ArticleNucleic acids research2024
Methods for constructing and evaluating consensus genomic interval sets.
Article in Nucleic acids research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Atacformer: A transformer-based foundation model for analysis and interpretation of ATAC-seq data.bioRxiv : the preprint server for biology · 2025Article
- High level of aneuploidy and recurrent loss of chromosome 11 as relevant features of somatotroph pituitary tumors.Journal of translational medicine · 2024Article
- Methods for evaluating unsupervised vector representations of genomic regions.NAR genomics and bioinformatics · 2024Article
- Fast clustering and cell-type annotation of scATAC data using pre-trained embeddings.NAR genomics and bioinformatics · 2024Article
- Joint Representation Learning for Retrieval and Annotation of Genomic Interval Sets.Bioengineering (Basel, Switzerland) · 2024Article
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Authors and funding
8 authors.
Funding
Abstract
The amount of genomic region data continues to increase. Integrating across diverse genomic region sets requires consensus regions, which enable comparing regions across experiments, but also by necessity lose precision in region definitions. We require methods to assess this loss of precision and build optimal consensus region sets. Here, we introduce the concept of flexible intervals and propose three novel methods for building consensus region sets, or universes: a coverage cutoff method, a likelihood method, and a Hidden Markov Model. We then propose three novel measures for evaluating how well a proposed universe fits a collection of region sets: a base-level overlap score, a region boundary distance score, and a likelihood score. We apply our methods and evaluation approaches to several collections of region sets and show how these methods can be used to evaluate fit of universes and build optimal universes. We describe scenarios where the common approach of merging regions to create consensus leads to undesirable outcomes and provide principled alternatives that provide interoperability of interval data while minimizing loss of resolution.
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Registered trials
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