1 Introduction

The QuasR package (short for Quantify and annotate short reads in R) integrates the functionality of several R packages (such as IRanges (Lawrence et al. 2013) and Rsamtools) and external software (e.g. bowtie, through the Rbowtie package, and HISAT2, through the Rhisat2 package). The package aims to cover the whole analysis workflow of typical high throughput sequencing experiments, starting from the raw sequence reads, over pre-processing and alignment, up to quantification. A single R script can contain all steps of a complete analysis, making it simple to document, reproduce or share the workflow containing all relevant details.

The current QuasR release supports the analysis of single read and paired-end ChIP-seq (chromatin immuno-precipitation combined with sequencing), RNA-seq (gene expression profiling by sequencing of RNA) and Bis-seq (measurement of DNA methylation by sequencing of bisulfite-converted genomic DNA) experiments. It has been successfully used with data from Illumina, 454 Life Technologies and SOLiD sequencers, the latter by using bam files created externally of QuasR.

2 Preliminaries

2.1 Citing QuasR

If you use QuasR (Gaidatzis et al. 2015) in your work, you can cite it as follows:

## Please use the QuasR reference below to cite the software itself. If
## you were using qAlign with Rbowtie as aligner, it can be cited as
## Langmead et al. (2009) (unspliced alignments) or Au et al. (2010)
## (spliced alignments). If you were using qAlign with Rhisat2 as aligner,
## it can be cited as Kim et al. (2015).
##   Gaidatzis D, Lerch A, Hahne F, Stadler MB. QuasR: Quantification and
##   annotation of short reads in R. Bioinformatics 31(7):1130-1132
##   (2015).
##   Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and
##   memory-efficient alignment of short DNA sequences to the human
##   genome. Genome Biology 10(3):R25 (2009).
##   Au KF, Jiang H, Lin L, Xing Y, Wong WH. Detection of splice junctions
##   from paired-end RNA-seq data by SpliceMap. Nucleic Acids Research,
##   38(14):4570-8 (2010).
##   Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with
##   low memory requirements. Nat Methods, 12(4):357-60 (2015).
## This free open-source software implements academic research by the
## authors and co-workers. If you use it, please support the project by
## citing the appropriate journal articles.
## To see these entries in BibTeX format, use 'print(<citation>,
## bibtex=TRUE)', 'toBibtex(.)', or set
## 'options(citation.bibtex.max=999)'.

2.2 Installation

QuasR is a package for the R computing environment and it is assumed that you have already installed R. See the R project at (http://www.r-project.org). To install the latest version of QuasR, you will need to be using the latest version of R. QuasR is part of the Bioconductor project at (http://www.bioconductor.org). To get QuasR together with its dependencies you can use

if (!require("BiocManager"))

Bioconductor works on a 6-monthly official release cycle. As with other Bioconductor packages, there are always two versions of QuasR. Most users will use the current official release version, which will be installed by BiocManager::install if you are using the current release version of R. There is also a development version of QuasR that includes new features due for the next official release. The development version will be installed if you are using the development version of Bioconductor (see version = "devel" in BiocManager). The official release version always has an even second number (for example 1.20.1), whereas the developmental version has an odd second number (for example 1.21.4).

2.3 Loading of QuasR and other required packages

In order to run the code examples in this vignette, the QuasR package and a few additional packages need to be loaded:


2.4 How to get help

Most questions about QuasR will hopefully be answered by the documentation or references. If you’ve run into a question which isn’t addressed by the documentation, or you’ve found a conflict between the documentation and software, then there is an active support community which can offer help.

The authors of the package (maintainer: Michael Stadler ) always appreciate receiving reports of bugs in the package functions or in the documentation. The same goes for well-considered suggestions for improvements.

Any other questions or problems concerning QuasR should be posted to the Bioconductor support site (https://support.bioconductor.org). Users posting to the support site for the first time should read the helpful posting guide at (https://support.bioconductor.org/info/faq/). Note that each function in QuasR has it’s own help page, as described in the section 3.1. Posting etiquette requires that you read the relevant help page carefully before posting a problem to the site.

3 Quick Start

3.1 A brief introduction to R

If you already use R and know about its interface, just skip this section and continue with section 3.2.

The structure of this vignette and in particular this section is based on the excellent user guide of the limma package, which we would like to hereby acknowledge. R is a program for statistical computing. It is a command-driven language meaning that you have to type commands into it rather than pointing and clicking using a mouse. In this guide it will be assumed that you have successfully downloaded and installed R from (http://www.r-project.org) as well as QuasR (see section 2.2). A good way to get started is to type


at the R prompt or, if you’re using R for Windows, to follow the drop-down menu items Help \(\succ\) Html help. Following the links Packages \(\succ\) QuasR from the html help page will lead you to the contents page of help topics for functions in QuasR.

Before you can use any QuasR commands you have to load the package by typing


at the R prompt. You can get help on any function in any loaded package by typing ? and the function name at the R prompt, for example


or equivalently


for detailed help on the preprocessReads function. The function help page is especially useful to learn about its arguments and its return value.

Working with R usually means creating and transforming objects. Objects might include data sets, variables, functions, anything at all. For example

x <- 2

will create a variable x and will assign it the value 2. At any stage of your R session you can type


to get a list of all the objects currently existing in your session. You can display an object by typing its name on the prompt. The following displays the object x:


We hope that you can use QuasR without having to spend a lot of time learning about the R language itself but a little knowledge in this direction will be very helpful, especially when you want to do something not explicitly provided for in QuasR or in the other Bioconductor packages. For more details about the R language see An Introduction to R which is available from the online help, or one of the many great online resources, like the documentation at r-project.org, the growing list of free books at bioconductor.org, or the books from rstudio.com (many of which are also available for free). For more background on using R for statistical analysis see (Dalgaard 2002).

