FAN-C: a feature-rich framework for the analysis and visualisation of chromosome conformation capture data

Kai Kruse,Clemens B. Hug,Juan M. Vaquerizas

doi:10.1186/s13059-020-02215-9

Abstract

Chromosome conformation capture data, particularly from high-throughput approaches such as Hi-C, are typically very complex to analyse. Existing analysis tools are often single-purpose, or limited in compatibility to a small number of data formats, frequently making Hi-C analyses tedious and time-consuming. Here, we present FAN-C, an easy-to-use command-line tool and powerful Python API with a broad feature set covering matrix generation, analysis, and visualisation for C-like data (https://github.com/vaquerizaslab/fanc). Due to its compatibility with the most prevalent Hi-C storage formats, FAN-C can be used in combination with a large number of existing analysis tools, thus greatly simplifying Hi-C matrix analysis.

Highlights

IntroductionThe development over the last decade of high-throughput techniques to study the three-dimensional organisation of the genome [1,2,3] in the nucleus has fuelled the characterisation of chromatin conformation in a wide variety of biological systems.These range from the organisation of the bacterial nucleoid [4], to the in vitro characterisation of the molecular mechanisms that govern chromatin organisation in eukaryotes [5,6,7,8,9,10], reviewed in [11], how this organisation is dynamically regulated during cell cycle [12, 13], development and differentiation [14,15,16,17,18,19], reviewed in [20], and how it is affected in disease [21,22,23], reviewed in [24].Given the fundamental role that the correct organisation of chromatin in the nucleus plays for proper cell physiology, there is a growing need to integrate chromatin contact data in current studies examining different aspects of gene and genome regulation
Feature analysis tools act on the Hi-C matrix to derive measures, models, and statistics that answer specific biological questions, such as the identification of topologically associating domains [30,31,32] and chromatin loops [25, 33], the 3D modelling of the chromatin fibre [34, 35], or the identification of differential contacts between samples
Hi-C matrix generation: from raw sequencing output to chromatin contacts The first component of the FAN-C analysis framework consists of tools for matrix generation (Fig. 1b)

Summary

Introduction

The development over the last decade of high-throughput techniques to study the three-dimensional organisation of the genome [1,2,3] in the nucleus has fuelled the characterisation of chromatin conformation in a wide variety of biological systems.These range from the organisation of the bacterial nucleoid [4], to the in vitro characterisation of the molecular mechanisms that govern chromatin organisation in eukaryotes [5,6,7,8,9,10], reviewed in [11], how this organisation is dynamically regulated during cell cycle [12, 13], development and differentiation [14,15,16,17,18,19], reviewed in [20], and how it is affected in disease [21,22,23], reviewed in [24].Given the fundamental role that the correct organisation of chromatin in the nucleus plays for proper cell physiology, there is a growing need to integrate chromatin contact data in current studies examining different aspects of gene and genome regulation. The development over the last decade of high-throughput techniques to study the three-dimensional organisation of the genome [1,2,3] in the nucleus has fuelled the characterisation of chromatin conformation in a wide variety of biological systems. These range from the organisation of the bacterial nucleoid [4], to the in vitro characterisation of the molecular mechanisms that govern chromatin organisation in eukaryotes [5,6,7,8,9,10], reviewed in [11], how this organisation is dynamically regulated during cell cycle [12, 13], development and differentiation [14,15,16,17,18,19], reviewed in [20], and how it is affected in disease [21,22,23], reviewed in [24]. Visualisation tools enable the static display, and sometimes interactive exploration of the Hi-C matrix, often together with associated genomic data derived using other methods, such as ChIP-seq [29, 36]

Methods

Results

Conclusion