Evidence map›Paper›PMID 37682480›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2023

Gene Regulatory Network Modeling Using Single-Cell Multi-Omics in Plants.

Tran Chau, Prakash Timilsena, Song Li

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

3 authors.

Tran ChauGraduate Program in Genetics, Bioinformatics and Computational Biology (GBCB), Blacksburg, VA, USA.
Prakash TimilsenaSchool of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, USA.
Song LiGraduate Program in Genetics, Bioinformatics and Computational Biology (GBCB), Blacksburg, VA, USA. songli@vt.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Single-cell multi-omics technology can be applied to plant cells to characterize gene expression and open chromatin regions in individual cells. In this chapter, we describe a computational pipeline for the analysis of single-cell data to construct gene regulatory networks. The major steps of this pipeline include the following: (1) normalize and integrate scRNA-seq and scATAC-seq data (2) identify cluster maker genes (3) perform motif finding for selected marker genes, and (4) identify regulatory networks with machine learning. The pipeline has been tested using data from the model species Arabidopsis and is generally applicable to other plant and animal species to characterize regulatory networks using single-cell multi-omics data.

Indexed as

ArabidopsisGene Regulatory NetworksAnimalsChromatinMachine LearningMultiomicsChromatinGene regulatory networkMachine learningMotif analysisMulti-omicsRootscATAC-seqscRNA-seqSingle-cell sequencing

Identifiers

PMID37682480

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.