Dataset Information

A roadmap for the generation of benchmarking resources for antimicrobial resistance detection using next generation sequencing.

ABSTRACT: Next Generation Sequencing technologies significantly impact the field of Antimicrobial Resistance (AMR) detection and monitoring, with immediate uses in diagnosis and risk assessment. For this application and in general, considerable challenges remain in demonstrating sufficient trust to act upon the meaningful information produced from raw data, partly because of the reliance on bioinformatics pipelines, which can produce different results and therefore lead to different interpretations. With the constant evolution of the field, it is difficult to identify, harmonise and recommend specific methods for large-scale implementations over time. In this article, we propose to address this challenge through establishing a transparent, performance-based, evaluation approach to provide flexibility in the bioinformatics tools of choice, while demonstrating proficiency in meeting common performance standards. The approach is two-fold: first, a community-driven effort to establish and maintain "live" (dynamic) benchmarking platforms to provide relevant performance metrics, based on different use-cases, that would evolve together with the AMR field; second, agreed and defined datasets to allow the pipelines' implementation, validation, and quality-control over time. Following previous discussions on the main challenges linked to this approach, we provide concrete recommendations and future steps, related to different aspects of the design of benchmarks, such as the selection and the characteristics of the datasets (quality, choice of pathogens and resistances, etc.), the evaluation criteria of the pipelines, and the way these resources should be deployed in the community.

SUBMITTER: Petrillo M

PROVIDER: S-EPMC9243550 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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A roadmap for the generation of benchmarking resources for antimicrobial resistance detection using next generation sequencing.

Petrillo Mauro M Fabbri Marco M Kagkli Dafni Maria DM Querci Maddalena M Van den Eede Guy G Alm Erik E Aytan-Aktug Derya D Capella-Gutierrez Salvador S Carrillo Catherine C Cestaro Alessandro A Chan Kok-Gan KG Coque Teresa T Endrullat Christoph C Gut Ivo I Hammer Paul P Kay Gemma L GL Madec Jean-Yves JY Mather Alison E AE McHardy Alice Carolyn AC Naas Thierry T Paracchini Valentina V Peter Silke S Pightling Arthur A Raffael Barbara B Rossen John J Ruppé Etienne E Schlaberg Robert R Vanneste Kevin K Weber Lukas M LM Westh Henrik H Angers-Loustau Alexandre A

F1000Research 20210208

Next Generation Sequencing technologies significantly impact the field of Antimicrobial Resistance (AMR) detection and monitoring, with immediate uses in diagnosis and risk assessment. For this application and in general, considerable challenges remain in demonstrating sufficient trust to act upon the meaningful information produced from raw data, partly because of the reliance on bioinformatics pipelines, which can produce different results and therefore lead to different interpretations. With ...[more]

PMID: 35847383

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Project description:Mounting evidence indicates that worldwide, innovation systems are increasing unsustainable. Equally, concerns about inequities in the science and innovation process, and in access to its benefits, continue. Against a backdrop of growing health, economic and scientific challenges global stakeholders are urgently seeking to spur innovation and maximize the just distribution of benefits for all. Open Science collaboration (OS) - comprising a variety of approaches to increase open, public, and rapid mobilization of scientific knowledge - is seen to be one of the most promising ways forward. Yet, many decision-makers hesitate to construct policy to support the adoption and implementation of OS without access to substantive, clear and reliable evidence. In October 2017, international thought-leaders gathered at an Open Science Leadership Forum in the Washington DC offices of the Bill and Melinda Gates Foundation to share their views on what successful Open Science looks like. Delegates from developed and developing nations, national governments, science agencies and funding bodies, philanthropy, researchers, patient organizations and the biotechnology, pharma and artificial intelligence (AI) industries discussed the outcomes that would rally them to invest in OS, as well as wider issues of policy and implementation. This first of two reports, summarizes delegates' views on what they believe OS will deliver in terms of research, innovation and social impact in the life sciences. Through open and collaborative process over the next months, we will translate these success outcomes into a toolkit of quantitative and qualitative indicators to assess when, where and how open science collaborations best advance research, innovation and social benefit. Ultimately, this work aims to develop and openly share tools to allow stakeholders to evaluate and re-invent their innovation ecosystems, to maximize value for the global public and patients, and address long-standing questions about the mechanics of innovation.

Project description:MotivationOver the last decades, vast numbers of sequences were deposited in public databases. Bioinformatics tools allow homology and consequently functional inference for these sequences. New profile-based homology search tools have been introduced, allowing reliable detection of remote homologs, but have not been systematically benchmarked. To provide such a comparison, which can guide bioinformatics workflows, we extend and apply our previously developed benchmark approach to evaluate the 'next generation' of profile-based approaches, including CS-BLAST, HHSEARCH and PHMMER, in comparison with the non-profile based search tools NCBI-BLAST, USEARCH, UBLAST and FASTA.MethodWe generated challenging benchmark datasets based on protein domain architectures within either the PFAM + Clan, SCOP/Superfamily or CATH/Gene3D domain definition schemes. From each dataset, homologous and non-homologous protein pairs were aligned using each tool, and standard performance metrics calculated. We further measured congruence of domain architecture assignments in the three domain databases.ResultsCSBLAST and PHMMER had overall highest accuracy. FASTA, UBLAST and USEARCH showed large trade-offs of accuracy for speed optimization.ConclusionProfile methods are superior at inferring remote homologs but the difference in accuracy between methods is relatively small. PHMMER and CSBLAST stand out with the highest accuracy, yet still at a reasonable computational cost. Additionally, we show that less than 0.1% of Swiss-Prot protein pairs considered homologous by one database are considered non-homologous by another, implying that these classifications represent equivalent underlying biological phenomena, differing mostly in coverage and granularity.Availability and implementationBenchmark datasets and all scripts are placed at (http://sonnhammer.org/download/Homology_benchmark).Contactforslund@embl.deSupplementary informationSupplementary data are available at Bioinformatics online.

Project description:Areas of open science (OS) policy and practice are already relatively well-advanced in several countries and sectors through the initiatives of some governments, funders, philanthropy, researchers and the community. Nevertheless, the current research and innovation system, including in the focus of this report, the life sciences, remains weighted against OS. In October 2017, thought-leaders from across the world gathered at an Open Science Leadership Forum in the Washington DC office of the Bill and Melinda Gates Foundation to share their views on what successful OS looks like. We focused on OS partnerships as this is an emerging model that aims to accelerate science and innovation. These outcomes are captured in a first meeting report: Defining Success in Open Science. On several occasions, these conversations turned to the challenges that must be addressed and new policies required to effectively and sustainably advance OS practice. Thereupon, in this report, we describe the concerns raised and what is needed to address them supplemented by our review of the literature, and suggest the stakeholder groups that may be best placed to begin to take action. It emerges that to be successful, OS will require the active engagement of all stakeholders: while the research community must develop research questions, identify partners and networks, policy communities need to create an environment that is supportive of experimentation by removing barriers. This report aims to contribute to ongoing discussions about OS and its implementation. It is also part of a step-wise process to develop and mobilize a toolkit of quantitative and qualitative indicators to assist global stakeholders in implementing high value OS collaborations. Currently in co-development through an open and international process, this set of measures will allow the generation of needed evidence on the influence of OS partnerships on research, innovation, and critical social and economic goals.

Dataset Information

A roadmap for the generation of benchmarking resources for antimicrobial resistance detection using next generation sequencing.

Publications

A roadmap for the generation of benchmarking resources for antimicrobial resistance detection using next generation sequencing.

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