3.2 Sample QuasR session

This is a quick overview of what an analysis could look like for users preferring to jump right into an analysis. The example uses data that is provided with the QuasR package, which is first copied to the current working directory, into a subfolder called "extdata":

file.copy(system.file(package = "QuasR", "extdata"), ".", recursive = TRUE)
## [1] TRUE

The sequence files to be analyzed are listed in sampleFile (see section 5.1 for details). The sequence reads will be aligned using bowtie (Langmead et al. 2009) (from the Rbowtie package (Hahne, Lerch, and Stadler 2012)) to a small reference genome (consisting of three short segments from the hg19 human genome assembly, available in full for example in the BSgenome.Hsapiens.UCSC.hg19 package). Information on selecting an appropriate reference genome is summarized in section 5.3.

Make sure that you have sufficient disk space, both in your R temporary directory (tempdir()) as well as to store the resulting alignments (see section 7.2).

sampleFile <- "extdata/samples_chip_single.txt"
genomeFile <- "extdata/hg19sub.fa"

proj <- qAlign(sampleFile, genomeFile)
## Creating .fai file for: /tmp/RtmpDdAZQl/Rbuild5185ec3f67e2/QuasR/vignettes/extdata/hg19sub.fa
## alignment files missing - need to:
##     create alignment index for the genome
##     create 2 genomic alignment(s)
## Creating an Rbowtie index for /tmp/RtmpDdAZQl/Rbuild5185ec3f67e2/QuasR/vignettes/extdata/hg19sub.fa
## Finished creating index
## Testing the compute nodes...OK
## Loading QuasR on the compute nodes...preparing to run on 1 nodes...done
## Available cores:
## nebbiolo2: 1
## Performing genomic alignments for 2 samples. See progress in the log file:
## /tmp/RtmpDdAZQl/Rbuild5185ec3f67e2/QuasR/vignettes/QuasR_log_52dfba45834e.txt
## Genomic alignments have been created successfully
## Project: qProject
##  Options   : maxHits         : 1
##              paired          : no
##              splicedAlignment: FALSE
##              bisulfite       : no
##              snpFile         : none
##              geneAnnotation  : none
##  Aligner   : Rbowtie v1.45.0 (parameters: -m 1 --best --strata)
##  Genome    : /tmp/RtmpDdAZQl/Rbuild5185ec3f67e2/QuasR/vigne.../hg19sub.fa (file)
##  Reads     : 2 files, 2 samples (fastq format):
##    1. chip_1_1.fq.bz2  Sample1 (phred33)
##    2. chip_2_1.fq.bz2  Sample2 (phred33)
##  Genome alignments: directory: same as reads
##    1. chip_1_1_52dfb2769ee90.bam
##    2. chip_2_1_52dfb5f19b23e.bam
##  Aux. alignments: none

The proj object keeps track of all the information of a sequencing experiment, for example where sequence and alignment files are stored, and what aligner and reference genome was used to generate the alignments.

Now that the alignments have been generated, further analyses can be performed. A quality control report is saved to the "extdata/qc_report.pdf" file using the qQCReport function.

qQCReport(proj, "extdata/qc_report.pdf")
## collecting quality control data
## creating QC plots

The number of alignments per promoter region is quantified using qCount. Genomic coordinates for promoter regions are imported from a gtf file (annotFile) into the GRanges-object with the name promReg:

annotFile <- "extdata/hg19sub_annotation.gtf"
txStart <- import.gff(annotFile, format = "gtf", feature.type = "start_codon")
promReg <- promoters(txStart, upstream = 500, downstream = 500)
names(promReg) <- mcols(promReg)$transcript_name

promCounts <- qCount(proj, query = promReg)
## counting alignments...done
##                width Sample1 Sample2
## TNFRSF18-003    1000      20       4
## TNFRSF18-002    1000      20       4
## TNFRSF18-001    1000      20       4
## TNFRSF4-001     1000       5       2
## SDF4-007        1000       8       2
## SDF4-001        1000       8       2
## SDF4-002        1000       8       2
## SDF4-201        1000       8       2
## B3GALT6-001     1000      25     274
## RPS7-001        1000     121     731
## RPS7-008        1000     121     731
## RPS7-009        1000     121     731
## RPS7-005        1000     121     731
## C3orf10-201     1000     176     496
## C3orf10-001     1000     176     496
## AC034193.1-201  1000       5       2
## VHL-001         1000      61     336
## VHL-002         1000      61     336
## VHL-201         1000      61     336

4 QuasR Overview

The following scheme shows the major components of QuasR and their relationships:

QuasR works with data (sequences and alignments, reference genome, etc.) that are stored as files on your storage (the gray cylinder on the lower left of Figure above, see section 4.1 for details on storage locations). QuasR does not need a database management system, or these files to be named and organized according to a specific scheme.

In order to keep track of directory paths during an analysis, QuasR makes use of a qProject object that is returned by the qAlign function, which at the minimum requires two inputs: the name of a samples text file (see section 5.1 for details), and the reference genome for the alignments (see section 5.3).

The qProject object is the main argument passed to subsequent functions such as qQCReport and qCount. The qProject object contains all necessary information on the current project and eliminates the need to repeatedly enter the same information. All functions that work on qProject objects can be recognized by their names starting with the letter q.

Read quantification (apart from quantification of methylation which has its own function qMeth) is done using the qCount function: It counts the alignments in regions of interest (e.g. promoters, genes, exons, etc.) and produces a count table (regions in rows, samples in columns) for further visualization and analysis. The count table can also be used as input to a statistical analysis using packages such as edgeR (Robinson, McCarthy, and Smyth 2010), DESeq (Anders and Huber 2010), DESeq2 (Love, Huber, and Anders 2014), TCC (Sun et al. 2013), DEXSeq (Anders, Reyes, and Huber 2012) or baySeq (Hardcastle and Kelly 2010).

