Journal of Open Source Education 2, no. 19 (2019): 53. http://dx.doi.org/10.21105/jose.00053.

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Platero, Ana Isabel, Jose Ignacio Pueyo, Sarah Anne Bishop, Emile Gerard Magny, and Juan Pablo Couso. "Pervasiveness of Microprotein Function Amongst Drosophila Small Open Reading Frames (SMORFS)." Cells 13, no. 24 (2024): 2090. https://doi.org/10.3390/cells13242090.

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Small Open Reading Frames (smORFs) of less than 100 codons remain mostly uncharacterised. About a thousand smORFs per genome encode peptides and microproteins about 70–80 aa long, often containing recognisable protein structures and markers of translation, and these are referred to as short Coding Sequences (sCDSs). The characterisation of individual sCDSs has provided examples of smORFs’ function and conservation, but we cannot infer the functionality of all other metazoan smORFs from these. sCDS function has been characterised at a genome-wide scale in yeast and bacteria, showing that hundreds can produce a phenotype, but attempts in metazoans have been less successful. Either most sCDSs are not functional, or classic experimental techniques do not work with smORFs due to their shortness. Here, we combine extensive proteomics with bioinformatics and genetics in order to detect and corroborate sCDS function in Drosophila. Our studies nearly double the number of sCDSs with detected peptides and microproteins and an experimentally corroborated function. Finally, we observe a correlation between proven sCDS protein function and bioinformatic markers such as conservation and GC content. Our results support that sCDSs peptides and microproteins act as membrane-related regulators of canonical proteins, regulators whose functions are best understood at the cellular level, and whose mutants produce little, if any, overt morphological phenotypes.

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Harris, Nomi L., Heather Wiencko, Brad Chapman, et al. "The 2018 Bioinformatics Open Source Conference (GCCBOSC 2018)." F1000Research 7 (August 17, 2018): 1309. http://dx.doi.org/10.12688/f1000research.15936.1.

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In 2018, the annual Bioinformatics Open Source Conference was held for the first time in conjunction with the Galaxy Community Conference, as an experiment to see if we could reach people in the bioinformatics community who aren’t part of the audience attracted by ISMB. Held in June 2018 at Reed College in Portland, Oregon, GCCBOSC (Galaxy Community Conference and Bioinformatics Open Source Conference) attracted over 300 participants from around the world. The meeting started with two days of training, followed by two days of talks and poster/demo sessions (with some joint and some parallel sessions). The joint sessions included well-received keynote talks by Tracy Teal, Fernando Pérez and Lucia Peixoto, as well as a panel discussion about documentation and training. After the main meeting, many attendees stayed for up to four additional collaboration days, an extended version of the Codefests that have been held in conjunction with previous BOSCs. GCCBOSC was a successful experiment. The organizers concluded that the best way to serve the broadest community of potential BOSC attendees will be to partner some years with the International Society for Computational Biology (ISMB) and others with GCC.

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Harris, Nomi L., Peter J. A. Cock, Hilmar Lapp, et al. "The 2015 Bioinformatics Open Source Conference (BOSC 2015)." PLOS Computational Biology 12, no. 2 (2016): e1004691. http://dx.doi.org/10.1371/journal.pcbi.1004691.

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Robinson, Clare. "The European Bioinformatics Institute (EBI) — open for business." Trends in Biotechnology 12, no. 10 (1994): 391–92. http://dx.doi.org/10.1016/0167-7799(94)90024-8.

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Garden, P., R. Alm, and J. Hakkinen. "PROTEIOS: an open source proteomics initiative." Bioinformatics 21, no. 9 (2005): 2085–87. http://dx.doi.org/10.1093/bioinformatics/bti291.

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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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Harris, Nomi L., Peter J. A. Cock, Christopher J. Fields, et al. "BOSC 2021, the 22nd Annual Bioinformatics Open Source Conference." F1000Research 10 (October 18, 2021): 1054. http://dx.doi.org/10.12688/f1000research.74074.1.

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The 22nd annual Bioinformatics Open Source Conference (BOSC 2021, open-bio.org/events/bosc-2021/) was held online as a track of the 2021 Intelligent Systems for Molecular Biology / European Conference on Computational Biology (ISMB/ECCB) conference. Launched in 2000 and held every year since, BOSC is the premier meeting covering topics related to open source software and open science in bioinformatics. In 2020, BOSC partnered with the Galaxy Community Conference to form the Bioinformatics Community Conference (BCC2020); that was the first BOSC to be held online. This year, BOSC returned to its roots as part of ISMB/ECCB 2021. As in 2020, the Covid-19 pandemic made it impossible to hold the conference in person, so ISMB/ECCB 2021 took place as an online meeting attended by over 2000 people from 79 countries. Nearly 200 people participated in BOSC sessions, which included 27 talks reviewed and selected from submitted abstracts, and three invited keynote talks representing a range of global perspectives on the role of open science and open source in driving research and inclusivity in the biosciences, one of which was presented in French with English subtitles.

