Статті в журналах: "BIOINFORMATICIANS"

1

Chang, Jeffrey. "Core services: Reward bioinformaticians." Nature 520, no. 7546 (April 2015): 151–52. http://dx.doi.org/10.1038/520151a.

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2

Smaglik, Paul. "Who makes the best bioinformaticians?" Nature 409, no. 6822 (February 2001): 963. http://dx.doi.org/10.1038/35057448.

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3

Niiler, Eric. "Bioinformaticians develop new data mining tools." Nature Medicine 6, no. 10 (October 2000): 1071. http://dx.doi.org/10.1038/80375.

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4

Gómez-López, Gonzalo, Joaquín Dopazo, Juan C. Cigudosa, Alfonso Valencia, and Fátima Al-Shahrour. "Precision medicine needs pioneering clinical bioinformaticians." Briefings in Bioinformatics 20, no. 3 (October 25, 2017): 752–66. http://dx.doi.org/10.1093/bib/bbx144.

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5

Pérez-Wohlfeil, Esteban, Oscar Torreno, Louisa J. Bellis, Pedro L. Fernandes, Brane Leskosek, and Oswaldo Trelles. "Training bioinformaticians in High Performance Computing." Heliyon 4, no. 12 (December 2018): e01057. http://dx.doi.org/10.1016/j.heliyon.2018.e01057.

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6

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

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7

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

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8

Xue, Yu, and Xiu-Jie Wang. "Bioinformaticians wrestling with the big biomedical data." Journal of Genetics and Genomics 44, no. 5 (May 2017): 223–25. http://dx.doi.org/10.1016/j.jgg.2017.05.002.

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9

Müller, H. M., H. Fiegl, M. Widschwendter, and G. Goebel. "Gene Methylation Data – a New Challenge for Bioinformaticians?" Methods of Information in Medicine 44, no. 04 (2005): 516–19. http://dx.doi.org/10.1055/s-0038-1634002.

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Summary Objectives: Changes in the status of DNA methylation, known as epigenetic alterations, are among the most common molecular alterations in human neoplasia. For the first time, we reported on the analysis of fecal DNA from patients with CRC to determine the feasibility, sensitivity and specificity of this approach. We want to present basic information about DNA methylation analysis in the context of bioinformatics, the study design and several statistical experiences with gene methylation data. Additionally we outline chances and new research questions in the field of DNA methylation. Methods: We present current approaches to DNA methylation analysis based on one reference study. Its study design and the statistical analysis is reflected in the context of biomarker development. Finally we outline perspectives and research questions for statisticians and bioinformaticians. Results: Identification of at least three genes as potential DNA methylation-based tumor marker genes (SFRP2, SFRP5, PGR). Conclusions: DNA methylation analysis is a rising topic in molecular genetics. Gene methylation will push the extension of biobanks to include new types of genetic data. Study design and statistical methods for the detection of methylation biomarkers must be improved. For the purpose of establishing methylation analysis as a new diagnostic/prognostic tool the adaptation of several approaches has become a challenging field of research activity.

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10

Oshlack, Alicia. "A 10-step guide to party conversation for bioinformaticians." Genome Biology 14, no. 1 (2013): 104. http://dx.doi.org/10.1186/gb-2013-14-1-104.

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11

van der Velde, K. Joeri, Floris Imhann, Bart Charbon, Chao Pang, David van Enckevort, Mariska Slofstra, Ruggero Barbieri, et al. "MOLGENIS research: advanced bioinformatics data software for non-bioinformaticians." Bioinformatics 35, no. 6 (August 26, 2018): 1076–78. http://dx.doi.org/10.1093/bioinformatics/bty742.

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12

Bruskiewich, Richard. "Meeting Review: Plant Bioinformatics at the NSF and NPGI (PAMGX Satellite) Meetings." Comparative and Functional Genomics 3, no. 2 (2002): 176. http://dx.doi.org/10.1002/cfg.158.

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13

Kihara, Daisuke, Yifeng David Yang, and Troy Hawkins. "Bioinformatics Resources for Cancer Research with an Emphasis on Gene Function and Structure Prediction Tools." Cancer Informatics 2 (January 2006): 117693510600200. http://dx.doi.org/10.1177/117693510600200020.

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The immensely popular fields of cancer research and bioinformatics overlap in many different areas, e.g. large data repositories that allow for users to analyze data from many experiments (data handling, databases), pattern mining, microarray data analysis, and interpretation of proteomics data. There are many newly available resources in these areas that may be unfamiliar to most cancer researchers wanting to incorporate bioinformatics tools and analyses into their work, and also to bioinformaticians looking for real data to develop and test algorithms. This review reveals the interdependence of cancer research and bioinformatics, and highlight the most appropriate and useful resources available to cancer researchers. These include not only public databases, but general and specific bioinformatics tools which can be useful to the cancer researcher. The primary foci are function and structure prediction tools of protein genes. The result is a useful reference to cancer researchers and bioinformaticians studying cancer alike.

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14

Mun, Jihyeob, Gildon Choi, and Byungho Lim. "A guide for bioinformaticians: ‘omics-based drug discovery for precision oncology." Drug Discovery Today 25, no. 11 (November 2020): 1897–904. http://dx.doi.org/10.1016/j.drudis.2020.08.004.

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15

Kunin, Victor, Alex Copeland, Alla Lapidus, Konstantinos Mavromatis, and Philip Hugenholtz. "A Bioinformatician's Guide to Metagenomics." Microbiology and Molecular Biology Reviews 72, no. 4 (December 2008): 557–78. http://dx.doi.org/10.1128/mmbr.00009-08.

