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Qualimap Documentation
Release 0.8

Fernando Garcia-Alcalde, et al

March 05, 2014

CONTENTS

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Workflow
2.1 Starting a new analysis . . . . . .
2.2 Viewing the results of the analysis
2.3 Exporting results . . . . . . . . .
2.4 Using tools . . . . . . . . . . . .

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Analysis types
3.1 BAM QC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Counts QC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Tools
4.1 Compute counts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Command Line Interface
5.1 General Description
5.2 BAM QC . . . . . .
5.3 Counts QC . . . . .
5.4 Clustering . . . . .
5.5 Compute counts . .

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Examples
6.1 Sample Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.2 Sample Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Frequently Asked Questions
7.1 General . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.2 Command line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.3 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Introduction
1.1 What is Qualimap? . . . . . . .
1.2 Installation . . . . . . . . . . .
1.3 Requirements . . . . . . . . . .
1.4 Installing Qualimap on Ubuntu
1.5 Citing Qualimap . . . . . . . .

Bibliography

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CHAPTER

ONE

INTRODUCTION
1.1 What is Qualimap?
Qualimap is a platform-independent application written in Java and R that provides both a Graphical User Interface (GUI) and a command-line interface to facilitate the quality control of alignment sequencing data. Shortly,
Qualimap:
1. Examines sequencing alignment data according to the features of the mapped reads and their genomic
properties
2. Povides an overall view of the data that helps to to the detect biases in the sequencing and/or mapping of
the data and eases decision-making for further analysis.
The main features offered by Qualimap are:
• fast analysis across the reference genome of mapping coverage and nucleotide distribution;
• easy-to-interpret summary of the main properties of the alignment data;
• analysis of the reads mapped inside/outside of the regions defined in an annotation reference;
• analysis of the adequacy of the sequencing depth in RNA-seq experiments;
• clustering of epigenomic profiles.

1.2 Installation
Download the ZIP file from the Qualimap web page.
Unpack it to desired directory.
Run Qualimap from this directory using the prebuilt script:
./qualimap
Qualimap was tested on GNU Linux and MacOS.
Note: On MS Windows use script qualimap.bat to launch Qualimap.

1.3 Requirements
Qualimap requires:
• JAVA runtime version 6 or above.
• R enviroment version 2.14 or above.

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The JAVA runtime can be downloaded from the official web-site. There are prebuilt binaries available for many
platforms.
R enviroment can be downloaded from R project web-site.
Note: In general the installation of R environment is platform-specific and may require additional efforts.
Several Qualimap features are implemented in R, using a number of external packages.
Note: If R environment is not available or required R-packages are missing, “Counts QC” and “Clustering”
features will be disabled.
Currently Qualimap requires the following R-packages:
• optparse (available from CRAN)
• Repitools, Rsamtools, GenomicFeatures, rtracklayer (available from Bioconductor)
One can install these packages manually or by executing the script found in the installation folder:
Rscript scripts/installDependencies.r

1.4 Installing Qualimap on Ubuntu
This manual is specific for Ubuntu(Debian) Linux distribution, however with slight differences this can be applied
for other GNU Linux systems.

1.4.1 Install JAVA
It is possible to use openjdk:
sudo apt-get install openjdk-6-jre

1.4.2 Install R
The R latest version can be installed from public repos.
The repos must be added to the sources file. Open sources.list:
sudo gedit /etc/apt/sources.list
Add the following line:
deb http:///bin/linux/ubuntu /
List of cran mirrors can be found here
Here is an example for Ubuntu 10.04 (Lucid):
deb http://cran.stat.ucla.edu/bin/linux/ubuntu lucid/
Then install R:
sudo apt-get update
sudo apt-get install r-base-core
If you don’t have the public key for the mirror add it:
gpg --keyserver subkeys.pgp.net --recv-key
gpg -a --export | sudo apt-key add More details available here:
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http://cran.r-project.org/bin/linux/ubuntu/README
Qualimap needs R version 2.14 or above. This can be checked with the following command:
Rscript --version
Note: Alternatively it is possible to build R enviroment directly from sources downloaded from r-project.org.

1.4.3 Install required R-packages
Some packages depend on external libraries, so you might need to install them either:
sudo apt-get install libxml2-dev
sudo apt-get install libcurl4-openssl-dev
You can install required packages manually or use special script from Qualimap installation folder:
sudo Rscript $QUALIMAP_HOME/scripts/installDependencies.r
where $QUALIMAP_HOME is the full path to the Qualimap installation folder.