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Harris, Nomi L., Peter J. A. Cock, Christopher J. Fields, et al. "BOSC 2019, the 20th annual Bioinformatics Open Source Conference." F1000Research 8 (December 20, 2019): 2132. http://dx.doi.org/10.12688/f1000research.21568.1.

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The Bioinformatics Open Source Conference is a volunteer-organized meeting that covers open source software development and open science in bioinformatics. Launched in 2000, BOSC has been held every year since. BOSC 2019, the 20th annual BOSC, took place as one of the Communities of Special Interest (COSIs) at the Intelligent Systems for Molecular Biology meeting (ISMB/ECCB 2019). The two-day meeting included a total of 46 talks and 55 posters, as well as eight Birds of a Feather interest groups. The keynote speaker was University of Cape Town professor Dr. Nicola Mulder, who spoke on “Building infrastructure for responsible open science in Africa”. Immediately after BOSC 2019, about 50 people participated in the two-day CollaborationFest (CoFest for short), an open and free community-driven event at which participants work together to contribute to bioinformatics software, documentation, training materials, and use cases.

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Kim, Jin-Dong, Yue Wang, Toyofumi Fujiwara, Shujiro Okuda, Tiffany J. Callahan, and K. Bretonnel Cohen. "Open Agile text mining for bioinformatics: the PubAnnotation ecosystem." Bioinformatics 35, no. 21 (2019): 4372–80. http://dx.doi.org/10.1093/bioinformatics/btz227.

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Abstract Motivation Most currently available text mining tools share two characteristics that make them less than optimal for use by biomedical researchers: they require extensive specialist skills in natural language processing and they were built on the assumption that they should optimize global performance metrics on representative datasets. This is a problem because most end-users are not natural language processing specialists and because biomedical researchers often care less about global metrics like F-measure or representative datasets than they do about more granular metrics such as precision and recall on their own specialized datasets. Thus, there are fundamental mismatches between the assumptions of much text mining work and the preferences of potential end-users. Results This article introduces the concept of Agile text mining, and presents the PubAnnotation ecosystem as an example implementation. The system approaches the problems from two perspectives: it allows the reformulation of text mining by biomedical researchers from the task of assembling a complete system to the task of retrieving warehoused annotations, and it makes it possible to do very targeted customization of the pre-existing system to address specific end-user requirements. Two use cases are presented: assisted curation of the GlycoEpitope database, and assessing coverage in the literature of pre-eclampsia-associated genes. Availability and implementation The three tools that make up the ecosystem, PubAnnotation, PubDictionaries and TextAE are publicly available as web services, and also as open source projects. The dictionaries and the annotation datasets associated with the use cases are all publicly available through PubDictionaries and PubAnnotation, respectively.

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Prlic, A., A. Yates, S. E. Bliven, et al. "BioJava: an open-source framework for bioinformatics in 2012." Bioinformatics 28, no. 20 (2012): 2693–95. http://dx.doi.org/10.1093/bioinformatics/bts494.

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Lafita, Aleix, Spencer Bliven, Andreas Prlić, et al. "BioJava 5: A community driven open-source bioinformatics library." PLOS Computational Biology 15, no. 2 (2019): e1006791. http://dx.doi.org/10.1371/journal.pcbi.1006791.

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Harris, Nomi L., Christopher J. Fields, Karsten Hokamp, et al. "BOSC 2023, the 24th annual Bioinformatics Open Source Conference." F1000Research 12 (December 7, 2023): 1568. http://dx.doi.org/10.12688/f1000research.143015.1.

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The 24th annual Bioinformatics Open Source Conference (BOSC 2023) was part of the 2023i conference on Intelligent Systems for Molecular Biology and the European Conference on Computational Biology (ISMB/ECCB 2023). Launched in 2000 and held yearly since, BOSC is the premier meeting covering open-source bioinformatics and open science. Like ISMB 2022, the 2023 meeting was a hybrid conference, with the in-person component hosted in Lyon, France. ISMB/ECCB attracted a near-record number of attendees, with over 2100 in person and about 900 more online. Approximately 200 people participated in BOSC sessions. In addition to 43 talks and 49 posters, BOSC featured two keynotes: Sara El-Gebali, who spoke about “A New Odyssey: Pioneering the Future of Scientific Progress Through Open Collaboration”, and Joseph Yracheta, who spoke about “The Dissonance between Scientific Altruism & Capitalist Extraction: The Zero Trust and Federated Data Sovereignty Solution.” Once again, a joint session brought together BOSC and the Bio-Ontologies COSI. The conference ended with a panel on Open and Ethical Data Sharing. As in prior years, BOSC was preceded by a CollaborationFest, a collaborative work event that brought together about 40 participants interested in synergistically combining ideas, shaping project plans, developing software, and more.

