Evidence map›Paper›PMID 38346081›Full record

ArticlePLoS computational biology2024

magpie: A power evaluation method for differential RNA methylation analysis in N6-methyladenosine sequencing.

Zhenxing Guo, Daoyu Duan, Wen Tang, Julia Zhu, William S Bush, Liangliang Zhang, Xiaofeng Zhu, Fulai Jin, Hao Feng

Abstract read
In one paragraph

Article in PLoS computational biology, 2024. 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

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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

9 authors.

Zhenxing GuoSchool of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), Shenzhen, Guangdong, China.
Daoyu DuanDepartment of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States of America.
Wen TangDepartment of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States of America.ORCID 0000-0003-3710-9029
Julia ZhuHathaway Brown School, Shaker Heights, Ohio, United States of America.
William S BushDepartment of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States of America.
Liangliang ZhangDepartment of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States of America.ORCID 0000-0001-5986-2260
Xiaofeng ZhuDepartment of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States of America.
Fulai JinDepartment of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, United States of America.
Hao FengDepartment of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, United States of America.ORCID 0000-0003-2243-9949

Funding

TUMOR METABOLISM PROGRAMP30CA043703 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI MARK R CHANCE · 1987 to 2026
$142.3M
Robust mapping of chromatin loops from sparse or single cell Hi-C data with DeepLoopR01HG009658 · NHGRI · CASE WESTERN RESERVE UNIVERSITY · PI Fulai Jin · 2017 to 2026
$5.1M
STAG2 mutations and 3D genome organization in glioblastoma multiformeR01CA267872 · NCI · GEORGETOWN UNIVERSITY · PI Fulai Jin, TODD A WALDMAN · 2022 to 2026
$2.7M
Mapping heritable chromatin loop variants with allele-specific Hi-C analysisR01HG012384 · NHGRI · CASE WESTERN RESERVE UNIVERSITY · PI Alan D Attie, Fulai Jin · 2023 to 2026
$2.6M
Understanding the variation of induced β-cell differentiation.R01DK131437 · NIDDK · CASE WESTERN RESERVE UNIVERSITY · PI JIN, FULAI, LI, YAN · 2022 to 2025
$2.4M
Simultaneous mapping of somatic mosaicism and kb-resolution 3D genome in single cells.UG3NS132061 · NINDS · CASE WESTERN RESERVE UNIVERSITY · PI JIN, FULAI, LI, YAN · 2023 to 2024
$805k
Case Western Reserve UniversityThe Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen) INTRAMURAL FUNDING UDF01002936
6 · The paper itself

Abstract

Recently, novel biotechnologies to quantify RNA modifications became an increasingly popular choice for researchers who study epitranscriptome. When studying RNA methylations such as N6-methyladenosine (m6A), researchers need to make several decisions in its experimental design, especially the sample size and a proper statistical power. Due to the complexity and high-throughput nature of m6A sequencing measurements, methods for power calculation and study design are still currently unavailable. In this work, we propose a statistical power assessment tool, magpie, for power calculation and experimental design for epitranscriptome studies using m6A sequencing data. Our simulation-based power assessment tool will borrow information from real pilot data, and inspect various influential factors including sample size, sequencing depth, effect size, and basal expression ranges. We integrate two modules in magpie: (i) a flexible and realistic simulator module to synthesize m6A sequencing data based on real data; and (ii) a power assessment module to examine a set of comprehensive evaluation metrics.

Indexed as

RNARNA MethylationHigh-Throughput Nucleotide SequencingMethylationRNA

Identifiers

PMID38346081
PMCPMC10890765

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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.