In summary, a typical QuasR analysis consists of the following steps (some of them are optional):

  • preprocessReads (optional): Remove adapters from start or end of reads, filter out reads of low quality, short length or low complexity (section 5.4).
  • Prepare samples file: List sequence files or alignments, provide sample names (section 5.1).
  • Prepare auxiliary file (optional): List additional reference sequences for alignment of reads not matching the reference genome (section 5.2).
  • qAlign: Create qProject object and specify project parameters. Also download BSgenome package, create aligner indices and align reads if not already existing (section 7.2).
  • qQCReport (optional): Create quality control report with plots on sequence qualities and alignment statistics (section 7.4).
  • qExportWig (optional): Export genomic alignments as wiggle tracks for genome browser visualization (section 7.6).
  • qCount: Quantify alignments in regions of interest (section 7.7).

Recurrent example tasks that may be part of any typical analysis are described in section 5. Example workflows for specific experiment types (ChIP-seq, RNA-seq and Bis-seq) are described in section 6.

4.1 File storage locations

Apart from qExportWig and qQCReport, which generate wig files and pdf reports, qAlign is the only function in QuasR that stores files on the disk (see section 7.2 for details). All files generated by qAlign are listed here by type, together with their default location and how locations can be changed.

  • Temporary files (default: tempdir()): Temporary files include reference genomes in fasta format, decompressed input sequence files, and temporary alignments in text format, and can require a large amount of disk space. By default, these files will be written to the temporary directory of the R process (as reported by tempdir()). If using clObj for parallel processing, this may be the tempdir() from the cluster node(s). An alternative location can be set using the TMPDIR environment variable (see ?tempdir).
  • Alignment files (bam format) (default: same directory as the input sequence files): Alignments against reference genome and auxiliary targets are stored in bam format in the same directory that also contains the input sequence file (listed in sampleFile). Please note that if the input sequence file corresponds to a symbolic link, QuasR will follow the link and use the directory of the original file instead. An alternative directory can be specified with the alignmentsDir argument from qAlign, which will store all bam files in that directory even if the input sequence files are located in different directories.
  • Alignment index files (default: depends on genome and snpFile arguments): Many alignment tools including bowtie require an index of the reference sequence to perform alignments. If necessary, qAlign will build this index automatically and store it in a default location that depends on the genome argument:
    • BSgenome: If genome is the name of a BSgenome package (such as "BSgenome.Hsapiens.UCSC.hg19"), the index will be stored as a new R package in the default library path (as reported by .libPaths()[1], see ?install.packages for details), or alternatively in the library specified by the lib.loc argument of qAlign. The name of this index package will be the name of the original BSgenome package with a suffix for the index type, for example "BSgenome.Hsapiens.UCSC.hg19.Rbowtie".
    • fasta: If genome refers to a reference genome file in fasta format, the index will be stored in a subdirectory at the same location. Similarly, the indices for files listed in auxiliaryFile are store at the location of these files. For example, the Rbowtie index for the genome at "./genome/mm9.fa" is stored in "./genome/mm9.fa.Rbowtie".
    • Allele-specific analysis: A special case is the allele-specific analysis, where reference and alternative alleles listed in snpFile (e.g. "./mySNPs.tab") are injected into the genome (e.g. "BSgenome.Mmusculus.UCSC.mm9") to create two variant genomes to be indexed. These indices are saved at the location of the snpFile in a directory named after snpFile, genome and the index type (e.g. "./mySNPs.tab.BSgenome.Mmusculus.UCSC.mm9.A.fa.Rbowtie").

5 Example tasks

5.1 Create a sample file

The sample file is a tab-delimited text file with two or three columns. The first row contains the column names: For a single read experiment, these are ‘FileName’ and ‘SampleName’; for a paired-end experiment, these are ‘FileName1’, ‘FileName2’ and ‘SampleName’. If the first row does not contain the correctly spelled column names, QuasR will not accept the samples file. Subsequent rows contain the input sequence files.

Here are examples of such sample files for a single read experiment:

FileName    SampleName
chip_1_1.fq.bz2 Sample1
chip_2_1.fq.bz2 Sample2

and for a paired-end experiment:

FileName1   FileName2   SampleName
rna_1_1.fq.bz2  rna_1_2.fq.bz2  Sample1
rna_2_1.fq.bz2  rna_2_2.fq.bz2  Sample2

These example files are also contained in the QuasR package and may be used as templates. The path of the files can be determined using:

sampleFile1 <- system.file(package="QuasR", "extdata",
sampleFile2 <- system.file(package="QuasR", "extdata",

The columns FileName for single-read, or FileName1 and FileName2 for paired-end experiments contain paths and names to files containing the sequence data. The paths can be absolute or relative to the location of the sample file. This allows combining files from different directories in a single analysis. For each input sequence file, qAlign will create one alignment file and by default store it in the same directory as the sequence file. Already existing alignment files with identical parameters will not be re-created, so that it is easy to reuse the same sequence files in multiple projects without unnecessarily copying sequence files or recreating alignments.

The SampleName column contains sample names for each sequence file. The same name can be used on several lines to indicate multiple sequence files that belong to the same sample (qCount can use this information to automatically combine counts for one sample from multiple files).

Three file formats are supported for input files (but cannot be mixed within a single sample file):

  • fasta files have names that end with ‘.fa’, ‘.fna’ or ‘.fasta’. They contain only sequences (and no base qualities) and will thus by default be aligned on the basis of mismatches (the best alignment is the one with fewest mismatches).
  • fastq files have names that end with ‘.fq’ or ‘.fastq’. They contain sequences and corresponding base qualities and will be aligned by default using these qualities. The encoding scheme of base qualities is automatically detected for each individual fastq file.
  • bam files have names that end with ‘.bam’. They can be used if the sequence reads have already been aligned outside of QuasR, and QuasR will only be used for downstream analysis based on the alignments contained in the bam files. This makes it possible to use alignment tools that are not available within QuasR, but making use of this option comes with a risk and should only be used by experienced users. For example, it cannot be guaranteed any more that certain assumptions made by qCount are fulfilled by the external aligner (see below). When using external bam files, we recommend to use files which contain only one alignment per read. This may also include multi-hit reads, for which one of the alignments is randomly selected. This allows QuasR to count the total number of reads by counting the total number of alignments. Furthermore, if the bam files also contain the unmapped reads, QuasR will be able to calculate the fraction of mapped reads. For bisulfite samples we require ungapped alignments stored in unpaired or paired ff orientation (even if the input reads are fr). For allele-specific bam files, QuasR requires an additional tag for each alignment called XV of type A (printable character) with the possible values R (Reference), U (Unknown) and A (Alternative).

fasta and fastq files can be compressed with gzip, bzip2 or xz (file extensions ‘.gz’, ‘.bz2’ or ‘xz’, respectively) and will be automatically decompressed when necessary.