1.5 Citing Qualimap
If you use Qualimap for your research, please cite the following:
García-Alcalde, et al. “Qualimap: evaluating next generation sequencing alignment data.” Bioinformatics(2012)
28 (20): 2678-2679

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Chapter 1. Introduction

CHAPTER

TWO

WORKFLOW
2.1 Starting a new analysis
• To start new analysis activate main menu item File → New Analysis and select the desired type of analysis.
Read more about different types of analysis here.

• After the corresponding item is selected a dialog will appear that allows customizing analysis options (input
files, algorithm parameters, etc.).

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• To run the analysis click the Start analysis button.
• During the computation a status message and a graphic bar will indicate the progress of the computation.

2.2 Viewing the results of the analysis
• After the selected analysis is finished the results are shown as an interactive report in the Qualimap main
window. Several reports can be opened at the same time in different tabs.

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• In the left part of the report window one can find a list containing available result items. Clicking on an item
will automatically show the corresponding information report or graph. Some report items are common for
different types of analysis.
• For example, the Summary section provides a short summary of performed quality control checks, while the
Input section lists all the input parameters. Further information about each specific result is provided here.

2.3 Exporting results
• The resulting report along with raw statistics data can be saved to HTML page or PDF document.
• To export results to HTML use a main menu item File → Export to HTML. In the dialog window one can
select the output folder. After clicking OK button the web-page, containing analysis results along with raw
statistics data will be saved to the specified directory.
• Similarly one can save the report to a PDF document by using a main menu item File → Export to PDF.
• Note that for plots in BAM QC and Counts QC it is also possible to export the underlying raw data using the
context menu, with appears by clicking the right mouse button in the corresponding plot. In addition, when
the report is exported to HTML, the raw data for all plots can be found in the output folder.

2.4 Using tools
• Qualimap is desgined to provide NGS-related tools that can be used aside from the quality control analysis.
Currently two tools are available (more are planned to be added in the future):
1. Compute Counts for counting how many reads are mapped to each region of interest at the desired
level (genes, transcripts, etc.)
2. Clustering for obtaining groups of genomic features that share similar coverage profiles

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Chapter 2. Workflow

CHAPTER

THREE

ANALYSIS TYPES
3.1 BAM QC
BAM QC reports information for the evaluation of the quality of the provided alignment data (a BAM file). In
short, the basic statistics of the alignment (number of reads, coverage, GC-content, etc.) are summarized and a
number of useful graphs are produced. This analysis can be performed with any kind of sequencing data, e.g.
whole-genome sequencing, exome sequencing, RNA-seq, ChIP-seq, etc.
In addition, it is possible to provide an annotation file so the results are computed for the reads mapping inside
(and optionally outside) of the corresponding genomic regions, which can be especially useful for evaluating
target-enrichment sequencing studies.
To start a new BAM QC analysis activate main menu item File → New Analysis → BAM QC.

3.1.1 Examples
• Whole-genome sequencing: HG00096.chrom20.bam. Report for sample alignment file from 1000 Genomes
project.
• Whole-genome sequencing: ERRR089819.bam. Report created using the whole-genome sequencing data
of Caenorhabditis elegans from the following study.
• See the Sample data section for more details about the data used in the examples.

3.1.2 Input Parameters
BAM file Path to the sequence alignment file in BAM format. Note, that the BAM file has to be sorted by
chromosomal coordinates. Sorting can be performed with samtools sort.
Analyze regions Activating this option allows the analysis of the alignment data for the regions of interest.
Regions file(GFF/BED file) The path to the annotation file that defines the regions of interest. The file must be
tab-separated and have GFF/GTF or BED format.
Note: A typical problem when working with human genome annotations is the inconsistency between chromosome names due to “chr” prefix. For example, Ensemble annotations do not include this prefix, while UCSC
annotations do. This can become a problem when asscociating regions file with the BAM alignment. Qualimap
handles this problem: if the reference sequence of a region has “chr” prefix, it tries to search for sequence name
with prefix and without prefix.
Library strand specificity
The sequencing protocol strand specificity: non-strand-specific, forward-stranded or reversestranded. This information is required to calculate the number of correct strand reads.

