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18 Publications visible to you, out of a total of 18
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

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

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

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

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

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

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

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

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