ArticleGenome research2017
Sasquatch: predicting the impact of regulatory SNPs on transcription factor binding from cell- and tissue-specific DNase footprints.
Article in Genome research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 37 citations in OpenAlex.
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- Correction of transposase sequence bias in ATAC-seq data with rule ensemble modeling.NAR genomics and bioinformatics · 2023Article
- Pulmonary embolism and 529 human blood metabolites: genetic correlation and two-sample Mendelian randomization study.BMC genomic data · 2022Article
- Predicting 3D chromatin interactions from DNA sequence using Deep Learning.Computational and structural biotechnology journal · 2022Review
- Mapping Chromatin Accessibility in Human Naïve Pluripotent Stem Cells Using ATAC-Seq.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Genome-wide association and functional interrogation identified a variant at 3p26.1 modulating ovarian cancer survival among Chinese women.Cell discovery · 2021Article
- Identification of LZTFL1 as a candidate effector gene at a COVID-19 risk locus.Nature genetics · 2021Article
- Genetic and functional insights into CDA-I prevalence and pathogenesis.Journal of medical genetics · 2021Article
- ATACdb: a comprehensive human chromatin accessibility database.Nucleic acids research · 2021Article
- Tissue-specific DNase I footprint analysis confirms the association ofJournal of genetics · 2021Article
- Chromatin accessibility profiling methods.Nature reviews. Methods primers · 2021Article
- Deep learning of pharmacogenomics resources: moving towards precision oncology.Briefings in bioinformatics · 2020Article
- DeepC: predicting 3D genome folding using megabase-scale transfer learning.Nature methods · 2020Article
- Atlas of Transcription Factor Binding Sites from ENCODE DNase Hypersensitivity Data across 27 Tissue Types.Cell reports · 2020Article
- regSNPs-ASB: A Computational Framework for Identifying Allele-Specific Transcription Factor Binding From ATAC-seq Data.Frontiers in bioengineering and biotechnology · 2020Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
In the era of genome-wide association studies (GWAS) and personalized medicine, predicting the impact of single nucleotide polymorphisms (SNPs) in regulatory elements is an important goal. Current approaches to determine the potential of regulatory SNPs depend on inadequate knowledge of cell-specific DNA binding motifs. Here, we present Sasquatch, a new computational approach that uses DNase footprint data to estimate and visualize the effects of noncoding variants on transcription factor binding. Sasquatch performs a comprehensive
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.