Computational workflows typically consist of many tools that are usually distributed as compiled binaries or source code. Each of these software tools typically depends on other installed software, and performance could potentially vary due to versions, updates, and operating systems. We show here that the analysis of mRNA-seq data can depend on the computing environment, and we demonstrate that software containers represent practical solutions that ensure the reproducibility of RNAseq data analyses
Obtaining RNA-seq measurements involves a complex data analytical process with a large number of com...
MotivationRNA-Seq is a method for profiling transcription using high-throughput sequencing and is an...
<p>An example RNA-seq analysis workflow is depicted for a typical gene expression and differential e...
RNA-seq is now the primary technology used to measure transcriptional abundance. The analysis of RNA...
We present the advancements and novelties recently introduced in RNASeqGUI, a graphical user interfa...
High-throughput sequencing is now routinely performed in many experiments. But the analysis of the m...
<p>This book contains material from a workshop directed toward life scientists with little to no exp...
High-throughput sequencing is now routinely performed in many experiments. But the analysis of the m...
The extensive generation of RNA sequencing (RNA-seq) data in the last decade has resulted in a myria...
The ever-growing number of methods for the generation of synthetic bulk and single cell RNA-seq data...
Abstract Background The throughput of commercially available sequencers has recently significantly i...
As a revolutionary technology for life sciences, RNA-seq has many applications and the computation p...
Posing complex research questions poses complex reproducibility challenges. Datasets may need to be ...
RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be ...
RNA-sequencing, commonly referred to as RNA-seq, is the most recently developed method for the analy...
Obtaining RNA-seq measurements involves a complex data analytical process with a large number of com...
MotivationRNA-Seq is a method for profiling transcription using high-throughput sequencing and is an...
<p>An example RNA-seq analysis workflow is depicted for a typical gene expression and differential e...
RNA-seq is now the primary technology used to measure transcriptional abundance. The analysis of RNA...
We present the advancements and novelties recently introduced in RNASeqGUI, a graphical user interfa...
High-throughput sequencing is now routinely performed in many experiments. But the analysis of the m...
<p>This book contains material from a workshop directed toward life scientists with little to no exp...
High-throughput sequencing is now routinely performed in many experiments. But the analysis of the m...
The extensive generation of RNA sequencing (RNA-seq) data in the last decade has resulted in a myria...
The ever-growing number of methods for the generation of synthetic bulk and single cell RNA-seq data...
Abstract Background The throughput of commercially available sequencers has recently significantly i...
As a revolutionary technology for life sciences, RNA-seq has many applications and the computation p...
Posing complex research questions poses complex reproducibility challenges. Datasets may need to be ...
RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be ...
RNA-sequencing, commonly referred to as RNA-seq, is the most recently developed method for the analy...
Obtaining RNA-seq measurements involves a complex data analytical process with a large number of com...
MotivationRNA-Seq is a method for profiling transcription using high-throughput sequencing and is an...
<p>An example RNA-seq analysis workflow is depicted for a typical gene expression and differential e...