Prerequisites

1. Python 2.7+
2. Java

Installation

New way (Linux and Mac): grab the latest release, extract, then add to PATH. You should be able to execute shi7.py on the commandline.

How to add to PATH? Well, like this:

echo 'PATH=$PATH: <path_to_binary>' >> ~/.bashrc
. ~/.bashrc

Usage examples:

Assuming you have a bunch of fastq files, of forward and reverse reads, split up by sample, that have Nextera adaptors:

shi7.py -i MyFastQFolder -o MyOutputFolder --adaptor Nextera

Assuming you only have R1 reads (no paired end):

shi7.py -i MyFastQFolder -o MyOutputFolder --adaptor Nextera -SE

If you have V4 16S metagenomic reads, you can get fancier:

-m 285 -M 300

This sets the minimum read length to 285 and the maximum to 300 when stitching, which is the canonical HMP V4 16S primer coverage region. This can be a powerful QC step in and of itself. Note: if using the EMP V4 protocol, omit these arguments.

If you have shotgun sequences, you might want to try not stitching (we recommend trying first and seeing how many stitch -- see the percent combined reported in the shi7.log file):

--flash False

Including --drop_r2 True here returns only the R1 reads.

We recommend the following format for sequence file names:

sampleID_other_information_R1.fastq
sampleID_other_information_R2.fastq

Then, using strip_underscore True will return processed reads with just the sampleID, simplifying downstream processing. For example, an efficient command for non-stitching shotgun sequences:

shi7.py -i MyFastQFolder -o MyOutputFolder --adaptor Nextera --flash False --strip_underscore True --drop_r2 True

Cite

To cite SHI7: Al-Ghalith GA, Ang K, Hillmann B, Knights D. (2017). SHI7: A Streamlined short-read iterative trimming pipeline. DOI:10.5281/zenodo.808832

Installation (old way)

These installation instructions are streamlined for Linux. The tool SHI7EN is installable on OSX/Windows with a few minor tweaks to this tutorial. This package requires anaconda, which is a system agnostic package and virtual environment manager. Follow the installation instructions for your system at http://conda.pydata.org/miniconda.html.

Once anaconda is installed, create a new virtual environment with python3.

conda create -n shi7en python=3

Now activate the environment.

# OSX, Linux
source activate shi7en

With the shogun environment activated, install the developmental SHI7EN toolchain.

# If you want to use flash
conda install -c bioconda flash

# If you want to use trimmomatic
conda install -c bioconda trimmomatic

# Install shi7en
pip install git+https://github.com/knights-lab/shi7en --upgrade --no-cache-dir

With the flags provided to pip, copying and pasting any of these commands will redo the installation if a failure happened.

The final step of the procedure is to add the binary shi7en_trimmer to your path. The binary is available on the release page. It is either ninja_shi7_linux or ninja_shi7_mac, depending on your machine. Please rename it to shi7en_trimmer. The tutorial for adding the binary to your path is shown as following:

echo 'PATH=$PATH: <path_to_binary>' >> ~/.bashrc
. ~/.bashrc

Example

If your binary is in your /home/username/Downloads/shi7en_trimmer, you can add the binary to your path in this way:

echo 'PATH=$PATH:/home/username/Downloads/shi7en_trimmer' >> ~/.bashrc
.~/.bashrc

Now that everything is installed, the command 'shi7en' will be on your path when the conda environment is active. Here is the helpfile for the command:

$ shi7en --help
shi7en --help
usage: shi7en -i <input> -o <output> -t_trim <threads>...

This is the commandline interface for shi7en

optional arguments:
  -h, --help            show this help message and exit
  --gotta_split {True,False}
                        Split one giant fastq (well, one pair -- an R1 and R2)
                        into samples
  --debug               Enable debug (default: Disabled)
  --adaptor {None,Nextera,TruSeq3,TruSeq2,TruSeq3-2}
                        Set the type of the adaptor (default: None)
  -SE                   Run in Single End mode (default: Disabled)
  --flash {True,False}  Enable (True) or Disable (False) FLASH stiching
                        (default: True)
  --trim {True,False}   Enable (True) or Disable (False) the TRIMMER (default:
                        True)
  --allow_outies {True,False}
                        Enable (True) or Disable (False) the "outie"
                        orientation (default: True)
  --convert_fasta {True,False}
                        Enable (True) or Disable (False) the conversion of
                        FASTQS to FASTA (default: True)
  --combine_fasta {True,False}
                        Enable (True) or Disable (False) the FASTA append mode
                        (default: True)
  --shell               Use shell in Python system calls, NOT RECOMMENDED
                        (default: Disabled)
  -i INPUT, --input INPUT
                        Set the directory path of the fastq directory
  -o OUTPUT, --output OUTPUT
                        Set the directory path of the output (default: cwd)
  -t THREADS, --threads THREADS
                        Set the number of threads (default: 4)
  -m MIN_OVERLAP, --min_overlap MIN_OVERLAP
                        Set the minimum overlap length between two reads. If
                        V4 set to 285 (default: 20)
  -M MAX_OVERLAP, --max_overlap MAX_OVERLAP
                        Set the maximum overlap length between two reads. If
                        V4 set to 300 (default: 700)
  -trim_l TRIM_LENGTH, --trim_length TRIM_LENGTH
                        Set the trim length (default: 150)
  -trim_q TRIM_QUAL, --trim_qual TRIM_QUAL
                        Set the trim qual (default: 20)