Accès ouvert
2026
article
OpenAlex
Pooja Kathail, Forest Yang, Gabriel B. Loeb, Nilah M. Ioannidis
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 …
us, pl
(code pays fourni par la source)
Accès ouvert
2026
preprint
OpenAlex
Runyang Nicolas Lou, Daven Lim, Minoli Daigavane, Landen Gozashti et autres
. Here, using haplotype-resolved genome assemblies from >600 vertebrate species, we comprehensively survey the landscape of SVs across >500 million years of evolution. We identify 35.3 million SVs and 3.12 billion single nucleotide variants (SNVs) segregating between two representative haplotypes across species, …
us, ca
(code pays fourni par la source)
Accès ouvert
2026
article
OpenAlex
Eyes S Robson, Nilah M. Ioannidis
There has been much debate about the benefits of supervised versus unsupervised learning on genomes. Determining which is better in what contexts requires developing comprehensive benchmarks spanning functional and evolutionary tasks. Importantly, such benchmarks need large sample sizes to enable well-powered ranking …
us
(code pays fourni par la source)
Accès ouvert
2026
article
OpenAlex
Ruchir Rastogi, Aniketh Janardhan Reddy, Ryan K. Chung, Nilah M. Ioannidis
BACKGROUND: Genomic sequence-to-expression deep learning models, which are trained to predict gene expression and other molecular phenotypes across the reference genome, have recently been shown to have poor out-of-the-box performance in predicting gene expression variation across individuals based on their personal genome …
us
(code pays fourni par la source)
Accès ouvert
2026
preprint
OpenAlex
Margarita Geleta, Daniel Mas Montserrat, Nilah M. Ioannidis, Alexander G. Ioannidis
Abstract Local ancestry inference (LAI) predicts a discrete ancestry label for each segment of an individual’s genome and has become integral to studying population history, genetic variation, and polygenic trait association. We present a new local ancestry paradigm that eschews discrete categorical …
us
(code pays fourni par la source)
Accès ouvert
2026
peer-review
OpenAlex
Alan J. Aw, Lionel Chentian Jin, Nilah M. Ioannidis, Yun S Song
In statistical fine-mapping, signals stable across stratified subgroups can capture functionally important loci missed by covariate adjustment approaches, and prioritizing agreement between both approaches enhances functional variant discovery.
us
(code pays fourni par la source)
Accès ouvert
2026
software
OpenAlex
Ruchir Rastogi, Aniketh Janardhan Reddy, Ryan Chung, Nilah M. Ioannidis
The code base for our work on improving the performance of sequence-to-expression models for making individual-specific gene expression predictions by fine-tuning them on personal genome and transcriptome data. This code was used to produce the results in our paper, please refer to …
us
(code pays fourni par la source)
Accès ouvert
2026
software
OpenAlex
Ruchir Rastogi, Aniketh Janardhan Reddy, Ryan Chung, Nilah M. Ioannidis
The code base for our work on improving the performance of sequence-to-expression models for making individual-specific gene expression predictions by fine-tuning them on personal genome and transcriptome data. This code was used to produce the results in our paper, please refer to …
us
(code pays fourni par la source)
Accès ouvert
2026
article
OpenAlex
Alan J. Aw, Lionel Chentian Jin, Nilah M. Ioannidis, Yun S Song
Fine-mapping methods, which aim to identify genetic variants responsible for complex traits following genetic association studies, typically assume that sufficient adjustments for confounding within the association study cohort have been made, for example, through regressing out the top principal components (i.e., residualization). …
us
(code pays fourni par la source)
Accès ouvert
2026
preprint
OpenAlex
Aniketh Janardhan Reddy, Peter H. Sudmant, Nilah M. Ioannidis
Abstract Predicting how RNA splicing varies across tissues is important for understanding the impact of genetic variation and identifying splicing-based disease mechanisms. Although many sequence-based deep learning models have been developed to predict splicing, most predict splice sites rather than full splicing …
us
(code pays fourni par la source)
Accès ouvert
2026
preprint
OpenAlex
Alan J. Aw, Lionel Chentian Jin, Nilah M. Ioannidis, Yun S. Song
Fine-mapping methods, which aim to identify genetic variants responsible for complex traits following genetic association studies, typically assume that sufficient adjustments for confounding within the association study cohort have been made, e.g., through regressing out the top principal components (i.e., residualization). Despite …
us
(code pays fourni par la source)
Accès ouvert
2025
preprint
OpenAlex
Daven Lim, Runyang Nicolas Lou, Nilah M. Ioannidis, Peter H. Sudmant
Structural variants (SVs) represent a major source of genetic diversity and play key roles in human disease and evolution. Yet, the extent to which local sequence context shapes the likelihood of structural variant formation remains poorly quantified. Here, we develop machine learning …
ch, us
(code pays fourni par la source)