Publications

INSaFLU: an automated open web-based bioinformatics suite “from-reads” for influenza whole-genome-sequencing-based surveillance

Abstract (Expand)

Background A new era of flu surveillance has already started based on the genetic characterization and exploration of influenza virus evolution at whole-genome scale. Although this has been prioritized …d by national and international health authorities, the demanded technological transition to whole-genome sequencing (WGS)-based flu surveillance has been particularly delayed by the lack of bioinformatics infrastructures and/or expertise to deal with primary next-generation sequencing (NGS) data. Results We developed and implemented INSaFLU (“INSide the FLU”), which is the first influenza-oriented bioinformatics free web-based suite that deals with primary NGS data (reads) towards the automatic generation of the output data that are actually the core first-line “genetic requests” for effective and timely influenza laboratory surveillance (e.g., type and sub-type, gene and whole-genome consensus sequences, variants’ annotation, alignments and phylogenetic trees). By handling NGS data collected from any amplicon-based schema, the implemented pipeline enables any laboratory to perform multi-step software intensive analyses in a user-friendly manner without previous advanced training in bioinformatics. INSaFLU gives access to user-restricted sample databases and projects management, being a transparent and flexible tool specifically designed to automatically update project outputs as more samples are uploaded. Data integration is thus cumulative and scalable, fitting the need for a continuous epidemiological surveillance during the flu epidemics. Multiple outputs are provided in nomenclature-stable and standardized formats that can be explored in situ or through multiple compatible downstream applications for fine-tuned data analysis. This platform additionally flags samples as “putative mixed infections” if the population admixture enrolls influenza viruses with clearly distinct genetic backgrounds, and enriches the traditional “consensus-based” influenza genetic characterization with relevant data on influenza sub-population diversification through a depth analysis of intra-patient minor variants. This dual approach is expected to strengthen our ability not only to detect the emergence of antigenic and drug resistance variants but also to decode alternative pathways of influenza evolution and to unveil intricate routes of transmission. Conclusions In summary, INSaFLU supplies public health laboratories and influenza researchers with an open “one size fits all” framework, potentiating the operationalization of a harmonized multi-country WGS-based surveillance for influenza virus.

Authors: Vítor Borges, Miguel Pinheiro, Pedro Pechirra, Raquel Guiomar, João Paulo Gomes

Date Published: 1st Dec 2018

Publication Type: InProceedings

BioExcel Building Blocks, a software library for interoperable biomolecular simulation workflows

Abstract (Expand)

In the recent years, the improvement of software and hardware performance has made biomolecular simulations a mature tool for the study of biological processes. Simulation length and the size and … complexity of the analyzed systems make simulations both complementary and compatible with other bioinformatics disciplines. However, the characteristics of the software packages used for simulation have prevented the adoption of the technologies accepted in other bioinformatics fields like automated deployment systems, workflow orchestration, or the use of software containers. We present here a comprehensive exercise to bring biomolecular simulations to the “bioinformatics way of working”. The exercise has led to the development of the BioExcel Building Blocks (BioBB) library. BioBB’s are built as Python wrappers to provide an interoperable architecture. BioBB’s have been integrated in a chain of usual software management tools to generate data ontologies, documentation, installation packages, software containers and ways of integration with workflow managers, that make them usable in most computational environments.

Authors: Pau Andrio, Adam Hospital, Javier Conejero, Luis Jordá, Marc Del Pino, Laia Codo, Stian Soiland-Reyes, Carole Goble, Daniele Lezzi, Rosa M. Badia, Modesto Orozco, Josep Ll. Gelpi

Date Published: 1st Dec 2019

Publication Type: Journal

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

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

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

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 …mposition 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

FAIR Computational Workflows

Abstract (Expand)

Computational workflows describe the complex multi-step methods that are used for data collection, data preparation, analytics, predictive modelling, and simulation that lead to new data products. They … can inherently contribute to the FAIR data principles: by processing data according to established metadata; by creating metadata themselves during the processing of data; and by tracking and recording data provenance. These properties aid data quality assessment and contribute to secondary data usage. Moreover, workflows are digital objects in their own right. This paper argues that FAIR principles for workflows need to address their specific nature in terms of their composition of executable software steps, their provenance, and their development.

