knitr::opts_chunk$set(echo = TRUE)

Introduction

The docker4seq package is an R control engine, which is at the core of the SeqBox ecosystem. It was developed to facilitate the use of computing demanding applications in the field of NGS data analysis.

The docker4seq package uses docker containers that embed demanding computing tasks (e.g. short reads mapping) into isolated docker images.

This approach provides multiple advantages:

Requirements

The minimal hardware requirements are a 4 cores 64 bits Linux computer, 32 Gb RAM, one SSD 250GB, with a folder with read/write permission for any users (chmod 777), and docker installed.

Setup

docker4seq and its graphical interface (optional) 4SeqGUI can fit ideally in the NUC6I7KYK, Intel mini-computer equipped with Kingston Technology HyperX Impact 32GB Kit (2x16GB), 2133MHz DDR4 CL13 260-Pin SODIMM and Samsung 850 EVO - 250GB - M.2 SATA III Internal SSD.

MANDATORY: The first time docker4seq is installed the downloadContainers function has to be executed to download, in the local repository, the docker images that are needed by docker4seq.

library(docker4seq)
downloadContainers(group="docker")

Dockers containers

At the present time all functions requiring some sort of calculation are embedded in the following docker images:

docker container nomenclature

Considering the following version encoding docker.io/rcaloger/XXXXX.YYYY.ZZ, the field ZZ will be updated in case of updates required to solve bugs, which do not affect the calculation.

Instead, the field YYYY will be updated in case of updates which affect the calculation (e.g. new release of Bioconductor libraries).

Previous versions will be maintained to guarantee the reproducibility of any previous analysis.

Reproducibility

The file containers.txt, which indicates the Docker images available in the local release of docker4seq is saved within any folder generated with docker4seq functions.

In case, user would like to download a set of dockers images different from those provided as part of the package, then these images must be specified in a file with the following format docker.repository/user/docker.name, which has to be passed to downloadContainers function:

downloadContainers(group="docker", containers.file="my_containers.txt")
#an example of the my_containers.txt file content
docker.io/rcaloger/bwa.2017.01
docker.io/rcaloger/chipseq.2017.01
docker.io/rcaloger/r340.2017.01

Available workflows

At the present time are available the following workflows:

The most expensive computing steps of the analyses are embedded in the following docker4seq functions: rnaseqCounts, mirnaCounts, chipseqCounts. These functions are also the only having RAM and computing power requirements not usually available in consumer computers. Hereafter it is shown the time required to run the above three functions increasing the number of sequenced reads.

rnaseqCounts performances

Counts generation from fastq files is the most time consuming step in RNAseq data analysis and it is usually calculated using high-end servers. We compare the behavior of rnaseqCounts on SeqBox and on a high-end server:

+ SeqBox: NUC6I7KYK CPU i7-6770HQ 3.5 GHz (1 core, 8 threads), 32 Gb RAM, HD 250 GB SSD
+ SGI UV2000 server: CPU E5-4650 v2 2.40GHz (8 cores, 160 threads), 1 Tb RAM, RAID 6, 100 TB SATA

We run respectively 26, 52, 78, and 105 million reads using different number of threads, values shown in parenthesis in Figure 1. It is notable that SeqBox, mapping in 5 hours more than 100 million reads, it is able to handle in 20 hours the throughput of the Illumina benchtop sequencer NextSeq 500, which produces up to 400 million reads in a run of 30 hours.

rnaseqCounts overall performance

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mirnaCounts performances

We run resepctively 3, 6, 12, and 24 miRNA samples in parallel using mirnaCounts, with different number of threads, values shown in parenthesis in Figure 2.

mirnaCounts overall performance

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chipseqCounts performances

We run respectively 37, 70, 111, and 149 million reads using different number of threads, values shown in parenthesis in Figure 3.

chipseqCounts overall performance

From the point of view of parallelization the rnaseqCounts is the one that embeds the most computing demanding tools: i) mapping with STAR and ii) quantifying transcripts with RSEM. Both these tools were design to take advantage of multiple cores hardware architecture and they also require massive I/O. On the basis of the results shown in Figure 1 parallelization does not improve very much the overall performances, even if it can mitigate the gap w.r.t. SeqBox due to the poor I/O performance of the SATA disk array. On the other side the presence of a SSD with very high I/O performance can cope with the limited amount of cores of SeqBox.

