adapt_find

adapt_find identifies adapters sequences from single-end raw sequencing files in FASTQ format. To run the script on raw FASTQ files, the following dependencies are required: PYTHON pandas module, scipy, numpy, cutadapt, blast, and bowtie.

adapt_find usage

$ python adapt_find.py <sequencing_platform> [-- min_len] [-- max_len] [-- index] [-- input_path] [-- output_path] [-- files]

"sequencing_platform" is a mandatory argument and has to be specified. A list of arguments and available options can be found here. The results from testing adapt_find on sequencing files can be found in the output file - master_adapters.csv. This file has the information regarding the predicted adapters, type of adapter and % of reads with adapters .

adapt_find usage examples

For Illumina as sequencing platform and if the current directory has all FASTQ files

$ python adapt_find.py ILLUMINA

For Illumina as sequencing platform and to specify filenames explicitly

$ python adapt_find.py ILLUMINA --files filename1.fastq filename2.fastq

For Illumina as sequencing platform and to specify path to folder containing FASTQ files

$ python adapt_find.py ILLUMINA --input_path path/to/folder

For Ion Torrent as sequencing platform and if the current directory has all FASTQ files

$ python adapt_find.py ION_TORRENT

For SOLiD as sequencing platform and if the current directory has all FASTQ files

$ python adapt_find.py SOLID

Note: cutadapt version 1.18 or an earlier version has to be used for SOLiD sequencing files, as colorspace format is not supported from cutadapt version 2.0 onwards.

random_mer

random_mer identifies random_mer sequences from single-end adapter-trimmed FASTQ files. To run the script on raw FASTQ files, the following dependencies are required: PYTHON pandas module, cutadapt, blast, and bowtie.

random_mer usage

$ python random_mer.py path/to/genome/file.fa [--index path/to/index][--input_path path/to/folder][--output_path path/to/folder] [--files list of files]

Example

For running random_mer on all FASTQ files in the current working directory

$ python random_mer.py ~/genome/file.fa

For running random_mer only on selected FASTQ files

$ python random_mer.py ~/genome/file.fa --files filename1.fastq filename2.fastq

fastqc_parser

fastqc_parser can parse the output of FASTQC reports: consolidate the results and extract the relevant information from all the output files. Depending on input arguments, fastqc_parser can either run FASTQC on raw FASTQ files and then parse the output. Alternatively, it can be run directly on FASTQC output files. To run this script, the following dependencies are required: PYTHON, pandas module, and FASTQC.

fastqc_parser usage

$ python fastqc_parser.py --run_fastqc [--fastqc_folder] [-- index] [--param] [-- input_path] [-- output_path] [-- files]

Example

For running FASTQC and parse the FASTQC reports.

$ python fastqc_parser.py

Only parse fastqc report

$ python fastqc_parser.py --run_fastqc NO --fastqc_folder /path/to/fasqc_report

The different arguments and options for the three tools are described in the manual