FI-Chrom Transforms Hi-C Chromosome Maps into 3D Models

Chromosomes, long strands of DNA, are experts at folding themselves into compact forms that organize genetic material efficiently. They keep essential genes accessible while storing away less active regions. Decoding how these large and intricate systems achieve such organization has proven difficult, requiring a blend of data-driven experiments and theoretical modeling. A study published in PNAS by José Onuchic and Vinícius Contessoto from Rice University introduces FI-Chrom, a new method that generates three-dimensional chromosome maps using real experimental data.

FI-Chrom works with Hi-C maps, which divide chromosomes into segments known as beads, each representing about 50,000 DNA bases. These maps provide information on how often different beads are near one another, capturing probabilities rather than precise 3D coordinates. As Onuchic put it, “We had chromosome maps that gave us, theoretically, 3D information, but we were really reading them in 2D space.” FI-Chrom bridges that gap by reconstructing chromosomes in three dimensions, converting millions of probabilistic interactions into a coherent model.

To design FI-Chrom, postdoctoral researcher Antonio Oliveira Jr applied inverse statistical mechanics, using a maximum entropy framework. Rather than preprogramming specific structures, Oliveira trained FI-Chrom directly on experimental data. Over time, the program produced 3D chromosome models that naturally reflected known biological features without prior instruction. “I have not, for example, told the program that human chromosomes generally separate into two compartments and minimize knots,” Oliveira noted. “Yet after multiple rounds of training, FI-Chrom began producing 3D models with these features.” These characteristics emerge from the data rather than assumptions, offering a clearer picture of chromosome architecture.

Available as an open-access tool, FI-Chrom enables scientists to explore chromosome structures across species—from humans to yeast and bacteria. Contessoto emphasized that while Hi-C data captures average interactions across many cells, FI-Chrom uncovers an ensemble of structures, revealing the dynamic behavior underlying them. Beyond static maps, the tool helps researchers infer the movement of chromatin loops and other structural changes over time. “We were able to use this dynamic Hi-C information to model chromatin loops,” Onuchic explained. “While we previously knew the loops existed, through the FI-Chrom model we were able to demonstrate that the loops form transiently rather than being static features of the chromosomes.”