Evidence map›Paper›PMID 35634361›Full record

ArticleSTAR protocols2022

Protocol for analysis of RNA-sequencing and proteome profiling data for subgroup identification and comparison.

Kevin C Yang, Sharon M Gorski

Abstract read
In one paragraph

Article in STAR protocols, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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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

2 authors.

Kevin C YangCanada's Michael Smith Genome Sciences Centre at BC Cancer, Vancouver, BC V5Z 1L3, Canada.
Sharon M GorskiCanada's Michael Smith Genome Sciences Centre at BC Cancer, Vancouver, BC V5Z 1L3, Canada.

Funding

CIHR PJT-159536
6 · The paper itself

Abstract

RNA-sequencing and quantitative proteomic profiling simultaneously measure thousands of molecules and provide opportunities to decipher the transcriptomic and proteomic landscapes of cohort specimens for basic and health research. We present a protocol for the analysis of paired transcriptome and proteome data to identify and compare molecular subgroups among cohort specimens. We demonstrate a streamlined analysis workflow, applicable for both transcriptome and proteome data, which allows the comparison of two data types for RNA-protein variations and for derivation of biological implications. For complete details on the use and execution of this protocol, please refer to Yang et al. (2021).

Indexed as

ProteomeProteomicsBase SequenceHumansRNASequence Analysis, RNAProteomeRNABioinformaticsCancerGene ExpressionProteomicsRNAseq

Identifiers

PMID35634361
PMCPMC9133752

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.