Authors: Carole Goble, Sarah Cohen-Boulakia, Stian Soiland-Reyes, Daniel Garijo, Yolanda Gil, Michael R. Crusoe, Kristian Peters, Daniel Schober

Date Published: 2020

Publication Type: Journal

FAIR Computational Workflows

Abstract

Not specified

Authors: Carole Goble, Sarah Cohen-Boulakia, Stian Soiland-Reyes, Daniel Garijo, Yolanda Gil, Michael R. Crusoe, Kristian Peters, Daniel Schober

Date Published: 2020

Publication Type: Journal

Landscape Analysis for the Specimen Data Refinery

Abstract (Expand)

This report reviews the current state-of-the-art applied approaches on automated tools, services and workflows for extracting information from images of natural history specimens and their labels. We … consider the potential for repurposing existing tools, including workflow management systems; and areas where more development is required. This paper was written as part of the SYNTHESYS+ project for software development teams and informatics teams working on new software-based approaches to improve mass digitisation of natural history specimens.

Authors: Stephanie Walton, Laurence Livermore, Olaf Bánki, Robert W. N. Cubey, Robyn Drinkwater, Markus Englund, Carole Goble, Quentin Groom, Christopher Kermorvant, Isabel Rey, Celia M Santos, Ben Scott, Alan Williams, Zhengzhe Wu

Date Published: 14th Aug 2020

Publication Type: Journal

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

Ten simple rules for making a software tool workflow-ready

Abstract (Expand)

Workflows have become a core part of computational scientific analysis in recent years. Automated computational workflows multiply the power of researchers, potentially turning “hand-cranked” data …data processing by informaticians into robust factories for complex research output. However, in order for a piece of software to be usable as a workflow-ready tool, it may require alteration from its likely origin as a standalone tool. Research software is often created in response to the need to answer a research question with the minimum expenditure of time and money in resource-constrained projects. The level of quality might range from “it works on my computer” to mature and robust projects with support across multiple operating systems. Despite significant increase in uptake of workflow tools, there is little specific guidance for writing software intended to slot in as a tool within a workflow; or on converting an existing standalone research-quality software tool into a reusable, composable, well-behaved citizen within a larger workflow. In this paper we present 10 simple rules for how a software tool can be prepared for workflow use.

Authors: Paul Brack, Peter Crowther, Stian Soiland-Reyes, Stuart Owen, Douglas Lowe, Alan R. Williams, Quentin Groom, Mathias Dillen, Frederik Coppens, Björn Grüning, Ignacio Eguinoa, Philip Ewels, Carole Goble

Date Published: 24th Mar 2022

Publication Type: Journal

The Specimen Data Refinery: A Canonical Workflow Framework and FAIR Digital Object Approach to Speeding up Digital Mobilisation of Natural History Collections

Abstract (Expand)

A key limiting factor in organising and using information from physical specimens curated in natural science collections is making that information computable, with institutional digitization tending … to focus more on imaging the specimens themselves than on efficiently capturing computable data about them. Label data are traditionally manually transcribed today with high cost and low throughput, rendering such a task constrained for many collection-holding institutions at current funding levels. We show how computer vision, optical character recognition, handwriting recognition, named entity recognition and language translation technologies can be implemented into canonical workflow component libraries with findable, accessible, interoperable, and reusable (FAIR) characteristics. These libraries are being developed in a cloud- based workflow platform—the ‘Specimen Data Refinery’ (SDR)—founded on Galaxy workflow engine, Common Workflow Language, Research Object Crates (RO-Crate) and WorkflowHub technologies. The SDR can be applied to specimens’ labels and other artefacts, offering the prospect of greatly accelerated and more accurate data capture in computable form. Two kinds of FAIR Digital Objects (FDO) are created by packaging outputs of SDR workflows and workflow components as digital objects with metadata, a persistent identifier, and a specific type definition. The first kind of FDO are computable Digital Specimen (DS) objects that can be consumed/produced by workflows, and other applications. A single DS is the input data structure submitted to a workflow that is modified by each workflow component in turn to produce a refined DS at the end. The Specimen Data Refinery provides a library of such components that can be used individually, or in series. To cofunction, each library component describes the fields it requires from the DS and the fields it will in turn populate or enrich. The second kind of FDO, RO-Crates gather and archive the diverse set of digital and real-world resources, configurations, and actions (the provenance) contributing to a unit of research work, allowing that work to be faithfully recorded and reproduced. Here we describe the Specimen Data Refinery with its motivating requirements, focusing on what is essential in the creation of canonical workflow component libraries and its conformance with the requirements of an emerging FDO Core Specification being developed by the FDO Forum.

