Biology subjects

Santos, L. G. C.

Publications and source records attributed to Santos, L. G. C..

2 recordsLinked to original sources

bioRxiv2021

Biotext: Exploiting Biological-like format for text mining

The large amount of existing textual data justifies the development of new text mining tools. Bioinformatics tools can be brought to Text Mining, increasing the arsenal of resources. Here, we present BIOTEXT, a package of strategies for converting natural language text into biological-like information data, providing a general protocol with standardized functions, allowing to share, encode and decode textual data for amino acid and DNA. The package was used to encode the arbitrary information present in the headings of the biological sequences found in a BLAST survey. The protocol implemented in this study consists of 12 steps, which can be easily executed and/ or changed by the user, depending on the study area. BIOTEXT empowers users to perform text mining using bioinformatics tools. BIOTEXT is freely available at https://pypi.org/project/BIOTEXT/ (Python package) and https://sourceforge.net/projects/BIOTEXTtools/files/AMINOcode_GUI/ (Standalone tool).

bioinformatics↗

bioRxiv2018

RAFTS3G - An efficient and versatile clustering software to analyses in large protein datasets

The need to develop computational tools and techniques that can predict efficiently consistent groups of family proteins in large volume of biological information is still a great perspective in Bioinformatic studies. Besides that, it is difficult to increase speed demanding low computational processing to minimize the information complexity. Tools already consolidated as the CD-HIT and UCLUST generates very compact data that makes the Data Mining difficult and have low efficiency when used for detect homology among proteins requiring manual intervention, therefore it is necessary a tool that is also efficient in low similarity. Here we present a new approach for the Data Mining and analysis of homology in large dataset of protein sequences, the RAFTS3G. We used the UniProtKB/Swiss-Prot database with the most popular clustering tools and RAFTS3G proved to be more than 10 times faster than CD-HIT and its strategy increases the performance in low similarity to detect protein families.\n\nContact: raittz@ufpr.br

bioinformatics↗