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SUMMARY As random shotgun metagenomic projects proliferate and become the dominant source of publicly available sequence data, procedures for the best practices in their execution and analysis become increasingly important. Based on our experience at the Joint Genome Institute, we describe the chain of decisions accompanying a metagenomic project from the viewpoint of the bioinformatic analysis step by step. We guide the reader through a standard workflow for a metagenomic project beginning with presequencing considerations such as community composition and sequence data type that will greatly influence downstream analyses. We proceed with recommendations for sampling and data generation including sample and metadata collection, community profiling, construction of shotgun libraries, and sequencing strategies. We then discuss the application of generic sequence processing steps (read preprocessing, assembly, and gene prediction and annotation) to metagenomic data sets in contrast to genome projects. Different types of data analyses particular to metagenomes are then presented, including binning, dominant population analysis, and gene-centric analysis. Finally, data management issues are presented and discussed. We hope that this review will assist bioinformaticians and biologists in making better-informed decisions on their journey during a metagenomic project.

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16

Ather, Syed Hussain, Olaitan Igbagbo Awe, Thomas J. Butler, Tamiru Denka, Stephen Andrew Semick, Wanhu Tang, and Ben Busby. "SeqAcademy: an educational pipeline for RNA-Seq and ChIP-Seq analysis." F1000Research 7 (May 22, 2018): 628. http://dx.doi.org/10.12688/f1000research.14880.1.

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Quantification of gene expression and characterization of gene transcript structures are central problems in molecular biology. RNA sequencing (RNA-Seq) and chromatin immunoprecipitation sequencing (ChIP-Seq) are important methods, but can be cumbersome and difficult for beginners to learn. To teach interested students and scientists how to analyze RNA-Seq and ChIP-Seq data, we present a start-to-finish tutorial for analyzing RNA-Seq and ChIP-Seq data: SeqAcademy (source code: https://github.com/NCBI-Hackathons/seqacademy, webpage: http://www.seqacademy.org/). This user-friendly pipeline, fully written in Jupyter Notebook, emphasizes the use of publicly available RNA-Seq and ChIP-Seq data and strings together popular tools that bridge that gap between raw sequencing reads and biological insight. We demonstrate practical and conceptual considerations for various RNA-Seq and ChIP-Seq analysis steps with a biological use case - a previously published yeast experiment. This work complements existing sophisticated RNA-Seq and ChIP-Seq pipelines designed for advanced users by gently introducing the critical components of RNA-Seq and ChIP-Seq analysis to the novice bioinformatician. In conclusion, this well-documented pipeline will introduce state-of-the-art RNA-Seq and ChIP-Seq analysis tools to beginning bioinformaticians and help facilitate the analysis of the burgeoning amounts of public RNA-Seq and ChIP-Seq data.

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17

Ather, Syed Hussain, Olaitan Igbagbo Awe, Thomas J. Butler, Tamiru Denka, Stephen Andrew Semick, Wanhu Tang, and Ben Busby. "SeqAcademy: an educational pipeline for RNA-Seq and ChIP-Seq analysis." F1000Research 7 (November 30, 2018): 628. http://dx.doi.org/10.12688/f1000research.14880.2.

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Quantification of gene expression and characterization of gene transcript structures are central problems in molecular biology. RNA sequencing (RNA-Seq) and chromatin immunoprecipitation sequencing (ChIP-Seq) are important methods, but can be cumbersome and difficult for beginners to learn. To teach interested students and scientists how to analyze RNA-Seq and ChIP-Seq data, we present a start-to-finish tutorial for analyzing RNA-Seq and ChIP-Seq data: SeqAcademy (source code: https://github.com/NCBI-Hackathons/seqacademy, webpage: http://www.seqacademy.org/). This user-friendly pipeline, fully written in Jupyter Notebook, emphasizes the use of publicly available RNA-Seq and ChIP-Seq data and strings together popular tools that bridge that gap between raw sequencing reads and biological insight. We demonstrate practical and conceptual considerations for various RNA-Seq and ChIP-Seq analysis steps with a biological use case - a previously published yeast experiment. This work complements existing sophisticated RNA-Seq and ChIP-Seq pipelines designed for advanced users by gently introducing the critical components of RNA-Seq and ChIP-Seq analysis to the novice bioinformatician. In conclusion, this well-documented pipeline will introduce state-of-the-art RNA-Seq and ChIP-Seq analysis tools to beginning bioinformaticians and help facilitate the analysis of the burgeoning amounts of public RNA-Seq and ChIP-Seq data.

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18

Ather, Syed Hussain, Olaitan Igbagbo Awe, Thomas J. Butler, Tamiru Denka, Stephen Andrew Semick, Wanhu Tang, and Ben Busby. "SeqAcademy: an educational pipeline for RNA-Seq and ChIP-Seq analysis." F1000Research 7 (May 9, 2019): 628. http://dx.doi.org/10.12688/f1000research.14880.3.

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Quantification of gene expression and characterization of gene transcript structures are central problems in molecular biology. RNA sequencing (RNA-Seq) and chromatin immunoprecipitation sequencing (ChIP-Seq) are important methods, but can be cumbersome and difficult for beginners to learn. To teach interested students and scientists how to analyze RNA-Seq and ChIP-Seq data, we present a start-to-finish tutorial for analyzing RNA-Seq and ChIP-Seq data: SeqAcademy (source code: https://github.com/NCBI-Hackathons/seqacademy, webpage: http://www.seqacademy.org/). This user-friendly pipeline, fully written in markdown language, emphasizes the use of publicly available RNA-Seq and ChIP-Seq data and strings together popular tools that bridge that gap between raw sequencing reads and biological insight. We demonstrate practical and conceptual considerations for various RNA-Seq and ChIP-Seq analysis steps with a biological use case - a previously published yeast experiment. This work complements existing sophisticated RNA-Seq and ChIP-Seq pipelines designed for advanced users by gently introducing the critical components of RNA-Seq and ChIP-Seq analysis to the novice bioinformatician. In conclusion, this well-documented pipeline will introduce state-of-the-art RNA-Seq and ChIP-Seq analysis tools to beginning bioinformaticians and help facilitate the analysis of the burgeoning amounts of public RNA-Seq and ChIP-Seq data.

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19

Ather, Syed Hussain, Olaitan Igbagbo Awe, Thomas J. Butler, Tamiru Denka, Stephen Andrew Semick, Wanhu Tang, and Ben Busby. "SeqAcademy: an educational pipeline for RNA-Seq and ChIP-Seq analysis." F1000Research 7 (September 22, 2020): 628. http://dx.doi.org/10.12688/f1000research.14880.4.