5.1.1 Working only with bam files after performing alignments

Once alignments have been created, most analyses will only require the bam files and will not access the original raw sequence files anymore. However, re-creating a qProject object by a later identical call to qAlign will still need access to the raw sequences to verify consistency between raw data and alignments. It may be desirable to remove this dependency, for example to archive or move away the raw sequence files and to reclaim used disk space.

This can be achieved using the following procedure involving two sequential calls to qAlign. First, qAlign is called with the orignial sample file (sampleFile1) that lists the raw sequence files, and subsequently with a second sample file (sampleFile2) that lists the bam files generated in the first call. Such a second sample file can be easily generated given the qProject object (proj1) returned by the first call:

sampleFile1 <- "samples_fastq.txt"
sampleFile2 <- "samples_bam.txt"

proj1 <- qAlign(sampleFile1, genomeFile)

write.table(alignments(proj1)$genome, sampleFile2, sep="\t", row.names=FALSE)

proj2 <- qAlign(sampleFile2, genomeFile)

The analysis can now be exclusively based on the bam files using sampleFile2 and proj2.

5.1.2 Consistency of samples within a project

The sample file implicitly defines the type of samples contained in the project: single read or paired-end read, sequences with or without qualities. This type will have a profound impact on the downstream analysis. For example, it controls whether alignments will be performed in single or paired-end mode, either with or without base qualities. That will also determine availability of certain options for quality control and quantification in qQCReport and qCount. For consistency, it is therefore required that all samples within a project have the same type; it is not possible to mix both single and paired-end read samples, or fasta and fastq files in a single project (sample file). If necessary, it may be possible to analyse different types of files in separate QuasR projects and combine the derived results at the end.

5.2 Create an auxiliary file (optional)

By default QuasR aligns reads only to the reference genome. However, it may be interesting to align non-matching reads to further targets, for example to identify contamination from vectors or a different species, or in order to quantify spike-in material not contained in the reference genome. In QuasR, such supplementary reference files are called auxiliary references and can be specified to qAlign using the auxiliaryFile argument (see section 7.2 for details). The format of the auxiliary file is similar to the one of the sample file described in section 5.1: It contains two columns with column names ‘FileName’ and ‘AuxName’ in the first row. Additional rows contain names and files of one or several auxiliary references in fasta format.

An example auxiliary file looks like this:

FileName    AuxName
NC_001422.1.fa  phiX174

and is available from your QuasR installation at

auxFile <- system.file(package = "QuasR", "extdata", "auxiliaries.txt")

5.3 Select the reference genome

Sequence reads are primarily aligned against the reference genome (see section 5.3.1 on how to choose a suitable reference assembly). If necessary, QuasR will create an aligner index for the genome. The reference genome can be provided in one of two different formats:

