Chromosome Conformation Capture (3C) technology is a method used for investigating three-dimensional (3D) genome structure, whereby segments of a genome that are in close-proximity can be identified and used to infer their spatial relationship. A 3C-derived method, High-resolution Chromosome Conformation Capture sequencing (HiC-seq) have been used to identify genes that can be affected by distal interactions such as long-range promoter-enhancer contacts that interact with immune system regulators. Although HiC-seq has been widely used to identify 3D interactions genome-wide in many species, many of the analysis tools have yet to be critically assessed. Here, we used publically available HiC-seq data to investigate and compare three major steps of HiC-seq data analysis workflow, including raw HiC-seq data processing, topologically-associated domains (TADs) identification algorithms and visualisation tools. We then applied our validated toolset to a DNaseI-treated, HiC-seq dataset sampled from human conventional T cells (Tconv cells) to investigate the ability of the tools at analysing relative low-coverage datasets. Whilst HiC-seq data analysis requires a significant sequencing coverage, applying HiC-Pro, an insulation score algorithm for TAD identification and HiCPlotter for visualisation, we identified a total of 4,818,855 long-range interactions, leading to the prediction of 3275 TADs genome-wide. Using this HiC-seq data along with other conformation assays (i.e. 4C-seq), we show that an upstream super-enhancer and promoter of the master T cell regulator SATB1 are located within the same TAD region, supporting the hypothesis that long-range interactions regulate the function of SATB1, and that sequence variants in enhancer elements may effect the pathogenicity of autoimmune diseases.