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Analyze outside regions If checked, the information about the reads that are mapped outside of the regions of
interest will be also computed and shown in a separate section.
Chromosome limits If selected, vertical dotted lines will be placed at the beginning of each chromosome according to the information found in the header of the BAM file.
Compare GC content distribution with This allows to compare the GC distribution of the sample with the selected pre-calculated genome GC distribution. Currently two genome distributions are available: human
(hg19) and mouse (mm9). More species will be included in future releases.
Advanced parameters
Number of windows Number of windows used to split the reference genome. This value is used for computing
the graphs that plot information across the reference. Basically, reads falling in the same window are
aggregated in the same bin. The higher the number, the bigger the resolution of the plots but also longer
time will be used to process the data. By default 400 windows are used.
Homopolymer size Only homopolymers of this size or larger will be considered when estimating homopolymer
indels count.
Number of threads In order to speed up the computation, the BAM QC analysis computation can be performed
in parallel on a multicore system using the given number of threads. More information on the parallelization
of qualimap can be found in FAQ. The default number of threads equals number of available processors.
Size of the chunk In order to reduce the load of I/O, reads are analyzed in chunks. Each chunk contains the
selected number of reads which will be loaded into memory and analyzed by a single thread. Smaller
numbers may result in lower performance, but also the memory consumption will be reduced. The default
value is 1000 reads.

3.1.3 Output
Summary
Basic information and statistics for the alignment data. Qualimap reports here information about
the total number of reads, number of mapped reads, paired-end mapping performance, read length
distribution, insert size, nucleotide content, coverage, number of indels, mapping quaility and
chromosome-based statistics.
For region-based analysis the information is given inside of regions, including some additional information like, for example, number of correct strand reads.
Input
Here one can check the input data and the parameters used for the analysis.
Coverage Across Reference
This plot consists of two figures. The upper figure provides the coverage distribution (red line) and
coverage deviation across the reference sequence. The coverage is measured in X 1 . The lower figure
shows GC content across reference (black line) together with its average value (red dotted line).
Coverage Histogram
Histogram of the number of genomic locations having a given coverage rate. The bins of the x-axis
are conveniently scaled by aggregating some coverage values in order to produce a representative
histogram also in presence of the usual NGS peaks of coverage.
Coverage Histogram (0-50X)
Histogram of the number of genomic locations having a given coverage rate. In this graph genome
locations with a coverage greater than 50X are grouped into the last bin. By doing so a higher
resolution of the most common values for the coverage rate is obtained.
1 Example for the meaning of X: If one genomic region has a coverage of 10X, it means that, on average, 10 different reads are mapped to
each nucleotide of the region.

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Genome Fraction by Coverage
Provides a visual way of knowing how much reference has been sequenced with at least a given
coverage rate. This graph should be interpreted as in this example:
If one aims a coverage rate of at least 25X (x-axis), how much of reference (y-axis) will be considered? The answer to this question in the case of the whole-genome sequencing provided example is
~83%.
Mapped Reads Nucleotide Content
This plot shows the nucleotide content per position of the mapped reads.
Mapped Reads Clipping Profile
Represents the percentage of clipped bases across the reads. The clipping is detected via SAM format
CIGAR codes ‘H’ (hard clipping) and ‘S’ (soft clipping). In addition, the total number of clipped
reads can be found in the report Summary. The plot is not shown if there are no clipped-reads are
found. Total number of clipped reads can be found in Summary. Example.
Mapped Reads GC Content Distribution
This graph shows the distribution of GC content per mapped read. If compared with a precomputed
genome distribution, this plot allows to check if there is a shift in the GC content.
Homopolymer Indels
This bar plot shows separately the number of indels that are within a homopolymer of A’s, C’s,
G’s or T’s together with the number of indels that are not within a homopolymer. Large numbers
of homopolymer indels may indicate a problem in a sequencing process. An indel is considered
homopolymeric if it is found within a homopolymer (defined as at least 5 equal consecutive bases).
Owing to the fact that Qualimap works directly from BAM files (and not from reference genomes),
we make use of the CIGAR code from the corresponding read for this task. Indel statistics cam be
found in a dedicated section of the report Summary.
This chart is not shown if the sample doesn’t contain any indels.
Duplication Rate Histogram
This plot shows the distribution of duplicated read starts. Due to several factors (e.g. amount
of starting material, sample preparation, etc) it is possible that the same fragments are sequenced
several times. For some experiments where enrichment is used (e.g. ChIP-seq ) this is expected at
some low rate. If most of the reads share the exact same genomic positions there is very likely an
associated bias.
Mapping Quality Across Reference
This plot provides the mapping quality distribution across the reference.
Mapping Quality Histogram
Histogram of the number of genomic locations having a given mapping quality. According to
Specification of the SAM format the range for the mapping quality is [0-255].

