Hands On

Before running PIMBA, let’s examine whether the base quality of the dataset you have chosen is good or bad. Go to the folder disciplina_metabarcoding/your_dataset/ and replace your_dataset with your chosen dataset. If you use the ls command, you can list and view all the FASTQ files in the dataset.

cd disciplina_metabarcoding/your_dataset/
ls

Considering you have installed FastQC successfully, just type the command below and wait for FastQC to analyze your dataset.

mkdir fastqc_output
fastqc -o fastqc_output *

FastQC generated the HTML files that will allow you to see the base quality of your data.

Now let’s PIMBA!

The first step in running PIMBA is to prepare your data. PIMBA can be used with paired-end or single-end reads (the latter being single-index or dual-index). The output will be a Fasta file that can be used in the next step. In this tutorial, we will use only paired-end reads, but you can check at the end how to prepare single-end reads with dual and single-indexes.

Paired-end reads

Please place all your forward and reverse reads in one directory and ensure that forward reads contain “R1” and reverse reads contain “R2” in the file name.

./pimba_prepare.sh illumina <rawdata_dir> <output_reads> <num_threads> <adapters.txt> <min_length> <min_phred>

rawdata_dir = path with all the R1 and R2 reads file;
output_reads = name for the output file;
num_threads = number of threads;
adapters.txt = tab separated 2-column file with all adapters and primers used for sequencing;
min_lenght = The minimum length of the read after quality treatment;
min_phred = Minimum PHRED score of a read after quality treatment.

Example:

  ./pimba_prepare.sh ilumina rawdata/ AllSamples 24 adapters.txt 100 20

When pimba_prepare finishes, it will create a FASTA file with the name of the parameter output_reads you have chosen. Let’s assume it is called AllSamples.
This FASTA file gatheres all reads from all samples in a unique file (hence the name AllSamples). If you wish to know how many reads remained after quality treatment in this file, you can use the command bellow:

  grep -c ">" AllSamples.fasta

Another created FASTA file ends with `withSingleton. This file contains the same reads that the other FASTA file, but also with the reads that have good quality, but were not paired.

single-end reads with dual-index:

In case your single-end reads have been multiplexed with dual-index, use the following command:

./pimba_prepare.sh iontorrent-dualindex  <rawdata.fastq> <barcodes.txt> <barcodes_reverse.txt> <barcodes.fasta> <barcodes_for_dir> <Primer_forward> <Primer_reverse> <num_threads> <output_name> <min_length> <min_phred>

rawdata.fastq = single file with all the reads to demultiplex;
barcodes.txt = barcodes used as index in the 3’ of the fragment;
barcodes_reverse.txt = reverse complement of barcodes.txt;
barcodes.fasta = fasta file for the barcodes.txt;
barcodes_for_dir = path to all the barcodes.fasta and barcodes.txt used in the 5’ of the fragment. Each 3’ barcode must have a fasta and txt file with all the associated 5’ barcodes;
Primer_forward = sequence of the forward primer;
Primer_reverse = sequence of the reverse primer;
num_threads = number of threads;
output_name = name for the output fastq file;
min_length = The minimum length of the read after quality treatment;
min_phred = Minimum PHRED score of a read after quality treatment.

Example:

./pimba_prepare.sh iontorrent-dualindex rawdata_chip-3-4.fastq barcodes.txt barcodes_reverse.txt barcodes.fasta barcode_for/ TCCACTAATCACAAAGANATNGGNAC AGAAAATCATAATNAANGCNTGNGC 24 AllSamplesCOI.fastq 100 20

single-end reads with single-index:

In case your single-end reads have been multiplexed with single-index, use the following command:

./pimba_prepare.sh iontorrent-singleindex <rawdata.fastq> <prefix> <barcodes.txt> <barcodes.fasta> <primer> <num_threads> <output_name> <min_length> <min_phred>

rawdata.fastq = single file with all the reads to demultiplex;
prefix = name that will precede the barcodes names;
barcodes.txt = barcodes used as index in the 5’ of the fragment;
barcodes.fasta = fasta file for the barcodes.txt;
primer = primer sequence;
num_threads = number of threads;
output_name = name for the output fastq file;
min_lenght = The minimum length of the read after quality treatment;
min_phred = Minimum PHRED score of a read after quality treatment.

Example:

./pimba_prepare.sh iontorrent-singleindex SN1-45.fastq SN1-45-ITS barcodes.txt barcodes.fasta ATGCGATACTTGGTGTGAAT 24 AllSamples

Questões para o relatório

1 - Escolha duas amostras específicas do seu dataset