In the case of mirnaCounts and chipseqCounts the parallelization is very little and it is only available for the reads mapping procedure. Moreover, both functions have a massive I/O. The reduced parallelization of these two analyses combined with the higher I/O throughput of the SSD with respect to the SATA array makes SeqBox extremely effective even when very high number of reads has to be processed, Figure 2 and 3.

Test sets

A folder including a set of datasets to test each of the workflows available in docker4seq/4SeqGUI can be found here

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RNAseq workflow: Howto

The mRNAseq workflow can be run using 4SeqGUI graphical interface (linux/MAC):

mRNAseq workflow

Sample quantification is made of these steps:

All the parameters can be setup using 4SeqGUI

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Creating a STAR index file for mRNAseq:

The index can be easily created using the graphical interface:

Creating a STAR genome index

A detailed description of the parameters is given hereafter.

Creating a STAR index file by line command

\fontsize{8}{8}\selectfont

rsemstarIndex(group="docker",genome.folder="/data/scratch/hg38star",
ensembl.urlgenome="ftp://ftp.ensembl.org/pub/release-87/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna.toplevel.fa.gz",
ensembl.urlgtf="ftp://ftp.ensembl.org/pub/release-87/gtf/homo_sapiens/Homo_sapiens.GRCh38.87.gtf.gz")

In brief, rsemstarIndex uses ENSEMBL genomic data. User has to provide the URL (ensembl.urlgenome) for the file XXXXX_dna.toplevel.fa.gz related to the organism of interest, the URL (ensembl.urlgtf) for the annotation GTF XXX.gtf.gz and the path to the folder where the index will be generated (genome.folder). The parameter threads indicate the number of cores dedicated to this task.

Precompiled index folders are available:

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Quantifying genes/isoforms:

Gene, Isoform counting

A detailed description of the parameters is given below.

Sample quantification by line command

The sample quantification can be also executed using R and it is completely embedded in a single function:

#test example
system("wget http://130.192.119.59/public/test.mrnaCounts.zip")
unzip("test.mrnaCounts.zip")
setwd("./test.mrnaCounts")
library(docker4seq)
rnaseqCounts(group="docker",fastq.folder=getwd(), scratch.folder=getwd(),
adapter5="AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT",
adapter3="AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT",
seq.type="se", threads=8,  min.length=40,
genome.folder="/data/scratch/mm10star", strandness="none", save.bam=FALSE,
org="mm10", annotation.type="gtfENSEMBL")

User needs to create the fastq.folder, where the fastq.gz file(s) for the sample 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 needs to provide also the sequence of the sequencing adapters, adapter5 and adapter3 parameters. In case Illumina platform the adapters sequences can be easily recovered here.

seq.type indicates if single-end (se) or pair-end (pe) data are provided, threads indicates the max number of cores used by skewer and STAR, all the other steps are done on a single core.

The min.length refers to the minimal length that reads should have after adapters trimming. Since today the average read length for a RNAseq experiment is 50 or 75 nts then it would be better to bring to 40 nts the min.length parameter to increase the precision in assigning the correct position on the genome.

The genome.folder parameter refers to the location of the genomic index generated by STAR using the docker4seq function rsemstarIndex, see above paragraph.

strandness, is a parameter referring to the kit used for the library prep. If the kit does not provide strand information it is set to "none", if provides strand information is set to "forward" for Illumina stranded kit and it set to "reverse" for Illumina ACCESS kit. save.bam set to TRUE indicates that genomic bam file and transcriotomic bam files are also saved at the end of the analysis. annotation.type refers to the type of available gene-level annotation. At the present time is only available ENSEMBL annotation defined by the gtf downloaded during the creation of the indexed genome files, see paragraph at the endCreating a STAR index file for mRNAseq*.

