CeTF: an R/Bioconductor package for transcription factor co-expression networks using regulatory impact factors (RIF) and partial correlation and information (PCIT) analysis.

BACKGROUND: Finding meaningful gene-gene interaction and the main Transcription Factors (TFs) in co-expression networks is one of the most important challenges in gene expression data mining. RESULTS: Here, we developed the R package "CeTF" that integrates the Partial Correlation with Information Theory (PCIT) and Regulatory Impact Factors (RIF) algorithms applied to gene expression data from microarray, RNA-seq, or single-cell RNA-seq platforms. This approach allows identifying the transcription factors most likely to regulate a given network in different biological systems - for example, regulation of gene pathways in tumor stromal cells and tumor cells of the same tumor. This pipeline can be easily integrated into the high-throughput analysis. To demonstrate the CeTF package application, we analyzed gastric cancer RNA-seq data obtained from TCGA (The Cancer Genome Atlas) and found the HOXB3 gene as the second most relevant TFs with a high regulatory impact (TFs-HRi) regulating gene pathways in the cell cycle. CONCLUSION: This preliminary finding shows the potential of CeTF to list master regulators of gene networks. CeTF was designed as a user-friendly tool that provides many highly automated functions without requiring the user to perform many complicated processes. It is available on Bioconductor ( http://bioconductor.org/packages/CeTF ) and GitHub ( http://github.com/cbiagii/CeTF ).

Whole-genome resequencing of common bean elite breeding lines.

The expansion of bean genome technologies has prompted new perspectives on generating resources and knowledge essential to research and implementing biotechnological tools for the practical operations of plant breeding programs. This study aimed to resequence the entire genome (whole genome sequencing-WGS) of 40 bean genotypes selected based on their significance in breeding programs worldwide, with the objective of generating an extensive database for the identification of single nucleotide polymorphisms (SNPs). Over 6 million SNPs were identified, distributed across the 11 bean chromosomes. After quality variant filtering, 420,509 high-quality SNPs were established, with an average of 38,228 SNPs per chromosome. These variants were categorized based on their predicted effects, revealing that the majority exerted a modifier impact on non-coding genome regions (94.68%). Notably, a significant proportion of SNPs occurred in intergenic regions (62.89%) and at least one SNP was identified in 58.63% of the genes annotated in the bean genome. Of particular interest, 7841 SNPs were identified in 85% of the putative plant disease defense-related genes, presenting a valuable resource for crop breeding efforts. These findings provide a foundation for the development of innovative and broadly applicable technologies for the routine selection of superior genotypes in global bean improvement and germplasm characterization programs.