The genome isn’t a string — it’s a folded 3D object, and where a gene sits in that fold decides which enhancers can reach it. This week we read the sequence; today we watch it fold. Fudenberg et al. built Akita, ‘a convolutional neural network … that accurately predicts genome folding from DNA sequence alone,’ learning ‘an orientation-specific grammar for CTCF binding sites.’ Schwessinger et al.’s DeepC used ‘megabase-scale transfer learning’ to ‘predict the impact of both large-scale structural and single base-pair variations.’ Zhou’s Orca went multiscale — ‘from kilobase to whole-chromosome scale’ — recapitulating variant effects from ‘300 bp to 90 Mb.’ Tan et al.’s C.Origami does ‘de novo prediction of cell-type-specific chromatin organization,’ powering ‘high-throughput in silico genetic screening’ in leukemia vs normal T cells. Yang et al.’s Epiphany predicts ‘cell-type-specific Hi-C contact maps from widely available epigenomic tracks,’ with a GAN ’to encourage contact map realism.’ And GraphReg closes the loop, using ‘graph attention networks to exploit the connectivity of distal elements up to 2 Mb away’ to predict gene expression — validated by ‘CRISPRi-FlowFISH and TAP-seq.’ Decoding genome function from sequence, through structure.