3.2 Counts QC
In RNA-seq experiments, the reads are usually first mapped to a reference genome. It is assumed that if the
number of reads mapping to a certain biological feature of interest (gene, transcript, exon, ...) is sufficient, it can
be used as an estimation of the abundance of that feature in the sample and interpreted as the quantification of
the expression level of the corresponding region.
These count data can be utilized for example to assess differential expression between two or more experimental
conditions. Before assesing differential expression analysis, researchers should be aware of some potential limitations of RNA-seq data, as for example: Has the saturation been reached or more features could be detected
by increasing the sequencing depth? Which type of features are being detected in the experiment? How good

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is the quantification of expression in the sample? All of these questions are answered by interpreting the plots
generated by Qualimap.
For assesing this analysis just activate from the main menu File → New Analysis → Counts QC.
Note: If count data need to be generated, one can use the provided tool Compute counts.

Note: For this option to work, the R language must be installed along with the R package optparse (both are
freely available from http://cran.r-project.org/).

3.2.1 Example
• RNA-seq count data. This report was produced using the counts from the RNA-seq of Homo sapiens kidney
and liver samples [Marioni].
• These counts can be downloaded from here or generated using the Compute counts tool.

3.2.2 Input Parameters
First sample (counts)
File containing the count data from the sample. This must be a two-column tab-delimited text file,
with the feature IDs in the first column and the number of counts in the second column. This file must
not contain header nor column names. See Counts for examples
First sample name
Name for the first sample that will be used as legend in the plots.
Second sample (counts)
Optional. If a second sample is available, this file should contain the same information as in First
sample for the second sample, i.e. the same feature IDs (first column) and the corresponding number
of counts (second column). Mark the Compare with other sample checkbox to enable this option.
Second sample name
Name for the second sample that will be used as legend in the plots.
Count threshold
In order to remove the influence of spurious reads, a feature is considered as detected if its corresponding number of counts is greater than this threshold. By default, the theshold value is set to 5
counts, meaning that features having less than 5 counts will not be taken into account.
Group File
Optional. File containing a classification of the features of the count files. It must be a two columns
tab-delimited text file, with the features names or IDs in the first column and the group (e.g. the
biotype from Ensembl database) in the second column (see human.64.genes.biotypes for an example).
Again, the file must not contain any header or column names. If this file is provided, specific plots for
each defined group are generated. Please, make sure that the features IDs on this file are the same in
the count files.
Species
Optional. For convinience, Qualimap provides the Ensembl biotype classification 2 for certain
species (currently Human and Mouse). In order to use these annotations, Ensembl Gene IDs should
be used as the feature IDs on the count files (e.g. ENSG00000251282). If so, mark the box to enable
2

Downloaded from Biomart v.61.

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this option and select the corresponding species. More annotations and species will be made available
in future releases.

3.2.3 Output
Global Plots
Global Saturation
This plot provides information about the level of saturation in the sample, so it helps the user to decide
if more sequencing is needed or if no many more features will detected when increasing the number
of reads. These are some tips for the interpretation of the plot:
• The increasing sequencing depth of the sample is represented at the x-axis. The maximum value
is the real sequencing depth of the sample(s). Smaller sequencing depths correspond to samples
randomly generated from the original sample(s).
• The curves are associated to the left y-axis. They represent the number of detected features at
each of the sequencing depths in the x-axis. By “detected features” we refer to features with
more than k counts, where k is the Count threshold selected by the user.
• The bars are associated to the right y-axis. They represent the number of newly detected features
when increasing the sequencing depth in one million reads at each sequencing depth value.
An example for this plot can be seen here.
When a Group File is provided by the user or chosen from those supplied by Qualimap, a series of
plots are additionally generated:
Samples Correlation
When two samples are provided, this plot determines the correlation level between both samples.
Due to the often wide range of expression data (counts), a log2-transformation is applied in order to
improve the graphical representation. Features not detected in any of the two samples are removed
for this analysis. To avoid infinite values in the case of genes with 0 counts in one of the samples,
log2(expression + 1) is used. Thus, sample 1 is depicted in X-axis and sample 2 in Y-axis. The colors
of the plot should be interpreted as a map. The blue color is the level of the sea and the white color
the top of the mountain. Hence, the higher you are over the sea level, the more genes you have in that
range of X-Y values. In addition, the title of the plot includes the Pearson’s correlation coefficient,
which indicates if both samples present a linear relationship.
Detection Per Class
This barplot allows the user to know which kind of features are being detected his sample(s). The
x-axis shows all the groups included in the Group File (or the biotypes supplied by Qualimap). The
grey bars are the percentage of features of each group within the reference genome (or transcriptome,
etc.). The striped color bars are the percentages of features of each group detected in the sample with
regard to the genome. The solid color bars are the percentages that each group represents in the total
detected features in the sample.
Counts Per Class
A boxplot per each group describes the counts distribution for the detected features in that group.
Individual Group Plots
Saturation per group
For each group, a saturation plot is generated like the one described in Global Saturation.
Counts & Sequencing Depth

