Comparison of software packages for detecting differential expression in RNA-seq studies

Seyednasrollah, Fatemeh; Laiho, Asta; Elo, Laura L.

doi:10.1093/bib/bbt086

Cited by 357 publications

(384 citation statements)

References 28 publications

(55 reference statements)

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“…Using linear models for microarray analysis (LIMMA), we looked at differentially expressed genes between the CD49f Hi and CD49f Lo populations (36). A total of 1,501 genes were differentially expressed between the CD49f Hi and CD49f Lo populations, with 527 genes up-regulated in the Hi population and 923 genes up-regulated in the Lo population.…”

Section: Benign and Cancer Gene Expression Profiles From The Same Epimentioning

confidence: 99%

A basal stem cell signature identifies aggressive prostate cancer phenotypes

Smith

Sokolov

Uzunangelov

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

177

172

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Evidence from numerous cancers suggests that increased aggressiveness is accompanied by up-regulation of signaling pathways and acquisition of properties common to stem cells. It is unclear if different subtypes of late-stage cancer vary in stemness properties and whether or not these subtypes are transcriptionally similar to normal tissue stem cells. We report a gene signature specific for human prostate basal cells that is differentially enriched in various phenotypes of late-stage metastatic prostate cancer. We FACSpurified and transcriptionally profiled basal and luminal epithelial populations from the benign and cancerous regions of primary human prostates. High-throughput RNA sequencing showed the basal population to be defined by genes associated with stem cell signaling programs and invasiveness. Application of a 91-gene basal signature to gene expression datasets from patients with organconfined or hormone-refractory metastatic prostate cancer revealed that metastatic small cell neuroendocrine carcinoma was molecularly more stem-like than either metastatic adenocarcinoma or organ-confined adenocarcinoma. Bioinformatic analysis of the basal cell and two human small cell gene signatures identified a set of E2F target genes common between prostate small cell neuroendocrine carcinoma and primary prostate basal cells. Taken together, our data suggest that aggressive prostate cancer shares a conserved transcriptional program with normal adult prostate basal stem cells.RNA-seq | prostate cancer | stem cell signature | basal cell | neuroendocrine prostate cancer

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Section: Benign and Cancer Gene Expression Profiles From The Same Epimentioning

confidence: 99%

A basal stem cell signature identifies aggressive prostate cancer phenotypes

Smith

Sokolov

Uzunangelov

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

177

172

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“…The results presented here are based on synthetic datasets to allow controlled experiments and exploration of a widerange of parameters, with no emphasis on a particular application. Note that these comparisons are not meant to serve as a benchmark (more sophisticated comparisons and benchmarks can be found in other studies [6,11,16,[18][19][20][21]28]) but instead to motivate the need for and the specific design decisions in EDDA. In the following section, we discuss the validity of the modeling assumptions and parameter ranges that we investigated and used to guide the design of EDDA.…”

Section: Resultsmentioning

confidence: 99%

“…The digital nature of associated data has allowed for several model-based approaches including the use of exact tests (for example, Fisher's Exact Test [11]), Poisson [17], and Negative-Binomial [6,12] models as well as Bayesian [15] and Non-parametric [16] methods. Recent comparative evaluations of DATs in a few different application settings (for example, for RNA-Seq [6,16,[18][19][20][21] and Metagenomics [11]) have further suggested that there is notable variability in their performance, though a consensus on the right DATs to be used remains elusive. In addition, it is not clear, which (if any) of the DATs are broadly applicable across experimental settings despite the generality of the statistical models employed.…”

Section: Introductionmentioning

confidence: 99%

The importance of study design for detecting differentially abundant features in high-throughput experiments

Luo

Chia

et al. 2014

Preprint

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High-throughput assays, such as RNA-seq, to detect differential abundance are widely used. Variable performance across statistical tests, normalizations, and conditions leads to resource wastage and reduced sensitivity. EDDA represents a first, general design tool for RNA-seq, Nanostring, and metagenomic analysis, that rationally selects tests, predicts performance, and plans experiments to minimize resource wastage. Case studies highlight EDDA's ability to model single-cell RNA-seq, suggesting ways to reduce sequencing costs up to five-fold and improving metagenomic biomarker detection through improved test selection. EDDA's novel mode-based normalization for detecting differential abundance improves robustness by 10% to 20% and precision by up to 140%.

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“…In some cases, no differences have been found in the final results between various methods (Seyednasrollah et al 2015).…”

Section: Normalizationmentioning

confidence: 96%

Computational Methods for Quality Check, Preprocessing and Normalization of RNA-Seq Data for Systems Biology and Analysis

Mazzoni

Kadarmideen

2016

Systems Biology in Animal Production and Health, Vol. 2

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The use of RNA sequencing (RNA-Seq) technologies is increasing mainly due to the development of new next-generation sequencing machines that have reduced the costs and the time needed for data generation.Nevertheless, microarrays are still the more common choice and one of the reasons is the complexity of the RNA-Seq data analysis. Furthermore, numerous biases can arise from RNA-Seq technology, and these biases have to be identified and removed properly in order to obtain accurate results.Nowadays, many tools have been developed which allow to perform each step without high-level programming skills. However, each step of the pipeline needs to be performed carefully and requires a good knowledge of both the technology and the algorithms.In this comprehensive review, we describe the fundamental steps of the pipeline for RNA-Seq analysis to identify differentially expressed genes: raw data quality control, trimming and filtering procedures, alignment, postmapping quality control, counting, normalization and differential expression test.For each step, we present the most common tools and we give a complete description of their main characteristics and advantages focusing on the statistics that they perform and the assumptions that they make about the data.The choice of the right tool can have a big impact on the final results. Until now, no gold standard has been established for this type of analysis. 62In conclusion, this review can be useful for both educational purposes as well as for less experienced practitioners of animal genomic research. In the absence of a commonly accepted standard procedure, the general overview presented in this review can help to make the best choices during the implementation of an RNA-Seq pipeline.

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Comparison of software packages for detecting differential expression in RNA-seq studies

Cited by 357 publications

References 28 publications

A basal stem cell signature identifies aggressive prostate cancer phenotypes

A basal stem cell signature identifies aggressive prostate cancer phenotypes

The importance of study design for detecting differentially abundant features in high-throughput experiments

Computational Methods for Quality Check, Preprocessing and Normalization of RNA-Seq Data for Systems Biology and Analysis

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