Publications

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66 Publications visible to you, out of a total of 66
MGnify: the microbiome analysis resource in 2020

Abstract (Expand)

MGnify (http://www.ebi.ac.uk/metagenomics) provides a free to use platform for the assembly, analysis and archiving of microbiome data derived from sequencing microbial populations that are present in … particular environments. Over the past 2 years, MGnify (formerly EBI Metagenomics) has more than doubled the number of publicly available analysed datasets held within the resource. Recently, an updated approach to data analysis has been unveiled (version 5.0), replacing the previous single pipeline with multiple analysis pipelines that are tailored according to the input data, and that are formally described using the Common Workflow Language, enabling greater provenance, reusability, and reproducibility. MGnify's new analysis pipelines offer additional approaches for taxonomic assertions based on ribosomal internal transcribed spacer regions (ITS1/2) and expanded protein functional annotations. Biochemical pathways and systems predictions have also been added for assembled contigs. MGnify's growing focus on the assembly of metagenomic data has also seen the number of datasets it has assembled and analysed increase six-fold. The non-redundant protein database constructed from the proteins encoded by these assemblies now exceeds 1 billion sequences. Meanwhile, a newly developed contig viewer provides fine-grained visualisation of the assembled contigs and their enriched annotations.

Authors: Alex L Mitchell, Alexandre Almeida, Martin Beracochea, Miguel Boland, Josephine Burgin, Guy Cochrane, Michael R Crusoe, Varsha Kale, Simon C Potter, Lorna J Richardson, Ekaterina Sakharova, Maxim Scheremetjew, Anton Korobeynikov, Alex Shlemov, Olga Kunyavskaya, Alla Lapidus, Robert D Finn

Date Published: 7th Nov 2019

Publication Type: Journal Article

MicroPIPE: validating an end-to-end workflow for high-quality complete bacterial genome construction.

Abstract (Expand)

BACKGROUND: Oxford Nanopore Technology (ONT) long-read sequencing has become a popular platform for microbial researchers due to the accessibility and affordability of its devices. However, easy and … automated construction of high-quality bacterial genomes using nanopore reads remains challenging. Here we aimed to create a reproducible end-to-end bacterial genome assembly pipeline using ONT in combination with Illumina sequencing. RESULTS: We evaluated the performance of several popular tools used during genome reconstruction, including base-calling, filtering, assembly, and polishing. We also assessed overall genome accuracy using ONT both natively and with Illumina. All steps were validated using the high-quality complete reference genome for the Escherichia coli sequence type (ST)131 strain EC958. Software chosen at each stage were incorporated into our final pipeline, MicroPIPE. Further validation of MicroPIPE was carried out using 11 additional ST131 E. coli isolates, which demonstrated that complete circularised chromosomes and plasmids could be achieved without manual intervention. Twelve publicly available Gram-negative and Gram-positive bacterial genomes (with available raw ONT data and matched complete genomes) were also assembled using MicroPIPE. We found that revised basecalling and updated assembly of the majority of these genomes resulted in improved accuracy compared to the current publicly available complete genomes. CONCLUSIONS: MicroPIPE is built in modules using Singularity container images and the bioinformatics workflow manager Nextflow, allowing changes and adjustments to be made in response to future tool development. Overall, MicroPIPE provides an easy-access, end-to-end solution for attaining high-quality bacterial genomes. MicroPIPE is available at https://github.com/BeatsonLab-MicrobialGenomics/micropipe .

Authors: V. Murigneux, L. W. Roberts, B. M. Forde, M. D. Phan, N. T. K. Nhu, A. D. Irwin, P. N. A. Harris, D. L. Paterson, M. A. Schembri, D. M. Whiley, S. A. Beatson

Date Published: 25th Jun 2021

Publication Type: Journal Article

Modern Approaches to the Monitoring of Biоdiversity (MAMBO)