Sample quantification output files

The mRNAseq workflow produces the following output files:

+ XXXXX-trimmed.log, containing the information related to the adapters trimming
+ gtf_annotated_genes.results, the output of RSEM gene quantification with gene-level annotation
+ Log.final.out, the statistics of the genome mapping generated by STAR  
+ rsem.info, summary of the parameters used in the run
+ genes.results, the output of RSEM gene quantification
+ isoforms.results, the output of RSEM isoform quantification
+ run.info, some statistics on the run
+ skewerd_xxxxxxxxxxxx.log, log of the skewer docker container
+ stard.yyyyyyyyyyyy.log, log of the star docker container

gtf_annotated_genes.results

The first column in gtf_annotated_genes.results is the ensembl gene id, the second column is the biotype, the third column is the annotation source, the fourth column contains the set of transcripts included in the ensembl gene id. Then there is the length of the gene, the length of the gene to which is subtracted the average length of the sequenced fragments, the expected counts are the counts to be used for differential expression analysis. TPM and FPM are normalized gene quantities to be used only for visualization purposes.

Alignment-dependent tools versus aligner-free methods

Recently Zhang and coworkers (BMC Genomics 2017, 18,583) compared, at transcript level, alignment-dependent tools (Salmon_aln, eXpress, RSEM and TIGAR2) and aligner-free methods (Salmon, Kallisto Sailfish). In their paper, STAR was used as mapping tool for all alignment-dependent tools. In terms of isoform quantification, the authors indicated that there is strong concordance among quantification results from RSEM, Salmon, Salmon_aln, Kallisto and Sailfish (R2 > 0.89), suggesting that the impact of mappers on isoform quantification is small. Furthermore, the paper of Teng and coworkers (Genome Biology 2016, 17,74) reported that,in term of gene-level quantification, differences between alignment-dependent tools and aligner-free methods are shrinking with respect to transcripts level analysis. On the basis of the above papers it seems that from the quantification point of view the difference between alignment free and alignment-dependent tools is very limited. However, aligner-free methods have low memory requirements and we have added Salmon in the development version of docker4seq in github. We are planning to introduce Salmon in the stable version of docker4seq in the first quarter of 2018. Salmon implementation will allow to increase the sample throughput, by running multiple samples. Currently samples are run serially because of the high RAM requirement of STAR.

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From samples to experiment

The RSEM output is sample specific, thus it is necessary to assemble the single sample in an experiment table including in the header of the columns both the covariates and the batches, if any. The header sample name is separated by the covariate with an underscore, e.g. mysample1_Cov1, mysample2_Cov2.

counts table with covariates

A batch can be added to the sample name through a further underscore, e.g. mysample1_Cov1_batch1, mysample2_Cov_batch2.

counts table with covariates and batch

The addition of the covariates to the various samples can be done using the 4seqGUI using the button: From samples to experiment.

generating a table with covariates

From samples to experiments by line command

#test example
system("wget http://130.192.119.59/public/test.samples2experiment.zip")
unzip("test.samples2experiment.zip")
setwd("test.samples2experiment")
library(docker4seq)
sample2experiment(sample.folders=c("./e1g","./e2g","./e3g",
"./p1g", "./p2g", "./p3g"),
covariates=c("Cov.1","Cov.1","Cov.1","Cov.2","Cov.2","Cov.2"),
bio.type="protein_coding", output.prefix=".")

User needs to provide the paths of the samples, sample.folder parameter, a vector of the covariates, covariates, and the biotype(s) of interest, bio.type parameter. The parameter output.prefix refers to the path where the output will be created, as default this is the current R working folder.

From samples to experiments output files

This task produces the following output files:

+ _counts.txt: gene-level raw counts table for differential expression analysis
+ _isoforms_counts.txt: isoform-level raw counts table for differential expression analysis
+ _isoforms_log2TPM.txt: isoform-level log2TPM for visualization purposes
+ _log2TPM.txt: gene-level log2TPM for visualization purposes
+ _isoforms_log2FPKM.txt: isoform-level log2FPKM for visualization purposes
+ _log2FPKM.txt: gene-level log2FPKM for visualization purposes
+ XXXXX.Rout: logs of the execution

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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

PCA

The plot is saved in pca.pdf in the selected folder.

