---
author: Sam White
toc-title: Contents
toc-depth: 5
toc-location: left
layout: post
title: Metagenomics Annotation - P.generosa Water Samples Using BLASTn on Mox and KronaTools Visualization to Compare pH Treatments
date: '2019-04-16 14:30'
tags:
  - Panopea generosa
  - geoduck
  - blastn
  - mox
  - metagenomics
categories:
  - 2019
  - Miscellaneous
---
Nearing the end of this quick metagenomics comparison of taxonomic differences between the two pH treatments (pH=7.1 and pH=8.2). Previously ran:

- [MEGAHIT assembly](https://robertslab.github.io/sams-notebook/posts/2019/2019-04-15-Metagenome-Assemblies---P.generosa-Water-Samples-Trimmed-HiSeqX-Data-Using-Megahit-on-Mox-to-Compare-pH-Treatments/)

- [MetaGeneMark gene prediction](https://robertslab.github.io/sams-notebook/posts/2019/2019-04-16-Metagenomics-Gene-Prediction---P.generosa-Water-Samples-Using-MetaGeneMark-on-Mox-to-Compare-pH-Treatments/)

Here's how the sample names breakdown:

| Sample | Develomental Stage (days post-fertilization) | pH Treatment |
|--------|-------------------------|--------------|
| MG1    | 13                      | 8.2          |
| MG2    | 17                      | 8.2          |
| MG3    | 6                       | 7.1          |
| MG5    | 10                      | 8.2          |
| MG6    | 13                      | 7.1          |
| MG7    | 17                      | 7.1          |

After this completes, I'll run [KronaTools](https://github.com/marbl/Krona/wiki/KronaTools) to get a rundown on taxonomic makeup of these two different pH treatments. I don't expect BLASTn to take terribly long (based on previous metagenomics runs wit this data set); I'd guess around 6hrs.

SBATCH script (GitHub):

- [20190416_metagenomics_pgen_blastn.sh](https://github.com/RobertsLab/sams-notebook/blob/master/sbatch_scripts/20190416_metagenomics_pgen_blastn.sh)

```shell
#!/bin/bash
## Job Name
#SBATCH --job-name=blastn_metagenomics
## Allocation Definition
#SBATCH --account=coenv
#SBATCH --partition=coenv
## Resources
## Nodes
#SBATCH --nodes=1
## Walltime (days-hours:minutes:seconds format)
#SBATCH --time=25-00:00:00
## Memory per node
#SBATCH --mem=120G
##turn on e-mail notification
#SBATCH --mail-type=ALL
#SBATCH --mail-user=samwhite@uw.edu
## Specify the working directory for this job
#SBATCH --workdir=/gscratch/scrubbed/samwhite/outputs/20190416_metagenomics_pgen_blastn

# Load Python Mox module for Python module availability

module load intel-python3_2017

# Document programs in PATH (primarily for program version ID)

date >> system_path.log
echo "" >> system_path.log
echo "System PATH for $SLURM_JOB_ID" >> system_path.log
echo "" >> system_path.log
printf "%0.s-" {1..10} >> system_path.log
echo ${PATH} | tr : \\n >> system_path.log


wd="$(pwd)"
threads=28

# Paths to programs
blast_dir="/gscratch/srlab/programs/ncbi-blast-2.8.1+/bin"
blastn="${blast_dir}/blastn"

# Paths to blastdbs
blastdb_dir="/gscratch/srlab/blastdbs/ncbi-nr-nt-v5"
blast_db="${blastdb_dir}/nt"

# Directory with metagenemark FastAs
fasta_dir="/gscratch/scrubbed/samwhite/outputs/20190416_metagenomics_pgen_metagenemark"


# Export BLAST database directory
export BLASTDB=${blastdb_dir}

# Loop through metagenemark nucleotide FastAs
# Create list of those FastAs for reference
# Parse out sample names
# Run BLASTn on each FastA
for fasta in ${fasta_dir}/*nucleotides.fasta
do
  echo ${fasta} >> input.fasta.list.txt
  no_ext=${fasta%%.*}
  sample_name=$(echo ${no_ext##*/})
  # Run blastx on Trinity fasta
  ${blastn} \
  -query ${fasta} \
  -db ${blast_db} \
  -max_target_seqs 1 \
  -outfmt "6 std staxids" \
  -evalue 1e-10 \
  -num_threads ${threads} \
  > ${wd}/${sample_name}.blastn.outfmt6
done
```


# RESULTS

Runtime was ~9hrs for these.

Output folder:

- [20190416_metagenomics_pgen_blastn/](http://gannet.fish.washington.edu/Atumefaciens/20190416_metagenomics_pgen_blastn/)

pH=7.1 BLASTn Output:

- [20190416_metagenomics_pgen_blastn/pH71.blastn.outfmt6](http://gannet.fish.washington.edu/Atumefaciens/20190416_metagenomics_pgen_blastn/pH71.blastn.outfmt6)

- 547,999 matches

- 2,188,436 seqs in input FastA

- ~25% of input seqs were matched

pH=8.2 BLASTn Output:

- [20190416_metagenomics_pgen_blastn/pH82.blastn.outfmt6](http://gannet.fish.washington.edu/Atumefaciens/20190416_metagenomics_pgen_blastn/pH82.blastn.outfmt6)

- 298,564

- 2,047,907 seqs in input FastA

- ~15% of input seqs were matched

Interactive taxonomic Krona Plot (HTML):

- [20190416_metagenomics_pgen_blastn/krona_megahit_MGM_blastn.html](http://gannet.fish.washington.edu/Atumefaciens/20190416_metagenomics_pgen_blastn/krona_megahit_MGM_blastn.html)

Here are just a couple snapshots of the plots to add some "spice" to this notebook entry:

---

##### pH=7.1 Bacteria

![pH=7.1 bacteria krona plot](https://github.com/RobertsLab/sams-notebook/blob/master/images/screencaps/20190417_krona_pH7.1_bacteria.png?raw=true)

---

##### pH=8.2 Bacteria

![pH=8.2 bacteria krona plot](https://github.com/RobertsLab/sams-notebook/blob/master/images/screencaps/20190417_krona_pH8.2_bacteria.png?raw=true)

---

##### pH=7.1 Intramacronucleata (ciliate subphylum)

![pH=7.1 Intramacronucleata krona plot](https://github.com/RobertsLab/sams-notebook/blob/master/images/screencaps/20190417_krona_pH7.1_Intramacronucleata.png?raw=true)

---

##### pH=8.2 Intramacronucleata (ciliate subphylum)

![pH=8.2 Intramacronucleata krona plot](https://github.com/RobertsLab/sams-notebook/blob/master/images/screencaps/20190417_krona_pH8.2_Intramacronucleata.png?raw=true)