Jiecong Lin

This study identifies 15 novel RLS-associated loci and links them to neurodevelopmental programs, while revealing over 100 comorbidities and brain structural associations through genome- and phenome-wide analyses.

The paper introduces UKBioBERT, a DNA language model pre-trained on UK BioBank variants, which improves gene expression prediction and enables eQTL identification when combined with sequence-to-function models.

The paper identifies noncoding variants that alter gene regulatory networks using a combination of single-molecule deaminase footprinting and variant effect prediction, applied to UK Biobank genomes.

scCAFE is a deep learning model that detects multi-scale chromatin architectural features in single-cell Hi-C data, outperforming existing methods and enabling cell-type annotation from 3D genome features.

UniPTM predicts multiple PTM sites on full-length protein sequences without using sliding windows, outperforming existing models and providing interpretable results.

EPInformer predicts gene expression by integrating promoter-enhancer sequences, epigenomic signals, and chromatin contacts, outperforming existing models in cross-chromosome validation and CRISPR experiments.

The paper develops ABSOLVE-seq to assess genome editing off-targets linked to genetic variants using primary cells and sequencing, enabling evaluation of human genetic diversity's impact on gene editing specificity.

The paper develops CRISPRme, a tool that integrates genetic diversity data to assess off-target risks of gene editing, finding allele-specific off-target effects in clinical gRNAs.

DeepMotifSyn synthesizes heterodimeric DNA motifs from monomeric pairs using deep learning, outperforming existing methods on CAP-SELEX data.