  • a string, referring to the name of a BSgenome package:
##   [1] "BSgenome.Alyrata.JGI.v1"                           
##   [2] "BSgenome.Amellifera.BeeBase.assembly4"             
##   [3] "BSgenome.Amellifera.NCBI.AmelHAv3.1"               
##   [4] "BSgenome.Amellifera.UCSC.apiMel2"                  
##   [5] "BSgenome.Amellifera.UCSC.apiMel2.masked"           
##   [6] "BSgenome.Aofficinalis.NCBI.V1"                     
##   [7] "BSgenome.Athaliana.TAIR.04232008"                  
##   [8] "BSgenome.Athaliana.TAIR.TAIR9"                     
##   [9] "BSgenome.Btaurus.UCSC.bosTau3"                     
##  [10] "BSgenome.Btaurus.UCSC.bosTau3.masked"              
##  [11] "BSgenome.Btaurus.UCSC.bosTau4"                     
##  [12] "BSgenome.Btaurus.UCSC.bosTau4.masked"              
##  [13] "BSgenome.Btaurus.UCSC.bosTau6"                     
##  [14] "BSgenome.Btaurus.UCSC.bosTau6.masked"              
##  [15] "BSgenome.Btaurus.UCSC.bosTau8"                     
##  [16] "BSgenome.Btaurus.UCSC.bosTau9"                     
##  [17] "BSgenome.Btaurus.UCSC.bosTau9.masked"              
##  [18] "BSgenome.Carietinum.NCBI.v1"                       
##  [19] "BSgenome.Celegans.UCSC.ce10"                       
##  [20] "BSgenome.Celegans.UCSC.ce11"                       
##  [21] "BSgenome.Celegans.UCSC.ce2"                        
##  [22] "BSgenome.Celegans.UCSC.ce6"                        
##  [23] "BSgenome.Cfamiliaris.UCSC.canFam2"                 
##  [24] "BSgenome.Cfamiliaris.UCSC.canFam2.masked"          
##  [25] "BSgenome.Cfamiliaris.UCSC.canFam3"                 
##  [26] "BSgenome.Cfamiliaris.UCSC.canFam3.masked"          
##  [27] "BSgenome.Cjacchus.UCSC.calJac3"                    
##  [28] "BSgenome.Cjacchus.UCSC.calJac4"                    
##  [29] "BSgenome.CneoformansVarGrubiiKN99.NCBI.ASM221672v1"
##  [30] "BSgenome.Creinhardtii.JGI.v5.6"                    
##  [31] "BSgenome.Dmelanogaster.UCSC.dm2"                   
##  [32] "BSgenome.Dmelanogaster.UCSC.dm2.masked"            
##  [33] "BSgenome.Dmelanogaster.UCSC.dm3"                   
##  [34] "BSgenome.Dmelanogaster.UCSC.dm3.masked"            
##  [35] "BSgenome.Dmelanogaster.UCSC.dm6"                   
##  [36] "BSgenome.Drerio.UCSC.danRer10"                     
##  [37] "BSgenome.Drerio.UCSC.danRer11"                     
##  [38] "BSgenome.Drerio.UCSC.danRer5"                      
##  [39] "BSgenome.Drerio.UCSC.danRer5.masked"               
##  [40] "BSgenome.Drerio.UCSC.danRer6"                      
##  [41] "BSgenome.Drerio.UCSC.danRer6.masked"               
##  [42] "BSgenome.Drerio.UCSC.danRer7"                      
##  [43] "BSgenome.Drerio.UCSC.danRer7.masked"               
##  [44] "BSgenome.Dvirilis.Ensembl.dvircaf1"                
##  [45] "BSgenome.Ecoli.NCBI.20080805"                      
##  [46] "BSgenome.Gaculeatus.UCSC.gasAcu1"                  
##  [47] "BSgenome.Gaculeatus.UCSC.gasAcu1.masked"           
##  [48] "BSgenome.Ggallus.UCSC.galGal3"                     
##  [49] "BSgenome.Ggallus.UCSC.galGal3.masked"              
##  [50] "BSgenome.Ggallus.UCSC.galGal4"                     
##  [51] "BSgenome.Ggallus.UCSC.galGal4.masked"              
##  [52] "BSgenome.Ggallus.UCSC.galGal5"                     
##  [53] "BSgenome.Ggallus.UCSC.galGal6"                     
##  [54] "BSgenome.Gmax.NCBI.Gmv40"                          
##  [55] "BSgenome.Hsapiens.1000genomes.hs37d5"              
##  [56] "BSgenome.Hsapiens.NCBI.GRCh38"                     
##  [57] "BSgenome.Hsapiens.NCBI.T2T.CHM13v2.0"              
##  [58] "BSgenome.Hsapiens.UCSC.hg17"                       
##  [59] "BSgenome.Hsapiens.UCSC.hg17.masked"                
##  [60] "BSgenome.Hsapiens.UCSC.hg18"                       
##  [61] "BSgenome.Hsapiens.UCSC.hg18.masked"                
##  [62] "BSgenome.Hsapiens.UCSC.hg19"                       
##  [63] "BSgenome.Hsapiens.UCSC.hg19.masked"                
##  [64] "BSgenome.Hsapiens.UCSC.hg38"                       
##  [65] "BSgenome.Hsapiens.UCSC.hg38.dbSNP151.major"        
##  [66] "BSgenome.Hsapiens.UCSC.hg38.dbSNP151.minor"        
##  [67] "BSgenome.Hsapiens.UCSC.hg38.masked"                
##  [68] "BSgenome.Hsapiens.UCSC.hs1"                        
##  [69] "BSgenome.Mdomestica.UCSC.monDom5"                  
##  [70] "BSgenome.Mfascicularis.NCBI.5.0"                   
##  [71] "BSgenome.Mfascicularis.NCBI.6.0"                   
##  [72] "BSgenome.Mfuro.UCSC.musFur1"                       
##  [73] "BSgenome.Mmulatta.UCSC.rheMac10"                   
##  [74] "BSgenome.Mmulatta.UCSC.rheMac2"                    
##  [75] "BSgenome.Mmulatta.UCSC.rheMac2.masked"             
##  [76] "BSgenome.Mmulatta.UCSC.rheMac3"                    
##  [77] "BSgenome.Mmulatta.UCSC.rheMac3.masked"             
##  [78] "BSgenome.Mmulatta.UCSC.rheMac8"                    
##  [79] "BSgenome.Mmusculus.UCSC.mm10"                      
##  [80] "BSgenome.Mmusculus.UCSC.mm10.masked"               
##  [81] "BSgenome.Mmusculus.UCSC.mm39"                      
##  [82] "BSgenome.Mmusculus.UCSC.mm8"                       
##  [83] "BSgenome.Mmusculus.UCSC.mm8.masked"                
##  [84] "BSgenome.Mmusculus.UCSC.mm9"                       
##  [85] "BSgenome.Mmusculus.UCSC.mm9.masked"                
##  [86] "BSgenome.Osativa.MSU.MSU7"                         
##  [87] "BSgenome.Ppaniscus.UCSC.panPan1"                   
##  [88] "BSgenome.Ppaniscus.UCSC.panPan2"                   
##  [89] "BSgenome.Ptroglodytes.UCSC.panTro2"                
##  [90] "BSgenome.Ptroglodytes.UCSC.panTro2.masked"         
##  [91] "BSgenome.Ptroglodytes.UCSC.panTro3"                
##  [92] "BSgenome.Ptroglodytes.UCSC.panTro3.masked"         
##  [93] "BSgenome.Ptroglodytes.UCSC.panTro5"                
##  [94] "BSgenome.Ptroglodytes.UCSC.panTro6"                
##  [95] "BSgenome.Rnorvegicus.UCSC.rn4"                     
##  [96] "BSgenome.Rnorvegicus.UCSC.rn4.masked"              
##  [97] "BSgenome.Rnorvegicus.UCSC.rn5"                     
##  [98] "BSgenome.Rnorvegicus.UCSC.rn5.masked"              
##  [99] "BSgenome.Rnorvegicus.UCSC.rn6"                     
## [100] "BSgenome.Rnorvegicus.UCSC.rn7"                     
## [101] "BSgenome.Scerevisiae.UCSC.sacCer1"                 
## [102] "BSgenome.Scerevisiae.UCSC.sacCer2"                 
## [103] "BSgenome.Scerevisiae.UCSC.sacCer3"                 
## [104] "BSgenome.Sscrofa.UCSC.susScr11"                    
## [105] "BSgenome.Sscrofa.UCSC.susScr3"                     
## [106] "BSgenome.Sscrofa.UCSC.susScr3.masked"              
## [107] "BSgenome.Tgondii.ToxoDB.7.0"                       
## [108] "BSgenome.Tguttata.UCSC.taeGut1"                    
## [109] "BSgenome.Tguttata.UCSC.taeGut1.masked"             
## [110] "BSgenome.Tguttata.UCSC.taeGut2"                    
## [111] "BSgenome.Vvinifera.URGI.IGGP12Xv0"                 
## [112] "BSgenome.Vvinifera.URGI.IGGP12Xv2"                 
## [113] "BSgenome.Vvinifera.URGI.IGGP8X"
genomeName <- "BSgenome.Hsapiens.UCSC.hg19"

