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biotoolsSchema: a formalized schema for bioinformatics software description

GIGASCIENCE 2021年第1期10卷 p1-19页

作者： Ison, Jon Ienasescu, Hans Rydza, Emil Chmura, Piotr Rapacki, Kristoffer Gaignard, Alban Schwaemmle, Veit van Helden, Jacques Kalas, Matus Menager, Herve Inst Francais Bioinformat IFB Core CNRS UMS 3601 2 Rue Gaston Cremieux F-91000 Evry France Tech Univ Denmark Natl Life Sci Supercomp Ctr Bldg 208 DK-2800 Lyngby Denmark Univ Copenhagen Novo Nordisk Fdn Ctr Prot Res Fac Hlth & Med Sci Blegdamsvej 3B DK-2200 Copenhagen Denmark Orsteds Plads Dept Hlth Technol Bldg 345C DK-2800 Lyngby Denmark Univ Nantes Inst Thorax CNRS INSERM F-44007 Nantes France Univ Southern Denmark Dept Biochem & Mol Biol Campusvej 55 DK-5230 Odense Denmark Univ Southern Denmark VILLUM Ctr Bioanalyt Sci Campusvej 55 DK-5230 Odense Denmark Aix Marseille Univ AMU Dept Biol 3 Pl Victor Hugo F-13003 Marseille France Univ Bergen Dept Informat Computat Biol Unit N-5008 Bergen Norway Inst Pasteur Hub Bioinformat & Biostat CNRS Dept Biol ComputatUSR 3756 F-75015 Paris France

Background: Life scientists routinely face massive and heterogeneous data analysis tasks and must find and access the most suitable databases or software in a jungle of web-accessible resources. The diversity of information used to describe life-scientific digital resources presents an obstacle to their utilization. Although several standardization efforts are emerging, no information schema has been sufficiently detailed to enable uniform semantic and syntactic description-and cataloguing-of bioinformatics resources. Findings: Here we describe biotoolsSchema, a formalized information model that balances the needs of conciseness for rapid adoption against the provision of rich technical information and scientific context. biotoolsSchema results from a series of community-driven workshops and is deployed in the *** registry, providing the scientific community with >17,000 machine-readable and human-understandable descriptions of software and other digital life-science resources. We compare our approach to related initiatives and provide alignments to foster interoperability and reusability. Conclusions: biotoolsSchema supports the formalized, rigorous, and consistent specification of the syntax and semantics of bioinformatics resources, and enables cataloguing efforts such as *** that help scientists to find, comprehend, and compare resources. The use of biotoolsSchema in *** promotes the FAIRness of research software, a key element of open and reproducible developments for data-intensive sciences.

关键词： bioinformatics software Tools Standards Data sharing

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Elucidating the Role and Use of bioinformatics software in Life Science Research 15

Elucidating the Role and Use of Bioinformatics Software in L...

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British HCI Conference

作者： Morrison-Smith, Sarah Boucher, Christina Bunt, Andrea Ruiz, Jaime Colorado State Univ Dept Comp Sci Ft Collins CO 80523 USA Univ Manitoba Dept Comp Sci Winnipeg MB Canada

ISBN: (纸本)9781450336437

Life science research requires critical evaluation of data handling and analytical software usability. We present the results of semi-structured interviews which provide insight into the effects of bioinformatics software usability on life science research. Results from our study confirm much of the prior anecdotal evidence of standalone bioinformatics software usability. More importantly, we show that usability issues and life scientists' lack of expertise in applying computational methods to biological research is limiting their research objectives and contributing to researchers' reliance on computational experts to conduct their research.

关键词： Data-Driven Research Qualitative Study bioinformatics software

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Focus on bioinformatics, software, and MS-Based "Omics," Honoring Dr. Michael J. MacCoss, Recipient of the 2015 ASMS Biemann Medal

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JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2016年第11期27卷 1715-1718页

作者： Loo, Joseph A. Matthews, Dwight E. Yates, John R., III Univ Calif Los Angeles Dept Chem & Biochem Dept Biol Chem 405 Hilgard Ave Los Angeles CA 90024 USA Univ Vermont Dept Chem Burlington VT 05405 USA Univ Vermont Dept Med Burlington VT USA Scripps Res Inst Chem Physiol & Mol & Cellular Neurobiol La Jolla CA 92037 USA

The article features Michael J. MacCoss, a professor at the University of Washington in Seattle and the recipient of the 2015 American Society for Mass Spectometry Biemann Medal. He graduated from the University of Vermont in Burlington with a degree in chemistry and completed postdoctoral research at The Scripps Research Institute in La Jolla, California. MacCoss and his team have developed bioinformatics software tools that facilitate different aspects of mass spectrometry data analysis.

