ArticleGenome biology2015
De novo ChIP-seq analysis.
Article in Genome biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- ATACompass enables cross-species cell identity mapping for scATAC-seq without gene annotations.Cell reports methods · 2026Article
- PAPerFly: Partial Assembly-based Peak Finder for ab initio binding site reconstruction.BMC bioinformatics · 2023Article
- Seq'ing identity and function in a repeat-derived noncoding RNA world.Chromosome research : an international journal on the molecular, supramolecular and evolutionary aspects of chromosome biology · 2020Review
- Islands of retroelements are major components of Drosophila centromeres.PLoS biology · 2019Article
- Recent advances in ChIP-seq analysis: from quality management to whole-genome annotation.Briefings in bioinformatics · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Methods for the analysis of chromatin immunoprecipitation sequencing (ChIP-seq) data start by aligning the short reads to a reference genome. While often successful, they are not appropriate for cases where a reference genome is not available. Here we develop methods for de novo analysis of ChIP-seq data. Our methods combine de novo assembly with statistical tests enabling motif discovery without the use of a reference genome. We validate the performance of our method using human and mouse data. Analysis of fly data indicates that our method outperforms alignment based methods that utilize closely related species.
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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.