In this example, the BSgenome package "BSgenome.Hsapiens.UCSC.hg19" refers to an unmasked genome; alignment index and alignments will be performed on the full unmasked genome sequence (recommended). If using a masked genome (e.g. "BSgenome.Hsapiens.UCSC.hg19.masked"), masked regions will be replaced with "N" bases, and this hard-masked version of the genome will be used for creating the alignment index and further alignments. Please also see section 5.3.1 for potential issues with redundant sequences contained in the reference genome, e.g. in BSgenome.Hsapiens.UCSC.hg19 or BSgenome.Hsapiens.UCSC.hg38.

  • a file name, referring to a sequence file containing one or several reference sequences (e.g. chromosomes) in fasta format:
genomeFile <- system.file(package="QuasR", "extdata", "hg19sub.fa")

5.3.1 Choosing a suitable (non-redundant) reference genome

For some organisms, several versions of the genome assembly exist. These differ for example in whether or not they include alternative variants for sequences that are variable within the species. This may lead to redundant sequences in the assembly, and thus reads mapping to such sequences being wrongly classified as “multi-mapping”, and comparing data aligned to different assembly version may give rise to incorrect results. A nice summary of this issue is provided in this blog post from Heng Li.

The BSgenome packages BSgenome.Hsapiens.UCSC.hg19 (versions newer than 1.4.0 from Bioconductor 3.10) and BSgenome.Hsapiens.UCSC.hg38 (all versions) do contain such redundant sequences and are therefore not ideal references for alignment of human data. Specific “analysis set” or “primary assembly” versions of the assembly should be used instead (see the before-mentioned blog post for details).

When using a BSgenome reference, QuasR will check in the qAlign function whether the chromosome names and lengths contained in the header of any pre-existing bam files are identical to the ones provided by the genome and warn if this is not the case.

5.4 Sequence data pre-processing

The preprocessReads function can be used to prepare the input sequence files prior to alignment. The function takes one or several sequence files (or pairs of files for a paired-end experiment) in fasta or fastq format as input and produces the same number of output files with the processed reads.

In the following example, we truncate the reads by removing the three bases from the 3’-end (the right side), remove the adapter sequence AAAAAAAAAA from the 5’-end (the left side) and filter out reads that, after truncation and adapter removal, are shorter than 14 bases or contain more than 2 N bases:

td <- tempdir()
infiles <- system.file(package = "QuasR", "extdata",
outfiles <- file.path(td, basename(infiles))
res <- preprocessReads(filename = infiles,
                       outputFilename = outfiles,
                       truncateEndBases = 3,
                       Lpattern = "AAAAAAAAAA",
                       minLength = 14, 
                       nBases = 2)
##   filtering /tmp/RtmpDdAZQl/Rinst5185e44fa7916/QuasR/extdata/rna_1_1.fq.bz2
##   filtering /tmp/RtmpDdAZQl/Rinst5185e44fa7916/QuasR/extdata/rna_2_1.fq.bz2
##                  rna_1_1.fq.bz2 rna_2_1.fq.bz2
## totalSequences             3002           3000
## matchTo5pAdapter            466            463
## matchTo3pAdapter              0              0
## tooShort                    107             91
## tooManyN                      0              0
## lowComplexity                 0              0
## totalPassed                2895           2909

preprocessReads returns a matrix with a summary of the pre-processing. The matrix contains one column per (pair of) input sequence files, and contains the total number of reads (totalSequences), the number of reads that matched to the five prime or three prime adapters (matchTo5pAdapter and matchTo3pAdapter), the number of reads that were too short (tooShort), contained too many non-base characters (tooManyN) or were of low sequence complexity (lowComplexity, deactivated by default). Finally, the number of reads that passed the filtering steps is reported in the last row (totalPassed).

In the example below we process paired-end reads, removing all pairs with one or several N bases. Even if only one sequence in a pair fulfills the filtering criteria, both reads in the pair are removed, thereby preserving the matching order of the sequences in the two files:

td <- tempdir()
infiles1 <- system.file(package = "QuasR", "extdata", "rna_1_1.fq.bz2")
infiles2 <- system.file(package = "QuasR", "extdata", "rna_1_2.fq.bz2")
outfiles1 <- file.path(td, basename(infiles1))
outfiles2 <- file.path(td, basename(infiles2))
res <- preprocessReads(filename = infiles1,
                       filenameMate = infiles2,
                       outputFilename = outfiles1,
                       outputFilenameMate = outfiles2,
                       nBases = 0)
##   filtering /tmp/RtmpDdAZQl/Rinst5185e44fa7916/QuasR/extdata/rna_1_1.fq.bz2 and
##     /tmp/RtmpDdAZQl/Rinst5185e44fa7916/QuasR/extdata/rna_1_2.fq.bz2
##                  rna_1_1.fq.bz2:rna_1_2.fq.bz2
## totalSequences                            3002
## matchTo5pAdapter                            NA
## matchTo3pAdapter                            NA
## tooShort                                     0
## tooManyN                                     3
## lowComplexity                                0
## totalPassed                               2999

More details on the preprocessReads function can be found in the function documentation (see ?preprocessReads) or in the section 7.1.