关键词： bioinformatics software MASS spectrometry

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Buying in to bioinformatics: an introduction to commercial sequence analysis software

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BRIEFINGS IN bioinformatics 2015年第4期16卷 700-709页

作者： Smith, David Roy Univ Western Ontario Biol London ON N6A 5B7 Canada

Advancements in high-throughput nucleotide sequencing techniques have brought with them state-of-the-art bioinformatics programs and software packages. Given the importance of molecular sequence data in contemporary life science research, these software suites are becoming an essential component of many labs and classrooms, and as such are frequently designed for non-computer specialists and marketed as one-stop bioinformatics toolkits. Although beautifully designed and powerful, user-friendly bioinformatics packages can be expensive and, as more arrive on the market each year, it can be difficult for researchers, teachers and students to choose the right software for their needs, especially if they do not have a bioinformatics background. This review highlights some of the currently available and most popular commercial bioinformatics packages, discussing their prices, usability, features and suitability for teaching. Although several commercial bioinformatics programs are arguably overpriced and overhyped, many are well designed, sophisticated and, in my opinion, worth the investment. If you are just beginning your foray into molecular sequence analysis or an experienced genomicist, I encourage you to explore proprietary software bundles. They have the potential to streamline your research, increase your productivity, energize your classroom and, if anything, add a bit of zest to the often dry detached world of bioinformatics.

关键词： bioinformatics software CLC bio Geneious genome assembly nucleotide alignment phylogenetics software

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Ten recommendations for creating usable bioinformatics command line software

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GIGASCIENCE 2013年第1期2卷 1-3页

作者： Seemann, Torsten Monash Univ Victorian Bioinformat Consortium Clayton Vic 3800 Australia Victorian Life Sci Computat Initiat Life Sci Computat Ctr Melbourne Vic 3010 Australia

bioinformatics software varies greatly in quality. In terms of usability, the command line interface is the first experience a user will have of a tool. Unfortunately, this is often also the last time a tool will be used. Here I present ten recommendations for command line software author's tools to follow, which I believe would greatly improve the uptake and usability of their products, waste less user's time, and improve the quality of scientific analyses.

关键词： bioinformatics software software quality User interface Unix Tools

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Computational Biology in the 21st Century: Scaling with Compressive Algorithms

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COMMUNICATIONS OF THE ACM 2016年第8期59卷 72-80页

作者： Berger, Bonnie Daniels, Noah M. Yu, Y. William MIT CSAIL 77 Massachusetts Ave Cambridge MA 02139 USA MIT Dept Math Cambridge MA 02139 USA MIT EECS 77 Massachusetts Ave Cambridge MA 02139 USA

Algorithmic advances take advantage of the structure of massive biological data landscape.

关键词： Big data Computational biology DNA sequencing RNA-seq bioinformatics software

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SIMLIN: a bioinformatics tool for prediction of S-sulphenylation in the human proteome based on multi-stage ensemble-learning models

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BMC bioinformatics 2019年第1期20卷 1-12页

作者： Wang, Xiaochuan Li, Chen Li, Fuyi Sharma, Varun S. Song, Jiangning Webb, Geoffrey, I Monash Univ Monash Ctr Data Sci Fac Informat Technol Melbourne Vic 3800 Australia Canc Council Victoria Div Canc Epidemiol Melbourne Vic 3004 Australia Swiss Fed Inst Technol Dept Biol Inst Mol Syst Biol CH-8093 Zurich Switzerland Monash Univ Infect & Immun Program Biomed Discovery Inst Melbourne Vic 3800 Australia Monash Univ Dept Biochem & Mol Biol Melbourne Vic 3800 Australia Monash Univ ARC Ctr Excellence Adv Mol Imaging Melbourne Vic 3800 Australia

Background S-sulphenylation is a ubiquitous protein post-translational modification (PTM) where an S-hydroxyl (-SOH) bond is formed via the reversible oxidation on the Sulfhydryl group of cysteine (C). Recent experimental studies have revealed that S-sulphenylation plays critical roles in many biological functions, such as protein regulation and cell signaling. State-of-the-art bioinformatic advances have facilitated high-throughput in silico screening of protein S-sulphenylation sites, thereby significantly reducing the time and labour costs traditionally required for the experimental investigation of S-sulphenylation. Results In this study, we have proposed a novel hybrid computational framework, termed SIMLIN, for accurate prediction of protein S-sulphenylation sites using a multi-stage neural-network based ensemble-learning model integrating both protein sequence derived and protein structural features. Benchmarking experiments against the current state-of-the-art predictors for S-sulphenylation demonstrated that SIMLIN delivered competitive prediction performance. The empirical studies on the independent testing dataset demonstrated that SIMLIN achieved 88.0% prediction accuracy and an AUC score of 0.82, which outperforms currently existing methods. Conclusions In summary, SIMLIN predicts human S-sulphenylation sites with high accuracy thereby facilitating biological hypothesis generation and experimental validation. The web server, datasets, and online instructions are freely available at / for academic purposes.

