ReviewCancer informatics2015
TIN: An R Package for Transcriptome Instability Analysis.
Review in Cancer informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
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.
Who cites it
3 citing papers in PubMed.
- Transcriptome analysis of alternative splicing in the pathogen life cycle in human foreskin fibroblasts infected with Trypanosoma cruzi.Scientific reports · 2020Article
- A bioinformatic analysis identifies circadian expression of splicing factors and time-dependent alternative splicing events in the HD-MY-Z cell line.Scientific reports · 2019Article
- Knockdown of EWSR1/FLI1 expression alters the transcriptome of Ewing sarcoma cells in vitro.Journal of bone oncology · 2016Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alternative splicing is a key regulatory mechanism for gene expression, vital for the proper functioning of eukaryotic cells. Disruption of normal pre-mRNA splicing has the potential to cause and reinforce human disease. Owing to rapid advances in high-throughput technologies, it is now possible to identify novel mRNA isoforms and detect aberrant splicing patterns on a genome scale, across large data sets. Analogous to the genomic types of instability describing cancer genomes (eg, chromosomal instability and microsatellite instability), transcriptome instability (TIN) has recently been proposed as a splicing-related genome-wide characteristic of certain solid cancers. We present the R package TIN, available from Bioconductor, which implements a set of methods for TIN analysis based on exon-level microarray expression profiles. TIN provides tools for estimating aberrant exon usage across samples and for analyzing correlation patterns between TIN and splicing factor expression levels.
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Registered trials
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.