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Анотація:

Quantification of gene expression and characterization of gene transcript structures are central problems in molecular biology. RNA sequencing (RNA-Seq) and chromatin immunoprecipitation sequencing (ChIP-Seq) are important methods, but can be cumbersome and difficult for beginners to learn. To teach interested students and scientists how to analyze RNA-Seq and ChIP-Seq data, we present a start-to-finish tutorial for analyzing RNA-Seq and ChIP-Seq data: SeqAcademy (source code: https://github.com/NCBI-Hackathons/seqacademy, webpage: http://www.seqacademy.org/). This user-friendly pipeline, fully written in markdown language, emphasizes the use of publicly available RNA-Seq and ChIP-Seq data and strings together popular tools that bridge that gap between raw sequencing reads and biological insight. We demonstrate practical and conceptual considerations for various RNA-Seq and ChIP-Seq analysis steps with a biological use case - a previously published yeast experiment. This work complements existing sophisticated RNA-Seq and ChIP-Seq pipelines designed for advanced users by gently introducing the critical components of RNA-Seq and ChIP-Seq analysis to the novice bioinformatician. In conclusion, this well-documented pipeline will introduce state-of-the-art RNA-Seq and ChIP-Seq analysis tools to beginning bioinformaticians and help facilitate the analysis of the burgeoning amounts of public RNA-Seq and ChIP-Seq data.

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20

An, Omer, Kar-Tong Tan, Ying Li, Jia Li, Chan-Shuo Wu, Bin Zhang, Leilei Chen, and Henry Yang. "CSI NGS Portal: An Online Platform for Automated NGS Data Analysis and Sharing." International Journal of Molecular Sciences 21, no. 11 (May 28, 2020): 3828. http://dx.doi.org/10.3390/ijms21113828.

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Next-generation sequencing (NGS) has been a widely-used technology in biomedical research for understanding the role of molecular genetics of cells in health and disease. A variety of computational tools have been developed to analyse the vastly growing NGS data, which often require bioinformatics skills, tedious work and a significant amount of time. To facilitate data processing steps minding the gap between biologists and bioinformaticians, we developed CSI NGS Portal, an online platform which gathers established bioinformatics pipelines to provide fully automated NGS data analysis and sharing in a user-friendly website. The portal currently provides 16 standard pipelines for analysing data from DNA, RNA, smallRNA, ChIP, RIP, 4C, SHAPE, circRNA, eCLIP, Bisulfite and scRNA sequencing, and is flexible to expand with new pipelines. The users can upload raw data in FASTQ format and submit jobs in a few clicks, and the results will be self-accessible via the portal to view/download/share in real-time. The output can be readily used as the final report or as input for other tools depending on the pipeline. Overall, CSI NGS Portal helps researchers rapidly analyse their NGS data and share results with colleagues without the aid of a bioinformatician. The portal is freely available at: https://csibioinfo.nus.edu.sg/csingsportal.

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21

Jackson, Michael, Kostas Kavoussanakis, and Edward W. J. Wallace. "Using prototyping to choose a bioinformatics workflow management system." PLOS Computational Biology 17, no. 2 (February 25, 2021): e1008622. http://dx.doi.org/10.1371/journal.pcbi.1008622.

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Workflow management systems represent, manage, and execute multistep computational analyses and offer many benefits to bioinformaticians. They provide a common language for describing analysis workflows, contributing to reproducibility and to building libraries of reusable components. They can support both incremental build and re-entrancy—the ability to selectively re-execute parts of a workflow in the presence of additional inputs or changes in configuration and to resume execution from where a workflow previously stopped. Many workflow management systems enhance portability by supporting the use of containers, high-performance computing (HPC) systems, and clouds. Most importantly, workflow management systems allow bioinformaticians to delegate how their workflows are run to the workflow management system and its developers. This frees the bioinformaticians to focus on what these workflows should do, on their data analyses, and on their science. RiboViz is a package to extract biological insight from ribosome profiling data to help advance understanding of protein synthesis. At the heart of RiboViz is an analysis workflow, implemented in a Python script. To conform to best practices for scientific computing which recommend the use of build tools to automate workflows and to reuse code instead of rewriting it, the authors reimplemented this workflow within a workflow management system. To select a workflow management system, a rapid survey of available systems was undertaken, and candidates were shortlisted: Snakemake, cwltool, Toil, and Nextflow. Each candidate was evaluated by quickly prototyping a subset of the RiboViz workflow, and Nextflow was chosen. The selection process took 10 person-days, a small cost for the assurance that Nextflow satisfied the authors’ requirements. The use of prototyping can offer a low-cost way of making a more informed selection of software to use within projects, rather than relying solely upon reviews and recommendations by others.

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22

Paul, Piby, Vimala Antonydhason, Judy Gopal, Steve W. Haga, Nazim Hasan, and Jae-Wook Oh. "Bioinformatics for Renal and Urinary Proteomics: Call for Aggrandization." International Journal of Molecular Sciences 21, no. 3 (January 31, 2020): 961. http://dx.doi.org/10.3390/ijms21030961.

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The clinical sampling of urine is noninvasive and unrestricted, whereby huge volumes can be easily obtained. This makes urine a valuable resource for the diagnoses of diseases. Urinary and renal proteomics have resulted in considerable progress in kidney-based disease diagnosis through biomarker discovery and treatment. This review summarizes the bioinformatics tools available for this area of proteomics and the milestones reached using these tools in clinical research. The scant research publications and the even more limited bioinformatic tool options available for urinary and renal proteomics are highlighted in this review. The need for more attention and input from bioinformaticians is highlighted, so that progressive achievements and releases can be made. With just a handful of existing tools for renal and urinary proteomic research available, this review identifies a gap worth targeting by protein chemists and bioinformaticians. The probable causes for the lack of enthusiasm in this area are also speculated upon in this review. This is the first review that consolidates the bioinformatics applications specifically for renal and urinary proteomics.