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EU policies, such as the EU biodiversity strategy 2030 and the Birds and Habitats Directives, demand unbiased, integrated and regularly updated biodiversity and ecosystem service data. However, efforts … to monitor wildlife and other species groups are spatially and temporally fragmented, taxonomically biased, and lack integration in Europe. To bridge this gap, the MAMBO project will develop, test and implement enabling tools for monitoring conservation status and ecological requirements of species and habitats for which knowledge gaps still exist. MAMBO brings together the technical expertise of computer science, remote sensing, social science expertise on human-technology interactions, environmental economy, and citizen science, with the biological expertise on species, ecology, and conservation biology. MAMBO is built around stakeholder engagement and knowledge exchange (WP1) and the integration of new technology with existing research infrastructures (WP2). MAMBO will develop, test, and demonstrate new tools for monitoring species (WP3) and habitats (WP4) in a co-design process to create novel standards for species and habitat monitoring across the EU and beyond. MAMBO will work with stakeholders to identify user and policy needs for biodiversity monitoring and investigate the requirements for setting up a virtual lab to automate workflow deployment and efficient computing of the vast data streams (from on the ground sensors, and remote sensing) required to improve monitoring activities across Europe (WP4). Together with stakeholders, MAMBO will assess these new tools at demonstration sites distributed across Europe (WP5) to identify bottlenecks, analyze the cost-effectiveness of different tools, integrate data streams and upscale results (WP6). This will feed into the co-design of future, improved and more cost-effective monitoring schemes for species and habitats using novel technologies (WP7), and thus lead to a better management of protected sites and species.

Authors: Toke Høye, Tom August, Mario V Balzan, Koos Biesmeijer, Pierre Bonnet, Tom Breeze, Christophe Dominik, France Gerard, Alexis Joly, Vincent Kalkman, W. Daniel Kissling, Teodor Metodiev, Jesper Moeslund, Simon Potts, David Roy, Oliver Schweiger, Deepa Senapathi, Josef Settele, Pavel Stoev, Dan Stowell

Date Published: 7th Dec 2023

Publication Type: Journal Article

MOLGENIS VIP: an open-source and modular pipeline for high-throughput and integrated DNA variant analysis

Abstract (Expand)

Abstract In silico variant interpretation pipelines have become an integral part of genetics research and genome diagnostics. However, challenges remain for automated variant interpretation and candidate …ant interpretation and candidate shortlisting. Their reliability is affected by variability in input data caused due the use of differing sequencing platforms, erroneous nomenclature and changing experimental conditions. Similarly, differences in predictive algorithms can result in discordant results. Finally, scalability is essential to accommodate large amounts of input data, such as in whole genome sequencing (WGS). To accelerate causal variant detection and innovation in genome diagnostics and research, we developed the MOLGENIS Variant Interpretation Pipeline (VIP). VIP is a flexible open-source computational pipeline that generates interactive reports of variants in whole exome sequencing (WES) and WGS data for expert interpretation. VIP can process short- and long-read data from different platforms and offers tools for increased sensitivity: a configurable decision-tree, filters based on human phenotype ontology (HPO) and gene inheritance that can be used to pinpoint disease-causing variants or finetune a query for specific variants. Here, alongside presenting VIP, we provide a step-by-step protocol for how to use VIP to annotate, classify and filter genetic variants of patients with a rare disease that has a suspected genetic cause. Finally, we demonstrate how VIP performs using 25,664 previously classified variants from the data sharing initiative of the Vereniging van Klinisch Genetische Laboratoriumdiagnostiek (VKGL), a cohort of 18 diagnosed patients from routine diagnostics and a cohort of 41 patients with a rare disease (RD) who were not diagnosed in routine diagnostics but were diagnosed using novel omics approaches within the EU-wide project to solve rare diseases (EU-Solve-RD). VIP requires bioinformatic knowledge to configure, but once configured, any diagnostic professional can perform an analysis within 5 hours.

Authors: W.T.K. Maassen, L.F. Johansson, B. Charbon, D. Hendriksen, S. van den Hoek, M.K. Slofstra, R. Mulder, M.T. Meems-Veldhuis, R. Sietsma, H.H. Lemmink, C.C. van Diemen, M.E. van Gijn, M.A. Swertz, K.J. van der Velde

Date Published: 15th Apr 2024

Publication Type: Preprint

Multi-task analysis of gene expression data on cancer public datasets

Abstract (Expand)

