Last updated: 2023-02-24

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File Version Author Date Message
Rmd 1839ef1 Pedro Baldoni 2023-02-23 Adding case study pages
html 1839ef1 Pedro Baldoni 2023-02-23 Adding case study pages

Our case study analysis is based on three RNA-seq experiments from the human lung cancer cell lines H1975 and HCC827: the paired-end Illumina short read RNA-seq data (GSE172421), the single-end Illumina short read RNA-seq data (GSE86337) and the long read Oxford Nanopore Technologies (ONT) data (GSE172421).

All the RNA-seq experiments are quantified with Salmon Patro et al. (2017) using an decoy-aware indexed transcriptome based on the Ensembl-oriented Gencode annotation version 33 (Ensembl annotation version 99) with added transcripts from spiked-in sequins (see Dong et al. (2022)). All necessary annotation files used to build the transcriptome index are publicly available and can also be downloaded from the GitHub repository associated to this page.

Creating the decoy-aware transcriptome index

  1. Run the script code/lung/index/combineAnnotation.sh to combine the Ensembl-oriented Gencode annotation with the sequins annotation. This script uses the picard-tools module.

  2. Run the script code/lung/index/buildIndex.sh to build Salmon’s transcriptome index. This script uses the following modules: Salmon, MashMap, bedtools and the generateDecoyTranscriptome.sh script.

Download and quantifying the Illumina paired-end short read experiments

  1. Run code/lung/data/runAspera.sh to download all necessary FASTQ files. This script uses the module aspera.

  2. Run code/lung/salmon/runSalmon.sh to quantify the RNA-seq reads. This scripts uses the Salmon module.

  3. Run code/lung/salmon/runWasabi.R to generate .h5 files from Salmon’s output that can be read by the DTE method sleuth.

The targets file for this experiment is provided in our GitHub repository for convenience. See file data/lung/misc/targets.txt.

Download and quantifying the Illumina single-end short read experiments

  1. Run code/lung-se/data/runAspera.sh to download all necessary FASTQ files. This script uses the module aspera.

  2. Run code/lung-se/salmon/runSalmon.sh to quantify the RNA-seq reads. This scripts uses the Salmon module.

  3. Run code/lung-se/salmon/runWasabi.R to generate .h5 files from Salmon’s output that can be read by the DTE method sleuth.

The targets file for this experiment is provided in our GitHub repository for convenience. See file data/lung-se/misc/targets.txt.

About the ONT long read data

For convenience, we provide the ONT long read data in an already processed DGEList object that can be used in the analysis1. The R .rds file containing such object is data/lung-ont/220928_dge.rds. The targets file for the ONT experiment is can be found at data/lung-ont/misc/targets.txt.

References

Dong, Xueyi, Mei RM Du, Quentin Gouil, Luyi Tian, Pedro L Baldoni, Gordon K Smyth, Shanika L Amarasinghe, Charity W Law, and Matthew E Ritchie. 2022. “Benchmarking Long-Read RNA-Sequencing Analysis Tools Using in Silico Mixtures.” bioRxiv, 2022–07.
Patro, Rob, Geet Duggal, Michael I Love, Rafael A Irizarry, and Carl Kingsford. 2017. “Salmon Provides Fast and Bias-Aware Quantification of Transcript Expression.” Nature Methods 14 (4): 417. 

sessionInfo()
R version 4.2.1 (2022-06-23)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS:   /stornext/System/data/apps/R/R-4.2.1/lib64/R/lib/libRblas.so
LAPACK: /stornext/System/data/apps/R/R-4.2.1/lib64/R/lib/libRlapack.so

locale:
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attached base packages:
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other attached packages:
[1] workflowr_1.7.0

loaded via a namespace (and not attached):
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[41] utf8_1.2.3       stringi_1.7.12   cachem_1.0.6

  1. Thanks to Dr. Xueyi Dong for processing and generating this file.↩︎