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For each group, a plot is generated containing a boxplot with the distribution of counts at each sequencing depth. The x-axis shows the increasing sequencing depths of randomly generated samples
from the original one till the true sequencing depth is reached. This plot allows the user to see how
the increase of sequencing depth is changing the expression level quantification.

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FOUR

TOOLS
4.1 Compute counts
• Given a BAM file and an annotation (GTF file), this tool calculates how many reads are mapped to each
region of interest.
• The user can decide:
– At which level wants to perform the counting (genes, transcripts...).
– What to do whith reads mapped to multiple locations.
– The strand-specifity.
– When a transcriptome GTF file is provided the tool allows to calculate 5’ and 3’ prime coverage bias.
To access the tool use Tools → Compute counts.
Note: For paired-end reads currently each mate of a pair is considered independently (taking into account the
strand-specificity of the protocol). We will add full support for paired-end reads in future versions of Qualimap.

4.1.1 Example
• Input data:
– BAM file: liver.bam. RNA-seq of liver tissue from Marioni JC et al
– GTF file: human.64.gtf . Human annotation from Ensembl (v. 64)
– Parameters:
* Feature ID: gene_id (to count at the level of genes)
* Feature type: exon (to ignore other features like start/end codons)
* Multimapped reads: uniquely-mapped-reads (to ignore not unique alignments)
• Output:
– liver.counts. Two-column tab-delimited text file, with the feature IDs in the first column and the
number of counts in the second column.

4.1.2 Input
BAM file Path to the BAM alignment file.
Annotation file Path to the GTF or BED file containing regions of interest.
Protocol

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Controls when to consider reads and features to be overlapping:
non-strand-specific Reads overlap features if they share genomic regions regardless of the strand.
forward-stranded For single-end reads, the read and the feature must have the same strand to be
overlapping. For paired-end reads, the first read of the pair must be mapped to the same strand
as the feature, while the second read must be mapped to the opposite strand.
reverse-strand For single-end reads, the read and the feature must have the opposite strand. For
paired-end reads, the first read of pair must be mapped to the opposite strand of the feature,
while the second read of the pair must be on the same strand as the feature.
Feature ID The user can select the attribute of the GTF file to be used as the feature ID. Regions with the same
ID will be aggregated as part of the same feature. The application preload the first 1000 lines of the file so
a list with possible feature IDs is conveniently provided.
Feature type The user can select the feature type (value of the third column of the GTF) considered for counting.
Other types will be ignored. The application preload the first 1000 lines of the file so a list with possible
feature IDs is conveniently provided.
Output Path to the ouput file.
Save computation summary This option controls whether to save overall computation statistics. If selected, the
statistics will be saved in a file named $INPUT_BAM.counts
Multi-mapped reads This option controls what to do whith reads mapped to multiple location:
uniquely-mapped-reads Reads mapped to multiple locations will be ignored.
proportional Each read is weighted according to the number of mapped locations. For example, a read
mapped to 4 different locations will add 0.25 to the counts of each location.
Calculate 5’ and 3’ coverage bias If a GTF file is provided, the user has the possibility of computing 5’ - 3’ bias.
The application automatically constructs the 5’ and 3’ UTR (100 bp) from the gene definitions of the GTF
file and determines the coverage rate of the 1000 most highly expressed transcripts in the UTR regions.
This information is then stored in the computation summary file, together with the statistics of the counting
procedure.
Note: This option requires a standard gene model definition. The UTRs are computed for the first and last exons
of each transcript. Therefore, exon is the feature of interest (third field of the GTF) and gene_id, transcript_id
should be attributes (ninth field of the GTF).