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23

Nanjala, Ruth, Festus Nyasimi, Daniel Masiga, and Caleb Kipkurui Kibet. "A mentorship and incubation program using project-based learning to build a professional bioinformatics pipeline in Kenya." PLOS Computational Biology 19, no. 3 (March 2, 2023): e1010904. http://dx.doi.org/10.1371/journal.pcbi.1010904.

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The demand for well-trained bioinformaticians to support genomics research continues to rise. Unfortunately, undergraduate training in Kenya does not prepare students for specialization in bioinformatics. Graduates are often unaware of the career opportunities in bioinformatics, and those who are may lack mentors to help them choose a specialization. The Bioinformatics Mentorship and Incubation Program seeks to bridge the gap by laying the foundation for a bioinformatics training pipeline using project-based learning. The program selects six participants through an intensive open recruitment exercise for highly competitive students to join the program for four months. The six interns undergo intensive training within the first one and a half months before being assigned to mini-projects. We track the progress of the interns weekly through code review sessions and a final presentation at the end of the four months. We have trained five cohorts, most of whom have secured master’s scholarships within and outside the country and job opportunities. We demonstrate the benefit of structured mentorship using project-based learning in filling the training gap after undergraduate programs to generate well-trained bioinformaticians who are competitive in graduate programs and bioinformatics jobs.

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24

Agaoglu, Nihat Bugra, Busra Unal, Ozlem Akgun Dogan, Martin Orlinov Kanev, Payam Zolfagharian, Sebnem Ozemri Sag, Sehime Gulsun Temel, and Levent Doganay. "Consistency of variant interpretations among bioinformaticians and clinical geneticists in hereditary cancer panels." European Journal of Human Genetics 30, no. 3 (February 8, 2022): 378–83. http://dx.doi.org/10.1038/s41431-022-01060-7.

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25

du Plessis, L., N. Skunca, and C. Dessimoz. "The what, where, how and why of gene ontology--a primer for bioinformaticians." Briefings in Bioinformatics 12, no. 6 (February 17, 2011): 723–35. http://dx.doi.org/10.1093/bib/bbr002.

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26

Snyder, Holly. "P560: Development of an educational forum for clinical bioinformaticians to share best practices." Genetics in Medicine Open 1, no. 1 (2023): 100607. http://dx.doi.org/10.1016/j.gimo.2023.100607.

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27

Pettifer, S. R., J. R. Sinnott, and T. K. Attwood. "UTOPIA—User-Friendly Tools for Operating Informatics Applications." Comparative and Functional Genomics 5, no. 1 (2004): 56–60. http://dx.doi.org/10.1002/cfg.359.

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Bioinformaticians routinely analyse vast amounts of information held both in large remote databases and in flat data files hosted on local machines. The contemporary toolkit available for this purpose consists of anad hoccollection of data manipulation tools, scripting languages and visualization systems; these must often be combined in complex and bespoke ways, the result frequently being an unwieldy artefact capable of one specific task, which cannot easily be exploited or extended by other practitioners. Owing to the sizes of current databases and the scale of the analyses necessary, routine bioinformatics tasks are often automated, but many still require the unique experience and intuition of human researchers: this requires tools that support real-time interaction with complex datasets. Many existing tools have poor user interfaces and limited real-time performance when applied to realistically large datasets; much of the user's cognitive capacity is therefore focused on controlling the tool rather than on performing the research. The UTOPIA project is addressing some of these issues by building reusable software components that can be combined to make useful applications in the field of bioinformatics. Expertise in the fields of human computer interaction, high-performance rendering, and distributed systems is being guided by bioinformaticians and end-user biologists to create a toolkit that is both architecturally sound from a computing point of view, and directly addresses end-user and application-developer requirements.

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Vincent, Antony T., Yves Bourbonnais, Jean-Simon Brouard, Hélène Deveau, Arnaud Droit, Stéphane M. Gagné, Michel Guertin, et al. "Implementing a web-based introductory bioinformatics course for non-bioinformaticians that incorporates practical exercises." Biochemistry and Molecular Biology Education 46, no. 1 (September 13, 2017): 31–38. http://dx.doi.org/10.1002/bmb.21086.

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29

Weninger, F., S. Merk, A. Kohlmann, T. Haferlach, and M. Dugas. "A Generic Concept for Large-scale Microarray Analysis Dedicated to Medical Diagnostics." Methods of Information in Medicine 45, no. 02 (2006): 146–52. http://dx.doi.org/10.1055/s-0038-1634058.

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Summary Background: The development of diagnostic procedures based on microarray analysis confronts the bioinformatician and the biomedical researcher with a variety of challenges. Microarrays generate a huge amount of data. There are many, not yet clearly defined, data processing steps and many clinical response variables which may not match gene expression patterns. Objectives: To design a generic concept for large-scale microarray experiments dedicated to medical diagnostics; to create a system capable of handling several 1000 microarrays per analysis and more than 100 clinical response variables; to design a standardized workflow for quality control, data calibration, identification of differentially expressed genes and estimation of classification accuracy; and to provide a user-friendly interface for clinical researchers with respect to biomedical interpretation. Methods: We designed a database structure suitable for the storage of microarray data and analysis results. We applied statistical procedures to identify differential genes and developed a technique to estimate classification accuracy of gene patterns with confidence intervals. Results: We implemented a Gene Analysis Management System (GAMS) based on this concept, using MySQL for data storage, R/Bioconductor for analysis and PHP for a web-based front-end for the exploration of microarray data and analysis results. This system was utilized with large data sets from several medical disciplines, mainly from oncology (~ 2000 micro-arrays). Conclusions: A systematic approach is necessary for the analysis of microarray experiments in a medical diagnostics setting to get comprehensible results. Due to the complexity of the analysis, data processing (by bioinformaticians) and interactive exploration of results (by biomedical experts) should be separated.

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Shyr, Casper, Andre Kushniruk, Clara D. M. van Karnebeek, and Wyeth W. Wasserman. "Dynamic software design for clinical exome and genome analyses: insights from bioinformaticians, clinical geneticists, and genetic counselors." Journal of the American Medical Informatics Association 23, no. 2 (June 27, 2015): 257–68. http://dx.doi.org/10.1093/jamia/ocv053.