Authors: Alex Hardisty, Paul Brack, Carole Goble, Laurence Livermore, Ben Scott, Quentin Groom, Stuart Owen, Stian Soiland-Reyes

Date Published: 7th Mar 2022

Publication Type: Journal

A Community Roadmap for Scientific Workflows Research and Development

Abstract (Expand)

Preprint: https://arxiv.org/abs/2110.02168 The landscape of workflow systems for scientific applications is notoriously convoluted with hundreds of seemingly equivalent workflow systems, many isolated …d research claims, and a steep learning curve. To address some of these challenges and lay the groundwork for transforming workflows research and development, the WorkflowsRI and ExaWorks projects partnered to bring the international workflows community together. This paper reports on discussions and findings from two virtual "Workflows Community Summits" (January and April, 2021). The overarching goals of these workshops were to develop a view of the state of the art, identify crucial research challenges in the workflows community, articulate a vision for potential community efforts, and discuss technical approaches for realizing this vision. To this end, participants identified six broad themes: FAIR computational workflows; AI workflows; exascale challenges; APIs, interoperability, reuse, and standards; training and education; and building a workflows community. We summarize discussions and recommendations for each of these themes.

Authors: Rafael Ferreira da Silva, Henri Casanova, Kyle Chard, Ilkay Altintas, Rosa M Badia, Bartosz Balis, Taina Coleman, Frederik Coppens, Frank Di Natale, Bjoern Enders, Thomas Fahringer, Rosa Filgueira, Grigori Fursin, Daniel Garijo, Carole Goble, Dorran Howell, Shantenu Jha, Daniel S. Katz, Daniel Laney, Ulf Leser, Maciej Malawski, Kshitij Mehta, Loic Pottier, Jonathan Ozik, J. Luc Peterson, Lavanya Ramakrishnan, Stian Soiland-Reyes, Douglas Thain, Matthew Wolf

Date Published: 1st Nov 2021

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

Collection of wing images for conservation of honey bees (Apis mellifera) biodiversity in Europe

Abstract (Expand)

Identification of honey bee (Apis mellifera) from various parts of the world is essential for protection of their biodiversity. The identification can be based on wing measurements which is inexpensive … and easy available. In order to develop such identification there are required reference samples from various parts or the world. We provide collection of 26481 honey bee fore wing images from 13 countries in Europe: Austria (AT), Croatia (HR), Greece (GR), Moldova (MD), Montenegro (ME), Poland (PL), Portugal (PT), Romania (RO), Serbia (RS), Slovenia (SI), Spain (ES), Turkey (TR). For each country there are three files starting with the two letter country code (indicated earlier in the parentheses): XX-wing-images.zip, XX-raw-coordinates.csv and XX-data.csv, which contain wing images, raw landmark coordinates and geographic coordinates, respectively. Files with prefix EU contain combined data from all countries.

Authors: Andrzej Oleksa, Eliza Căuia, Adrian Siceanu, Zlatko Puškadija, Marin Kovačić, M. Alice Pinto, Pedro João Rodrigues, Fani Hatjina, Leonidas Charistos, Maria Bouga, Janez Prešern, Irfan Kandemir, Slađan Rašić, Szilvia Kusza, Adam Tofilski

Date Published: 1st Oct 2022

Publication Type: Journal

Dataset: Computer software for identification of honey bee subspecies and evolutionary lineages

Abstract (Expand)

Coordinates of 19 landmarks from honey bee (Apis mellifera) worker wings. They represent 1832 workers, 187 colonies, 25 subspecies and four evolutionary lineages. The material was obtained from the …e Morphometric Bee Data Bank in Oberursel, Germany.

