gene expression — papers

BiologyNature Methods · Oct 2021 Open access

Effective gene expression prediction from sequence by integrating long-range interactions

Žiga Avsec, Vikram Agarwal and David R. Kelley

This paper introduces Enformer, a transformer-based deep learning model that predicts gene expression and chromatin states directly from DNA sequence by integrating regulatory information from up to ~100 kb away. By using self-attention to capture long-range interactions, it substantially improves prediction accuracy over prior convolutional models. The approach also improves prediction of the effects of non-coding genetic variants on expression.

deep learning gene expression genomics transformer

BiologyScience · Jul 2016

Visualization and analysis of gene expression in tissue sections by spatial transcriptomics

Patrik L. Ståhl, Fredrik Salmén, Joakim Lundeberg and Jonas Frisén

The authors introduce 'spatial transcriptomics,' a method that places thin histological tissue sections onto a glass surface arrayed with barcoded reverse-transcription primers, so that mRNA captured at each position retains its two-dimensional spatial coordinates. Sequencing the barcoded cDNA reconstructs genome-wide expression maps directly on the tissue image. They demonstrate the approach on mouse brain and human breast cancer sections, recovering spatially resolved transcriptomes that align with tissue morphology.

spatial transcriptomics gene expression rna-seq tissue sections