ArticleBMC bioinformatics2009
Identifying differential exon splicing using linear models and correlation coefficients.
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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.
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
15 citing papers in PubMed, 22 citations in OpenAlex.
- Comprehensive insights on pivotal prognostic signature involved in clear cell renal cell carcinoma microenvironment using the ESTIMATE algorithm.Cancer medicine · 2020Article
- Unusual structure and splicing pattern of the vertebrate mitochondrial solute carrier SLC25A3 gene.Journal of genetics · 2018Article
- A random effects model for the identification of differential splicing (REIDS) using exon and HTA arrays.BMC bioinformatics · 2017Article
- A proteogenomic approach to understand splice isoform functions through sequence and expression-based computational modeling.Briefings in bioinformatics · 2016Article
- iGEMS: an integrated model for identification of alternative exon usage events.Nucleic acids research · 2016Article
- Algorithms for differential splicing detection using exon arrays: a comparative assessment.BMC genomics · 2015Article
- An integrative pipeline for multi-modal discovery of disease relationships.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2015Article
- ARH-seq: identification of differential splicing in RNA-seq data.Nucleic acids research · 2014Article
- Inferring the expression variability of human transposable element-derived exons by linear model analysis of deep RNA sequencing data.BMC genomics · 2013Article
- Gene Array Analyzer: alternative usage of gene arrays to study alternative splicing events.Nucleic acids research · 2012Article
- Exon array data analysis using Affymetrix power tools and R statistical software.Briefings in bioinformatics · 2011Article
- Genome-wide analysis of alternative splicing in medulloblastoma identifies splicing patterns characteristic of normal cerebellar development.Cancer research · 2011Article
- Exon array analysis using re-defined probe sets results in reliable identification of alternatively spliced genes in non-small cell lung cancer.BMC genomics · 2010Article
- The comparison of different pre- and post-analysis filters for determination of exon-level alternative splicing events using Affymetrix arrays.Journal of biomolecular techniques : JBT · 2010Article
- Exon array analysis of head and neck cancers identifies a hypoxia related splice variant of LAMA3 associated with a poor prognosis.PLoS computational biology · 2009Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWith the availability of the Affymetrix exon arrays a number of tools have been developed to enable the analysis. These however can be expensive or have several pre-installation requirements. This led us to develop an analysis workflow for analysing differential splicing using freely available software packages that are already being widely used for gene expression analysis. The workflow uses the packages in the standard installation of R and Bioconductor (BiocLite) to identify differential splicing. We use the splice index method with the LIMMA framework. The main drawback with this approach is that it relies on accurate estimates of gene expression from the probe-level data. Methods such as RMA and PLIER may misestimate when a large proportion of exons are spliced. We therefore present the novel concept of a gene correlation coefficient calculated using only the probeset expression pattern within a gene. We show that genes with lower correlation coefficients are likely to be differentially spliced.
resultsThe LIMMA approach was used to identify several tissue-specific transcripts and splicing events that are supported by previous experimental studies. Filtering the data is necessary, particularly removing exons and genes that are not expressed in all samples and cross-hybridising probesets, in order to reduce the false positive rate. The LIMMA approach ranked genes containing single or few differentially spliced exons much higher than genes containing several differentially spliced exons. On the other hand we found the gene correlation coefficient approach better for identifying genes with a large number of differentially spliced exons.
conclusionWe show that LIMMA can be used to identify differential exon splicing from Affymetrix exon array data. Though further work would be necessary to develop the use of correlation coefficients into a complete analysis approach, the preliminary results demonstrate their usefulness for identifying differentially spliced genes. The two approaches work complementary as they can potentially identify different subsets of genes (single/few spliced exons vs. large transcript structure differences).
Indexed as
Identifiers
What OpenQuestion holds
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.