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Abstract Background The transition of whole-exome and whole-genome sequencing (WES/WGS) from the research setting to routine clinical practice remains challenging. Objectives With almost no previous research specifically assessing interface designs and functionalities of WES and WGS software tools, the authors set out to ascertain perspectives from healthcare professionals in distinct domains on optimal clinical genomics user interfaces. Methods A series of semi-scripted focus groups, structured around professional challenges encountered in clinical WES and WGS, were conducted with bioinformaticians (n = 8), clinical geneticists (n = 9), genetic counselors (n = 5), and general physicians (n = 4). Results Contrary to popular existing system designs, bioinformaticians preferred command line over graphical user interfaces for better software compatibility and customization flexibility. Clinical geneticists and genetic counselors desired an overarching interactive graphical layout to prioritize candidate variants—a “tiered” system where only functionalities relevant to the user domain are made accessible. They favored a system capable of retrieving consistent representations of external genetic information from third-party sources. To streamline collaboration and patient exchanges, the authors identified user requirements toward an automated reporting system capable of summarizing key evidence-based clinical findings among the vast array of technical details. Conclusions Successful adoption of a clinical WES/WGS system is heavily dependent on its ability to address the diverse necessities and predilections among specialists in distinct healthcare domains. Tailored software interfaces suitable for each group is likely more appropriate than the current popular “one size fits all” generic framework. This study provides interfaces for future intervention studies and software engineering opportunities.

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Fernandes, Pedro, Pooja Jain, and Catarina Moita. "Training Experimental Biologists in Bioinformatics." Advances in Bioinformatics 2012 (January 31, 2012): 1–4. http://dx.doi.org/10.1155/2012/672749.

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Bioinformatics, for its very nature, is devoted to a set of targets that constantly evolve. Training is probably the best response to the constant need for the acquisition of bioinformatics skills. It is interesting to assess the effects of training in the different sets of researchers that make use of it. While training bench experimentalists in the life sciences, we have observed instances of changes in their attitudes in research that, if well exploited, can have beneficial impacts in the dialogue with professional bioinformaticians and influence the conduction of the research itself.

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32

van Iterson, Maarten, Herman H. H. B. M. van Haagen, and Jelle J. Goeman. "Resolving confusion of tongues in statistics and machine learning: A primer for biologists and bioinformaticians." PROTEOMICS 12, no. 4-5 (January 23, 2012): 543–49. http://dx.doi.org/10.1002/pmic.201100395.

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33

Damkliang, Kasikrit, Pichaya Tandayya, Unisa Sangket, and Ekawat Pasomsub. "Similarity Score Estimation and Gaps Trimming of Multiple Sequence Alignment for Phylogenetic Tree Analysis." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 11, no. 2 (November 28, 2017): 129–42. http://dx.doi.org/10.37936/ecti-cit.2017112.74783.

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Phylogenetic tree analysis is a process for finding the highest possible revolution tree history of an interested organism. The important step of the process is multiple sequences alignment (MSA) which is operated using any MSA tool that produces a result in blocks of the Phylip format. Bioinformaticians have to manually determine and trim gaps of the MSA blocks using relevant tools of a software package in the off-line mode. The data blocks need to be manually cut-and-pasted between these tools. This working steps tend to be error-prone and time consuming. In addition, improper algorithm selection for tree inferring without applying an MSA similarity score tends to generate the phylogenetic tree with low accuracy and also take much more time. In this work, we present a new practical approach for the phylogenetic tree analysis applying our enhancement for the similarity score estimation and gaps trimming of the MSA blocks. We propose \textit{in-silico} algorithms for automating the concerned similarity score estimation and gaps trimming, and deploy them as web services. We demonstrate the web services utilized by composing them into an integrated stateful WSDL workflow. Our case study datasets are a complete coding sequences (CDS) and sets of complete genome of Dengue Viruses - 2, fetched from the NCBI RefSeq nucleotide database. Our proposed algorithms have correctly returned results, verified and satisfied by our bioinformaticians. Our distributions, user manuals and endpoints of the web services, and the open source programs are available at https://bioservices.sci.psu.ac.th.

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34

Kalfatovic, Martin R., and Grace Costantino. "The Biodiversity Heritage Library: Empowering Discovery through Free Access to Biodiversity Knowledge." KULA: Knowledge Creation, Dissemination, and Preservation Studies 2 (November 29, 2018): 17. http://dx.doi.org/10.5334/kula.41.

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The advancement of knowledge about life on the planet—its origins, preservation, and loss of species and environments—is dependent on access and reference to library collections. The Biodiversity Heritage Library (BHL) is a global digital library that serves the biodiversity research community, as well as a widening circle of those interested in learning more about life. Through an international consortium of natural history and botanical libraries and in close collaboration with researchers, bioinformaticians, publishers, and information technology professionals, BHL has democratized access to biodiversity information and revolutionized research worldwide, allowing everyone, everywhere to study and explore life on Earth.

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Goettsch, Winfried, Niko Beerenwinkel, Li Deng, Lars Dölken, Bas E. Dutilh, Florian Erhard, Lars Kaderali, et al. "ITN—VIROINF: Understanding (Harmful) Virus-Host Interactions by Linking Virology and Bioinformatics." Viruses 13, no. 5 (April 27, 2021): 766. http://dx.doi.org/10.3390/v13050766.

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Many recent studies highlight the fundamental importance of viruses. Besides their important role as human and animal pathogens, their beneficial, commensal or harmful functions are poorly understood. By developing and applying tailored bioinformatical tools in important virological models, the Marie Skłodowska-Curie Initiative International Training Network VIROINF will provide a better understanding of viruses and the interaction with their hosts. This will open the door to validate methods of improving viral growth, morphogenesis and development, as well as to control strategies against unwanted microorganisms. The key feature of VIROINF is its interdisciplinary nature, which brings together virologists and bioinformaticians to achieve common goals.

