Automatic Filtering, Trimming, Error Removing and Quality Control for fastq data
AfterQC can simply go through all fastq files in a folder and then output three folders: good, bad and QC folders, which contains good reads, bad reads and the QC results of each fastq file/pair.
Currently it supports processing data from HiSeq 2000/2500/3000/4000, Nextseq 500/550, MiniSeq...and other Illumina 1.8 or newer formats
The author has reimplemented this tool in C++ with multithreading support to make it much faster. The new tool is called
fastp and can be found at: https://github.com/OpenGene/fastp . If you prefer a C++ based tool, please use
An Example of Report
The report of AfterQC is a single HTML page with figures contained in. See an example: http://opengene.org/AfterQC/report.html
AfterQC does following tasks automatically:
- Filters reads with too low quality, too short length or too many N
- Filters reads with abnormal PolyA/PolyT/PolyC/PolyG sequences
- Does per-base quality control and plots the figures
- Trims reads at front and tail, according to QC results
- For pair-end sequencing data,
AfterQCautomatically corrects low quality wrong bases in overlapped area of read1/read2
- Detects and eliminates bubble artifact caused by sequencer due to fluid dynamics issues
- Single molecule barcode sequencing support: if all reads have a single molecule barcode (see duplex sequencing),
AfterQCshifts the barcodes from the reads to the fastq query names
- Support both single-end sequencing and pair-end sequencing data
- Automatic adapter cutting for pair-end sequencing data
- Sequencing error estimation, and error distribution profiling
- with bioconda
conda install afterqc
git clone https://github.com/OpenGene/AfterQC.gitor download https://github.com/OpenGene/AfterQC/archive/master.zip
- stable: Releases
AfterQC is compitable with
PyPy to run
AfterQC is strongly suggested since it can make
AfterQC 3X faster than native Python (CPython). To run with
pypy, just replace
pypy in the commands.
- Prepare your fastq files in a folder
- For single-end sequencing, the filenames in the folder should be
*R1*, otherwise you should specify
- For pair-end sequencing, the filenames in the folder should be
*R2*, otherwise you should specify
cd /path/to/fastq/folder python path/to/AfterQC/after.py
- three folders will be automatically generated, a folder
goodstores the good reads, a folder
badstores the bad reads and a folder
QCstores the report of quality control
AfterQCwill print some statistical information after it is done, such how many good reads, how many bad reads, and how many reads are corrected.
- if you want to run
AfterQConly with a single file/pair:
# with a single file python after.py -1 R1.fq # with a single pair python after.py -1 R1.fq -2 R2.fq
Quality Control only
If you only want to get quality control statistics, run:
python after.py --qc_only
- If the input FastQ files are gzipped, then the output will be also gzipped.
- If the input FastQ files are not gzipped, you can enable
-zoption to force gzip compression.
--compressionto change the compression level (0~9), default is 2. The better the compression, the lower the speed.