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Ball, C. A., G. Sherlock, H. Parkinson, et al. "An open letter to the scientific journals." Bioinformatics 18, no. 11 (2002): 1409. http://dx.doi.org/10.1093/bioinformatics/18.11.1409.

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36

Voegele, Catherine, Baptiste Bouchereau, Nivonirina Robinot, James McKay, Philippe Damiecki, and Lucile Alteyrac. "A universal open-source Electronic Laboratory Notebook." Bioinformatics 29, no. 13 (2013): 1710–12. http://dx.doi.org/10.1093/bioinformatics/btt253.

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Peat, Gareth, William Jones, Michael Nuhn, et al. "The open targets post-GWAS analysis pipeline." Bioinformatics 36, no. 9 (2020): 2936–37. http://dx.doi.org/10.1093/bioinformatics/btaa020.

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Abstract Motivation Genome-wide association studies (GWAS) are a powerful method to detect even weak associations between variants and phenotypes; however, many of the identified associated variants are in non-coding regions, and presumably influence gene expression regulation. Identifying potential drug targets, i.e. causal protein-coding genes, therefore, requires crossing the genetics results with functional data. Results We present a novel data integration pipeline that analyses GWAS results in the light of experimental epigenetic and cis-regulatory datasets, such as ChIP-Seq, Promoter-Capture Hi-C or eQTL, and presents them in a single report, which can be used for inferring likely causal genes. This pipeline was then fed into an interactive data resource. Availability and implementation The analysis code is available at www.github.com/Ensembl/postgap and the interactive data browser at postgwas.opentargets.io.

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

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

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Quispe-Juli, Cender Udai, Victor Hugo Moquillaza-Alcántara, and Katherine Arapa-Apaza. "Massive open online courses on biomedical informatics." F1000Research 8 (February 14, 2019): 180. http://dx.doi.org/10.12688/f1000research.17693.1.

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This study aimed to identify the characteristics of massive open online courses (MOOCs) related to biomedical informatics offered in several plataforms. We conducted an observational study on specialized MOOCs platforms to find courses related to biomedical informatics, in 2018. Our search identified 67 MOOCs on biomedical informatics. The majority of MOOCs were offered by Coursera (71.6%, 48/67), English was the most common language (95.5%, 64/67). The United States developed the majority of courses (73.1%, 49/67), with the vast majority of MOOCs being offered by universities (94%, 63/67). The majority of MOOCs were in bioinformatics (56.7%, 38/67) and data science (47.7%, 32/67). In conclusion, the MOOCs on biomedical informatics were focused in bioinformatics and data science, and were offered in English by institutions in the developing world.

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Yi, Hai Bo, De Kang Zhu, Xiao Jia Wang, An Chun Cheng, and Ming Shu Wang. "Bioinformatics Analysis and Characteristics of Capsular Exopolysaccharide Family Gene and its Encoding Protein in Riemerella Anatipestifer." Advanced Materials Research 641-642 (January 2013): 630–37. http://dx.doi.org/10.4028/www.scientific.net/amr.641-642.630.

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To provide some evidence for further research on the biological function of capsular polysaccharide family gene and its endcoding protein of Riemerella anatipestifer.we analyzed the sequence of capsular exopolysaccharide gene and physicochemical properties , structure , function of capsular exopolysaccharide family protein by bioinformatics tools online.We found that capsular exopolysaccharide family gene is an open reading frame with 2373 bp in length and contained a single ORF, which consisted of 732 adenine,443 cytosine,321 guanine,877 thymine and a GC content 32.20%. The NC value of the nucleotides sequence of capsular exopolysaccharide family gene was 44.270. Capsular exopolysaccharide family protein consisted of 790 amino acids and had the following characteristics: numerous highly hydrophilic region, two transmembrane domains, zero signal peptide cleavage site, numerous functional sites. We obtain more biological information about capsular exopolysaccharide family gene and its endcoding protein by bioinformatic analysis, which provide some basic information for further research.

