Evidence map›Paper›PMID 38351456›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2024

Data Analysis Pipeline for scRNA-seq Experiments to Study Early Oogenesis.

Wei Ge, Teng Zhang, Yang Zhou, Wei Shen

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 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, top 96% of its field
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, 0 citations in OpenAlex.

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 at 2 institutions in 1 country.

Wei GeCollege of Life Sciences, Qingdao Agricultural University, Qingdao, China.
Teng ZhangCollege of Life Sciences, Inner Mongolia University, Hohhot, China.
Yang ZhouCollege of Life Sciences, Inner Mongolia University, Hohhot, China.
Wei ShenCollege of Life Sciences, Qingdao Agricultural University, Qingdao, China. wshen@qau.edu.cn.
Inner Mongolia University · CNQingdao Agricultural University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Germ cells as the means for the transmission of genetic information between generations have been a hot topic of research for decades. The analysis of the transcriptomes, that is of the RNA transcripts produced by the genotype at a given time, of germ cells and the surrounding somatic cells, is essential to unravel the cellular and molecular processes regulating gametogenesis. However, the asynchronized differentiation of germ cells and high cellular heterogeneity in the developing ovary or testis represent two unsurmountable challenges for delineating the transcription regulation mechanism of germ cells using traditional bulk RNA sequencing. By performing single-cell RNA sequencing (scRNA-seq), it is now possible to dissect the transcriptome of germ cell development at single-cell resolution, and apply powerful bioinformatics methods to translate raw sequencing data into meaningful information. Here, using the 10× Genomic platform and the most widely cited bioinformatics tools, we describe how to analyze early female germ cell development using scRNA-seq data generated from mouse E11.5 to E14.5 ovaries. This pipeline will provide a guide for exploring the processes of early germ cell development at single-cell resolution.

Indexed as

Single-Cell Gene Expression AnalysisTranscriptomeAnimalsData AnalysisFemaleGene Expression ProfilingMaleMiceOogenesisSequence Analysis, RNASingle-Cell AnalysisBulk RNA-seqMeiosisMeiotic transcriptomescRNA-seqSingle-cell transcriptome

Identifiers

PMID38351456
OpenAlexW4391818783

What OpenQuestion holds

Textmetadata
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Registered trials

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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.