The qplot program calculates various summary statistics some of which are plotted in a PDF file. These statistics can be used to assess the sequencing quality of sequence reads mapped to the reference genome. The main statistics are empirical Phred scores which are calculated based on the background mismatch rate. Background mismatch rate is the rate that sequenced bases are different from the reference genome, EXCLUDING dbSNP positions. Other statistics include GC biases, insert size distribution, depth distribution, genome coverage, empirical Q20 count, and so on.
In the following sections, we will guide you through: how to obtain qplot, how to use qplot, example outputs, interactive diagnostic plots, and real applications in which qplot has helped identify sequencing problems.
Where to Find It
You can obtain qplot in two ways:
(1) Download the pre-compiled binary along with the source code as described in Binary Download.
(2) Download source code only and compile it on your own machine. Please follow the instruction in Source Code Distribution on fetching source code and building instructions.
We have prepared a pre-compiled (under Ubuntu) qplot along with source code . You can download it from: qplot.20120602.tar.gz (File Size: 1.7G)
The executable file is under qplot/bin/qplot.
In addition, we provided the necessary input files under qplot/data/ (NCBI human genome build v37, dbSNP 130, and pre-computed GC file with windows size 100).
You can also find an example BAM input file under qplot/example/chrom20.9M.10M.bam. It is taken from the 1000 Genome Project with sequencing reads aligned to chromosome 20 positions 8M to 9M.
Source Code Distribution
We provide a source code only download in qplot-source.20120602.tar.gz. Optionally, you can download example file and/or data file:
resources data: necessary input files for qplot, including NCBI human genome build v37, dbSNP 130, and pre-computed GC file with windows size 100.
You can put above file(s) in the same folder and follow these steps:
- 1. Unarchive downloaded file
tar zvxf qplot-source.20120602.tar.gz
A new folder qplot will be created.
- 2. Build libStatGen
cd qplot make libStatGen
This step will download a necessary software library libStatGen and compile source code into a binary code library.
- 3. Build qplot
This step will then build qplot. Upon success, the executable qplot can be found under qplot/bin/.
- 4. (Optional) unarchive example and/or data
tar zvxf qplot-example.tar.gz
An example file, chrom20.9M.10M.bam, will be extracted to qplot/example/. It contains ~1.1 million aligned Illumina sequencing reads of NA12878 from 1000 Genome Project. Example command line, cmd.sh, example outputs, qplot.pdf, qplot.stats, and qplot.R are also provided and will be extracted qplot/example/ as well.
tar zvxf qplot-data.tar.gz
Three files will be extracted to qplot/data/: human.g1k.v37-bs.umfa is binary NCBI reference genome build 37; dbSNP130.UCSC.coordinates.tbl is dbSNP version 130; and human.g1k.w100.gc is pre-calculated GC content with windows size 100.
After you obtain the qplot executable (either by compiling the source code or by downloading the pre-compiled binary file), you will find the executable file under qplot/bin/qplot.
Here is the qplot help page by invoking qplot without any command line arguments:
some_linux_host > qplot/bin/qplot References : --reference [/net/fantasia/home/zhanxw/software/qplot/data/human.g1k.v37.fa], --dbsnp [/net/fantasia/home/zhanxw/software/qplot/data/dbSNP130.UCSC.coordinates.tbl], --gccontent [/net/fantasia/home/zhanxw/software/qplot/data/human.g1k.w100.gc] Create gcContent file : --create_gc , --winsize  Region list : --regions , --invertRegion Flag filters : --read1_skip, --read2_skip, --paired_skip, --unpaired_skip Dup and QCFail : --dup_keep, --qcfail_keep Mapping filters : --minMapQuality [0.00] Records to process : --first_n_record [-1] Lanes to process : --lanes  Read group to process : --readGroup  Input file options : --noeof Output files : --plot , --stats , --Rcode , --xml  Plot labels : --label , --bamLabel 
qplot runs on the input BAM/SAM file(s) specified on the command-line after all other parameters.
Additionally, three (3) precomputed files are required.