PCA by line command

#test example
system("wget 130.192.119.59/public/test.analysis.zip")
unzip("test.analysis.zip")
setwd("test.analysis")
library(docker4seq)
pca(experiment.table="_log2FPKM.txt", type="FPKM", legend.position="topleft", 
    covariatesInNames=FALSE, principal.components=c(1,2), pdf = TRUE, output.folder=getwd())

User needs to provide the paths of experiment table, experiment.table parameter, i.e. the file generated using the samples2experiment function. The type parameter indicates if FPKM, TPM or counts are used by the PCA generation. The parameter legend.position defines where to locate the covariates legend. The parameter covariatesInNames indicates if the header of the experiment table contains or not covariate information. The parameter principal.components indicates which principal components should be plotted. output.folder indicates where to save the pca.pdf file.

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pca.pdf

The values in parentesis on x and y axes are the amount of variance explained by each principal component.

IMPORTANT: The above analysis is suitable for miRNAseq data too.

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Evaluating sample size and experiment power

Sample size estimation is an important issue in the design of RNA sequencing experiments. Furthermore, experiment power provides an indication of which is the fraction of differentially expressed genes that can be detected given a specific number of samples and differential expression detection thresholds. RnaSeqSampleSize Bioconductor package provides the possibility to calculate, from a pilot experiment, the statistical power and to define the optimal sample size. We have implemented wrapper functions to call these RnaSeqSampleSize functions for the sample size estimation and for statistical power estimation.

4SeqGUI provides an interface to sample size estimation and to statistical power estimation.

sample size estimation

stat power estimation

Sample size estimation by line command

#test example
system("wget 130.192.119.59/public/test.analysis.zip")
unzip("test.analysis.zip")
setwd("test.analysis")
library(docker4seq)
sampleSize(group="docker", filename="_counts.txt", power=0.80, FDR=0.1, genes4dispersion=200, log2fold.change=1)

The requested parameters are the path of the counts experiment table generated by samples2experiment function. The param power indicates the expected fraction of differentially expressed gene, e.g 0.80. FDR and log2fold.change are the two thresholds used to define the set of differentially expressed genes of interest.

The output file is sample_size_evaluation.txt and it is saved in the R working folder, below an example of the file content:

sample_size_evaluation.txt

IMPORTANT: The above analysis is suitable for miRNAseq data too.

Experiment statistical power estimation by line command

#test example
system("wget 130.192.119.59/public/test.analysis.zip")
unzip("test.analysis.zip")
setwd("test.analysis")
library(docker4seq)
experimentPower(group="docker", filename="_counts.txt",replicatesXgroup=7, FDR=0.1, genes4dispersion=200, log2fold.change=1)

The requested parameters are the path of the counts experiment table generated by samples2experiment function. The param replicatesXgroup indicates the number of sample associated with each of the two covariates. FDR and log2fold.change are the two thresholds used to define the set of differentially expressed genes of interest. genes4dispersion indicates the number of genes used in the estimation of read counts and dispersion distribution.

The output file is power_estimation.txt and it is saved in the R working folder, below an example of the file content:

power_estimation.txt

IMPORTANT: The above analysis is suitable for miRNAseq data too.

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Differential expression analysis with DESeq2

A basic task in the analysis of count data from RNA-seq is the detection of differentially expressed genes. 4SeqGUI provides an interface to DESeq2 to simplify differential expression analysis.

DESeq2

The output files are:

DEfull.txt containing the full set of results generated by DESeq2

DEfull.txt

DEfiltered_log2fc_X_fdr_Y.Y.txt containing the set of differentially expressed genes passing the indicated thresholds

DEfiltered_log2fc_1_fdr_0.1.txt

genes4david.txt a file containing only the gene symbols to be used as input for DAVID or ENRICHR

log2normalized_counts.txt, log2 library size normalized counts, calculated by DESeq2, that can be used for visualization purposes.

