DNA structural properties of DNA binding sites for 21 transcription factors in the mycobacterial genome

Discerning the Role of DNA Sequence, Shape, and Flexibility in Recognition by Drosophila Transcription Factors

The precise spatial and temporal orchestration of gene expression is crucial for the ontogeny of an organism and is mainly governed by transcription factors (TFs). The mechanism of recognition of cognate sites amid millions of base pairs in the genome by TFs is still incompletely understood. In this study, we focus on DNA sequence composition, shape, and flexibility preferences of 28 quintessential TFs from Drosophila melanogaster that are critical to development and body patterning mechanisms. Our study finds that TFs exhibit distinct predilections for DNA shape, flexibility, and sequence compositions in the proximity of transcription factor binding sites (TFBSs). Notably, certain zinc finger proteins prefer GC-rich areas with less negative propeller twist, while homeodomains mainly seek AT-rich regions with a more negative propeller twist at their sites. Intriguingly, while numerous cofactors share similar binding site preferences and bind closer to each other in the genome, some cofactors that have different preferences bind farther apart. These findings shed light on TF DNA recognition and provide novel insights into possible cofactor binding and transcriptional regulation mechanisms.

CDBProm: the Comprehensive Directory of Bacterial Promoters

The decreasing cost of whole genome sequencing has produced high volumes of genomic information that require annotation. The experimental identification of promoter sequences, pivotal for regulating gene expression, is a laborious and cost-prohibitive task. To expedite this, we introduce the Comprehensive Directory of Bacterial Promoters (CDBProm), a directory of in-silico predicted bacterial promoter sequences. We first identified that an Extreme Gradient Boosting (XGBoost) algorithm would distinguish promoters from random downstream regions with an accuracy of 87%. To capture distinctive promoter signals, we generated a second XGBoost classifier trained on the instances misclassified in our first classifier. The predictor of CDBProm is then fed with over 55 million upstream regions from more than 6000 bacterial genomes. Upon finding potential promoter sequences in upstream regions, each promoter is mapped to the genomic data of the organism, linking the predicted promoter with its coding DNA sequence, and identifying the function of the gene regulated by the promoter. The collection of bacterial promoters available in CDBProm enables the quantitative analysis of a plethora of bacterial promoters. Our collection with over 24 million promoters is publicly available at https://aw.iimas.unam.mx/cdbprom/.