Evidence map›Paper›PMID 41909610›Full record

ArticleBioinformatics advances2026

MotifPeeker: benchmarking epigenomic profiling methods using motifs.

Hiranyamaya Dash, Thomas Roberts, Maria Weinert, Nathan Skene

Abstract read
In one paragraph

Article in Bioinformatics advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Hiranyamaya DashDepartment of Brain Sciences, Imperial College London, Sir Michael Uren Hub, White City Campus, London W12 0BZ, United Kingdom.ORCID https://orcid.org/0009-0005-5514-505X
Thomas RobertsDepartment of Brain Sciences, Imperial College London, Sir Michael Uren Hub, White City Campus, London W12 0BZ, United Kingdom.
Maria WeinertDepartment of Brain Sciences, Imperial College London, Sir Michael Uren Hub, White City Campus, London W12 0BZ, United Kingdom.ORCID https://orcid.org/0000-0001-6187-1000
Nathan SkeneDepartment of Brain Sciences, Imperial College London, Sir Michael Uren Hub, White City Campus, London W12 0BZ, United Kingdom.ORCID https://orcid.org/0000-0002-6807-3180

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Summary: MotifPeeker benchmarks epigenomic profiling methods targeting transcription factors (TFs) where no 'gold standard' reference exists, using motif enrichment as a key metric. With minimal input, users can analyse their data in a single function and receive an intuitive HTML report. Availability and implementation: MotifPeeker is available on Bioconductor (≥v3.21) at https://bioconductor.org/packages/MotifPeeker. The complete source code is available on GitHub at https://github.com/neurogenomics/MotifPeeker, with full documentation provided at https://neurogenomics.github.io/MotifPeeker. Additionally, the MotifPeeker Docker image is hosted on GitHub at https://github.com/neurogenomics/MotifPeeker/pkgs/container/motifpeeker.

Identifiers

PMID41909610
PMCPMC13020906

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.