4.1.3 Output
A two-column tab-delimited text file, with the feature IDs in the first column and the number of counts in the
second column, and overall calculation stats.
The calculation stats include:
Feature counts Number of reads assigned to various features
No feature Number of reads not aligned to any feature
Not unique alignment Number of reads with non-unique alignment
Ambiguous Number of reads that align to features ambigously
The following stats are calculate only if option Calulate 5’ and 3’ bias was set:
Median 5’ bias For 1000 most expressed genes the ratio between coverage of 100 leftmost bases and
mean coverage is calcualted and median value is provided.
Median 3’ bias For 1000 most expressed gene the ratio between coverage of 100 rightmost bases and
mean coverage is calculated and median value is provided.

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Median 5’ to 3 For 1000 most expressed genes the ratio between coverag of 100 leftmost and 100
rightmost bases is calculated and median value is provided.

4.2 Clustering
• Qualimap provides the possibility of clustering genomic features according to their surrounding coverage
profiles. This is particulary interesting in epigenomic studies (e.g. methylation). The user can import a set
of features (e.g. TSSs or CpG Islands) together with the BAM file. Then the application preprocess the data
and clusters the profiles using the Repitools package (Statham et al). The obtained groups of features are
displayed as a heatmap or as line graphs and can be exported for further analysis (e.g. for measuring the
correlation between promoter methylation and gene expression).
• Summary of the process:
– filter out the non-uniquely-mapped reads
– compute the smoothed coverages values of the samples at the desired locations
– apply k-means on the smoothed coverage values for the desired values of k
• To perform this analysis the user needs to provide at least two BAM files – one for the sample (enriched)
and other for the control (input) – and a list of features as BED file.
• Clustering analysis can be accesed using the menu item File → Tools → Clustering.
Note: Clustering coverage profiles is not a straightforward task and it may be necessary to perform a number
of empirical filter steps. In order to correctly interpret the approach the results we encourage the users to read
Repitools User Manual.

4.2.1 Input Parameters
Experiment ID The experiment name
Alignment data Here you can provide your replicates to analyze. Each replicate includes sample file and a control
file. For example, in an epigenomics experiment, the sample file could be the MeDIP-seq data and the
control the non-enriched data (the so-called INPUT data). Thus, for each replicate the following information
has to be provided:
Replicate name Name of the replicate
Sample file Path to sample BAM file
Control file Path to control BAM file
To add a replicate click Add button. To remove a replicate select it and click Remove button. You can modify
replicate by using Edit button.
Regions of interest Path to an annotation file in BED or GFF format, which contains regions of interest.
Location Relative location to analyze
Left offset Offset in bp upstream the selected regions
Right offset Offset in bp downstream the selected regions
Bin size Can be thought as the resolution of the plot. Bins of the desired size will be computed and the information
falling on each bin will be aggregated
Number of clusters Number of groups that you the user wants to divide the data. Several values can be used by
separating them with commas
Fragment length Length of the fragments that were initially sequenced. All reads will be enlarged to this length.
Visualization type You can visualize cluster using heatmaps or line-based graphs.

4.2. Clustering

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4.2.2 Output
After the analysis is performed, the regions of interest are clustered in groups based on the coverage pattern. The
output graph shows the coverage pattern for each cluster either as a heatmap or a line graph. There can be multiple
graphs based on the number of clusters provided as input. The name of each graph consists of the experiment
name and the number of clusters.
It is possible to export list of features beloning to the particular cluster. To do this use main menu item File →
Export gene list or context menu item Export gene list. After activating the item a dialog will appear where you
can choose some specific cluster. One can either copy the list of features belonging to this cluster in the clipboard
or export it to a text file.