DESeq2 by line command

#test example
system("wget 130.192.119.59/public/test.analysis.zip")
unzip("test.analysis.zip")
setwd("test.analysis")
library(docker4seq)
wrapperDeseq2(output.folder=getwd(), group="docker", 
      experiment.table="_counts.txt", log2fc=1, fdr=0.1, 
      ref.covar="Cov.1", type="gene", batch=FALSE)

User has to provide experiment table, experiment.table param, i.e. the counts table generated with samples2experiment function, the thresholds for the differential expression analysis, log2fc and fdr params, the reference covariate, ref.covar param, i.e. the covariate that is used as reference for differential expression detection, the type param, which refers to the type of experiment table in use: gene, isoform, mirna, batch parameter that indicates, if it is set to TRUE that the header of the experiment table also contains the extra information for the batch effect (see above).

IMPORTANT: the above analysis can be applied to miRNAseq data too.

Why DESeq2 was chosen as differential expression tool

Love and co-workers, in their paper on DESeq2 (Love at al. Genome Biol. 2014; 15, 550), showed that DESeq2 had comparable sensitivity to edgeR and voom. We introduced in our workflow DESeq2 because it has some specific features which increase the strength of the differential expression analysis, features that are not available in other tools. One of these features is, the Empirical Bayes shrinkage for fold-change estimation, which shrinks log fold change estimates toward zero. This feature reduces the noise due to low expressed genes, since shrinkage is stronger when the available information for a gene is low, which may be because the read counts are low, dispersion is high or there are few degrees of freedom. The other peculiar feature of DESeq2 is the identification of counts outliers, which might represent a source of false positives. Specifically, DESeq2 flags genes characterized by the presence of counts outliers, which are estimated with the standard outlier diagnostic Cook’s distance (Love at al. Genome Biol. 2014; 15, 550).

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miRNAseq workflow: Howto

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.

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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.

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. Moreover, user has to provide 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 then the trimmed fastq are not saved at the end of the analysis.

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

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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.2", "bath.2", "batch.1", "batch.1", 
               "batch.2", "batch.2","batch.1", "batch.1","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.

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chipseq workflow: HowTo

The chipseq workflow can be ran using 4SeqGUI graphical interface:

ChIPseq workflow

The ChIPseq consists of two main steps:

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Creating a BWA index file for Chipseq:

The index can be easily created using the graphical interface:

Creating a BWA index with Genome indexing BWA

bwaIndexUcsc(group="sudo",genome.folder="/sto2/data/scratch/mm10bwa", uscs.urlgenome=
"http://hgdownload.cse.ucsc.edu/goldenPath/mm10/bigZips/chromFa.tar.gz",
gatk=FALSE)

In brief, bwaIndexUcsc uses UCSC genomic data. User has to provide the URL (uscs.urlgenome) for the file chromFa.tar.gz related to the organism of interest and the path to the folder where the index will be generated (genome.folder). The parameter gatk has to be set to FALSE if it is not required for ChIPseq genomic index creation.

Precompiled index folders are available:

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Calling peaks and annotating:

All the parameters needed to run MACS or SICER can be setup using 4SeqGUI

MACS and SICER analysis

A detailed description of the parameters is given below.

Chipseq workflow by line command

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

system("wget 130.192.119.59/public/test.chipseqCounts.zip")
unzip("test.chipseqCounts.zip")
setwd("test.chipseqCounts")
library(docker4seq)
chipseqCounts(group = "docker", output.folder = "./prdm51.igg",
  mock.folder="./igg", test.folder="./prdm51", scratch.folder=getwd(),
  adapter5 = "AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT",
  adapter3 = "AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT",
  threads = 8, min.length = 30, genome.folder,
  mock.id = "igg", test.id = "tf", genome, read.size = 50,
  tool = "macs", macs.min.mfold = 10, macs.max.mfold = 30,
  macs.pval = "1e-5", sicer.wsize = 200, sicer.gsize = 200,
  sicer.fdr = 0.1, tss.distance = 0, max.upstream.distance = 10000,
  remove.duplicates = "N")

Specifically user needs to create three folders:

+ mock.folder, where the fastq.gz file for the control sample is located. 
  For control sample we refer to ChIP with IgG only or input DNA.
+ test.folder, where the fastq.gz file for the ChIP of the sample to be analysed.
+ output.folder, where the R script embedding the above script is located.

