QPLOT

Introduction

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.

Citing QPLOT

If you found QPLOT useful and wants to cite in your paper, please copy and paste the information below.

• Bingshan Li, Xiaowei Zhan, Mary-Kate Wing, Paul Anderson, Hyun Min Kang, and Goncalo R. Abecasis, “QPLOT: A Quality Assessment Tool for Next Generation Sequencing Data,” BioMed Research International, vol. 2013, Article ID 865181, 4 pages, 2013. doi:10.1155/2013/865181 http://www.hindawi.com/journals/bmri/2013/865181/

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.

Binary Download

We have prepared a pre-compiled (under Ubuntu) qplot along with source code . You can download it from: qplot.20130627.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.20130627.tar.gz. Optionally, you can download example file and/or data file:

example: example input file, and expected outputs if you following the direction.

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.20130627.tar.gz

A new folder qplot will be created.

• 2. Build libStatGen

cd qplot
(cd ../libStatGen; make cloneLib)

This step will download a necessary software library libStatGen and compile source code into a binary code library.

• 3. Build qplot

make 

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.

Usage

Command line

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
   The following parameters are available.  Ones with "[]" are in effect:
   
   
   
                   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]
      GC content file options : --winsize [100]
                  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 []
       Obsoleted (DO NOT USE) : --gccontent [], --create_gc

Input files

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.

• --reference

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.

• --dbsnp

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

• **OBSOLETED** --gccontent, --create_gc

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. GC content file name is automatically determined in this format: <reference_genome_base_file_name>.winsize<gc_content_window_size>.gc. For example, if your reference genome is human.g1k.v37.fa and the window size is 100, then the GC content file name is: human.g1k.v37.winsize100.gc .

As it said, there is no need to use --gccontent to specify GC content file in each run.

• input files

QPLOT take SAM/BAM files.

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.

Parameters

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

--dup_keep 

or

--qcfail_keep

• Records to process

The --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 :

The read group option can restrict qplot to process a subset of reads. For example, if the BAM contains 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

QPLOT will by default (without specifying --readgroup) process all reads.

If you specify "--readGroup UM0348", then only read groups UM0348_1, UM_0348_2, UM_0348_3, UM_0348_4 will be processed.

If you specify "--readGroup UM0348_1", then only one read group, UM0348_1, will be processed.

• Input file options :

BAM files are compressed using BGZF and should contain the EOF indicator by default. QPLOT will, by default, stop working if it does not find a valid EOF indicator inside the BAM files. However, you can force QPLOT to continue processing BAM files without an EOF indicator using --noeof. But you should be aware 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

Then specifying --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

Output files

There are three (optional) output files.

• --plot qa.pdf

Qplot will generate a PDF file named qa.pdf containing 2 pages each with 4 figures. The plot is generated using Rscript.

• --stats qa.stats

Qplot will generate a text file named qa.stats containing various summary statistics for each input BAM/SAM file.

• --Rcode qa.R

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).

Example

Qplot can generate diagnostic graphs, related R code, and summary statistics for each SAM/BAM file.

Built-in example

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.

QPlot of 24 samples(PDF)

• 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.

QPlot of 24 samples(HTML)

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

Example of qplot helping to identify wrong phred base quality

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

Example of qplot identifying the effect of ignoring bar-coding

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.

Contact

Questions and requests should be sent to Bingshan Li (bingshan@umich.edu) or Xiaowei Zhan (zhanxw@umich.edu) or Goncalo Abecasis (goncalo@umich.edu)