Qualimap 0.8 documentation
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Analysis types

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.

Examples

  • See the Sample data section for more details about the data used in the examples.

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 reverse-stranded. This information is required to calculate the number of correct strand reads.
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.

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.

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].

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 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/).

Example

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

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

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.
[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.
[2]Downloaded from Biomart v.61.
[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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