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PubMed · 42627343

Base-pair resolution conservation data improves cell type specific sequence-to-expression prediction.

Pooja Kathail·Forest Yang·Gabriel B Loeb·Nilah M Ioannidis
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Abstract

MOTIVATION: Genomic sequence-to-activity models can decipher gene regulatory mechanisms and predict the functional impact of regulatory variants. However, current models struggle to integrate information from sequences outside promoters, especially information from cell type specific regulatory elements. RESULTS: Here, we propose incorporating base-pair resolution evolutionary conservation data into genomic sequence-to-expression predictors. We explore two training strategies-training from scratch or fine-tuning an existing sequence-only model with additional conservation input. We find that in both cases, base-pair resolution conservation data improves cell type specific sequence-to-expression prediction, with training from scratch yielding the greatest benefit. The improvement in cell type specific expression prediction can be attributed in part to the fact that models trained on sequence and conservation data learn to better recognize cell type specific regulatory elements than models trained on sequence alone. AVAILABILITY: Code is available at https://github.com/ni-lab/basenji-phyloP.

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Conserved SequenceBase PairingSequence Analysis, DNAComputational BiologyGenomicsJournal Article

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BibTeXRIS

Pooja Kathail, Forest Yang, Gabriel B Loeb, Nilah M Ioannidis. 2026-08-31. Base-pair resolution conservation data improves cell type specific sequence-to-expression prediction.. https://doi.org/10.1093/bioinformatics%2Fbtag626

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PubMed2026

Lift&Add-rapid and robust addition of new species to alignments of conserved non-coding sequences.

MOTIVATION: Identifying sequence constraint across long evolutionary distances is a powerful method for the discovery of functional genomic sequences, especially putative non-coding elements. Conserved elements have been a mainstay of comparative genomic research, and can be further investigated for species-specific sequence acceleration to dissect the genetic basis of trait evolution. The conclusions of these comparative genomic studies are contingent on the number and range of species included in this phylogenetic analysis. However, while the number of metazoan genomes sequences is increasing rapidly, adding new genomes to existing whole-genome alignments remains computationally expensive. RESULTS: Here, we present a bioinformatic workflow, Lift&Add, that enables conserved elements, coding or non-coding, to be rapidly mapped to new genomes ("Lift") and subsequently be added to pre-existing multiple species alignments ("Add"), thus providing an avenue for easy exploration of these putative functional elements. Focusing here on a group of species that has been largely under-represented in genomic comparisons, the marsupials, we demonstrate the intuition behind this workflow and provide an example comparative genomic analysis that can be performed. IMPLEMENTATION AND AVAILABILITY: Lift&Add is implemented as a series of scripts in Snakemake and bash, which can be downloaded from https://github.com/navyashukladr/Lift_and_Add.

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