16S_illumina_preprocessreads

From Illumina raw PE reads (16S seq) to polished FASTQ files, ready to undergo taxonomical classification.

Reads information

Paired End reads obtained by Illumina sequencing, PE250. Primers for bacterial 16S rRNA gene V4 amplification:

• 515F: 5'-GTGCCAGCMGCCGCGGTAA-3'
• 806R: 5'-GGACTACHVGGGTWTCTAAT-3'

Barcodes are listed in 01.RawData/SampleSeq_info.xls. Reads are already demultiplexed and placed in individual folders in 01.RawData.

• *.raw_1.fq.gz [Read 1 sequences with barcodes and primers]
• *.raw_2.fq.gz [Read 2 sequences with barcodes and primers]
• *_1.fastq.gz [Read 1 sequences with barcode and primer removed]
• *_2.fastq.gz [Read 2 sequences with barcode and primer removed]
• *.extendedFrags.fastq.gz [Merged reads]

Steps and software

• Quality control: FastQC. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
• Trimming: AdapterRemoval. https://adapterremoval.readthedocs.io/en/stable/index.html Adapters at the 3' of reads, barcodes, low quality bases (minq=28) removed. --barcode-list barcodes.txt to point to the barcodes files Trimmomatic http://www.usadellab.org/cms/?page=trimmomatic Demultiplexed sequence files are subject to quality filtering using Trimmomatic [32] version 0.30, with a hard cutoff of PHRED score Q3 for 5′ and 3′ ends of the reads (parameters LEADING: 3 and TRAILING: 3), trimming of the 3′ end with a moving average score of Q15, with a window size of 4 bases (parameter SLIDINGWINDOW: 4:15), and removing any remaining reads shorter than 75% [10] of the original read length (for example, parameter MINLEN: 112 for 150 bp long reads, MINLEN: 187 for 250 bp long reads or MINLEN: 225 for 300 bp long reads). Reads with any ambiguous base calls are discarded. (from https://doi.org/10.1371/journal.pone.0114804)
• Merging: UPARSE or AdapterRemoval
• Chimera removal: UCHIME
• Filter by length
• OTU picking (SUMACLUST groups reads by 97% similarity threshold, as this is what is usually considered as the identity threshold to group sequences from the same species, and then picks a representative consensus sequence for that cluster), or ASV prediction (by calculation of the probability that a given sequenced read is due to error or if it is a real sequence, and counts identical sequences finally providing ASVs)
• Taxonomy assignment to the OTUs or ASVs sequences (seqs can be blasted using QIIME against a reference database such as SILVA, rrnDB, GreenGenes, NCBI nucleotide (nt))