This tutorial describes a standard micca pipeline for the analysis of single-end amplicon data. This pipeline is intended for different platforms, such as Roche 454, Illumina MiSeq/HiSeq and Ion Torrent. Although this tutorial explains how to apply the pipeline to 16S rRNA amplicons, it can be adapted to others markers gene/spacers, e.g. Internal Transcribed Spacer (ITS), 18S or 28S.
Table of Contents
The dataset used in this tutorial is taken from the Barelli et al. paper Habitat fragmentation is associated to gut microbiota diversity of an endangered primate: implications for conservation (https://doi.org/doi:10.1038/srep14862). The dataset contains only a subset of the entire study (Mwanihana samples only) for a total of 15 samples (in FASTQ format) and 235179 16S rRNA amplicon reads (V1-V3 hypervariable regions, 27F 5’-AGAGTTTGATCMTGGCTCAG, 533R 5’-TTACCGCGGCTGCTGGCAC). The 454 pyrosequencing was carried out on the GS FLX+ system using the XL+ chemistry.
Open a terminal, download the data and prepare the working directory:
wget ftp://ftp.fmach.it/metagenomics/micca/examples/mwanihana.tar.gz tar -zxvf mwanihana.tar.gz cd mwanihana
Now the FASTQ files must be merged in a single file. This operation can be performed with the merge command. Sample names will be included into the sequence indentifiers.
micca merge -i fastq/*.fastq -o merged.fastq
Segments which match PCR primers should be now removed. Typical Roche 454 reads start with a sequence key (e.g. TCAG) followed by the barcode (if it was not previously removed) and the forward primer. For these types of data (and in general, for single-end sequencing) we recommend to trim both forward reverse primers and discard reads that do not contain the forward primer. Moreover, sequence preceding (for the forward) or succeding (for the reverse, if found) primers should be removed:
These operations can be performed with the trim command:
micca trim -i merged.fastq -o trimmed.fastq -w AGAGTTTGATCMTGGCTCAG -r GTGCCAGCAGCCGCGGTAA -W
-W/--duforward ensures that reads that do not contain
the forward primer will be discarded.
Do not use the
-R/--dureverse with single-end reads.
Producing high-quality OTUs requires high-quality reads. The filter command filters sequences according to the maximum allowed expected error (EE) rate %. We recommend values <=1%. Moreover, to obtain good results in clustering (see otu), reads should be truncated at the same length when they cover partial amplicons or if quality deteriorates towards the end (common when you have long amplicons in 454 or Illumina single-end sequencing).
The command filterstats reports the fraction of reads that would pass for each specified maximum expected error (EE) rate %:
micca filterstats -i trimmed.fastq -o filterstats
Open the PNG file
In this case we are interested in the plot below (minimum length filtering +
truncation). A truncation length of 350 and a maximum error rate of 0.5%
seems to be a good compromise between read read length, expected error rate and
number of reads remaining. Inspecting the file
filterstats/trunclen_stats.txt, you can see that more than 92% reads
will pass the filter:
L 0.25 0.5 0.75 1.0 1.25 1.5 ... 349 78.905 92.472 97.425 99.135 99.705 99.897 350 78.639 92.385 97.389 99.126 99.704 99.896 351 78.369 92.300 97.357 99.116 99.700 99.892 ...
To obtain general sequencing statistics, run stats.
To characterize the taxonomic structure of the samples, the sequences are now organized into Operational Taxonomic Units (OTUs) at varying levels of identity. An identity of 97% represent the common working definition of bacterial species. The otu command implements several state-of-the-art approaches for OTU clustering, but in this tutorial we will focus on the de novo greedy clustering (see OTU picking and Denoising):
micca otu -i filtered.fasta -o denovo_greedy_otus -d 0.97 -c -t 4
The otu command returns several files in the output directory,
including the OTU table (
otutable.txt) and a FASTA file containing the
representative sequences (