Scheila de Avila e Silva

BacPP: Bacterial promoter prediction—A tool for accurate sigma-factor specific assignment in enterobacteria

Promoter sequences are well known to play a central role in gene expression. Their recognition and assignment in silico has not consolidated into a general bioinformatics method yet. Most previously available algorithms employ and are limited to σ70-dependent promoter sequences. This paper presents a new tool named BacPP, designed to recognize and predict Escherichia coli promoter sequences from background with specific accuracy for each σ factor (respectively, σ24, 86.9%; σ28, 92.8%; σ32, 91.5%; σ38, 89.3%, σ54, 97.0%; and σ70, 83.6%). BacPP is hence outstanding in recognition and assignment of sequences according to σ factor and provide circumstantial information about upstream gene sequences. This bioinformatic tool was developed by weighing rules extracted from neural networks trained with promoter sequences known to respond to a specific σ factor. Furthermore, when challenged with promoter sequences belonging to other enterobacteria BacPP maintained 76% accuracy overall.

Rules extraction from neural networks applied to the prediction and recognition of prokaryotic promoters

Promoters are DNA sequences located upstream of the gene region and play a central role in gene expression. Computational techniques show good accuracy in gene prediction but are less successful in predicting promoters, primarily because of the high number of false positives that reflect characteristics of the promoter sequences. Many machine learning methods have been used to address this issue. Neural Networks (NN) have been successfully used in this field because of their ability to recognize imprecise and incomplete patterns characteristic of promoter sequences. In this paper, NN was used to predict and recognize promoter sequences in two data sets: (i) one based on nucleotide sequence information and (ii) another based on stability sequence information. The accuracy was approximately 80% for simulation (i) and 68% for simulation (ii). In the rules extracted, biological consensus motifs were important parts of the NN learning process in both simulations.

Bacterial Promoter Features Description and Their Application on E. coli in silico Prediction and Recognition Approaches

© 2012 Silva and Echeverrigaray, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Bacterial Promoter Features Description and Their Application on E. coli in silico Prediction and Recognition Approaches

IntergenicDB: a database for intergenic sequences.

A whole genome contains not only coding regions, but also non-coding regions. These are located between the end of a given coding region and the beginning of the following coding region. For this reason, the information about gene regulation process underlies in intergenic regions. There is no easy way to obtain intergenic regions from current available databases. IntergenicDB was developed to integrate data of intergenic regions and their gene related information from NCBI databases. The main goal of INTERGENICDB is to offer friendly database for intergenic sequences of bacterial genomes. Availability http://intergenicdb.bioinfoucs.com/

Triplet entropy analysis of hemagglutinin and neuraminidase sequences measures influenza virus phylodynamics

The influenza virus has been a challenge to science due to its ability to withstand new environmental conditions. Taking into account the development of virus sequence databases, computational approaches can be helpful to understand virus behavior over time. Furthermore, they can suggest new directions to deal with influenza. This work presents triplet entropy analysis as a potential phylodynamic tool to quantify nucleotide organization of viral sequences. The application of this measure to segments of hemagglutinin (HA) and neuraminidase (NA) of H1N1 and H3N2 virus subtypes has shown some variability effects along timeline, inferring about virus evolution. Sequences were divided by year and compared for virus subtype (H1N1 and H3N2). The nonparametric Mann-Whitney test was used for comparison between groups. Results show that differentiation in entropy precedes differentiation in GC content for both groups. Considering the HA fragment, both triplet entropy as well as GC concentration show intersection in 2009, year of the recent pandemic. Some conclusions about possible flu evolutionary lines were drawn.

Bacillus subtilis promoter sequences data set for promoter prediction in Gram-positive bacteria

This paper presents a prediction of Bacillus subtilis promoters using a Support Vector Machine system. In the literature, there is a lack of information on Gram-positive bacterial promoter sequences compared to Gram-negative bacteria. Promoter sequence identification is essential for studying gene expression. Initially, we collected the B. subtilis genome sequence from the NCBI database, and promoters were identified by their sigma factors in the DBTBS database. We then grouped the promoters according to 15 factors in 2 domains, corresponding to sigma 54 and sigma 70 of Gram-negative bacteria. Based on these data we developed a script in Python to search for promoters in the B. subtilis genome. After processing the data, we obtained 767 promoter sequences for B. subtilis, most of which were recognized by sigma SigA. To validate the data we found, we developed a software package called BacSVM+, which receives promoters as input and returns the best combination of parameters in a LibSVM library to predict promoter regions in the bacteria used in the simulation. All data gathered as well as the BacSVM+ software is available for download at http://bacpp.bioinfoucs.com/rafael/Sigmas.zip.