The reference genome is the same as karma reference genome. If the index files do not exist, qplot will create the index files automatically using the input reference fasta file.
This file has two columns. First column is the chromosome name which must be consistent with the reference created above. Second column is 1-based SNP position. If you want to create your own dbSNP data from downloaded UCSC dbSNP file, one way to do it is:
cat dbsnp_129_b36.rod|grep "single" | awk '$4-$3==1' |cut -f2,4 > dbSNP_129_b36.tbl
Although GC content can be calculated on the fly each time, it is much more efficient to load a precomputed GC content from a file. To generate the file, use the following command:
qplot --rerefence reference.fa --windowsize winsize --create_gc reference.gc
Note: Before running qplot, it is critical to check how the chromosome names are coded. Some BAM/SAM files use just numbers, others use chr + numbers. You need to make sure that the chromosome names from the reference and dbSNP are consistent with the BAM/SAM files.
Some of the command line parameters are described here, but most are self explanatory.
- Flag filter
By default all reads are processed. If it is desired to check only the first read of a pair, use
--read2_skip to ignore the second read. And so on.
- Duplication and QCFail
By default reads marked as duplication and QCFail are ignored but can be retained by
- Records to process
--first_n_record option followed by a number, n, will enable qplot to read the first n reads to test the bam files and verify it works.
- Lanes to process (only works for Illumina sequences)
If the input bam files have more than one lane and only some of them need to be checked, use something like
--lanes 1,3,5 to specify that only lanes 1, 3, and 5 need to be checked.
NOTE In order for this to work, the lane info has to be encoded in the read name such that the lane number is the second field with the delimiter ":".
- Read group to process :
Read group option can restrict qplot to process a subset of reads. For example, if BAM contain the following @RG tags:
@RG ID:UM0348_1:1 PL:ILLUMINA LB:M5390 SM:M5390 CN:UM @RG ID:UM0348_2:1 PL:ILLUMINA LB:M5390 SM:M5390 CN:UM @RG ID:UM0348_3:1 PL:ILLUMINA LB:M5390 SM:M5390 CN:UM @RG ID:UM0348_4:1 PL:ILLUMINA LB:M5390 SM:M5390 CN:UM @RG ID:UM0360_1:1 PL:ILLUMINA LB:M5390 SM:M5390 CN:UM @RG ID:UM0360_2:1 PL:ILLUMINA LB:M5390 SM:M5390 CN:UM @RG ID:UM0360_3:1 PL:ILLUMINA LB:M5390 SM:M5390 CN:UM @RG ID:UM0360_4:1 PL:ILLUMINA LB:M5390 SM:M5390 CN:UM
If specify nothing or not using "--readGroup", QPLOT by default will process all reads; If specify "--readGroup UM0348", then only read group UM0348_1, UM_0348_2, UM_0348_3, UM_0348_4 will be processed; If specify "--readGroup UM0348_1", then only one read group UM0348_1 will be processed.
- Input file options :
BAM files are compress by BGZF algorithm and it should contain EOF by default. QPLOT will by default stop working when it does not found a valid EOF tag inside BAM files. However, you can force QPLOT to continue process using --noeof. But you should be award the input files may be corrupted.
- Mapping filters
Qplot will exclude reads with lower mapping qualities than the user specified parameter,
--minMapQuality. By default, mapped reads with all mapping quality will be included in the analysis.
- Region list
If the interest of qplot is a list of regions, e.g. exons, this can be achieved by providing a list of regions. The regions should be in the form of "chr start end label" each line in the file (NOTE: start and end position are inclusive and they follow the convention of BED file). In order for this option to work, within each chromosome (contig) the regions have to be sorted by starting position, and also the input bam files have to be sorted. For example, you can create a text file, region.txt like following:
1 100 500 region_A 1 600 800 region_B 2 100 300 region_C
--regions region.txt enables qplot to calculate various statistics out of sequenced bases only within the above 3 regions.
Qplot also provides the
--invertRegion option. Enabling this option tells qplot to operate on those sequence bases that are outside the given region.
