Nextera_Dada2

A wrapper around the processes needed to analyse MiSeq reads with Dada2 when the libraries have been made with Nextera adapters I have done this in UNIX (macOSX10.13.6 High Sierra - I will test it in Linux, macOSX Catalina).

The idea is to separate reads by locus (if more than 1) and sample, remove the primers with cutadapt (quicker than doing it in R) and then use DADA2 within each sample and locus.

Why use this?

You can also do all these steps by yourself. The advantage of this approach is that it will not only process the samples, but will also generate Rmarkdown files to document the process that got you there. In case things go wrong at some stage, you can retake the analysis with the fastq files from the intermediate processes

Dependecies

• cutadapt (v2.1)
• R (v3.6 or above), Rstudio(if not then I think you need pandoc, knitr, rmarkdown)
• R packages:
  • tidyverse
  • DADA2
  • rmarkdown
  • stringr
  • digest
  • knitr
  • kableExtra

Installation

For this pipeline, go to https://github.com/ramongallego/Nextera_Dada2 and clone or download this repository. For the dependecies, I will update this with links that will break in the next month

Input files

You will need:

• A folder with all the fastq files to be processed.

• A metadata file with one row per sample and locus with the following columns:

  - Sample_name - The name that makes sense to you and your project (No spaces in the name would be better)
  - Well  - in the 96-well plate
  - Set - In case there are more than 96 samples (1, 2, 3)
  - Locus: The name of the locus you want to use (e.g. Leray_COI)
  - PrimerF: The nucleotide sequence of the forward primer - supports IUPAC characters 
  - PrimerR: Ditto for the reverse primer (also in 5' -> 3' direction)
  - i7_Index_Name: N701... See file Nextera_adapters_i7.csv in the data_sub subfolder.
  - i5_Index_Name: N503... See file Nextera_adapters_i5.csv in the data_sub subfolder
  - file1: it should match exactly the output of the Miseq.
  - file2: Same for the second read.
  

• An output folder.

Note on metadata and filenames

In case you don't want to introduce the filenames manually, there is an RMarkdown called "Generate.metadata.and.samplesheet.Rmd". The idea is to parse the information from your original metadata (Sample_name, Well, Locus, PrimerF, PrimerR, and i5 and i7 indices), parse it with the Nextera Indices files and generate a metadata.csv and a SampleSheet for the MiSeq to use. The newly generated metadata file will have filenames there, based on the MiSeq format <Sample_name> _L001_R [1-2] _001.fastq. By all means check that this is the format in which your MiSeq / Sequencing facility returns the filenames.

Output files

The pipeline will work in two stages (or three if you use the metadata and sample sheet generator). I have done this to make sure that you only have to run the cutadapt part once, and then you can reuse the output of cutadapt with different parameters in Dada2.

The cutadapt-wrapper

The first one will split the reads by locus (if there is more than one) and remove the primer from the sequences. It will deploy in your folder:

• A series of fastq files, two per sample and locus (Forward and Reverse reads)

• A new metadata file, that will have one row per sample and locus and the following columns:

  • Sample_name
  • Locus (the name from the original.metadata)
  • file1 (with the format Locus_original_file1_name.fastq)
  • file2 (with the same format)

And this is all you need to run the second part of the analysis

The Dada2 tailing

This part of the process will quality trim the reads, merge them and detect the Amplicon Sequence Variants using Dada2. The inputs required are the series of fastq files and the new metadata we generated from step1.

The outputs that you will obtain are:

• ASV table: includes three columns: Hash, Sample, nReads
• Hash_key, in .csv format, two columns: Hash, Sequence
• Hash key, in .fasta format

The program also spits out a table with the summary statistics, a log file, and a Markdown. It will also create a copy of the parameter and metadata files

Usage

The cutadapt-wrapper

Operates in the bash shell. So open the terminal and run

bash path\to\script\cutadapt.wrapper.sh path\to\folder\with\fastqs {metadata_file} {output_folder}

Just to be on the safe side, use absolute paths to all arguments.

The Dada2-wrapper

Open the file dada2.Rmd in Rstudio. Click on the dropdown option Knitr with parameters. Add your selections - the folder is the one you set as an output folder in cutadapt, the

Currently working in getting a browse function that allows you to navigate to your target folder