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36

von Heijne, Gunnar. "Membrane proteins: from bench to bits." Biochemical Society Transactions 39, no. 3 (May 20, 2011): 747–50. http://dx.doi.org/10.1042/bst0390747.

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Membrane proteins currently receive a lot of attention, in large part thanks to a steady stream of high-resolution X-ray structures. Although the first few structures showed proteins composed of tightly packed bundles of very hydrophobic more or less straight transmembrane α-helices, we now know that helix-bundle membrane proteins can be both highly flexible and contain transmembrane segments that are neither very hydrophobic nor necessarily helical throughout their lengths. This raises questions regarding how membrane proteins are inserted into the membrane and fold in vivo, and also complicates life for bioinformaticians trying to predict membrane protein topology and structure.

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Hettne, Kristina, Reinout van Schouwen, Eleni Mina, Eelke van der Horst, Mark Thompson, Rajaram Kaliyaperumal, Barend Mons, Erik van Mulligen, Jan A. Kors, and Marco Roos. "Explain your data by Concept Profile Analysis Web Services." F1000Research 3 (July 25, 2014): 173. http://dx.doi.org/10.12688/f1000research.4830.1.

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The Concept Profile Analysis technology (overlapping co-occurring concept sets based on knowledge contained in biomedical abstracts) has led to new biomedical discoveries, and users have been able to interact with concept profiles through the interactive tool “Anni” (http://biosemantics.org/anni). However, Anni provides no way for users to save their procedures, results, or related provenance. Here we present a new suite of Web Service operations that allows bioinformaticians to design and execute their own Concept Profile Analysis workflow, possibly as part of a larger bioinformatics analysis. The source code can be downloaded from ZENODO at http://www.dx.doi.org/10.5281/zenodo.10963.

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38

Bartocci, E., M. R. Di Berardini, E. Merelli, and L. Vito. "UBioLab: a web-LABoratory for Ubiquitous in-silico experiments." Journal of Integrative Bioinformatics 9, no. 1 (March 1, 2012): 12–31. http://dx.doi.org/10.1515/jib-2012-192.

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Summary The huge and dynamic amount of bioinformatic resources (e.g., data and tools) available nowadays in Internet represents a big challenge for biologists -for what concerns their management and visualization- and for bioinformaticians -for what concerns the possibility of rapidly creating and executing in-silico experiments involving resources and activities spread over the WWW hyperspace. Any framework aiming at integrating such resources as in a physical laboratory has imperatively to tackle -and possibly to handle in a transparent and uniform way- aspects concerning physical distribution, semantic heterogeneity, co-existence of different computational paradigms and, as a consequence, of different invocation interfaces (i.e., OGSA for Grid nodes, SOAP for Web Services, Java RMI for Java objects, etc.). The framework UBioLab has been just designed and developed as a prototype following the above objective. Several architectural features -as those ones of being fully Web-based and of combining domain ontologies, Semantic Web and workflow techniques- give evidence of an effort in such a direction.The integration of a semantic knowledge management system for distributed (bioinformatic) resources, a semantic-driven graphic environment for defining and monitoring ubiquitous workflows and an intelligent agent-based technology for their distributed execution allows UBioLab to be a semantic guide for bioinformaticians and biologists providing (i) a flexible environment for visualizing, organizing and inferring any (semantics and computational) “type” of domain knowledge (e.g., resources and activities, expressed in a declarative form), (ii) a powerful engine for defining and storing semantic-driven ubiquitous in-silico experiments on the domain hyperspace, as well as (iii) a transparent, automatic and distributed environment for correct experiment executions.

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Rosellini, Will, and Frank McEachern. "The United States Database Debate – Proteins as virtual property." Journal of International Biotechnology Law 3, no. 1 (January 1, 2006): 1–11. http://dx.doi.org/10.1515/jibl.2006.001.

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AbstractA database is defined as “any organized collection of information” and can include any number of different categories, from clients list, to phonebooks to nucleotide sequence data for E. Coli. As the speed and storage capacity of next generation computers continue to Moore's law, doubling every 12–18 months, databases are becoming vital tools to extract information that to date has gone unnoticed. Nowhere is this more applicable or more evident than in the burgeoning field of bioinformatics, the science of applying computers to biological problems. Bioinformaticians are a loose consort of biologists, physicists, chemists, mathematicians who also understand principles of computer science. These Bioinformaticians invest substantial resources in the form of money, time, and personnel in gathering information, verifying the accuracy of that information, and bringing it together in one location. Until very recently, scientists who fit this definition have largely been academic researchers, but with the mapping of the Human Genome completed, these scientists have now begun to become more and more prevalent in commercial settings. To date, these academic scientists have participated in open source data sharing to speed scientific progress, but as the cost of development increases and commercial entities continue to exploit bioinformatics tools for the production of new drug candidates this compiled information has become the topic of hot debate. The difficulty associated with a discussion of such a highly technical subject both intellectual property and genomics, coupled with a paradigm shift in the science makes for a very difficult and perhaps insurmountable barrier. In spite of these difficulties, one should not be discouraged, as the innovation at this level is the beginning of the end of death and disease in human beings.

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Jarlier, Frédéric, Nicolas Joly, Nicolas Fedy, Thomas Magalhaes, Leonor Sirotti, Paul Paganiban, Firmin Martin, Michael McManus, and Philippe Hupé. "QUARTIC: QUick pArallel algoRithms for high-Throughput sequencIng data proCessing." F1000Research 9 (April 6, 2020): 240. http://dx.doi.org/10.12688/f1000research.22954.1.

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Life science has entered the so-called ’big data era’ where biologists, clinicians and bioinformaticians are overwhelmed with unprecedented amount of data. High-throughput sequencing has revolutionized genomics and offers new insights to decipher the genome structure. However, using these data for daily clinical practice care and diagnosis purposes is challenging as the data are bigger and bigger. Therefore, we implemented software using Message Passing Interface such that the alignment and sorting of sequencing reads can easily scale on high-performance computing architecture. Our implementation makes it possible to reduce the time to delivery to few minutes, even on large whole-genome data using several hundreds of cores.

