msBiodat analysis tool, big data analysis for high-throughput experiments

Background Mass spectrometry (MS) are a group of a high-throughput techniques used to increase knowledge about biomolecules. They produce a large amount of data which is presented as a list of hundreds or thousands of proteins. Filtering those data efficiently is the first step for extracting biologically relevant information. The filtering may increase interest by merging previous data with the data obtained from public databases, resulting in an accurate list of proteins which meet the predetermined conditions. Results In this article we present msBiodat Analysis Tool, a web-based application thought to approach proteomics to the big data analysis. With this tool, researchers can easily select the most relevant information from their MS experiments using an easy-to-use web interface. An interesting feature of msBiodat analysis tool is the possibility of selecting proteins by its annotation on Gene Ontology using its Gene Id, ensembl or UniProt codes. Conclusion The msBiodat analysis tool is a web-based application that allows researchers with any programming experience to deal with efficient database querying advantages. Its versatility and user-friendly interface makes easy to perform fast and accurate data screening by using complex queries. Once the analysis is finished, the result is delivered by e-mail. msBiodat analysis tool is freely available at http://msbiodata.irb.h

Structural Bioinformatics and Big Data Analytics: A mini-review

Structural Biology and Structural Bioinformatics are two complementary areas that deal with three dimensional structures of biomolecules. With the advent of high-throughput techniques and automation of identifying structures there is a barrage of data generated currently, which fall under the area of Big Data. In this review, we present examples and current approach to handle massive volume of structural data and some potential applications of Big Data from Structural Bioinformatics perspective.

Mapeamento, conversão e migração automática de bancos de dados relacionais para orientados a grafos

Relational Databases are the most used models in several applications in reason of the ease of use in its language of consultation and use in environments multi-users. With the great volume of information that we have today and, being that these are increasingly related, databases oriented graphs as a way to deal with this new demand, given the difficulties of the model relational to this new scenario. In view of this, this research dealt with the mapping processes, conversion and migration from the relational model to the graph-oriented model, above all, the semantic overload of constructors between the two models. The purpose of this study was the development of an application, called ThrusterDB, that performs this conversion process from the relational model to the graph-oriented one automatically. The research contributes by integrating the mapping, conversion and migration phases from a relational database to a graph-oriented one. This dissertation presents results that show that the generated database, after the process, provides a better performance in the average time of consultations carried out, in addition to preserving the semantics from the source relational database, without any loss or redundancy of Dice.Agência 1Bancos de Dados Relacionais são os modelos mais utilizados em diversas aplicações em razão da facilidade existente em sua linguagem de consulta e utilização em ambientes multi-usuários. Com o grande volume de informação que se tem nos dias de hoje e, sendo que estes encontram-se cada vez mais relacionadas, surgem os bancos de dados orientados a grafos como forma de lidar com esta nova demanda, frente às dificuldades do modelo relacional a este novo cenário. Diante disto, esta pesquisa tratou dos processos de mapeamento, conversão e migração do modelo relacional para o orientado a grafos, tratando, sobretudo, a sobrecarga semântica de construtores entre os dois modelos. O objetivo deste estudo foi o desenvolvimento de uma aplicação, denominada ThrusterDB, que realiza esse processo de conversão do modelo relacional para o orientado a grafos de forma automática. A pesquisa traz contribuição ao integrar as fases de mapeamento, conversão e migração automática de um banco de dados relacional para um orientado a grafos. Esta dissertação apresenta resultados que mostram que o banco de dados gerado, após o processo, provê um desempenho melhor no tempo médio de consultas realizadas, além de preservar a semântica do banco de dados relacional de origem, sem qualquer perda ou redundância de dados

Additional file 1 of msBiodat analysis tool, big data analysis for high-throughput experiments

Excel spreadsheets. XLSX file containing the data from Sousa Abreu et al. which is used in the example of the article. (XLSX 611 kb