Authors: Anna Nawrocka, Irfan Kandemir, Stefan Fuchs, Adam Tofilski

Date Published: 1st Apr 2018

Publication Type: Journal

Ten simple rules and a template for creating workflows-as-applications

Abstract

Not specified

Authors: Michael J. Roach, N. Tessa Pierce-Ward, Radoslaw Suchecki, Vijini Mallawaarachchi, Bhavya Papudeshi, Scott A. Handley, C. Titus Brown, Nathan S. Watson-Haigh, Robert A. Edwards

Date Published: 15th Dec 2022

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

Automatic, Efficient and Scalable Provenance Registration for FAIR HPC Workflows

Abstract (Expand)

Provenance registration is becoming more and more important, as we increase the size and number of experiments performed using computers. In particular, when provenance is recorded in HPC environments, … it must be efficient and scalable. In this paper, we propose a provenance registration method for scientific workflows, efficient enough to run in supercomputers (thus, it could run in other environments with more relaxed restrictions, such as distributed ones). It also must be scalable in order to deal with large workflows, that are more typically used in HPC. We also target transparency for the user, shielding them from having to specify how provenance must be recorded. We implement our design using the COMPSs programming model as a Workflow Management System (WfMS) and use RO-Crate as a well-established specification to record and publish provenance. Experiments are provided, demonstrating the run time efficiency and scalability of our solution.

Authors: Raul Sirvent, Javier Conejero, Francesc Lordan, Jorge Ejarque, Laura Rodriguez-Navas, Jose M. Fernandez, Salvador Capella-Gutierrez, Rosa M. Badia

Date Published: 1st Nov 2022

Publication Type: Proceedings

DSCrank: A Method for Selection and Ranking of Datasets

Abstract (Expand)

Considerable efforts have been made to build the Web of Data. One of the main challenges has to do with how to identify the most related datasets to connect to. Another challenge is to publish a local … dataset into the Web of Data, following the Linked Data principles. The present work is based on the idea that a set of activities should guide the user on the publication of a new dataset into the Web of Data. It presents the specification and implementation of two initial activities, which correspond to the crawling and ranking of a selected set of existing published datasets. The proposed implementation is based on the focused crawling approach, adapting it to address the Linked Data principles. Moreover, the dataset ranking is based on a quick glimpse into the content of the selected datasets. Additionally, the paper presents a case study in the Biomedical area to validate the implemented approach, and it shows promising results with respect to scalability and performance.

Authors: Yasmmin Cortes Martins, Fábio Faria da Mota, Maria Cláudia Cavalcanti

Date Published: 2016

Publication Type: Journal

EpiCurator: an immunoinformatic workflow to predict and prioritize SARS-CoV-2 epitopes

Abstract (Expand)

The ongoing coronavirus 2019 (COVID-19) pandemic, triggered by the emerging SARS-CoV-2 virus, represents a global public health challenge. Therefore, the development of effective vaccines is an urgent … need to prevent and control virus spread. One of the vaccine production strategies uses the in silico epitope prediction from the virus genome by immunoinformatic approaches, which assist in selecting candidate epitopes for in vitro and clinical trials research. This study introduces the EpiCurator workflow to predict and prioritize epitopes from SARS-CoV-2 genomes by combining a series of computational filtering tools. To validate the workflow effectiveness, SARS-CoV-2 genomes retrieved from the GISAID database were analyzed. We identified 11 epitopes in the receptor-binding domain (RBD) of Spike glycoprotein, an important antigenic determinant, not previously described in the literature or published on the Immune Epitope Database (IEDB). Interestingly, these epitopes have a combination of important properties: recognized in sequences of the current variants of concern, present high antigenicity, conservancy, and broad population coverage. The RBD epitopes were the source for a multi-epitope design to in silico validation of their immunogenic potential. The multi-epitope overall quality was computationally validated, endorsing its efficiency to trigger an effective immune response since it has stability, high antigenicity and strong interactions with Toll-Like Receptors (TLR). Taken together, the findings in the current study demonstrated the efficacy of the workflow for epitopes discovery, providing target candidates for immunogen development.