关键词： Protein post-translational modification S-sulphenylation bioinformatics software Machine learning Ensemble learning

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PopMLvis: a tool for analysis and visualization of population structure using genotype data from genome-wide association studies

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BMC bioinformatics 2024年第1期25卷 1页

作者： Elshrif, Mohamed Isufaj, Keivin Kunji, Khalid Saad, Mohamad Hamad Bin Khalifa Univ Qatar Comp Res Inst Doha Qatar

One of the aims of population genetics is to identify genetic differences/similarities among individuals of multiple ancestries. Many approaches including principal component analysis, clustering, and maximum likelihood techniques can be used to assign individuals to a given ancestry based on their genetic makeup. Although there are several tools that implement such algorithms, there is a lack of interactive visual platforms to run a variety of algorithms in one place. Therefore, we developed PopMLvis, a platform that offers an interactive environment to visualize genetic similarity data using several algorithms, and generate figures that can be easily integrated into scientific articles.

关键词： Population genetics bioinformatics software Machine learning GWAS analysis

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ValWorkBench: An open source Java library for cluster validation, with applications to microarray data analysis

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2015年第2期118卷 207-217页

作者： Giancarlo, R. Scaturro, D. Utro, F. Univ Palermo Dipartimento Matemat & Informat I-90133 Palermo Italy IBM TJ Watson Res Computat Ctr Biol Yorktown Hts NY 10598 USA

The prediction of the number of clusters in a dataset, in particular microarrays, is a fundamental task in biological data analysis, usually performed via validation measures. Unfortunately, it has received very little attention and in fact there is a growing need for software tools/libraries dedicated to it. Here we present ValWorkBench, a software library consisting of eleven well known validation measures, together with novel heuristic approximations for some of them. The main objective of this paper is to provide the interested researcher with the full software documentation of an open source cluster validation platform having the main features of being easily extendible in a homogeneous way and of offering software components that can be readily re-used. Consequently, the focus of the presentation is on the architecture of the library, since it provides an essential map that can be used to access the full software documentation, which is available at the supplementary material website [1]. The mentioned main features of ValWorkBench are also discussed and exemplified, with emphasis on software abstraction design and re-usability. A comparison with existing cluster validation software libraries, mainly in terms of the mentioned features, is also offered. It suggests that ValWorkBench is a much needed contribution to the microarray software development/algorithm engineering community. For completeness, it is important to mention that previous accurate algorithmic experimental analysis of the relative merits of each of the implemented measures [19,23,25], carried out specifically on microarray data, gives useful insights on the effectiveness of ValWorkBench for cluster validation to researchers in the microarray community interested in its use for the mentioned task. (C) 2014 Elsevier Ireland Ltd. All rights reserved.

关键词： Microarray cluster analysis bioinformatics software Pattern discovery in bioinformatics and biomedicine

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Computational methods in mass spectrometry-based structural proteomics for studying protein structure, dynamics, and interactions

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COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 2020年 18卷 1391-1402页

作者： Na, Seungjin Paek, Eunok Hanyang Univ Dept Comp Sci 222 Wangsimni Ro Seoul 04763 South Korea

Mass spectrometry (MS) has made enormous contributions to comprehensive protein identification and quantification in proteomics. MS is also gaining momentum for structural biology in a variety of ways, complementing conventional structural biology techniques. Here, we will review how MS-based techniques, such as hydrogen/deuterium exchange, covalent labeling, and chemical cross-linking, enable the characterization of protein structure, dynamics, and interactions, especially from a perspective of their data analyses. Structural information encoded by chemical probes in intact proteins is decoded by interpreting MS data at a peptide level, i.e., revealing conformational and dynamic changes in local regions of proteins. The structural MS data are not amenable to data analyses in traditional proteomics workflow, requiring dedicated software for each type of data. We first provide basic principles of data interpretation, including isotopic distribution and peptide sequencing. We then focus particularly on computational methods for structural MS data analyses and discuss outstanding challenges in a proteome-wide large scale analysis. (C) 2020 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.

关键词： Structural biology Mass spectrometry Hydrogen/deuterium exchange Covalent labeling Chemical cross-linking Computational proteomics bioinformatics software

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