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Bekkar, Amel, Anita Nasrallah, Nicolas Guex, Lluis Fajas, Ioannis Xenarios, and Isabel C. Lopez-Mejia. "PamgeneAnalyzeR: open and reproducible pipeline for kinase profiling." Bioinformatics 36, no. 20 (2020): 5117–19. http://dx.doi.org/10.1093/bioinformatics/btz858.

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Abstract Protein phosphorylation––catalyzed by protein kinases–is the most common post-translational modification. It increases the functional diversity of the proteome and influences various aspects of normal physiology and can be altered in disease states. High throughput profiling of kinases is becoming an essential experimental approach to investigate their activity and this can be achieved using technologies such as PamChip® arrays provided by PamGene for kinase activity measurement. Here, we present ‘pamgeneAnalyzeR’, an R package developed as an alternative to the manual steps necessary to extract the data from PamChip® peptide microarrays images in a reproducible and robust manner. The extracted data can be directly used for downstream analysis. Availability and implementation PamgeneAnalyzeR is implemented in R and can be obtained from https://github.com/amelbek/pamgeneAnalyzeR.

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Sandal, M., F. Benedetti, M. Brucale, A. Gomez-Casado, and B. Samori. "Hooke: an open software platform for force spectroscopy." Bioinformatics 25, no. 11 (2009): 1428–30. http://dx.doi.org/10.1093/bioinformatics/btp180.

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Dowst, H., B. Pew, C. Watkins, et al. "Acquire: an open-source comprehensive cancer biobanking system." Bioinformatics 31, no. 10 (2015): 1655–62. http://dx.doi.org/10.1093/bioinformatics/btv012.

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Floratos, Aris, Kenneth Smith, Zhou Ji, John Watkinson, and Andrea Califano. "geWorkbench: an open source platform for integrative genomics." Bioinformatics 26, no. 14 (2010): 1779–80. http://dx.doi.org/10.1093/bioinformatics/btq282.

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Wang, P., P. Yang, and J. Y. H. Yang. "OCAP: an open comprehensive analysis pipeline for iTRAQ." Bioinformatics 28, no. 10 (2012): 1404–5. http://dx.doi.org/10.1093/bioinformatics/bts150.

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Harris, Nomi L., Peter J. A. Cock, Christopher J. Fields, et al. "The 21st annual Bioinformatics Open Source Conference (BOSC 2020, part of BCC2020)." F1000Research 9 (September 21, 2020): 1160. http://dx.doi.org/10.12688/f1000research.26498.1.

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Launched in 2000 and held every year since, the Bioinformatics Open Source Conference (BOSC) is a volunteer-run meeting coordinated by the Open Bioinformatics Foundation (OBF) that covers open source software development and open science in bioinformatics. Most years, BOSC has been part of the Intelligent Systems for Molecular Biology (ISMB) conference, but in 2018, and again in 2020, BOSC partnered with the Galaxy Community Conference (GCC). This year’s combined BOSC + GCC conference was called the Bioinformatics Community Conference (BCC2020, bcc2020.github.io). Originally slated to take place in Toronto, Canada, BCC2020 was moved online due to COVID-19. The meeting started with a wide array of training sessions; continued with a main program of keynote presentations, talks, posters, Birds of a Feather, and more; and ended with four days of collaboration (CoFest). Efforts to make the meeting accessible and inclusive included very low registration fees, talks presented twice a day, and closed captioning for all videos. More than 800 people from 61 countries registered for at least one part of the meeting, which was held mostly in the Remo.co video-conferencing platform.

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Vetrivel, Umashankar, and Kalabharath Pilla. "Open discovery: An integrated live Linux platform of Bioinformatics tools." Bioinformation 3, no. 4 (2008): 144–46. http://dx.doi.org/10.6026/97320630003144.

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Vera, R., Y. Perez-Riverol, S. Perez, B. Ligeti, A. Kertesz-Farkas, and S. Pongor. "JBioWH: an open-source Java framework for bioinformatics data integration." Database 2013 (July 11, 2013): bat051. http://dx.doi.org/10.1093/database/bat051.

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Dionisio, John David N., and Kam D. Dahlquist. "Improving the computer science in bioinformatics through open source pedagogy." ACM SIGCSE Bulletin 40, no. 2 (2008): 115–19. http://dx.doi.org/10.1145/1383602.1383648.

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Niu, Bei-Fang, Xian-Yu Lang, Zhong-Hua Lu, and Xue-Bin Chi. "Parallel algorithm research on several important open problems in bioinformatics." Interdisciplinary Sciences: Computational Life Sciences 1, no. 3 (2009): 187–95. http://dx.doi.org/10.1007/s12539-009-0004-7.

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Journal articles on the topic 'Open Bioinformation'

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