Cloud-scale RNA-sequencing differential expression analysis with Myrna

Langmead, Ben; Hansen, Kasper D.; Leek, Jeffrey T.

doi:10.1186/gb-2010-11-8-r83

Cited by 293 publications

(195 citation statements)

References 29 publications

Supporting

Mentioning

191

Contrasting

Unclassified

Order By: Relevance

“…Of course, many other methods exist, including but not limited to: Cuffdiff (Trapnell et al (2010)), Cuffdiff2 (Trapnell et al (2013)), NBPSeq (Di, Schafer, Cumbie, and Chang (2011)), TSPM (Auer and Doerge (2011)), baySeq (Hardcastle and Kelly (2010)), EBSeq (Leng et al (2013)), NOISeq (Tarazona, García-Alcalde, Dopazo, Ferrer, and Conesa (2011)), SAMseq (J. Li and Tibshirani (2013)), ShrinkSeq (Van De Wiel et al (2012)), DEGSeq (Wang, Feng, Wang, Wang, and Zhang (2010)), BBSeq (Y.-H. Zhou, Xia, and Wright (2011)), FDM (Singh et al (2011)), RSEM (B. Li and Dewey (2011)), Myrna (Langmead, Hansen, and Leek (2010)), PANDORA (Moulos and Hatzis (2014)), ALDEx2 (Fernandes et al (2014)), PoissonSeq (J. Li, Witten, Johnstone, and Tibshirani (2011)), and GPSeq (Srivastava and Chen (2010)). We provide code that can be easily adapted to any method that runs in R and applied to the publicly available data sets we used, as well as others.…”

Section: Cc-by-nd 40 International License Peer-reviewed) Is the Autmentioning

confidence: 99%

Excess False Positive Rates in Methods for Differential Gene Expression Analysis using RNA-Seq Data

Rocke

Ruan

Zhang

et al. 2015

Preprint

View full text Add to dashboard Cite

Motivation: An important property of a valid method for testing for differential expression is that the false positive rate should at least roughly correspond to the p-value cutoff, so that if 10,000 genes are tested at a p-value cutoff of 10 −4 , and if all the null hypotheses are true, then there should be only about 1 gene declared to be significantly differentially expressed. We tested this by resampling from existing RNA-Seq data sets and also by matched negative binomial simulations.Results: Methods we examined, which rely strongly on a negative binomial model, such as edgeR, DESeq, and DESeq2, show large numbers of false positives in both the resampled real-data case and in the simulated negative binomial case. This also occurs with a negative binomial generalized linear model function in R. Methods that use only the variance function, such as limma-voom, do not show excessive false positives, as is also the case with a variance stabilizing transformation followed by linear model analysis with limma. The excess false positives are likely caused by apparently small biases in estimation of negative binomial dispersion and, perhaps surprisingly, occur mostly when the mean and/or the dispersion is high, rather than for low-count genes. Contact:dmrocke@ucdavis.edu, lruan@ucdavis.edu, yilzhang@ucdavis.edu, gt4636b@gatech.edu, bpdurbin@ucdavis.edu, saviran@ucdavis.edu.Supplementary Information: The computational tools developed for this study are freely available via our website http://dmrocke.ucdavis.edu/software.html. They can be downloaded as R code or run directly through an interactive web-based shiny application to reproduce the analysis presented here per a user's choice of dataset and the methods to be evaluated.

show abstract

Section: Cc-by-nd 40 International License Peer-reviewed) Is the Autmentioning

confidence: 99%

Excess False Positive Rates in Methods for Differential Gene Expression Analysis using RNA-Seq Data

Rocke

Ruan

Zhang

et al. 2015

Preprint

View full text Add to dashboard Cite

show abstract

“…have implemented REST ful API web services or SOAP to share or integrate data in the form of FTP, HTML, XML, JSON, plain text, or AWK commands [29].Moreover, cloud computing services were offered to handle, analyze, or interpret big datasets through various remote applications/servers. There are many cloud servers such as Cloud BLAST [30], Myrna [31], Cloud Burst [32], Hadoop-BAM [33], GPU-BLAST [34], Hydra [35], Peak Ranger [36],Crossbow [37], etc. were available over cloud for analyzing different types of big datasets [38][39][40][41].…”

Section: Comprehensive Data Integration Methodsmentioning

confidence: 99%

Prediction of Potential Lead Molecules through Systematic Integration of Multi-omics Datasets - A Mini-Review

2017

IJCRR

View full text Add to dashboard Cite

Prediction of a novel or potential lead molecules for a therapeutic drug target without adverse effects is a challenging task in the drug designing, discovery, and development process. The systematic integration of multi-omics data from various data/knowledge bases through computational techniques enables to identify potential lead molecules and study the therapeutic properties. Over the last decades, several drug discoveries using multi-omics and huge dataset integration methods proven with successive results. In this paper, we present different types of computational approaches for prediction of potential lead molecules through the systems-level integration of multi-omics datasets.

show abstract

“…Those methods can be divided into two categories according to their use or disuse of parametric models [7,[40][41][42][43][44][45][46][47][48]. Parametric approaches are based on known probability distributions, such as Binomial, Poisson, and Negative Binomial.…”

Section: Differential Expression Analysismentioning

confidence: 99%

Overview of available methods for diverse RNA-Seq data analyses

Chen

Wang

Shi

2011

Sci. China Life Sci.

View full text Add to dashboard Cite

RNA-Seq technology is becoming widely used in various transcriptomics studies; however, analyzing and interpreting the RNA-Seq data face serious challenges. With the development of high-throughput sequencing technologies, the sequencing cost is dropping dramatically with the sequencing output increasing sharply. However, the sequencing reads are still short in length and contain various sequencing errors. Moreover, the intricate transcriptome is always more complicated than we expect. These challenges proffer the urgent need of efficient bioinformatics algorithms to effectively handle the large amount of transcriptome sequencing data and carry out diverse related studies. This review summarizes a number of frequently-used applications of transcriptome sequencing and their related analyzing strategies, including short read mapping, exon-exon splice junction detection, gene or isoform expression quantification, differential expression analysis and transcriptome reconstruction. To date, RNA-Seq has been applied to a number of species for various research, such as inferring alternative splicing [4,5], quantifying the expression of genes and transcripts [6,7], detecting gene fusions [8,9], revealing long noncoding RNAs (lncRNAs) [10], and identifying single nucleotide variants (SNVs) in expressed exons [11]. Although RNA-Seq has brought tremendous benefits to these studies, it also faces many challenges from both sequencing technologies themselves and bioinformatics analyses of the data. In detail, RNA-Seq has biases in library construction, and strand-specific libraries are still not easy to be produced but are important for determining the orientation of transcripts [1]. Furthermore, RNA-Seq generates a large amount of data, and the read length is generally short and sequencing errors exist in the reads. These aspects challenge the corresponding methods and algorithms to effectively process the large amount of RNA-Seq data.

show abstract

Cloud-scale RNA-sequencing differential expression analysis with Myrna

Cited by 293 publications

References 29 publications

Excess False Positive Rates in Methods for Differential Gene Expression Analysis using RNA-Seq Data

Excess False Positive Rates in Methods for Differential Gene Expression Analysis using RNA-Seq Data

Prediction of Potential Lead Molecules through Systematic Integration of Multi-omics Datasets - A Mini-Review

Overview of available methods for diverse RNA-Seq data analyses

Contact Info

Product

Resources

About