ArticlePloS one2014
PolyaPeak: detecting transcription factor binding sites from ChIP-seq using peak shape information.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- EDClust: an EM-MM hybrid method for cell clustering in multiple-subject single-cell RNA sequencing.Bioinformatics (Oxford, England) · 2022Article
- Integration of Count Difference and Curve Similarity in Negative Regulatory Element Detection.Frontiers in genetics · 2022Article
- From reads to insight: a hitchhiker's guide to ATAC-seq data analysis.Genome biology · 2020Review
- Functional data analysis for computational biology.Bioinformatics (Oxford, England) · 2019Article
- Ritornello: high fidelity control-free chromatin immunoprecipitation peak calling.Nucleic acids research · 2017Article
- Features that define the best ChIP-seq peak calling algorithms.Briefings in bioinformatics · 2017Article
- Article
- Peak shape clustering reveals biological insights.BMC bioinformatics · 2015Article
- Uncovering correlated variability in epigenomic datasets using the Karhunen-Loeve transform.BioData mining · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
ChIP-seq is a powerful technology for detecting genomic regions where a protein of interest interacts with DNA. ChIP-seq data for mapping transcription factor binding sites (TFBSs) have a characteristic pattern: around each binding site, sequence reads aligned to the forward and reverse strands of the reference genome form two separate peaks shifted away from each other, and the true binding site is located in between these two peaks. While it has been shown previously that the accuracy and resolution of binding site detection can be improved by modeling the pattern, efficient methods are unavailable to fully utilize that information in TFBS detection procedure. We present PolyaPeak, a new method to improve TFBS detection by incorporating the peak shape information. PolyaPeak describes peak shapes using a flexible Pólya model. The shapes are automatically learnt from the data using Minorization-Maximization (MM) algorithm, then integrated with the read count information via a hierarchical model to distinguish true binding sites from background noises. Extensive real data analyses show that PolyaPeak is capable of robustly improving TFBS detection compared with existing methods. An R package is freely available.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.