Chapter 4. Tools

CHAPTER

FIVE

COMMAND LINE INTERFACE
5.1 General Description
Each analysis type presented in QualiMap GUI is also available as command line tool. The common pattern to
launch the tool is the following:
qualimap

is the name of the desired analysis. This could be: bamqc, countsqc, clustering or counts.
are specific to each type analysis. If not option is provided for the specific tool a full list of
available options will be shown
Note: If you are using Qualimap on Unix server without X11 system, make sure that the DISPLAY environment
variable is unset. Otherwise this might result in problems when running Qualimap. Here is an instruction how to
solve this issue.
To show available tools use command:
qualimap --help

5.2 BAM QC
The following command allows to perform BAM QC analysis:
usage: qualimap bamqc -bam [-c] [-gd ] [-gff ] [-nr ] [-nt
] [-nw ] [-os] [-outdir ] [-outformat ]
-bam
input mapping file
-c,--paint-chromosome-limits
paint chromosome limits inside charts
-gd
compare with genome distribution (possible
values: HUMAN or MOUSE)
-gff
region file (in GFF/GTF or BED format)
-hm
minimum size for a homopolymer to be considered
in indel analysis (default is 3)
-nr
number of reads in the chunk (default is 500)
-nt
number of threads (default equals the number of cores)
-nw
number of windows (default is 400)
-os,--outside-stats
compute region outside stats (only with -gff
option)
-outdir
output folder
-outformat
output report format (PDF or HTML, default is
HTML)
-p
specify protocol to calculate correct strand
reads (works only with -gff option, possible
values are STRAND-SPECIFIC-FORWARD or

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STRAND-SPECIFIC-REVERSE, default is
NON-STRAND-SPECIFIC)

The only required parameter is bam – the input mapping file.
If outdir is not provided, it will be created automatically in the same folder where BAM file is located.
Detailed explanation of available options can be found here.

Example (data available here):
qualimap bamqc -bam ERR089819.bam -c

5.3 Counts QC
To perform counts QC analysis (evaluation of RNA-seq data) use the following command:
usage: qualimap counts -d1 [-d2 ] [-i ] [-k ] [-n1 ]
[-n2 ] [-outdir ] [-outformat ] [-s ]
-d1,--data1
first file with counts
-d2,--data2
second file with counts
-i,--info
info file
-k,--threshold
threshold for the number of counts
-n1,--name1
name for the first sample
-n2,--name2
name for second sample
-outdir
output folder
-outformat
output report format (PDF or HTML, default is HTML)
-s,--species
use default file for the given species [human | mouse]

Detailed explanation of available options can be found here.

Example (data available here):
qualimap counts -d1 kidney.counts -d2 liver.counts -s human -outdir results

5.4 Clustering
To perform clustering of epigenomic signals use the following command:
usage: qualimap clustering [-b ] [-c ] -control [-expr ]
[-f ] [-l ] [-name ] [-outdir ] [-outformat ]
[-r ] -regions -sample [-viz ]
-b,--bin-size
size of the bin (default is 100)
-c,--clusters
comma-separated list of cluster sizes
-control
comma-separated list of control BAM files
-expr
name of the experiment
-f,--fragment-length
smoothing length of a fragment
-l
upstream offset (default is 2000)
-name
comma-separated names of the replicates
-outdir
output folder
-outformat
output report format (PDF or HTML, default is
HTML)
-r
downstream offset (default is 500)

Chapter 5. Command Line Interface

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-regions
-sample
-viz

path to regions file
comma-separated list of sample BAM files
visualization type: heatmap or line

Detailed explanation of available options can be found here.

Example (data available here):

qualimap clustering -sample clustering/hmeDIP.bam -control clustering/input.bam -regions annotatio

5.5 Compute counts
To compute counts from mapping data use the following command:
usage: qualimap comp-counts [-algorithm ] -bam -gtf [-id ]
[-out ] [-protocol ] [-type ]
-algorithm
uniquely-mapped-reads(default) or proportional
-b
calculate 5’ and 3’ coverage bias
-bam
mapping file in BAM format)
-gtf
region file in GTF format
-id
attribute of the GTF to be used as feature ID. Regions with
the same ID will be aggregated as part of the same feature.
Default: gene_id.
-out
path to output file
-protocol
forward-stranded,reverse-stranded or non-strand-specific
-type
Value of the third column of the GTF considered for
counting. Other types will be ignored. Default: exon

Detailed explanation of available options can be found here.

Example (data available here):
qualimap comp-counts -bam kidney.bam -gtf ../annotations/human.64.gtf

5.5. Compute counts

-out kidney.counts

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Chapter 5. Command Line Interface

CHAPTER

SIX

EXAMPLES
6.1 Sample Data
6.1.1 Alignments
• ERR089819.bam (2.6 GB) Whole genome sequencing data of C. elegans from the following study.
• HG00096.chrom20.bam (278 MB) Sequencing of the chromosome 20 from a H. sapiens sample from
1000 Genomes project. The header of the BAM file was changed in order to contain only chromosome
20. Original file can be found here.

