HiCdat: a fast and easy-to-use Hi-C data analysis tool.

Area of Science:

• Genomics and Molecular Biology
• Bioinformatics and Computational Biology

Background:

• Chromosome Conformation Capture (3C) technologies, including Hi-C, are crucial for studying nuclear architecture.
• Decreasing sequencing costs enhance Hi-C's potential for nuclear architecture phenotyping.
• Accessible Hi-C data processing is essential for comparative studies across biological contexts.

Purpose of the Study:

• To develop an accessible tool for Hi-C data processing and analysis.
• To enable biologists to utilize Hi-C data for phenotypic comparisons.
• To facilitate the study of large-scale chromosomal structures and their correlations.

Main Methods:

• Development of HiCdat, a software tool with a graphical user interface (GUI).
• Implementation of data pre-processing and higher-level analysis tools in R.
• Support for diverse data types including RNA-Seq, ChIP-Seq, and BS-Seq for comprehensive analysis.

Main Results:

• HiCdat offers a user-friendly GUI for efficient Hi-C data pre-processing.
• The tool provides advanced R-based analysis capabilities for deeper insights.
• HiCdat supports multiple data types, enhancing the scope of Hi-C analysis.

Conclusions:

• HiCdat provides an easy-to-use solution from raw reads to in-depth analysis.
• The software focuses on large structural features, genomic/epigenomic correlations, and comparative studies.
• HiCdat's simple I/O formats allow seamless integration into existing bioinformatics workflows.