Orchard: Building large cancer phylogenies using stochastic combinatorial search.

Phylogenies depicting the evolutionary history of genetically heterogeneous subpopulations of cells from the same cancer, i.e., cancer phylogenies, offer valuable insights about cancer development and guide treatment strategies.

At-RS31 orchestrates hierarchical cross-regulation of splicing factors and integrates alternative splicing with TOR-ABA pathways.

Alternative splicing is essential for plants, enabling a single gene to produce multiple transcript variants to boost functional diversity and fine-tune responses to environmental and developmental cues. At-RS31, a plant-specific splicing factor in the Serine/Arginine (SR)-rich protein family, responds to light and the Target of Rapamycin (TOR) signaling pathway, yet its downstream targets and regulatory impact remain unknown.To identify At-RS31 targets, we applied individual-nucleotide resolution crosslinking and immunoprecipitation (iCLIP) and RNAcompete assays.

Extensive binding of uncharacterized human transcription factors to genomic dark matter.

Most of the human genome is thought to be non-functional, and includes large segments often referred to as "dark matter" DNA. The genome also encodes hundreds of putative and poorly characterized transcription factors (TFs). We determined genomic binding locations of 166 uncharacterized human TFs in living cells.

Reconstructing the sequence specificities of RNA-binding proteins across eukaryotes.

RNA-binding proteins (RBPs) are key regulators of gene expression. Here, we introduce EuPRI (Eukaryotic Protein-RNA Interactions) - a freely available resource of RNA motifs for 34,736 RBPs from 690 eukaryotes. EuPRI includes binding data for 504 RBPs, including newly collected RNAcompete data for 174 RBPs, along with thousands of reconstructed motifs.

Orthrus: Towards Evolutionary and Functional RNA Foundation Models.

In the face of rapidly accumulating genomic data, our ability to accurately predict key mature RNA properties that underlie transcript function and regulation remains limited. Pre-trained genomic foundation models offer an avenue to adapt learned RNA representations to biological prediction tasks. However, existing genomic foundation models are trained using strategies borrowed from textual or visual domains that do not leverage biological domain knowledge.