6.1.2 Annotations
• human.64.gtf Human genome annotations from Ensembl database (v. 64).
• transcripts.human.64.bed Human transcripts in BED format from Ensembl database (v. 64).

6.1.3 Counts
Human RNA-seq data from the paper of Marioni JC et al.
• Counts:
kidney.counts and liver.counts
• BAM files used to produce the counts:
kidney.bam and liver.bam
• Genes Biotypes:
human.64.genes.biotypes.txt

6.1.4 Clustering
• hmeDIP.bam (988M) MeDIP-seq of human embryonic stem cells from the study of Stroud H et al.
• input.bam (1.8G) Input data of the same study

6.2 Sample Output
6.2.1 BAM QC
Analysis of the WG-seq data (ERR089819.bam): QualiMap HTML report.
23

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Analysis of the WG-seq data (HG00096.chrom20.bam): QualiMap HTML report.

6.2.2 Counts QC
Analysis of RNA-seq counts data: QualiMap HTML report.

6.2.3 Clustering
Analysis of MeDIP-seq data: QualiMap HTML report.

Chapter 6. Examples

CHAPTER

SEVEN

FREQUENTLY ASKED QUESTIONS
7.1 General
Q: How to cite Qualimap?
A: If you use Qualimap for your research, please cite the following:
García-Alcalde, et al. “Qualimap: evaluating next generation sequencing alignment data.” Bioinformatics(2012) 28 (20): 2678-2679

Q: How to increase maximum Java heap memory size?
A: The Qualimap launching script allows to set desired memory size using special command line argument
--java-mem-size. Here are some usage examples:
qualimap --java-mem-size=1200M
qualimap bamqc -bam very_large_alignment.bam --java-mem-size=4G
Note that there should be no whitespace between argument and its value.
Alternatively one can change default memory size parameter by modifying the following line in the launching
script:
JAVA_MEM_DEFAULT_SIZE=”1200M”
Also one can override this parameter by setting environment variable $JAVA_OPTS.

Q: Does Qualimap run on MS Windows?
A: Qualimap can be launched on Windows using script qualimap.bat. However, officially we do not support
MS Windows.

Q: Does Qualimap work with R version 3?
A: Yes, Qualimap works with R v3. There was a bug with R-version recogintion GUI, but starting from version
0.8 the bug was fixed.

Qualimap Documentation, Release 0.8

Q: I always get a message “Out of Memory”. What should I do?
A: You can try decreasing the number of reads in chunk or increasing maximum Java heap memory size.

7.2 Command line
Q: I launch Qualimap command-line tool on my big and powerful Linux server. However it doesn’t finish properly
and outputs some strange message like:

Exception in thread “main” java.lang.InternalError: Can’t connect to X11
window server using ‘foo:42.0’ as the value of the DISPLAY variable.

What is going on?

A: Java virtual machine uses DISPLAY environment variable to detect if the X11 system is available. Sometimes this variab
unset DISPLAY
or like this: export DISPLAY=:0

7.3 Performance
Q: Does Qualimap make use of multicore systems to improve computation speed?
A: Yes, Qualimap uses threads to perform BAM QC analysis.
In short, reads are processed in chunks and each chunk is analyzed in parallel.
Below you can find a schema, depicting the applied algorithm.

Chapter 7. Frequently Asked Questions

Qualimap Documentation, Release 0.8

Here each block denotes a certain algorithm step. The analysis starts dividing the reference genome into windows.
The first window is set to be the current one. Then the analysis continues processing BAM records belonging to
the current window.
When all the reads belonging to the current window are processed, the window is finalized in a newly created
thread.
The analysis is finished when all windows are processed.

Q: What is the scalability of QualiMap? Can it run on a cluster?
A: Currently qualimap is designed to run in a single multicore machine. In the future we plan to support cluster
and computational cloud execution for BAM QC.

Q: I have a powerful computer with a lot of memory. Can I make Qualimap run faster?
A: Sure, just increase your maximum JAVA heap size.

7.3. Performance

Qualimap Documentation, Release 0.8

Chapter 7. Frequently Asked Questions

BIBLIOGRAPHY

[Marioni] Marioni JC et al, “RNA-seq: An assessment of technical reproducibility and comparison with gene
expression arrays”. Genome Res. 2008. 18: 1509-1517.

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