Authors: Cristina S. Ferreira, Yasmmin C. Martins, Rangel Celso Souza, Ana Tereza R. Vasconcelos

Date Published: 2021

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

Large-Scale Protein Interactions Prediction by Multiple Evidence Analysis Associated With an In-Silico Curation Strategy

Abstract (Expand)

Predicting the physical or functional associations through protein-protein interactions (PPIs) represents an integral approach for inferring novel protein functions and discovering new drug targets … during repositioning analysis. Recent advances in high-throughput data generation and multi-omics techniques have enabled large-scale PPI predictions, thus promoting several computational methods based on different levels of biological evidence. However, integrating multiple results and strategies to optimize, extract interaction features automatically and scale up the entire PPI prediction process is still challenging. Most procedures do not offer an in-silico validation process to evaluate the predicted PPIs. In this context, this paper presents the PredPrIn scientific workflow that enables PPI prediction based on multiple lines of evidence, including the structure, sequence, and functional annotation categories, by combining boosting and stacking machine learning techniques. We also present a pipeline (PPIVPro) for the validation process based on cellular co-localization filtering and a focused search of PPI evidence on scientific publications. Thus, our combined approach provides means to extensive scale training or prediction of new PPIs and a strategy to evaluate the prediction quality. PredPrIn and PPIVPro are publicly available at https://github.com/YasCoMa/predprin and https://github.com/YasCoMa/ppi_validation_process.

Authors: Yasmmin Côrtes Martins, Artur Ziviani, Marisa Fabiana Nicolás, Ana Tereza Ribeiro de Vasconcelos

Date Published: 6th Sep 2021

Publication Type: Journal

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

Analysis of Protein-Protein Interactions networks and cross-species transfer learning comparison for seven organisms

Abstract (Expand)

Motivation Protein-protein interactions (PPIs) can be used for a plenty of applications like inferring protein functions or even helping the drug discovery process. For human specie, there is a lot of … validated information and functional annotations for the proteins in its interactome. In other species, the known interactome is much smaller compared with human and there are many proteins with few or no annotations by specialists. Understanding the interactome of other species helps to trace evolutionary characteristics, compare important biological processes and also build interactomes for new organisms according to other organisms more related with it instead of relying just to the human interactome. Results In this study, we evaluate the performance of PredPrIn workflow in predicting interactome for seven organisms in terms of scalability and precision showing that PredPrIn gets over than 70% of precision and it takes less than three days even on the largest datasets. We made a transfer learning analysis predicting an organism interactome from each other organism, we then showed an implication regarding to their evolutionary relation in the number of ortholog proteins shared between these organisms. We also present an analysis of functional enrichment showing the proportion of shared annotations between positive and false interactions predicted and extraction of topological features of each organism interactome such as proteins acting as hubs and bridge between modules. From each organism, one of the most frequent biological processes was selected and the proteins and pairs present in it were compared in terms of quantity in the interactome available in HINT database for that organism and the one predicted by PredPrIn. In this comparison we showed that we covered those proteins and pairs covered in HINT and also enriched these processes for almost all organisms. Conclusions In this work, we have proved the efficiency of PredPrIn workflow for protein interaction prediction for seven different organisms using scalability, performance and transfer learning analyses. We have also made cross-species interactome comparisons showing the most frequent biological processes for each organism as well as the topological features of each organism interactome showing the consistency with hypothesis about biological networks. Finally, we described the enrichment made by PredPrIn in selected biological processes showing that its prediction was important to enhance information about these organisms interactomes.