Background There is an availability of omics and often multi-omics cancer datasets on public databases such as Gene Expression Omnibus (GEO), International Cancer Genome Consortium and The Cancer Genome … Atlas Program. Most of these databases provide at least the gene expression data for the samples contained in the project. Multi-omics has been an advantageous strategy to leverage personalized medicine, but few works explore strategies to extract knowledge relying only on gene expression level for decisions on tasks such as disease outcome prediction and drug response simulation. The models and information acquired on projects based only on expression data could provide decision making background for future projects that have other level of omics data such as DNA methylation or miRNAs. Results We extended previous methodologies to predict disease outcome from the combination of protein interaction networks and gene expression profiling by proposing an automated pipeline to perform the graph feature encoding and further patient networks outcome classification derived from RNA-Seq. We integrated biological networks from protein interactions and gene expression profiling to assess patient specificity combining the treatment/control ratio with the patient normalized counts of the deferentially expressed genes. We also tackled the disease outcome prediction from the gene set enrichment perspective, combining gene expression with pathway gene sets information as features source for this task. We also explored the drug response outcome perspective of the cancer disease still evaluating the relationship among gene expression profiling with single sample gene set enrichment analysis (ssGSEA), proposing a workflow to perform drug response screening according to the patient enriched pathways. Conclusion We showed the importance of the patient network modeling for the clinical task of disease outcome prediction using graph kernel matrices strategy and showed how ssGSEA improved the prediction only using transcriptomic data combined with pathway scores. We also demonstrated a detailed screening analysis showing the impact of pathway-based gene sets and normalization types for the drug response simulation. We deployed two fully automatized Screening workflows following the FAIR principles for the disease outcome prediction and drug response simulation tasks.

Author: Yasmmin Martins

Date Published: 28th Sep 2023

Publication Type: Journal Article

OntoPPI: Towards Data Formalization on the Prediction of Protein Interactions

Abstract (Expand)

The Linking Open Data (LOD) cloud is a global data space for publishing and linking structured data on the Web. The idea is to facilitate the integration, exchange, and processing of data. The LOD cloud … already includes a lot of datasets that are related to the biological area. Nevertheless, most of the datasets about protein interactions do not use metadata standards. This means that they do not follow the LOD requirements and, consequently, hamper data integration. This problem has impacts on the information retrieval, specially with respect to datasets provenance and reuse in further prediction experiments. This paper proposes an ontology to describe and unite the four main kinds of data in a single prediction experiment environment: (i) information about the experiment itself; (ii) description and reference to the datasets used in an experiment; (iii) information about each protein involved in the candidate pairs. They correspond to the biological information that describes them and normally involves integration with other datasets; and, finally, (iv) information about the prediction scores organized by evidence and the final prediction. Additionally, we also present some case studies that illustrate the relevance of our proposal, by showing how queries can retrieve useful information.

Authors: Yasmmin Cortes Martins, Maria Cláudia Cavalcanti, Luis Willian Pacheco Arge, Artur Ziviani, Ana Tereza Ribeiro de Vasconcelos

Date Published: 2019

Publication Type: Journal Article

Optimized meta-scheduling in Galaxy using TPV Broker

Abstract (Expand)

Description Effective resource scheduling is critical in high-performance (HPC) and high-throughput computing (HTC) environments, where traditional scheduling systems struggle with resource contention, …, data locality, and fault tolerance. Meta-scheduling, which abstracts multiple schedulers for unified job allocation, addresses these challenges. Galaxy, a widely used platform for data-intensive computational analysis, employs the \textit{Total Perspective Vortex (TPV)} system for resource scheduling. With over 550,000 users, Galaxy aims to optimize scheduling efficiency in large-scale environments. While TPV offers flexibility, its decision-making can be enhanced by incorporating real-time resource availability and job status. This paper introduces the TPV Broker, a meta-scheduling framework that integrates real-time resource data to enable dynamic, data-aware scheduling. TPV Broker enhances scalability, resource utilization, and scheduling efficiency in Galaxy, offering potential for further improvements in distributed computing environments.

Authors: Abdulrahman Azab, Paul De Geest, Sanjay Kumar Srikakulam, Tomáš Vondra, Mira Kuntz, Björn Grüning

Date Published: 1st Feb 2025

Publication Type: Preprint

Perspectives on automated composition of workflows in the life sciences

Abstract (Expand)

