Uwe Ohler

The paper identifies 25% of 7,264 non-canonical open reading frames (ncORFs) as producing translated gene products, supported by over 3,000 peptides from 3.8 billion mass spectra.

The paper identifies CRISPRa-responsive enhancers and their target genes in a 2 Mb region around PHOX2B using TESLA-seq, revealing both known and novel regulatory relationships.

GeneSys generates synthetic single-cell transcriptomic data under developmental constraints, improving trajectory inference and gene prioritization in Arabidopsis and mouse datasets.

The paper argues against using Silhouette metrics for evaluating single-cell integration methods due to their unsuitability in handling batch effects and proposes alternative strategies.

The paper predicts RNA degradation and translation levels from RBP binding data using machine learning, finding in vivo binding sites are more important than predicted ones.

The paper identifies gene dosage effects on telomere maintenance and disease pathways in neuroblastoma using genomic and transcriptomic data from 115 tumors.

Liam integrates multimodal single-cell data by learning a joint low-dimensional representation, outperforming existing methods on Multiome and CITE-seq datasets.

The paper uses model interpretation to uncover sequence determinants of translation rates from synthetic 5UTRs and improves model performance using human reporter data.

The paper measures translation dynamics in mouse neocortex neurogenesis, revealing a critical period of translational control that regulates protein expression and neuronal differentiation.

The paper performs a rare variant association study of 30 blood biomarkers in 191,971 UK Biobank individuals, identifying 193 gene-biomarker associations using functionally informed kernel-based tests.

This paper outlines a community-driven initiative to create a consolidated catalog of human Ribo-seq ORFs, addressing the lack of standardized annotation for these features.

The paper creates a central repository for zebrafish developmental genomics data, identifies new cis-regulatory elements, and compares regulatory elements between zebrafish and mouse.

A NeurIPS 2021 benchmarking competition evaluated over 2600 submissions from 280 competitors across three multimodal single-cell integration tasks using a purpose-built benchmarking dataset and cloud evaluation pipeline.

This paper introduces a variational auto-encoder for single-cell ATAC-seq data that performs dimensionality reduction and batch correction simultaneously.

The paper maps human RBP-viral RNA interactions for SARS-CoV-2 and related coronaviruses using deep learning, identifies variant impacts, and links RBPs to potential biomarkers.

The paper uses spatial transcriptomics and single-cell RNA labeling to study mRNA localization and dynamics in early zebrafish embryos, revealing transport to primordial germ cells and evolutionary conservation.

The paper reveals Ebp1's role in neuronal proteostasis by analyzing ribosomal architecture and proteomic changes in the developing mouse neocortex.

A CRISPR screen identifies ZBTB11 and ZFP131 as factors maintaining pluripotency by pausing POL II at pro-differentiation genes.

SEMITONES identifies markers without relying on cluster assignments, showing its utility in healthy haematopoiesis data for marker gene identification and co-enrichment network construction.

Janggu is a Python library that simplifies deep learning in genomics by providing reusable data tools and model evaluation features, improving performance through high-order sequence features.

ORFquant quantifies translation at the open reading frame level using Ribo-seq, revealing alternative ORFs and gene-specific protein production differences across human cell lines.

The paper uses sci-ATAC-seq and a new tool, ScregSeg, to analyze chromatin dynamics in zebrafish embryos, revealing roles of npas4l/cloche in cell and regulatory states.

This study shows that the m6A reader Ythdf limits axonal growth in Drosophila by modulating Fmr1 target selection.

This study develops a multitask deep learning model to predict RNA-binding protein (RBP) binding preferences and identifies key sequence motifs and transcript context patterns.

The paper identifies functional miRNA::mRNA interactions in Drosophila using AGO CLIP experiments and quantifies their impact on gene expression and translation.

Ribo-seQC is a software package that evaluates ribosome profiling data quality and translational profiles across cytoplasmic and organellar ribosomes, outputting shareable platform-independent HTML reports.

The paper introduces a new method, PTex, to purify RNA-protein complexes without relying on polyadenylated RNA, and provides a global RNA-bound proteome for human and bacterial cells.

The paper compares DNase-seq and ATAC-seq for open chromatin and TF footprinting, showing protocol-specific biases affect performance and reproducibility.

The paper analyzes RBP-RNA interactions across 64 RBPs to identify regulatory modules and their functional roles in RNA metabolism and localization.

SSMART identifies sequence-structure motifs for RNA-binding proteins by integrating sequence and structural preferences from RNA targets.