Author: Yasmmin C Martins

Date Published: 7th Jun 2023

Publication Type: Journal

The impact of non-lineage defining mutations in the structural stability for variants of concern of SARS-CoV-2

Abstract (Expand)

Motivation The identification of the most important mutations, that lead to a structural and functional change in a highly transmissible virus variants, is essential to understand the impacts and the … possible chances of vaccine and antibody escape. Strategies to rapidly associate mutations to functional and conformational properties are needed to rapidly analyze mutations in proteins and their impacts in antibodies and human binding proteins. Results Comparative analysis showed the main structural characteristics of the essential mutations found for each variant of concern in relation to the reference proteins. The paper presented a series of methodologies to track and associate conformational changes and the impacts promoted by the mutations.

Authors: Yasmmin Martins, Ronaldo Francisco da Silva

Date Published: 22nd Jun 2023

Publication Type: Journal

survInTime - Exploring surveillance methods and data analysis on Brazilian respiratory syndrome dataset and community mobility changes

Abstract (Expand)

Background The covid-19 pandemic brought negative impacts in almost every country in the world. These impacts were observed mainly in the public health sphere, with a rapid raise and spread of the … disease and failed attempts to restrain it while there was no treatment. However, in developing countries, the impacts were severe in other aspects such as the intensification of social inequality, poverty and food insecurity. Specifically in Brazil, the miscommunication among the government layers conducted the control measures to a complete chaos in a country of continental dimensions. Brazil made an effort to register granular informative data about the case reports and their outcomes, while this data is available and can be consumed freely, there are issues concerning the integrity and inconsistencies between the real number of cases and the number of notifications in this dataset. Results We projected and implemented four types of analysis to explore the Brazilian public dataset of Severe Acute Respiratory Syndrome (srag dataset) notifications and the google dataset of community mobility change (mobility dataset). These analysis provides some diagnosis of data integration issues and strategies to integrate data and experimentation of surveillance analysis. The first type of analysis aims at describing and exploring the data contained in both datasets, starting by assessing the data quality concerning missing data, then summarizing the patterns found in this datasets. The Second type concerns an statistical experiment to estimate the cases from mobility patterns organized in periods of time. We also developed, as the third analysis type, an algorithm to help the understanding of the disease waves by detecting them and compare the time periods across the cities. Lastly, we build time series datasets considering deaths, overall cases and residential mobility change in regular time periods and used as features to group cities with similar behavior. Conclusion The exploratory data analysis showed the under representation of covid-19 cases in many small cities in Brazil that were absent in the srag dataset or with a number of cases very low than real projections. We also assessed the availability of data for the Brazilian cities in the mobility dataset in each state, finding out that not all the states were represented and the best coverage occurred in Rio de Janeiro state. We compared the capacity of place categories mobility change combination on estimating the number of cases measuring the errors and identifying the best components in mobility that could affect the cases. In order to target specific strategies for groups of cities, we compared strategies to cluster cities that obtained similar outcomes behavior along the time, highlighting the divergence on handling the disease.

Authors: Yasmmin Côrtes Martins, Ronaldo Francisco da Silva

Date Published: 27th Sep 2023

Publication Type: Journal

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

Framework para a Construção de Redes Filogenéticas em Ambiente de Computação de Alto Desempenho

Abstract (Expand)

No presente artigo é apresentado uma avaliação de desempenho de um Framework de Redes Filogenéticas no ambiente do supercomputador Santos Dumont. O trabalho reforça os benefícios de paralelizar o … paralelizar o framework usando abordagens paralelas baseadas em Computação de Alta Vazão (CAV), e Computação de Alto Desempenho (CAD). Os resultados da execução paralela do framework proposto, demonstram que este tipo de experimento da bioinformática é apropriado para ser executado em ambientes de CAD; apesar de que nem todas as tarefas e programas componentes do framework tenham sido criados para usufruir de escalabilidade em ambientes de CAD, ou de técnicas de paralelismo em diferentes níveis. A análise comparativa da execução dos cinco pipelines de forma sequencial (como desenhado e usado originalmente por bioinformatas) apresentou um tempo estimado de 81, 67 minutos. Já a execução do mesmo experimento por meio do framework executa os cinco pipelines de forma paralela e usufruindo de um melhor gerenciamento das tarefas, gerando um tempo total de execução de 38,73 minutos. Essa melhora é de aproximadamente 2, 11 vezes em tempo de execução sugere que a utilização de um framework otimizado leva à diminuição do tempo computacional, à melhora de alocação de recursos e ao tempo de espera na alocação.

