Evidence map›Paper›PMID 30891063›Full record

ArticleFrontiers in genetics2019

Assessment of a Highly Multiplexed RNA Sequencing Platform and Comparison to Existing High-Throughput Gene Expression Profiling Techniques.

Eric Reed, Elizabeth Moses, Xiaohui Xiao, Gang Liu, Joshua Campbell, Catalina Perdomo, Stefano Monti

Abstract read
In one paragraph

Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Label-Free Sample Preparation for Single-Cell Proteomics.Methods in molecular biology (Clifton, N.J.) · 2024
    Article
  2. Article
  3. Article
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

7 authors.

Eric ReedBioinformatics Program, Boston University, Boston, MA, United States.
Elizabeth MosesSection of Computational Biomedicine, School of Medicine, Boston University, Boston, MA, United States.
Xiaohui XiaoSection of Computational Biomedicine, School of Medicine, Boston University, Boston, MA, United States.
Gang LiuSection of Computational Biomedicine, School of Medicine, Boston University, Boston, MA, United States.
Joshua CampbellBioinformatics Program, Boston University, Boston, MA, United States.
Catalina PerdomoSection of Computational Biomedicine, School of Medicine, Boston University, Boston, MA, United States.
Stefano MontiBioinformatics Program, Boston University, Boston, MA, United States.

Funding

Training CoreP42ES007381 · NIEHS · BOSTON UNIVERSITY MEDICAL CAMPUS · PI SHERR, DAVID H · 1995 to 2019
$46.1M
NIEHS NIH HHS P42 ES007381
6 · The paper itself

Abstract

The need to reduce per sample cost of RNA-seq profiling for scalable data generation has led to the emergence of highly multiplexed RNA-seq. These technologies utilize barcoding of cDNA sequences in order to combine multiple samples into a single sequencing lane to be separated during data processing. In this study, we report the performance of one such technique denoted as sparse full length sequencing (SFL), a ribosomal RNA depletion-based RNA sequencing approach that allows for the simultaneous sequencing of 96 samples and higher. We offer comparisons to well established single-sample techniques, including: full coverage Poly-A capture RNA-seq, microarrays, as well as another low-cost highly multiplexed technique known as 3' digital gene expression (3'DGE). Data was generated for a set of exposure experiments on immortalized human lung epithelial (AALE) cells in a two-by-two study design, in which samples received both genetic and chemical perturbations of known oncogenes/tumor suppressors and lung carcinogens. SFL demonstrated improved performance over 3'DGE in terms of coverage, power to detect differential gene expression, and biological recapitulation of patterns of differential gene expression from

Indexed as

gene expressionmicroarraymultiplexingplatform comparisonRNA sequencing

Identifiers

PMID30891063
PMCPMC6411637

What OpenQuestion holds

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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.