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39 Publications visible to you, out of a total of 39
Making Canonical Workflow Building Blocks interoperable across workflow languages

Abstract (Expand)

We here introduce the concept of Canonical Workflow Building Blocks (CWBB), a methodology of describing and wrapping computational tools, in order for them to be utilized in a reproducible manner from … multiple workflow languages and execution platforms. We argue such practice is a necessary requirement for FAIR Computational Workflows [Goble 2020] to improve widespread adoption and reuse of a computational method across workflow language barriers.

Authors: Stian Soiland-Reyes, Genís Bayarri, Pau Andrio, Robin Long, Douglas Lowe, Ania Niewielska, Adam Hospital

Date Published: 7th Mar 2021

Publication Type: Journal

MetaGT: A pipeline for de novo assembly of metatranscriptomes with the aid of metagenomic data

Abstract (Expand)

While metagenome sequencing may provide insights on the genome sequences and composition of microbial communities, metatranscriptome analysis can be useful for studying the functional activity of a … activity of a microbiome. RNA-Seq data provides the possibility to determine active genes in the community and how their expression levels depend on external conditions. Although the field of metatranscriptomics is relatively young, the number of projects related to metatranscriptome analysis increases every year and the scope of its applications expands. However, there are several problems that complicate metatranscriptome analysis: complexity of microbial communities, wide dynamic range of transcriptome expression and importantly, the lack of high-quality computational methods for assembling meta-RNA sequencing data. These factors deteriorate the contiguity and completeness of metatranscriptome assemblies, therefore affecting further downstream analysis. Here we present MetaGT, a pipeline for de novo assembly of metatranscriptomes, which is based on the idea of combining both metatranscriptomic and metagenomic data sequenced from the same sample. MetaGT assembles metatranscriptomic contigs and fills in missing regions based on their alignments to metagenome assembly. This approach allows to overcome described complexities and obtain complete RNA sequences, and additionally estimate their abundances. Using various publicly available real and simulated datasets, we demonstrate that MetaGT yields significant improvement in coverage and completeness of metatranscriptome assemblies compared to existing methods that do not exploit metagenomic data. The pipeline is implemented in NextFlow and is freely available from https://github.com/ablab/metaGT .

Authors: Daria Shafranskaya, Varsha Kale, Rob Finn, Alla L. Lapidus, Anton Korobeynikov, Andrey D. Prjibelski

Date Published: 28th Oct 2022

Publication Type: Journal

Methods Included: Standardizing Computational Reuse and Portability with the Common Workflow Language

Abstract (Expand)

A widely used standard for portable multilingual data analysis pipelines would enable considerable benefits to scholarly publication reuse, research/industry collaboration, regulatory cost control, and … to the environment. Published research that used multiple computer languages for their analysis pipelines would include a complete and reusable description of that analysis that is runnable on a diverse set of computing environments. Researchers would be able to easier collaborate and reuse these pipelines, adding or exchanging components regardless of programming language used; collaborations with and within the industry would be easier; approval of new medical interventions that rely on such pipelines would be faster. Time will be saved and environmental impact would also be reduced, as these descriptions contain enough information for advanced optimization without user intervention. Workflows are widely used in data analysis pipelines, enabling innovation and decision-making for the modern society. In many domains the analysis components are numerous and written in multiple different computer languages by third parties. However, lacking a standard for reusable and portable multilingual workflows, then reusing published multilingual workflows, collaborating on open problems, and optimizing their execution would be severely hampered. Moreover, only a standard for multilingual data analysis pipelines that was widely used would enable considerable benefits to research-industry collaboration, regulatory cost control, and to preserving the environment. Prior to the start of the CWL project, there was no standard for describing multilingual analysis pipelines in a portable and reusable manner. Even today / currently, although there exist hundreds of single-vendor and other single-source systems that run workflows, none is a general, community-driven, and consensus-built standard. Preprint, submitted to Communications of the ACM (CACM).

Authors: Michael R. Crusoe, Sanne Abeln, Alexandru Iosup, Peter Amstutz, John Chilton, Nebojša Tijanić, Hervé Ménager, Stian Soiland-Reyes, Carole Goble

Date Published: 14th May 2021

Publication Type: Unpublished

MGnify Genomes: A Resource for Biome-specific Microbial Genome Catalogues

Abstract

Not specified

Authors: Tatiana A. Gurbich, Alexandre Almeida, Martin Beracochea, Tony Burdett, Josephine Burgin, Guy Cochrane, Shriya Raj, Lorna Richardson, Alexander B. Rogers, Ekaterina Sakharova, Gustavo A. Salazar, Robert D. Finn

Date Published: 1st Jul 2023

Publication Type: Journal

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

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

Modern Approaches to the Monitoring of Biоdiversity (MAMBO)

Abstract (Expand)

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

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 …omated variant 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: Unpublished

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

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

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