ArticleISRN bioinformatics2012
Differential Expression Analysis for RNA-Seq Data.
Article in ISRN bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Transcriptome analysis reveals ADAMTS15 is a potential inflammation-related gene in remote ischemic postconditioning.Frontiers in cardiovascular medicine · 2023Article
- Transcriptome analysis of the effect of diosgenin on autoimmune thyroiditis in a rat model.Scientific reports · 2021Article
- Comparative transcriptomics analysis revealing flower trichome development during flower development in two Lonicera japonica Thunb. cultivars using RNA-seq.BMC plant biology · 2020Article
- Transcript Profiles of Stria Vascularis in Models of Waardenburg Syndrome.Neural plasticity · 2020Article
- Transcriptome Analysis Reveals the Molecular Mechanisms Underlying Adenosine Biosynthesis in Anamorph Strain of Caterpillar Fungus.BioMed research international · 2019Article
- Burkitt lymphoma beyond MYC translocation: N-MYC and DNA methyltransferases dysregulation.BMC cancer · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
RNA-Seq is increasingly being used for gene expression profiling. In this approach, next-generation sequencing (NGS) platforms are used for sequencing. Due to highly parallel nature, millions of reads are generated in a short time and at low cost. Therefore analysis of the data is a major challenge and development of statistical and computational methods is essential for drawing meaningful conclusions from this huge data. In here, we assessed three different types of normalization (transcript parts per million, trimmed mean of M values, quantile normalization) and evaluated if normalized data reduces technical variability across replicates. In addition, we also proposed two novel methods for detecting differentially expressed genes between two biological conditions: (i) likelihood ratio method, and (ii) Bayesian method. Our proposed methods for finding differentially expressed genes were tested on three real datasets. Our methods performed at least as well as, and often better than, the existing methods for analysis of differential expression.
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.