- Plot labels
Two kinds of labels are enabled.
--label is the label for the plot (default is empty) which is appended to the title of each subplot.
--bamLabels followed by a column separated list of labels provides the labels for each input SAM/BAM file, e.g. sample ID (default is numbers 1, 2, ... until the number of input bam files). For example:
--label Run100 --bamLabels s1,s2,s3,s4,s5,s6,s7,s8
There are three (optional) output files.
Qplot will generate a PDF file named qa.pdf containing 2 pages each with 4 figures. The plot is generated using Rscript.
Qplot will generate a text file named qa.stats containing various summary statistics for each input BAM/SAM file.
Qplot will generate qa.R which is the R code used for plotting the figures in the qa.pdf file. If Rscript is not installed in the system, you can use the qa.R to generate the figures on other machines, or extract plotting data from each run and combine multiple runs together to generate more comprehensive plots (See Example).
Qplot can generate diagnostic graphs, related R code, and summary statistics for each SAM/BAM file.
In the pre-compiled binary download, you will find a subdirectory named examples. We provide a sample file from the 1000 Genome project, it contains aligned reads on chromosome 20 from position 8 Mbp to 9Mbp. You can invoke qplot using the following command line:
../bin/qplot --reference ../data/human.g1k.v37.umfa --dbsnp ../data/dbSNP130.UCSC.coordinates.tbl --gccontent ../data/human.g1k.w100.gc --plot qplot.pdf --stats qplot.stats --Rcode qplot.R --label "chr20:9M-10M" chrom20.9M.10M.bam
Sample outputs are listed below:
1) Figure: qplot.pdf
2) Summary statistics:
Stats\BAM chrom20.9M.10M.bam TotalReads(e6) 1.11 MappingRate(%) 97.24 MapRate_MQpass(%) 97.24 TargetMapping(%) 0.00 ZeroMapQual(%) 2.39 MapQual<10(%) 2.86 PairedReads(%) 83.76 ProperPaired(%) 71.34 MappedBases(e9) 0.04 Q20Bases(e9) 0.04 Q20BasesPct(%) 88.63 MeanDepth 42.22 GenomeCover(%) 0.03 EPS_MSE 1.81 EPS_Cycle_Mean 18.71 GCBiasMSE 0.01 ISize_mode 137 ISize_medium 184 DupRate(%) 5.90 QCFailRate(%) 0.00 BaseComp_A(%) 29.9 BaseComp_C(%) 20.1 BaseComp_G(%) 20.2 BaseComp_T(%) 29.8 BaseComp_O(%) 0.1
Gallery of examples
Here we show qplot can be applied in various sequencing scenarios. Also users can customize statistics generated by qplot to their needs.
- Whole genome sequencing with 24-multiplexing
With a customized script, we aggregated 24 bar-coded samples in the same graph. The graph will help compare sequencing quality between samples.
- Interactive qplot
Qplot can be interactive. In the following example, you can use mouse scroll to zoom in and zoom out on each graph and pan to a certain part of the graph. By presenting qplot data on a web page, users can easily identify problematic sequencing samples. Users of qplot can customize its outputs into web page format greatly easing the data exploring process.
Diagnose sequencing quality
Qplot is designed and implemented for the need of checking sequencing quality. Besides the example of analyzing RNA-seq data as shown in our manuscript, here we demonstrate two additional scenarios in which qplot can help identify problems after obtaining sequencing data.
- Base quality distributed abnormally
By checking the first graph "Empirical vs reported Phred score", we found reported base qualities are shifted to the right. In this particular example, '33' was incorrectly added to all base qualities. When such data used in variant calling, we may increase false positive SNP variants.
- Bar-coded samples
By checking "Empirical phred score by cycle" (top right graph on the first page), we noticed the empirical qualities in the first several cycles are abnormally low. This phenomenon leads us to hypothesize that the first several bases have different properties. Further investigation confirmed that this sequencing was done using bar-coded DNA samples, but the analysis did not properly de-multiplex each sample.