The scratch.folder can be the same as the output.folder. However, if the system has a high speed disk for temporary calculation, e.g. a SSD disk, the location of the scratch.folder on the SSD will reduce significantly the total execution time.

User needs to provide also the sequencing adapters, i.e. adapter5 and adapter3 parameters. In case of Illumina platform the adapters sequences can be easily recovered here.

Threads indicates the max number of cores used by skewer and bwa, all the other steps are done on a single core. The min.length refers to the minimal length that a reads should have after adapters trimming. Since today the average read length for a ChIP experiment is 50 or 75 nts we suggest to bring to 40 nts the min.length parameter to increase the precision in assigning the correct position on the genome.

The genome.folder parameter refers to the location of the genomic index generated by bwa using the docker4seq function bwaIndexUcsc.

mock.id and test.id identify the type of sample and are assigned to the ID parameter in the RG field of the bam file.

genome is the parameter referring to the annotation used to associate ChIP peaks with genes. In the present implementation hg38, hg19 for human and mm10 and mm9 for mouse annotations are available.

read.size is a parameter requested by MACS and SICER for their analysis. macs.min.mfold, macs.max.mfold, macs.pval are the default parameters requested to peaks definition for more info please refer to the documentation of MACS 1.4. sicer.wsize, sicer.gsize, sicer.fdr are the default parameters requested to peaks definition for more info please refer to the documentation of SICER 1.1. IMPORTANT: The optimal value for sicer.gsize in case of H3K4Me3 ChIP is 200 and in case of ChIP H3K27Me3 is 600.

tss.distance and max.upstream.distance are parameters required by ChIPseqAnno, which is the Bioconductor package used to assign the peaks to specific genes. Specifically max.upstream.distance refers to the max distance in nts that allows the association of a peak with a specific gene.

remove.duplicates is the parameter that indicates if duplicates have to be removed or not. It has two options: N duplicates are not removed, Y duplicates are removed.

Chipseq workflow output files

The chipseq workflow produces the following output files:

+ README: A file describing the content of the data folder
+ mypeaks.xls: All detected peaks alongside the nearest gene and its annotation
+ mytreat.counts: The total reads count for the provided treatment file
+ mycontrol.counts: The total reads count for the provided control/background file
+ peak_report.xls: Aggregate information regarding the peak and their position relative to the nearest gene
+ chromosome_distribution.pdf: Barplot of the distribution of the peaks on the chromosomes
+ relative_position_distribution.pdf: Barplot of the distribution of the peaks positions relative to their nearest gene
+ peak_width_distribution.pdf: Histogram of the distribution of the width of the peaks
+ distance_from_nearest_gene_distribution.pdf: Histogram of the distribution of the distance of each peak from its nearest gene
+ cumulative_coverage_total.pdf: Cumulative normalized gene coverage
+ cumulative_coverage_chrN.pdf: Cumulative normalized gene coverage for the specific chromosome
+ mycontrol_sorted.bw: bigWig file for UCSC Genome Browser visualization
+ mytreat_sorted.bw: bigWig file for UCSC Genome Browser visualization

Tutorials

RNAseq workflow

Tutorial experiment downloadable here:

+ Three replicates for two experimental conditions

+ single-end mode sequencing

+ 1 million reads for each sample

Experiment description:

+ 4T1 mouse cell line grown in standard DMEM medium (e) is compared with the same cells grown in low attachment medium (p)

The following data are available for download:

miRNAseq workflow

Tutorial experiment downloadable here:

Experiment description:

The following data are available for download:

ChIPseq workflow

Tutorial experiment downloadable here:

+ Two ChIPseq one IgG control and an other Prdm5 TF moAb (mouse)

+ single-end mode sequencing,

+ 1 million reads for each sample.

Experiment description:



kendomaniac/docker4seq documentation built on Dec. 16, 2018, 2:14 p.m.