Real and Virtual Sequences Supporting "Preference-Based Fine-Tuning of Genomic Sequence Models for Personal Expression Prediction with Data Augmentation"

This dataset contains real and virtual genomic sequences used in the study“Preference-Based Fine-Tuning of Genomic Sequence Models for Personal Expression Prediction with Data Augmentation”(bioRxiv preprint: https://doi.org/10.1101/2025.11.09.687505).

The initial release (v1) of the dataset included mixed-case nucleotide characters (lowercase/uppercase).These inconsistencies affected sequence preprocessing pipelines; therefore, we provide a corrected version (v2) with fully normalized uppercase sequences.All replication and downstream analyses for the above manuscript were performed using v2, and users should likewise rely on v2 for running the official code associated with the preprint.

🔹 Real Sequences

Real individual sequences were derived from the GEUVADIS cohort(E-GEUV-1),which provides paired whole-genome sequencing (WGS) and RNA-seq data for 421 phased individuals.Following the approach of Huang et al. (2023), Nature Genetics 55(12), 2056–2059,we extracted 196,608 bp (±98,304 bp around TSS) windows for selected chromosome 22 genes from the hg19 reference genome,substituting individual-specific variants to create personalized haplotype sequences.

Reference:Huang, C. et al. Personal transcriptome variation is poorly explained by current genomic deep learning models.Nature Genetics 55(12), 2056–2059 (2023). https://doi.org/10.1038/s41588-023-01574-w

🔹 Virtual Sequences

To mitigate data scarcity and expand genetic diversity, we generated 1,000 virtual individuals usingthe sim1000G framework (Dimitromanolakis et al., BMC Bioinformatics 20(1), 26, 2019).sim1000G simulates recombination and mutation events based on 1000 Genomes haplotypes, producing realistic population-level variant structures.

References:

Dimitromanolakis, A. et al. sim1000g: a user-friendly genetic variant simulator in R for unrelated individuals and family-based designs. BMC Bioinformatics 20(1), 26 (2019). https://doi.org/10.1186/s12859-019-2611-1

Huang, C. et al. Nature Genetics 55(12), 2056–2059 (2023). https://doi.org/10.1038/s41588-023-01574-w