Authors: Rafael Terra, Kary Ocaña, Carla Osthoff, Lucas Cruz, Philippe Navaux, Diego Carvalho

Date Published: 19th Oct 2022

Publication Type: InProceedings

Gerência e Análises de Workflows aplicados a Redes Filogenéticas de Genomas de Dengue no Brasil

Abstract (Expand)

Processos evolutivos e dispersão de genomas de Dengue no Brasil são relevantes na direção do impacto e vigilância endemo-epidêmico e social de arboviroses emergentes. Árvores e redes filogenéticas … filogenéticas permitem exibir eventos evolutivos e reticulados em vírus originados pela alta diversidade e taxa de mutação de recombinação homóloga frequente. Apresentamos um workflow científico paralelo e distribuído para redes filogenéticas desenhado para trabalhar com a diversidade de ferramentas e recursos em experimentos da biologia computacional e acoplados a ambientes de computação de alto desempenho. Apresentamos uma melhoria no tempo de execução de aproximadamente 5 vezes em comparação com a execução sequencial em análises de genomas de dengue e com identificação de eventos de recombinação.

Authors: Rafael Terra, Micaella Coelho, Lucas Cruz, Marco Garcia-Zapata, Luiz Gadelha, Carla Osthoff, Diego Carvalho, Kary Ocaña

Date Published: 18th Jul 2021

Publication Type: InProceedings

Framework para execução de workflows de redes filogenéticas em ambientes de computação de alto desempenho

Abstract (Expand)

In the last years, the development of technologies, such as next-generation sequencing and high-performance computing allowed the execution of Bioinformatics experiments of high complexity and … computationally intensives. Different Bioinformatics fields need to use high-performance computing platforms to take advantage of the parallelism and tasks distribution, through specialized technologies of scientific workflows management systems. One of the Bioinformatics fields that need high-performance computing is phylogeny, a field that expresses the evolutive relations between genes and organisms, establishing which of them are most related evolutively. The phylogeny is used in several approaches, such as in the species classification; in the discovery of individuals’ kinship; in the identification of pathogens origins, and even in conservation biology. A way of representing these phylogenetic relations is using phylogenetic networks. However, the construction of these networks uses computationally intensive algorithms that require the constant manipulation of different input data. This work aims the development of a framework for construction of explicit phylogenetic networks, modeling a scientific workflow that adds different methods for the construction of the networks and the required input data treatment. The framework was developed to allow the use of multiple flows from the workflow in an automated, parallel, and distributed manner in a single execution and also to be executable in high- performance computing environments, constituting a challenging task, once the tools used are not developed focused in this environment. To orchestrate the workflow tasks, the scalable parallel programing library Parsl was used, allowing to do optimizations in the workflow’s tasks execution, performing better management of the resources. Two versions of the framework were developed, called Single Partition and Multi Partition, differing in the manner in which the resources are used. In tests performed, there was an improvement in the execution time of about five times when compared to the sequential execution of a flow without the optimizations. The framework was validated using public data of Dengue virus genomes, which were processed, annotated, and executed in the framework using the Santos Dumont supercomputer. The construction of the genomes’ explicit phylogenetic networks indicates that the framework is a functional, efficient, and easy to use tool.

Authors: Rafael Terra, Kary Ocaña, Carla Osthoff, Diego Carvalho

Date Published: 18th Feb 2022

Publication Type: Master's Thesis

Powered by
 (v.1.14.1)
About WorkflowHub | Acknowledgements | Credits | Terms & Conditions | Privacy Policy | Cite us | Contact us
Copyright © 2008 - 2023 The University of Manchester and HITS gGmbH