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Auto-q

Qiime Analysis Automating Script.

This script is written to reduce the effort and time for Qiime analysis. It is designed to work on illumina pair-end reads FASTQ files.

Installation:

  1. This script is designed to be installed in Qiime virtual machine. To install QIIME, please check this link: http://qiime.org/install/install.html

  2. Install usearch: follow the instructions in this link https://www.drive5.com/usearch/download.html . QIIME works with version 6.1.544 32 bit. Please download it.

  • Create bin/ folder in qiime home folder in the virtual machine /home/qiime/bin
  • Copy usearch6.1.544_i86linux32 to /home/qiime/bin/usearch/ and rename the file to usearch61
  • Make usearch61 executable using this command
$ chmod +x usearch61
  1. Install BBtools from https://sourceforge.net/projects/bbmap/ or you can run these commands in /home/qiime/bin/ folder:
$ wget https://sourceforge.net/projects/bbmap/files/BBMap_37.66.tar.gz
$ tar -zxvf BBMap_37.66.tar.gz && rm BBMap_37.66.tar.gz
  1. Install Auto-q by executing these commands in /home/qiime/bin/ :
$ git clone https://github.com/Attayeb/auto-q/ && rm -rf auto-q/.git

Edit .bashrc in your home directory and add the following line at the end:

$ echo 'export PATH="/home/qiime/bin/auto-q/:/home/qiime/bin/bbtools/:/home/qiime/bin/usearch/:$PATH"' >> ~/.bashrc
  1. If you want to use SILVA database you can download it from here https://www.arb-silva.de/no_cache/download/archive/qiime/ use the latest one Silva_128_release.tgz, after downloading this file decompress it.
  2. Modify qiime.cfg file to indicate the folders of your database. The default preinstalled greengenes folder is: /home/qiime/lib/python2.7/site-packages/qiime_default_reference/gg_13_8_otus/ Please modify this file according to your settings.

Sequence files preparation:

Fastq files:

FASTQ files are named with the sample name and the sample number, which is a numeric assignment based on the order that the sample is listed in the sample sheet. Example:

R1 → SampleName_S1_L001_R1_001.fastq.gz

R2 → SampleName_S1_L001_R2_001.fastq.gz

keep a copy of the original compressed fastq files in a safe folder and use another copy after decompressing them. To decompress the fastq.gz file use this commnad inside the folder in terminal:

$ gunzip *.fastq.gz

Auto-q determines R1 and R2 using the names of the files, please do not modify the file names.

Steps of analysis:

usage: auto-q.py [-h] -i Input folder -o Output folder
                 [-t trim_phred_threshold] [-p fastq-join p]
                 [--adapter ADAPTER_REFERENCE] [-b starting step] [-s stop at]
                 [-j joining method] [-m] [-q quality control threshold]
                 [--continuation_reference newref_seq.fna]
                 [--continuation_otu_id C_OTU_ID] [-r Reference database]
                 [-c Configuration file name] [-a Mapping file name]
                 [--parameter_file_name PARAMETER_FILE_NAME]
                 [-n Number of jobs] [-e Sampling depth]
                 [--remove_intermediate_files] [--ml Minimum length]
                 [--primer-trim-f Primer Trim] [--primer-trim-r Primer Trim]



optional arguments:
  -h, --help            show this help message and exit
  -i Input folder       the input sequences filepath (fastq files) [REQUIRED]
  -o Output folder      the output directory [REQUIRED]
  -t trim_phred_threshold
                        phred quality threshold for trimming [default: 12]
  -p fastq-join p       fastq-join's percentage of mismatch [default: 16]
  --adapter ADAPTER_REFERENCE
                        Adapters reference file
  -b starting step      starting the analysis in the middle: (otu_picking),
                        (diversity_analysis), (chimera_removal)
  -s stop at            terminate the analysis at this step [choices:
                        (merging), (quality_control), (chimera_removal))
  -j joining method     choose the merging method (fastq-join) or (bbmerge)
                        [default: fastq-join]
  -m                    Assign maxloose to be true for bbmerge [default:
                        False]
  -q quality control threshold
                        quality control phred threshold [default: 19]
  --continuation_reference newref_seq.fna
                        reference sequence for continuation. If you want to
                        continue analysis using the reference data set from
                        previous analysis. you can find it in the last sample
                        otus folder new_refseqs.fna
  --continuation_otu_id C_OTU_ID
                        continuation reference new otus ids
  -r Reference database
                        silva, greengenes [default: silva]
  -c Configuration file name
                        Configuration file name [default: qiime.cfg]
  -a Mapping file name  Mapping file name
  --parameter_file_name PARAMETER_FILE_NAME
                        The name of the parameter file [if not assigned is
                        automatically produced using configuration file
  -n Number of jobs     Specify the number of jobs to start with [default: 2]
  -e Sampling depth     sampling depth for diversity analyses [default: 10000]
  --remove_intermediate_files
                        To remove intermediate files, to reduce the disk space
  --ml Minimum length   Minimum length of reads kept after merging [default:
                        380]
  --primer-trim-f Primer Trim
                        length of the forward primer [17]
  --primer-trim-r Primer Trim
                        length of the reverse primer [21]

Examples:

Using silva database:
$ auto-q.py -i /data/experiment1/fastqs/ -o /data/experiment1/results/ -t 12 -p 10 -r silva -n 10 -e 5000 -c /bin/auto-q/qiime.cfg
Stop at merging step:
$ auto-q.py -i /data/experiment1/fastqs/ -o /data/experiment1/results/ -t 10 -p 16 -s merging -n 10 -c /bin/auto-q/qiime.cfg
Begin analysis with otu picking using fasta files after chimera removal:
$ auto-q.py -i /data/experiment1/results/chi/ -o /data/experiment1/results/ -b otu_picking -n 10 -c /bin/auto-q/qiime.cfg
If not going run the script in parallel use (-n 1):
$ auto-q.py -i /data/experiment1/results/chi/ -o /data/experiment1/results/ -b otu_picking -n 1 -c /bin/auto-q/qiime.cfg

Results:

Full analysis output folder will has 7 subfolders:

Folder name content
others\ log file, Mapping file, parameter file
trimmed\ fastq files after trimming
merged\ fastq files after merging pair reads
qc\ fasta files after quality step
chi\ fastq files after chimera removed
otus\ picked otus standard Qiime output
div\ diversity analyses results

Stop at:

How to cite:

Mohsen, A., Park, J., Chen, YA., Kawashima, H., Mizuguchi, K., Impact of quality trimming on the efficiency of reads joining and diversity analysis of Illumina paired-end reads in the context of QIIME1 and QIIME2 microbiome analysis frameworks. BMC Bioinformatics 20, 581 (2019). https://doi.org/10.1186/s12859-019-3187-5

@article{Mohsen_Park_Chen_Kawashima_Mizuguchi_2019, title={Impact of quality trimming on the efficiency of reads joining and diversity analysis of Illumina paired-end reads in the context of QIIME1 and QIIME2 microbiome analysis frameworks}, volume={20}, ISSN={1471-2105}, DOI={10.1186/s12859-019-3187-5}, number={1}, journal={BMC Bioinformatics}, author={Mohsen, Attayeb and Park, Jonguk and Chen, Yi-An and Kawashima, Hitoshi and Mizuguchi, Kenji}, year={2019}, month={Nov}, pages={581} }