6 Example workflows

6.1 ChIP-seq: Measuring protein-DNA binding and chromatin modifications

Here we show an exemplary single-end ChIP-seq workflow using a small number of reads from a histone 3 lysine 4 trimethyl (H3K4me3) ChIP-seq experiment. This histone modification is known to locate to genomic regions with a high density of CpG dinucleotides (so called CpG islands); about 60% of mammalian genes have such a CpG island close to their transcript start site. All necessary files are included in the QuasR package, and we start the example workflow by copying those files into the current working directly, into a subfolder called "extdata":

file.copy(system.file(package = "QuasR", "extdata"), ".", recursive = TRUE)
## [1] TRUE

6.1.1 Align reads using the qAlign function

We assume that the sequence reads have already been pre-processed as described in section 5.4. Also, a sample file (section 5.1) that lists all sequence files to be analyzed has been prepared. A fasta file with the reference genome sequence(s) is also available (section 5.3), as well as an auxiliary file for alignment of reads that failed to match the reference genome (section 5.2).

By default, newly generated bam files will be stored at the location of the input sequence files, which should be writable and have sufficient capacity (an alternative location can be specified using the alignmentsDir argument). Make also sure that you have sufficient temporary disk space for intermediate files in tempdir() (see section 7.2). We start by aligning the reads using qAlign:

sampleFile <- "extdata/samples_chip_single.txt"
auxFile <- "extdata/auxiliaries.txt"
genomeFile <- "extdata/hg19sub.fa"

proj1 <- qAlign(sampleFile, genome = genomeFile, auxiliaryFile = auxFile)
## alignment files missing - need to:
##     create 2 auxiliary alignment(s)
## Creating an Rbowtie index for /tmp/RtmpDdAZQl/Rbuild5185ec3f67e2/QuasR/vignettes/extdata/NC_001422.1.fa
## Finished creating index
## Testing the compute nodes...OK
## Loading QuasR on the compute nodes...preparing to run on 1 nodes...done
## Available cores:
## nodeNames
## nebbiolo2 
##         1
## Performing auxiliary alignments for 2 samples. See progress in the log file:
## /tmp/RtmpDdAZQl/Rbuild5185ec3f67e2/QuasR/vignettes/QuasR_log_52dfb4d2ca376.txt
## Auxiliary alignments have been created successfully
## Project: qProject
##  Options   : maxHits         : 1
##              paired          : no
##              splicedAlignment: FALSE
##              bisulfite       : no
##              snpFile         : none
##              geneAnnotation  : none
##  Aligner   : Rbowtie v1.45.0 (parameters: -m 1 --best --strata)
##  Genome    : /tmp/RtmpDdAZQl/Rbuild5185ec3f67e2/QuasR/vigne.../hg19sub.fa (file)
##  Reads     : 2 files, 2 samples (fastq format):
##    1. chip_1_1.fq.bz2  Sample1 (phred33)
##    2. chip_2_1.fq.bz2  Sample2 (phred33)
##  Genome alignments: directory: same as reads
##    1. chip_1_1_52dfb2769ee90.bam
##    2. chip_2_1_52dfb5f19b23e.bam
##  Aux. alignments: 1 file, directory: same as reads
##    a. /tmp/RtmpDdAZQl/Rbuild5185ec3f67e2/QuasR/vignet.../NC_001422.1.fa  phiX174
##      1. chip_1_1_52dfb37c492c4.bam
##      2. chip_2_1_52dfb274f8877.bam

qAlign will build alignment indices if they do not yet exist (by default, if the genome and auxiliary sequences are given in the form of fasta files, they will be stored in the same folder). The qProject object (proj1) returned by qAlign now contains all information about the ChIP-seq experiment: the (optional) project name, the project options, aligner package, reference genome, and at the bottom the sequence and alignment files. For each input sequence file, there will be one bam file with alignments against the reference genome, and one for each auxiliary target sequence with alignments of reads without genome hits. Our auxFile contains a single auxiliary target sequence, so we expect two bam files per input sequence file:

list.files("extdata", pattern = ".bam$")
## [1] "chip_1_1_52dfb2769ee90.bam"    "chip_1_1_52dfb37c492c4.bam"   
## [3] "chip_2_1_52dfb274f8877.bam"    "chip_2_1_52dfb5f19b23e.bam"   
## [5] "phiX_paired_withSecondary.bam"

The bam file names consist of the base name of the sequence file with an added random string. The random suffix makes sure that newly generated alignment files do not overwrite existing ones, for example of the same reads aligned against an alternative reference genome. Each alignment file is accompanied by two additional files with suffixes .bai and .txt:

list.files("extdata", pattern = "^chip_1_1_")[1:3]
## [1] "chip_1_1_52dfb2769ee90.bam"     "chip_1_1_52dfb2769ee90.bam.bai"
## [3] "chip_1_1_52dfb2769ee90.bam.txt"

The .bai file is the bam index used for fast access by genomic coordinate. The .txt file contains all the parameters used to generate the corresponding bam file. Before new alignments are generated, qAlign will look for available .txt files in default locations (the directory containing the input sequence file, or the value of alignmentsDir), and read their contents to determine if a compatible bam file already exists. A compatible bam file is one with the same reads and genome, aligned using the same aligner and identical alignment parameters. If a compatible bam file is not found, or the .txt file is missing, qAlign will generate a new bam file. It is therefore recommended not to delete the .txt file - without it, the corresponding bam file will become unusable for QuasR.

6.1.2 Create a quality control report

QuasR can produce a quality control report in the form of a series of diagnostic plots with details on sequences and alignments (see QuasR scheme figure above). The plots are generated by calling the qQCReport function with the qProject object as argument. qQCReport uses ShortRead (Morgan et al. 2009) internally to obtain some of the quality metrics, and some of the plots are inspired by the FastQC quality control tool by Simon Andrews (http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/). The plots will be stored into a multipage PDF document defined by the pdfFilename argument, or else shown as individual plot windows on the current graphics device. In order to keep the running time reasonably short, some quality metrics are obtained from a random sub-sample of the sequences or alignments.