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Orengo, Christine, Sameer Velankar, Shoshana Wodak, Vincent Zoete, Alexandre M. J. J. Bonvin, Arne Elofsson, K. Anton Feenstra, et al. "A community proposal to integrate structural bioinformatics activities in ELIXIR (3D-Bioinfo Community)." F1000Research 9 (April 22, 2020): 278. http://dx.doi.org/10.12688/f1000research.20559.1.

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Structural bioinformatics provides the scientific methods and tools to analyse, archive, validate, and present the biomolecular structure data generated by the structural biology community. It also provides an important link with the genomics community, as structural bioinformaticians also use the extensive sequence data to predict protein structures and their functional sites. A very broad and active community of structural bioinformaticians exists across Europe, and 3D-Bioinfo will establish formal platforms to address their needs and better integrate their activities and initiatives. Our mission will be to strengthen the ties with the structural biology research communities in Europe covering life sciences, as well as chemistry and physics and to bridge the gap between these researchers in order to fully realize the potential of structural bioinformatics. Our Community will also undertake dedicated educational, training and outreach efforts to facilitate this, bringing new insights and thus facilitating the development of much needed innovative applications e.g. for human health, drug and protein design. Our combined efforts will be of critical importance to keep the European research efforts competitive in this respect. Here we highlight the major European contributions to the field of structural bioinformatics, the most pressing challenges remaining and how Europe-wide interactions, enabled by ELIXIR and its platforms, will help in addressing these challenges and in coordinating structural bioinformatics resources across Europe. In particular, we present recent activities and future plans to consolidate an ELIXIR 3D-Bioinfo Community in structural bioinformatics and propose means to develop better links across the community. These include building new consortia, organising workshops to establish data standards and seeking community agreement on benchmark data sets and strategies. We also highlight existing and planned collaborations with other ELIXIR Communities and other European infrastructures, such as the structural biology community supported by Instruct-ERIC, with whom we have synergies and overlapping common interests.

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42

Seto, Kelly, Wendy Mok, and Jonny Stone. "Bridging the gap between theory and practice in elucidating modular gene regulatory sequence organisation within genomes." Genome 63, no. 6 (June 2020): 281–89. http://dx.doi.org/10.1139/gen-2019-0150.

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Changes to promoter regions probably have been responsible for many morphological evolutionary transitions, especially in animals. This idea is becoming testable, as data from genome projects amass and enable bioinformaticians to conduct comparative sequence analyses and test for correlations between genotypic similarities or differences and phenotypic likeness or disparity. Although such practical pursuits have initiated some theoretical considerations, a conceptual framework for understanding promoter region evolution, potentially effecting morphological evolution, is only starting to emerge, predominantly resulting from computational research. We contribute to this framework by specifying three big problems for promoter region research; reviewing computational research on promoter region evolution; and exemplifying a topic for future promoter region research — module evolution.

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43

Hodgman, T. C., Y. Ugartechea-Chirino, G. Tansley, and I. Dryden. "The implications for Bioinformatics of integration across physical scales." Journal of Integrative Bioinformatics 3, no. 2 (December 1, 2006): 212–18. http://dx.doi.org/10.1515/jib-2006-39.

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Abstract Bioinformatics blossomed with research developments in molecular biology. But as the focus of research moves back up the physical scale to the biology of whole multicellular organisms, there are new integration challenges. Data integration is a perennial theme that will not be explored here. Rather, we outline a survey of the types of data generated in whole organisms, putting particular emphasis on complex image capture, management and analysis, bio-systems modelling techniques at this higher scale, and approaches to integrating models across these physical scales. This change in biological focus raises certain challenges, one of which may be the need to retrain bioinformaticians in sciences pertaining to whole plants and animals.

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Martin-Sanchez, F., and V. Maojo. "Public Health Implications of Bioinformatics." Yearbook of Medical Informatics 13, no. 01 (August 2004): 137–43. http://dx.doi.org/10.1055/s-0038-1638191.

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Abstract:Epidemiologists are reformulating their classical approaches to diseases by considering various issues associated to “omics” areas and technologies. Traditional differences between epidemiology and genetics include background, training, terminologies, study designs and others. Public health and epidemiology are increasingly looking forward to using methodologies and informatics tools, facilitated by the Bioinformatics community, for managing genomic information. Future microarray developments will also facilitate the analysis of entire genomes on single arrays, enhancing genetic epidemiology research. The use of biomarkers, biobanks, and integrated genomic/clinical databases poses serious challenges for bioinformaticians in order to extract useful information and knowledge for biomedical research and healthcare. In this regard, there are various ethical, privacy, informed consent and social implications that should be carefully addressed by researchers, practitioners and policy makers.

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45

Gisel, Andreas, Livia Stavolone, Temitayo Olagunju, Michael Landi, Renaud Van Damme, Adnan Niazi, Laurent Falquet, Trushar Shah, and Erik Bongcam-Rudloff. "EpiCass and CassavaNet4Dev Advanced Bioinformatics Workshop." EMBnet.journal 29 (June 8, 2023): e1045. http://dx.doi.org/10.14806/ej.29.0.1045.

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EpiCass and CassavaNet4Dev are collaborative projects funded by the Swedish Research Council between the Swedish University of Agriculture (SLU) and the International Institute of Tropical Agriculture (IITA). The projects aim to investigate the influence of epigenetic changes on agricultural traits such as yield and virus resistance while also providing African students and researchers with advanced bioinformatics training and opportunities to participate in big data analysis events. The first advanced bioinformatics training workshop took place from May 16th to May 18th, 2022, followed by an online mini-symposium titled "Epigenetics and crop improvement" on May 19th. The symposium featured international speakers covering a wide range of topics related to plant epigenetics, cassava viral diseases, and cassava breeding strategies. A new online and on-site teaching concept was developed for the three-day workshop to ensure maximum student participation across Western, Eastern, and Southern Africa. Initially planned in Nigeria, Kenya, Ethiopia, Tanzania, and Zambia, the workshop ultimately focused on Nigeria, Kenya, and Ethiopia due to a lack of qualified candidates in the other countries. Each classroom hosted 20 to 25 students, with at least one bioinformatician present for support. The classrooms were connected via video conferencing, whereas teachers located in different places in Africa and Europe joined the video stream to conduct teaching sessions. The workshop was divided into theoretical classes and hands-on sessions, where participants could run data analysis with support from online teachers and local bioinformaticians. To enable participants to run guided, CPU and RAM-intensive data analysis workflows and overcome local computing and internet access restrictions, a system of virtual machines (VMs) hosted in the cloud was developed. The teaching platform provided teaching and exercise materials to support the use of the VMs. Although some students could not run heavy data analysis workflows due to unforeseen restrictions in the cloud, these issues were solved. All participants had the opportunity to run the analysis steps independently in the cloud using the protocols hosted on the teaching platform.

