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. 2015 Aug;6(8):973-980.
doi: 10.1111/2041-210X.12399. Epub 2015 May 25.

PIPITS: an automated pipeline for analyses of fungal internal transcribed spacer sequences from the Illumina sequencing platform

Hyun S Gweon  1 , Anna Oliver  1 , Joanne Taylor  2 , Tim Booth  1 , Melanie Gibbs  1 , Daniel S Read  1 , Robert I Griffiths  1 , Karsten Schonrogge  1
Affiliations

PIPITS: an automated pipeline for analyses of fungal internal transcribed spacer sequences from the Illumina sequencing platform

Hyun S Gweon et al. Methods Ecol Evol. 2015 Aug.

Abstract

Studying fungal biodiversity using data generated from Illumina MiSeq sequencing platforms poses a number of bioinformatic challenges with the analysis typically involving a large number of tools for each analytical step from quality filtering to generating identified operational taxonomic unit (OTU) abundance tables.Here, we introduce PIPITS, an open-source stand-alone suite of software for automated processing of Illumina MiSeq sequences for fungal community analysis. PIPITS exploits a number of state of the art applications to process paired-end reads from quality filtering to producing OTU abundance tables.We provide detailed descriptions of the pipeline and show its utility in the analysis of 9 396 092 sequences generated on the MiSeq platform from Illumina MiSeq. PIPITS is the first automated bioinformatics pipeline dedicated for fungal ITS sequences which incorporates ITSx to extract subregions of ITS and exploits the latest RDP Classifier to classify sequences against the curated UNITE fungal data set.

Keywords: DNA metabarcoding; bioinformatics; fungi; internal transcribed spacer; pipeline.

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Figures

Figure 1
Figure 1
Overview/workflow of PIPITS for Illumina ITS sequences.
Figure 2
Figure 2
The number of sequences and time taken at each processing step. SYS1: Bio‐Linux 7, 16‐core Intel(R) Xeon(R) CPU @ 2·27GHz, 105 GB RAM; SYS2: Ubuntu 14·04, a standard desktop computer, 2·93 GHz quad‐core Intel Core i3, 8GB RAM.
Figure 3
Figure 3
Proportion of most abundant operational taxonomic units representing 90% of the samples. In total, 91·2% of the reads (blue) were assigned to phylum Ascomycota, 7·0% (green) to phylum Basidiomycota and 0·04% to phyla belonging to Glomeromycota, Zygomycota and Chytridiomycota, while < 2% of the reads (orange) were unassignable to any of the known phylum in the data base. The three most dominant OTUs were Lophodermium seditiosum [SH198382.06FU] (24·2%), Ascomycota unidentified species [SH235673.06FU] (16·4%) and Rhinocladiella similis [SH210380.06FU] (14·11%).
Figure 4
Figure 4
Length distribution of ITS2 sequences after extraction by PIPITS_FUNITS. Range: 101–461 bp, mean: 168, standard deviation: 28·7.
Figure 5
Figure 5
FASTQ quality scores across forward and reverse reads in one of the samples. The blue bar represents the inter‐quartile range (25–75%), the grey bar represents the 10% and 90%, and the black line represents the mean quality.
See this image and copyright information in PMC

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