## collecting quality control data
## creating QC plots
qQCReport(proj1, pdfFilename = "extdata/qc_report.pdf")
## collecting quality control data
## creating QC plots

Currently available plots are described in section 7.4 and following.

6.1.3 Alignment statistics

The alignmentStats gets the number of (un-)mapped reads for each sequence file in a qProject object, by reading the bam file indices, and returns them as a data.frame. The function also works for arguments of type character with one or several bam file names (for details see section 7.5).

##                 seqlength mapped unmapped
## Sample1:genome      95000   2339      258
## Sample2:genome      95000   3609      505
## Sample1:phiX174      5386    251        7
## Sample2:phiX174      5386    493       12

6.1.4 Export genome wig file from alignments

For visualization in a genome browser, alignment coverage along the genome can be exported to a (compressed) wig file using the qExportWig function. The created fixedStep wig file (see (http://genome.ucsc.edu/goldenPath/help/wiggle.html) for details on the wig format) will contain one track per sample in the qProject object. The resolution is defined using the binsize argument, and if scaling is set to TRUE, read counts per bin are scaled by the total number of aligned reads in each sample to improve comparability:

qExportWig(proj1, binsize = 100L, scaling = TRUE, collapseBySample = TRUE)
## collecting mapping statistics for scaling...done
## start creating wig files...
##   Sample1.wig.gz (Sample1)
##   Sample2.wig.gz (Sample2)
## done

6.1.5 Count alignments using qCount

Alignments are quantified using qCount, for example using a GRanges object as a query. In our H3K4me3 ChIP-seq example, we expect the reads to occur around the transcript start site of genes. We can therefore construct suitable query regions using genomic intervals around the start sites of known genes. In the code below, this is achieved with help from the txdbmaker package to first create a TxDb object from a .gtf file with gene annotation. With the promoters function from the GenomicFeatures package, we can then create the GRanges object with regions to be quantified. Finally, because most genes consist of multiple overlapping transcripts, we select the first transcript for each gene:

annotFile <- "extdata/hg19sub_annotation.gtf"
chrLen <- scanFaIndex(genomeFile)
chrominfo <- data.frame(chrom = as.character(seqnames(chrLen)),
                        length = width(chrLen),
                        is_circular = rep(FALSE, length(chrLen)))
txdb <- makeTxDbFromGFF(file = annotFile, format = "gtf",
                        chrominfo = chrominfo,
                        dataSource = "Ensembl",
                        organism = "Homo sapiens")
## Import genomic features from the file as a GRanges object ... OK
## Prepare the 'metadata' data frame ... OK
## Make the TxDb object ... OK
promReg <- promoters(txdb, upstream = 1000, downstream = 500,
                     columns = c("gene_id","tx_id"))
gnId <- vapply(mcols(promReg)$gene_id,
               FUN = paste, FUN.VALUE = "",
               collapse = ",")
promRegSel <- promReg[ match(unique(gnId), gnId) ]
names(promRegSel) <- unique(gnId)
## GRanges object with 12 ranges and 2 metadata columns:
##                   seqnames      ranges strand |         gene_id     tx_id
##                      <Rle>   <IRanges>  <Rle> | <CharacterList> <integer>
##   ENSG00000176022     chr1 31629-33128      + | ENSG00000176022         1
##   ENSG00000186891     chr1   6452-7951      - | ENSG00000186891         2
##   ENSG00000186827     chr1 14013-15512      - | ENSG00000186827         6
##   ENSG00000078808     chr1 31882-33381      - | ENSG00000078808         9
##   ENSG00000171863     chr2   1795-3294      + | ENSG00000171863        17
##               ...      ...         ...    ... .             ...       ...
##   ENSG00000254999     chr3   1276-2775      + | ENSG00000254999        28
##   ENSG00000238642     chr3 19069-20568      + | ENSG00000238642        30
##   ENSG00000134086     chr3 26692-28191      + | ENSG00000134086        31
##   ENSG00000238345     chr3 26834-28333      + | ENSG00000238345        32
##   ENSG00000134075     chr3 13102-14601      - | ENSG00000134075        36
##   -------
##   seqinfo: 3 sequences from an unspecified genome

Using promRegSel object as query, we can now count the alignment per sample in each of the promoter windows.

cnt <- qCount(proj1, promRegSel)
## counting alignments...done
##                 width Sample1 Sample2
## ENSG00000176022  1500     157     701
## ENSG00000186891  1500      22       5
## ENSG00000186827  1500      10       3
## ENSG00000078808  1500      73     558
## ENSG00000171863  1500      94     339
## ENSG00000252531  1500      59       9
## ENSG00000247886  1500     172     971
## ENSG00000254999  1500     137     389
## ENSG00000238642  1500       8       3
## ENSG00000134086  1500       9      18
## ENSG00000238345  1500      13      25
## ENSG00000134075  1500       7       3

The counts returned by qCount are the raw number of alignments per sample and region, without any normalization for the query region length, or the total number of aligned reads in a sample. As expected, we can find H3K4me3 signal at promoters of a subset of the genes with CpG island promoters, which we can visualize with help of the Gviz package:

gr1 <- import("Sample1.wig.gz")
## Warning in asMethod(object): NAs introduced by coercion
gr2 <- import("Sample2.wig.gz")
## Warning in asMethod(object): NAs introduced by coercion
axisTrack <- GenomeAxisTrack()
dTrack1 <- DataTrack(range = gr1, name = "Sample 1", type = "h")
dTrack2 <- DataTrack(range = gr2, name = "Sample 2", type = "h")
txTrack <- GeneRegionTrack(txdb, name = "Transcripts", showId = TRUE)
plotTracks(list(axisTrack, dTrack1, dTrack2, txTrack),
           chromosome = "chr3", extend.left = 1000)