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Sansom, Clare. "The Grid revisited." Biochemist 30, no. 2 (April 1, 2008): 37–38. http://dx.doi.org/10.1042/bio03002037.

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When Cyberbiochemist first looked at the Grid, 4 years ago, the term will not have entered the vocabulary of most bio chemists. It was the more numbercrunching disciplines, such as highenergy physics and fluid dynamics, which first em braced this much hyped ‘secondgeneration Internet’; career bioinformaticians were not far behind. Most biochemists and molecular biologists, however, whose experience of the computational side of their discipline is restricted to the occasional BLAST run or simple molecular visualization, have unsurprisingly been slower to see what these new developments might have to offer them. Now, 4 years on, it is useful to look into what practical use the Grid can be to the majority of Biochemist readers, who will never design a Hidden Markov Model or run a multinanosecond Molecular Dynamics simulation.

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Mohamed, Sofia B., Sumaya Kambal, Sabah A. E. Ibrahim, Esra Abdalwhab, Abdalla Munir, Arwa Ibrahim, and Qurashi Mohamed Ali. "Bioinformatics in Sudan: Status and challenges case study: The National University-Sudan." PLOS Computational Biology 17, no. 10 (October 21, 2021): e1009462. http://dx.doi.org/10.1371/journal.pcbi.1009462.

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The ever increasing applications of bioinformatics in providing effective interpretation of large and complex biological data require expertise in the use of sophisticated computational tools and advanced statistical tests, skills that are mostly lacking in the Sudanese research community. This can be attributed to paucity in the development and promotion of bioinformatics, lack of senior bioinformaticians, and the general status quo of inadequate research funding in Sudan. In this paper, we describe the challenges that have encountered the development of bioinformatics as a discipline in Sudan. Additionally, we highlight on specific actions that may help develop and promote its education and training. The paper takes the National University Biomedical Research Institute (NUBRI) as an example of an institute that has tackled many of these challenges and strives to drive powerful efforts in the development of bioinformatics in the country.

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Munro, Andrew W., and Nigel S. Scrutton. "Enzyme Mechanisms: Fast Reaction and Computational Approaches." Biochemical Society Transactions 37, no. 2 (March 20, 2009): 333–35. http://dx.doi.org/10.1042/bst0370333.

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Now, more than ever, enzymology and its development can be considered of vital importance to the progression of the biological sciences. With an increase in the numbers of enzymes being identified from genomic studies, enzymology is key to defining the structural and functional properties of these enzymes in order to establish their mechanisms of action and how they fit into metabolic networks. Along with the efforts of the bioinformaticians and systems biologists, such studies will ultimately lead to detailed descriptions of intricate biochemical pathways and allow identification of the most appropriate target enzymes for intervention in disease therapy. Thus the timing for the recent Biochemical Society Focused Meeting entitled ‘Enzyme Mechanisms: Fast Reaction and Computational Approaches’ was highly appropriate. The present paper represents an overview of this meeting, which was held at the Manchester Interdisciplinary Biocentre on 9–10 October 2008.

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49

Le, Vinh. "A computational framework to analyze human genomes." Journal of Computer Science and Cybernetics 35, no. 2 (June 3, 2019): 105–18. http://dx.doi.org/10.15625/1813-9663/35/2/13827.

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The advent of genomic technologies has led to the current genomic era. Large-scale human genome projects have resulted in a huge amount of genomic data. Analyzing human genomes is a challenging task including a number of key steps from short read alignment, variant calling, and variant annotating. In this paper, the state-of-the-art computational methods and databases for each step will be analyzed to suggest a practical and efficient guideline for whole human genome analyses. This paper also discusses frameworks to combine variants from various genome analysis pipelines to obtain reliable variants. Finally, we will address advantages as well as discordances of widely-used variant annotation methods to evaluate the clinical significance of variants. The review will empower bioinformaticians to efficiently perform human genome analyses, and more importantly, help genetic consultants understand and properly interpret mutations for clinical purposes.

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Wrzeszczynski, Kazimierz O., Mayu O. Frank, Takahiko Koyama, Kahn Rhrissorrakrai, Nicolas Robine, Filippo Utro, Anne-Katrin Emde, et al. "Comparing sequencing assays and human-machine analyses in actionable genomics for glioblastoma." Neurology Genetics 3, no. 4 (July 11, 2017): e164. http://dx.doi.org/10.1212/nxg.0000000000000164.

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Objective:To analyze a glioblastoma tumor specimen with 3 different platforms and compare potentially actionable calls from each.Methods:Tumor DNA was analyzed by a commercial targeted panel. In addition, tumor-normal DNA was analyzed by whole-genome sequencing (WGS) and tumor RNA was analyzed by RNA sequencing (RNA-seq). The WGS and RNA-seq data were analyzed by a team of bioinformaticians and cancer oncologists, and separately by IBM Watson Genomic Analytics (WGA), an automated system for prioritizing somatic variants and identifying drugs.Results:More variants were identified by WGS/RNA analysis than by targeted panels. WGA completed a comparable analysis in a fraction of the time required by the human analysts.Conclusions:The development of an effective human-machine interface in the analysis of deep cancer genomic datasets may provide potentially clinically actionable calls for individual patients in a more timely and efficient manner than currently possible.ClinicalTrials.gov identifier:NCT02725684.

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