---
author: Sam White
toc-title: Contents
toc-depth: 5
toc-location: left
layout: post
title: Data Wrangling - C.bairdi NanoPore 6129-403-26 Quality Filtering Using NanoFilt on Mox
date: '2020-09-28 19:42'
tags:
  - Chionoecetes bairdi
  - Tanner crab
  - NanoFilt
  - nanopore
  - 6129-403-26
  - mox
categories:
  - 2020
  - Miscellaneous
---
Last week, I [ran all of our Q7-filtered _C.baird_ NanoPore reads through MEGAN6 to evaluate the taxonomic breakdown (on 20200917)](https://robertslab.github.io/sams-notebook/posts/2020/2020-09-17-Taxonomic-Assignments---C.bairdi-NanoPore-Reads-Using-DIAMOND-BLASTx-on-Mox-and-MEGAN6-daa2rma-on-swoose/) and noticed that there were a large quantity of bases assigned to _E.canceri_ (a [known microsporidian agent of infection in crabs](https://academic.oup.com/icesjms/article/65/9/1578/629544)) and _Aquifex sp._ (a genus of thermophylic bacteria), in addition to the expected _Arthropoda_ assignments. Notably, _Alveolata_ assignments were remarkably low.

Since our NanoPore data was a combination of an uninfected sample and a _Hematodinium_-infected sample, I decided to find out which of the two samples was contributing to the _E.canceri_ and _Aquifex sp._ assignments. To do so, first I need to generate a singular Q7-filtered FastQ using [NanoFilt](https://github.com/wdecoster/nanofilt).

This set was the _Hematodinium_-infected hemolymph sample:

- [C.bairdi-6129_403_26](https://robertslab.github.io/sams-notebook/posts/2020/2020-09-04-Data-Wrangling---NanoPore-Fast5-Conversion-to-FastQ-of-C.bairdi-6129_403_26-on-Mox-with-GPU-Node/)

Job was run on Mox.


SBATCH script (GitHub):

- [20200928_cbai_nanofilt_Q7_6129_403_26_nanopore-data.sh](https://github.com/RobertsLab/sams-notebook/blob/master/sbatch_scripts/20200928_cbai_nanofilt_Q7_6129_403_26_nanopore-data.sh)


```shell
#!/bin/bash
## Job Name
#SBATCH --job-name=cbai_nanofilt_Q7_6129_403_26_nanopore-data
## Allocation Definition
#SBATCH --account=coenv
#SBATCH --partition=coenv
## Resources
## Nodes
#SBATCH --nodes=1
## Walltime (days-hours:minutes:seconds format)
#SBATCH --time=10-00:00:00
## Memory per node
#SBATCH --mem=200G
##turn on e-mail notification
#SBATCH --mail-type=ALL
#SBATCH --mail-user=samwhite@uw.edu
## Specify the working directory for this job
#SBATCH --chdir=/gscratch/scrubbed/samwhite/outputs/20200928_cbai_nanofilt_Q7_6129_403_26_nanopore-data




###################################################################################
# These variables need to be set by user

# Load Anaconda
# Uknown why this is needed, but Anaconda will not run if this line is not included.
. "/gscratch/srlab/programs/anaconda3/etc/profile.d/conda.sh"


# Activate the NanoPlot Anaconda environment
conda activate nanofilt_2.6.0_env


# Declare array
raw_reads_dir_array=()

# Paths to reads
raw_reads_dir_array=(
"/gscratch/srlab/sam/data/C_bairdi/DNAseq/ont_FAL86873_d8db260e_cbai_6129_403_26"
)

# FastQ concatenation filename
fastq_cat=20200928_cbai_nanopore_6129_403_26.fastq

fastq_filtered=20200928_cbai_nanopore_6129_403_26_quality-7.fastq

# Paths to programs
nanofilt=NanoFilt

# Set mean quality filter (integer)
quality=7

###################################################################################


# Exit script if any command fails
set -e

# Inititalize array
programs_array=()

# Programs array
programs_array=("${nanofilt}")


# Loop through NanoPore data directories
# to run NanoPlot, FastQC, and MultiQC
for directory in "${raw_reads_dir_array[@]}"
do

  # Find all FastQ files and concatenate into singel file
  while IFS= read -r -d '' filename
  do
    # Concatenate all FastQ files into single file
    # for NanoFilt and generate MD5 checksums
    echo "Now concatenating ${filename} to ${fastq_cat}..."
    cat "${filename}" >> ${fastq_cat}
    echo "Concatenation of ${filename} to ${fastq_cat} complete."

    # Create checksums file
    echo "Now generating checksum for ${filename}..."
    echo ""
    md5sum "${filename}" >> fastq_checksums.md5
    echo "Checksum for ${filename} complete."
    echo ""

  done < <(find "${directory}" -name "*.fastq" -type f -print0)

done

# Generate MD5 checksum for concatenated FastQ file
echo "Now generating checksum for ${fastq_cat}..."
echo ""
md5sum "${fastq_cat}" >> fastq_checksums.md5
echo "checksum for ${fastq_cat} complete."
echo ""

# Run NanoFilt
## Sets readtype to 1D (default)
## Filters on mean quality >= 7 (ONT "standard")
## FYI: seems to require piping stdin (i.e. cat fastq |)to NanoFilt...
echo "Running ${programs_array[nanofilt]}"
echo ""
cat ${fastq_cat} \
| ${programs_array[nanofilt]} \
--readtype 1D \
--quality ${quality} \
> ${fastq_filtered}
echo "${programs_array[nanofilt]} complete."
echo ""

# Generate MD5 checksum for concatenated FastQ file
echo "Now generating checksum for ${fastq_filtered}..."
echo ""
md5sum "${fastq_filtered}" >> fastq_checksums.md5
echo "checksum for ${fastq_filtered} complete."
echo ""

# Capture program options
for program in "${!programs_array[@]}"
do
	{
  echo "Program options for ${programs_array[program]}: "
	echo ""
	${programs_array[program]} -h
	echo ""
	echo ""
	echo "----------------------------------------------"
	echo ""
	echo ""
} &>> program_options.log || true
done

# Document programs in PATH (primarily for program version ID)
{
date
echo ""
echo "System PATH for $SLURM_JOB_ID"
echo ""
printf "%0.s-" {1..10}
echo "${PATH}" | tr : \\n
} >> system_path.log
```

---

# RESULTS

Runtime was very fast, ~4.5mins:

![NanoFilt runtime on mox for 6129-403-26](https://github.com/RobertsLab/sams-notebook/blob/master/images/screencaps/20200928_cbai_nanofilt_Q7_6129_403_26_nanopore-data_runtime.png?raw=true)

Output folder:

- [20200928_cbai_nanofilt_Q7_6129_403_26_nanopore-data/](https://gannet.fish.washington.edu/Atumefaciens/20200928_cbai_nanofilt_Q7_6129_403_26_nanopore-data/)


Q7 Filtered FastQ file (2.1GB):

- [20200928_cbai_nanofilt_Q7_6129_403_26_nanopore-data/20200928_cbai_nanopore_6129_403_26_quality-7.fastq](https://gannet.fish.washington.edu/Atumefaciens/20200928_cbai_nanofilt_Q7_6129_403_26_nanopore-data/20200928_cbai_nanopore_6129_403_26_quality-7.fastq)

  - MD5 checksum:

    - `803dbec872739826e3d6c0dc5cd4e678`

Will get taxonomy assignments using MEGAN6.