Cloud-scale RNA-sequencing differential expression analysis with Myrna

By Benjamin Langmead, Kasper D. Hansen, Jeffrey T. Leek. Genome Biology, 2010

346 citations recorded by the source corpus.

As sequencing throughput approaches dozens of gigabases per day, there is a growing need for efficient software for analysis of transcriptome sequencing (RNA-Seq) data. Myrna is a cloud-computing pipeline for calculating differential gene expression in large RNA-Seq datasets. We apply Myrna to the analysis of publicly available data sets and assess the goodness of fit of standard statistical models. Myrna is available from http://bowtie-bio.sf.net/myrna.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/58f7fd8492572f21582a1d21aebeb113d07a4f8d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

RNA Sequencing and Analysis (2015, positive): “high false-positive rates due to underestimation of sampling error (Anders and Huber 2010; Langmead et al. 2010; Robinson and Oshlack 2010).”
'Big data', Hadoop and cloud computing in genomics (2013, methodology): “1 billion RNA sequence reads in less than 2 h, at cost of about $66 [48].”
Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation (2012, methodology): “If aliquots of the same RNA sample are sequenced, then the read counts for a particular gene should vary according to a Poisson law (30).”
Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques (2014, background): “…made their mark in scientific and engineering disciplines as diverse as biology, astrophysics, and Internet phenomena such as Google or Facebook (Berriman et al., 2010; Langmead et al., 2010; Yang et al., 2011) and spawned new fields of research such as sensor networks (Ganguly et al., 2009a).”
Big Data Application in Biomedical Research and Health Care: A Literature Review (2016, background): “Myrna 48 is a cloud-based computing pipeline that calculates the differences of gene expression in large RNA-seq datasets.”
From RNA-seq reads to differential expression results (2010, methodology): “In the absence of such methods, researchers have transformed their count data and used tools appropriate for continuous data [31,47,61].”
Next-generation sequencing data interpretation: enhancing reproducibility and accessibility (2012, background): “…packages that can scale well on cloud resources, but these are typically single-purpose (such as Crossbow for variant discovery 25 , Myrna for RNA-seq analyses 26 and CloVR for metagenomic and small-genome annotation projects 27 ) and thus may not be easily integrated into larger workflows.”
Applications of next-generation sequencing to the study of biological invasions (2015, background): “RNA-Seq data typically conforms to a Poisson distribution, however analyses based on these will often fail to capture biological variability (Langmead et al., 2010; Oshlack, 2010).”
Computational methods for transcriptome annotation and quantification using RNA-seq (2011, methodology): “Ideally, if one had enough replicates the variability across replicates could be estimated empirically using a permutation-derived approach 70–72 , similar to that used by the Myrna method 73 .”
An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics (2010, methodology): “3) Myrna [46,47] uses Bowtie [48,49], another UMD tool for ultrafast short read alignment, and R/Bioconductor [50] for calculating differential gene expression from large RNA-seq data sets.”
RobiNA: a user-friendly, integrated software solution for RNA-Seq-based transcriptomics (2012, background): “Tools like Myrna (14) and Galaxy (15–17) take advantage of cloud and cluster computing to boost performance when processing large data volumes, but rely on an elaborate bioinformatic infrastructure and lack an intuitive user interface.”