Contents

0.0.1 miRNAseq workflow by GUI

Tutorial experiment downloadable here:

+ Six specimens for two experimental conditions

+ single-end mode sequencing, 

+ 1 million reads for each sample.

Experiment description:

+ Six blood circulating exosomes miRNA samples from healthy donors (hd) and six blood circulating exosomes miRNA samples from tumor patients (tum)

+ from 1 to 3 hd and tum samples were harvested on day 1, from 5 to 6 hd and tum samples were harvested on day 2. Thus the data are require the addition of the batch effect in differential expression analysis.

The following data are available for download:

The miRNAseq workflow can be run using 4SeqGUI graphical interface:

miRNAseq workflow

The miRNAseq docker container executes the following steps:

miRNAseq workflow

The full workflow is described in Cordero et al. Plos ONE 2012. In brief, fastq files are trimmed using cutadapt and the trimmed reads are mapped on miRNA precursors, i.e. harpin.fa file, from miRBase using SHRIMP. Using the location of the mature miRNAs in the precursor, countOverlaps function, from the Bioconductor package GenomicRanges is used to quantify the reads mapping on mature miRNAs.

All the parameters needed to run the miRNAseq workflow can be setup using 4SeqGUI

miRNAseq parameters

A detailed description of the parameters is given below.

0.0.2 miRNAseq workflow by line command

The miRNAseq workflow can be also executed using R and it is completely embedded in a unique function:

#test example
system("wget 130.192.119.59/public/test.mirnaCounts.zip")
unzip("test.mirnaCounts.zip")
setwd("test.mirnaCounts")
library(docker4seq)
mirnaCounts(group="docker",fastq.folder=getwd(), scratch.folder="/data/scratch", 
            mirbase.id="hsa",download.status=FALSE, adapter.type="NEB", trimmed.fastq=FALSE)

User has to create the fastq.folder, where the fastq.gz files for all miRNAs under analysis are located. The scratch.folder is the location where temporary data are created. The results will be then saved in the fastq.folder. User has to provide also the identifier of the miRBase organism, e.g. hsa for Homo sapiens, mmu for Mus musculus. If the download.status is set to FALSE, mirnaCounts uses miRBase release 21, if it is set to TRUE the lastest version of precursor and mature miRNAs will be downloaded from miRBase. Users need to provide the name of the producer of the miRNA library prep kit to identify which adapters need to be provided to cutadapt, adapter.type parameter. The available adapters are NEB and Illumina, but, upon request, we can add other adapters. Finally, if the trimmed.fastq is set to FALSE the trimmed fastq are not saved at the end of the analysis.

0.0.3 miRNAseq workflow output files

The miRNAseq workflow produces the following output files:

+ README: A file describing the content of the data folder
+ all.counts.txt: miRNAs raw counts, to be used for differential expression analysis
+ trimmimg.log: adapters trimming statistics
+ shrimp.log: mapping statistics
+ all.counts.Rda: miRNAs raw counts ready to be loaded in R.
+ analysis.log: logs of the full analysis pipeline

0.0.4 Adding covariates and batches to mirnaCounts output: all.counts.txt

4SeqGUI provides an interface to add covariates and batches to all.counts.txt

miRNAseq covariates and batches

The function mirnaCovar add to the header of all.counts.txt covariates and batches or covariates only.

#test example
system("wget 130.192.119.59/public/test.mirna.analysis.zip")
unzip("test.mirna.analysis.zip")
setwd("test.mirna.analysis")
library(docker4seq)
mirnaCovar(experiment.folder=paste(getwd(), "all.counts.txt", sep="/"),
     covariates=c("Cov.1", "Cov.1", "Cov.1", "Cov.1", "Cov.1", "Cov.1", 
                  "Cov.2", "Cov.2", "Cov.2", "Cov.2", "Cov.2", "Cov.2"),
     batches=c("bath.1", "bath.1", "bath.1", "bath.2", "batch.2", "batch.2", 
               "batch.1", "batch.1","batch.1", "batch.2","bath.2", "bath.2"), output.folder=getwd())

The output of mirnaCovar, i.e. w_covar_batch_all.counts.txt, is compliant with PCA, Sample size estimator, Experiment stat. power and DEseq2 analysis.

0.0.5 Visualizing experiment data with PCA

PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. This transformation is defined in such a way that the first principal component accounts for as much of the variability in the data as possible, and each succeeding component in turn has the highest variance possible under the constraint that it is orthogonal to the preceding components. 4SeqGUI provides an interface to the generation experiment samples PCA