Scientific data analyses often combine several computational tools in automated pipelines, or workflows. Thousands of such workflows have been used in the life sciences, though their composition has … remained a cumbersome manual process due to a lack of standards for annotation, assembly, and implementation. Recent technological advances have returned the long-standing vision of automated workflow composition into focus. This article summarizes a recent Lorentz Center workshop dedicated to automated composition of workflows in the life sciences. We survey previous initiatives to automate the composition process, and discuss the current state of the art and future perspectives. We start by drawing the “big picture” of the scientific workflow development life cycle, before surveying and discussing current methods, technologies and practices for semantic domain modelling, automation in workflow development, and workflow assessment. Finally, we derive a roadmap of individual and community-based actions to work toward the vision of automated workflow development in the forthcoming years. A central outcome of the workshop is a general description of the workflow life cycle in six stages: 1) scientific question or hypothesis, 2) conceptual workflow, 3) abstract workflow, 4) concrete workflow, 5) production workflow, and 6) scientific results. The transitions between stages are facilitated by diverse tools and methods, usually incorporating domain knowledge in some form. Formal semantic domain modelling is hard and often a bottleneck for the application of semantic technologies. However, life science communities have made considerable progress here in recent years and are continuously improving, renewing interest in the application of semantic technologies for workflow exploration, composition and instantiation. Combined with systematic benchmarking with reference data and large-scale deployment of production-stage workflows, such technologies enable a more systematic process of workflow development than we know today. We believe that this can lead to more robust, reusable, and sustainable workflows in the future.

Authors: Anna-Lena Lamprecht, Magnus Palmblad, Jon Ison, Veit Schwämmle, Mohammad Sadnan Al Manir, Ilkay Altintas, Christopher J. O. Baker, Ammar Ben Hadj Amor, Salvador Capella-Gutierrez, Paulos Charonyktakis, Michael R. Crusoe, Yolanda Gil, Carole Goble, Timothy J. Griffin, Paul Groth, Hans Ienasescu, Pratik Jagtap, Matúš Kalaš, Vedran Kasalica, Alireza Khanteymoori, Tobias Kuhn, Hailiang Mei, Hervé Ménager, Steffen Möller, Robin A. Richardson, Vincent Robert, Stian Soiland-Reyes, Robert Stevens, Szoke Szaniszlo, Suzan Verberne, Aswin Verhoeven, Katherine Wolstencroft

Date Published: 2021

Publication Type: Journal Article

Perspectives on automated composition of workflows in the life sciences

Abstract

Not specified

Authors: Anna-Lena Lamprecht, Magnus Palmblad, Jon Ison, Veit Schwämmle, Mohammad Sadnan Al Manir, Ilkay Altintas, Christopher J. O. Baker, Ammar Ben Hadj Amor, Salvador Capella-Gutierrez, Paulos Charonyktakis, Michael R. Crusoe, Yolanda Gil, Carole Goble, Timothy J. Griffin, Paul Groth, Hans Ienasescu, Pratik Jagtap, Matúš Kalaš, Vedran Kasalica, Alireza Khanteymoori, Tobias Kuhn, Hailiang Mei, Hervé Ménager, Steffen Möller, Robin A. Richardson, Vincent Robert, Stian Soiland-Reyes, Robert Stevens, Szoke Szaniszlo, Suzan Verberne, Aswin Verhoeven, Katherine Wolstencroft

Date Published: 2021

Publication Type: Journal Article

PPIntegrator: semantic integrative system for protein–protein interaction and application for host–pathogen datasets

Abstract (Expand)

Semantic web standards have shown importance in the last 20 years in promoting data formalization and interlinking between the existing knowledge graphs. In this context, several ontologies and data … integration initiatives have emerged in recent years for the biological area, such as the broadly used Gene Ontology that contains metadata to annotate gene function and subcellular location. Another important subject in the biological area is protein–protein interactions (PPIs) which have applications like protein function inference. Current PPI databases have heterogeneous exportation methods that challenge their integration and analysis. Presently, several initiatives of ontologies covering some concepts of the PPI domain are available to promote interoperability across datasets. However, the efforts to stimulate guidelines for automatic semantic data integration and analysis for PPIs in these datasets are limited. Here, we present PPIntegrator, a system that semantically describes data related to protein interactions. We also introduce an enrichment pipeline to generate, predict and validate new potential host–pathogen datasets by transitivity analysis. PPIntegrator contains a data preparation module to organize data from three reference databases and a triplification and data fusion module to describe the provenance information and results. This work provides an overview of the PPIntegrator system applied to integrate and compare host–pathogen PPI datasets from four bacterial species using our proposed transitivity analysis pipeline. We also demonstrated some critical queries to analyze this kind of data and highlight the importance and usage of the semantic data generated by our system.

Authors: Yasmmin Côrtes Martins, Artur Ziviani, Maiana de Oliveira Cerqueira e Costa, Maria Cláudia Reis Cavalcanti, Marisa Fabiana Nicolás, Ana Tereza Ribeiro de Vasconcelos

Date Published: 2023

Publication Type: Journal Article

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