Trun

Vorderrhein

This is the amplicon data analysis of my early SNF project at a hatchery in Trun, Switzerland. Briefly, I washed fish sperm with antibiotics and then used it to fertilize eggs. I am contrasting offspring of the same parents that originated from washed sperm with offspring that originated from 'natural' sperm.

I am reaching the following landmarks

1. Preprocessing of reads  

• I did the first quality control with dada2
• I did two rounds of sequencing so I performed two different rounds of quality control before merging the two datasets.
• The following flags were used for quality control: the following flags for filterAndTrim(): truncLen=c(280,200), maxEE=c(2,5), maxN=0
• To see the effect of these flags, I also ran a second, more relaxed quality control: truncLen=c(280,200), maxN=0
• As a reference for the reads I used SILVA (silva_nr_v132_train_set.fa).
• However, I also used GREENGENES because this is the reference I had used during my PhD and I wanted to compare my 'current' figh egg bacteria to the ones of previous projects (gg_13_8_train_set_97.fa).  

The script for this was named: "Trun_seq-prep.Rmd" --> All scripts here are RMarkdown script and can be found in the directory RMarkdown

   

2. Analysis of the effects of treatment - Sequencing Run 1 / Vals  

• Using only the reads from the first sequencing run I made a subset of embryos in both treatments (sham-treatment & antibacterial cocktail)
• Several Ordination metrices to visualize the data
• DESeq2 to find statistically significant differences between bacteria on embryos in the two different treatment groups  

The script for this was named: "Vals_APS_Run1_Analysis.Rmd"

   

3. Analysis of the effects of treatment - Sequencing Run 1 / Tavanasa  

• Using only the reads from the first sequencing run I made a subset of embryos in both treatments (sham-treatment & Virkon S)
• Several Ordination metrices to visualize the data
• DESeq2 to find statistically significant differences between bacteria on embryos in the two different treatment groups  

The script for this was named: "Tavanasa_APS_Run1_Analysis.Rmd"

   

4. Analysis of the effects of treatment - Both Sequencing Runs Combined / Vals  

• After qc and separate error profiling, data from both sequencing runs were combined.
• I made a subset of embryos in both treatments (sham-treatment & antibacterial cocktail)
• Several Ordination metrices to visualize the data
• DESeq2 to find statistically significant differences between bacteria on embryos in the two different treatment groups --> Here I controlled for sequencing run with: diagdds = phyloseq_to_deseq2(kostic.Vals, ~ Sequencing + Treatment)  

The script for this was named: "whole_dataset_Vals_APS_analysis.Rmd"

   

5. Is there vertical transmission??? - Both Sequencing Runs Combined / Vals  

• For each male in Vals and Tavanasa (n=12) look for differences in offspring (combined large dataset and DESeq). Then search for these sequences in milt.
• I ran DESeq2 with Sequencing Run and Treatment for each family/male.
• Then I plotted the significantly different bacteria in all milt samples (barplot).

The script for this was named: "vert_trans_Vals.Rmd"

   

6. Characterize milt bacteria

The script for this was named: "milt.Rmd"

   

My temporary to do list:

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And here is my cheat sheet:

https://guides.github.com/features/mastering-markdown/