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9 Publications visible to you, out of a total of 9
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

DSCrank: A Method for Selection and Ranking of Datasets

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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

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

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

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

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

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

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