Update README.md

README.md

@@ -57,29 +57,26 @@ configs:
 - [Citation](#citation)
 - [Contact](#contact)
 
+## Overview
 ## Overview
 
 This dataset contains promoter sequences used for validation purposes in genetic research, focusing on prokaryotic promoters. It serves to facilitate the study of gene expression regulation, providing a comprehensive set of promoter sequences from various organisms.
 
-This figure 1 provides a visual representation of the sequence sources and their distribution within the study. 
-The dataset comprises known promoter sequences from 75 organisms, retrieved from the Prokaryotic Promoter Database (PPD), 
-alongside non-promoter sequences obtained from the NCBI RefSeq database (specifically sampled from CDS regions).
-It also includes non-promoter sequences constructed via higher and zero-order Markov chains that mirror  compositional characteristics of known promoters. 
-Additionally, an independent test set, focusing on \textit{E. coli} sigma70 promoters, was employed, curated by Cassiano and Silva-Rocha (2020).  
-A balanced distribution approach was adopted to even out the number of positive and negative samples, with the dataset being systematically divided into 
-training, validation, and test subsets. This stratification underpins a thorough evaluation of the model efficacy.
+The dataset comprises known promoter sequences from 75 organisms, retrieved from the Prokaryotic Promoter Database (PPD), alongside non-promoter sequences obtained from the NCBI RefSeq database (specifically sampled from CDS regions). It also includes non-promoter sequences constructed via higher and zero-order Markov chains that mirror compositional characteristics of known promoters. Additionally, an independent test set, focusing on *E. coli* sigma70 promoters, was employed, curated by Cassiano and Silva-Rocha (2020).
+
+A balanced distribution approach was adopted to even out the number of positive and negative samples, with the dataset being systematically divided into training, validation, and test subsets. This stratification underpins a thorough evaluation of the model efficacy.
 
-![Promoter prediction performance metrics](https://github.com/nbrg-ppcu/prokbert/blob/main/assets/Figure4_promoter_db.png)
+![Promoter Dataset Overview](https://github.com/nbrg-ppcu/prokbert/blob/main/assets/Figure4_promoter_db.png)
 *Figure 1: Promoter dataset - overview*
 
+The prokaryotic promoter sequences are typically 81bp long, ensuring compatibility with most tools' input prerequisites, particularly around the putative TSS region interval $[-60, +20]$. Our positive dataset encompasses promoter sequences from various species, predominantly found on both chromosomes and plasmids. Promoters included in the independent test set, based on exact match, were excluded from the training data. Species and contigs were mapped to NCBI assembly and sequence accessions.
+
+To curate comprehensive non-promoter sequences (negative samples), we employed three strategies:
+1. Using non-promoter sequences (CDS -- Coding Sequences).
+2. Random sequences generated with a 3rd-order Markov chain.
+3. Pure random sequences (0-order Markov chain) as proposed by Cassiano and Silva-Rocha (2020).
 
-The prokaryotic promoter sequences are typically 81bp long, ensuring compatibility with most tools' input prerequisites, particularly around the putative TSS region interval $[-60, +20]$. Our positive dataset encompasses promoter sequences from various species, predominantly found on both chromosomes and plasmids. Promoters included in the independent test set, based on exact match, were excluded from the training data. Species and contigs were mapped to NCBI assembly and sequence accessions. To curate comprehensive non-promoter sequences (negative samples), we employed three strategies:
-\begin{enumerate}
-    \item Using non-promoter sequences (CDS -- Coding Sequences).
-    \item Random sequences generated with a 3rd-order Markov chain.
-    \item Pure random sequences (0-order Markov chain) as proposed by Cassiano and Silva-Rocha (2020). 
-\end{enumerate}
-The distribution of this composite dataset was 40\% CDS, 40\% Markov-derived random sequences, and 20\% pure random sequences (0-order Markov chain). One practical application of promoter detection in coding sequences is to check whether an unintentional promoter is injected or can be located inside a modified or designed coding sequence region, causing disruption. To cover this use-case, we incorporated the coding regions into our training and evaluation dataset. The CDS sequences were extracted from the genomic sequences of contigs, based on annotations from NCBI. The 81bp long CDS region samples were selected based on the NCBI-provided annotations for the available contigs with respect to the underlying species. The promoter regions often contain AT-rich sequences, i.e., TATA box. To capture and model the AT-rich regions, we applied 3rd and 0 order Markov chains to generate sequence examples that reflect the compositional property of known promoters. 
+The distribution of this composite dataset was 40% CDS, 40% Markov-derived random sequences, and 20% pure random sequences (0-order Markov chain). One practical application of promoter detection in coding sequences is to check whether an unintentional promoter is injected or can be located inside a modified or designed coding sequence region, causing disruption. To cover this use-case, we incorporated the coding regions into our training and evaluation dataset. The CDS sequences were extracted from the genomic sequences of contigs, based on annotations from NCBI. The 81bp long CDS region samples were selected based on the NCBI-provided annotations for the available contigs with respect to the underlying species. The promoter regions often contain AT-rich sequences, i.e., TATA box. To capture and model the AT-rich regions, we applied 3rd and 0 order Markov chains to generate sequence examples that reflect the compositional property of known promoters. 
 
 
 ## Dataset Description