--version show program's version number and exit -h, --help show this help message and exit
File (name) options
-1 READ1_FILE, --read1_file=READ1_FILE file name of read1, required. If input_dir is specified, then this arg is ignored. -2 READ2_FILE, --read2_file=READ2_FILE file name of read2, if paired. If input_dir is specified, then this arg is ignored. -7 INDEX1_FILE, --index1_file=INDEX1_FILE file name of 7' index. If input_dir is specified, then this arg is ignored. -5 INDEX2_FILE, --index2_file=INDEX2_FILE file name of 5' index. If input_dir is specified, then this arg is ignored. -d INPUT_DIR, --input_dir=INPUT_DIR the input dir to process automatically. If read1_file are input_dir are not specified, then current dir (.) is specified to input_dir -g GOOD_OUTPUT_FOLDER, --good_output_folder=GOOD_OUTPUT_FOLDER the folder to store good reads, by default it is the same folder contains read1 -b BAD_OUTPUT_FOLDER, --bad_output_folder=BAD_OUTPUT_FOLDER the folder to store bad reads, by default it is same as good_output_folder --read1_flag=READ1_FLAG specify the name flag of read1, default is R1, which means a file with name *R1* is read1 file --read2_flag=READ2_FLAG specify the name flag of read2, default is R2, which means a file with name *R2* is read2 file --index1_flag=INDEX1_FLAG specify the name flag of index1, default is I1, which means a file with name *I1* is index2 file --index2_flag=INDEX2_FLAG specify the name flag of index2, default is I2, which means a file with name *I2* is index2 file
-f TRIM_FRONT, --trim_front=TRIM_FRONT number of bases to be trimmed in the head of read. -1 means auto detect -t TRIM_TAIL, --trim_tail=TRIM_TAIL number of bases to be trimmed in the tail of read. -1 means auto detect --trim_pair_same=TRIM_PAIR_SAME use same trimming configuration for read1 and read2 to keep their sequence length identical, default is true lots of dedup algorithms require this feature -q QUALIFIED_QUALITY_PHRED, --qualified_quality_phred=QUALIFIED_QUALITY_PHRED the quality value that a base is qualifyed. Default 20 means base quality >=Q20 is qualified. -u UNQUALIFIED_BASE_LIMIT, --unqualified_base_limit=UNQUALIFIED_BASE_LIMIT if exists more than unqualified_base_limit bases that quality is lower than qualified quality, then this read/pair is bad. Default 0 means do not filter reads by low quality base count -p POLY_SIZE_LIMIT, --poly_size_limit=POLY_SIZE_LIMIT if exists one polyX(polyG means GGGGGGGGG...), and its length is >= poly_size_limit, then this read/pair is bad. Default is 35 -a ALLOW_MISMATCH_IN_POLY, --allow_mismatch_in_poly=ALLOW_MISMATCH_IN_POLY the count of allowed mismatches when evaluating poly_X. Default 5 means disallow any mismatches -n N_BASE_LIMIT, --n_base_limit=N_BASE_LIMIT if exists more than maxn bases have N, then this read/pair is bad. Default is 5 -s SEQ_LEN_REQ, --seq_len_req=SEQ_LEN_REQ if the trimmed read is shorter than seq_len_req, then this read/pair is bad. Default is 35
Debubble options (not suggested for regular tasks)
If you want to eliminate bubble artifact, turn debubble option on (this is slow, usually you don't need to do this):
--debubble enable debubble algorithm to remove the reads in the bubbles. Default is False --debubble_dir=DEBUBBLE_DIR specify the folder to store output of debubble algorithm, default is debubble --draw=DRAW specify whether draw the pictures or not, when use debubble or QC. Default is on
Barcoded sequencing options
--barcode=BARCODE specify whether deal with barcode sequencing files, default is on --barcode_length=BARCODE_LENGTH specify the designed length of barcode --barcode_flag=BARCODE_FLAG specify the name flag of a barcoded file, default is barcode, which means a file with name *barcode* is a barcoded file --barcode=BARCODE specify whether deal with barcode sequencing files, default is on, which means all files with barcode_flag in filename will be treated as barcode sequencing files
--qc_only enable this option, only QC result will be output, this can be much faster --qc_sample=QC_SAMPLE sample up to qc_sample when do QC, default is 1000,000 --qc_kmer=QC_KMER specify the kmer length for KMER statistics for QC, default is 8
Understand the report
AfterQCwill generate a QC folder, which contains lots of figures.
- For pair-end sequencing data, both read1 and read2 figures will be in the same folder with the folder name of read1's filename.
- For single-end sequencing data, it will still have
- For pair-end sequencing data,
Afterwill do an
overlap analysis. read1 and read2 will be overlapped when
read1_length + read2_length > DNA_template_length.
Shifu Chen, Tanxiao Huang, Yanqing Zhou, Yue Han, Mingyan Xu and Jia Gu. AfterQC: automatic filtering, trimming, error removing and quality control for fastq data. BMC Bioinformatics 2017 18(Suppl 3):80 https://doi.org/10.1186/s12859-017-1469-3