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

Abstract

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.

De novo Origination of Bacillus subtilis 168 Promoters from Random Sequences

“…Coelho., et al [1]. The data set of 769 promoters, from now referred to as baseline sequences, was used for comparison against the random sequences generated to determine putative promoters.…”

“…10,000 random sequences were generated to investigate their probability of being putative promoters. The lengths of these random sequences were set between 38 and 93 nucleotides, with nucleotide compositions of 3535 adenine (A), 3218 thymine (T), 1763 guanine (G) and 1485 cytosine (C) per 10,000 bases; in accordance to the length and sequence compositions of B. subtilis promoters [1]. Start codons and stop codons were excluded.…”

“…Promoters are responsible for directing RNA polymerase towards the transcription of genes; hence, are key factors of transcriptional regulation. A typical prokaryotic promoter consists of a Pribnow box and -35 region [1], which lies between 9-18 base pairs (bp) and 35 bp upstream from the start point of transcription. The Pribnow box has a sequence similar to 5′-TATAAT-3′ while the -35 region has a sequence similar to 5′-TTGACA-3′.…”

“…These consensus sequences are not strictly conserved in prokaryotes; however, the promoters of Bacillus subtilis are known to have similar sequences [2]. Initiation factors known as sigma (σ) factors determine the specific promoter sequences RNAP bind to in prokaryotes [1]. Different σ factors are produced in response to specific stimuli; such as, stress, stationary and growth phases, star-vation and morphological differentiation [3]; therefore, driving the transcription of necessary genes.…”

iPromoter-BnCNN: a novel branched CNN based predictor for identifying and classifying sigma promoters

“…Recently, a few computational methods have been proposed to classify DNA sequences as promoters or non-promoters, some aiming at identifying a certain class of sigma promoters. For instance, Coelho et al (2018) provided BacSVM+, a software package using LibSVM library for promoter prediction in Bacillus subtilis. Work of Scheila de Avila e Silva* (2014) integrated DNA duplex stability as feature of neural network to identify σ 28 and σ 54 class of promoter in E. coli bacteria.…”