PLANiTS: a curated sequence reference dataset for plant ITS DNA metabarcoding

Abstract

DNA metabarcoding combines DNA barcoding with high-throughput sequencing to identify different taxa within environmental communities. The ITS has already been proposed and widely used as universal barcode marker for plants, but a comprehensive, updated and accurate reference dataset of plant ITS sequences has not been available so far. Here, we constructed reference datasets of Viridiplantae ITS1, ITS2 and entire ITS sequences including both Chlorophyta and Streptophyta. The sequences were retrieved from NCBI, and the ITS region was extracted. The sequences underwent identity check to remove misidentified records and were clustered at 99% identity to reduce redundancy and computational effort. For this step, we developed a script called ‘better clustering for QIIME’ (bc4q) to ensure that the representative sequences are chosen according to the composition of the cluster at a different taxonomic level. The three datasets obtained with the bc4q script are PLANiTS1 (100 224 sequences), PLANiTS2 (96 771 sequences) and PLANiTS (97 550 sequences), and all are pre-formatted for QIIME, being this the most used bioinformatic pipeline for metabarcoding analysis. Being curated and updated reference databases, PLANiTS1, PLANiTS2 and PLANiTS are proposed as a reliable, pivotal first step for a general standardization of plant DNA metabarcoding studies. The bc4q script is presented as a new tool useful in each research dealing with sequences clustering. Database URL: https://github.com/apallavicini/bc4q; https://github.com/apallavicini/PLANiTS.

Applied Barcoding: The Practicalities of DNA Testing for Herbals

“…A flow diagram of a pragmatic approach to choosing a DNA barcode for herbal authentication is shown in Figure 1 . Although an increasingly large number of DNA barcode sequences have been published, the quality and veracity of these are often questionable, and databases have numerous examples of sequences assigned to incorrect species [ 20 , 21 ]. For this reason, the decision tree in Figure 1 was developed to avoid some of the common pitfalls and provide a method to select high-quality sequences.…”

“…As a result, a BLAST search of the database with a genuine H. perforatum ITS sequence will often include sequences from a number of different species in the top 50 hits, some having higher scores than genuine H. perforatum accessions. In order to filter this “background noise” effect, irrelevant and unreliable sequences were identified using the BLAST distance tree facility to identify and discard obvious outliers (typically singleton accessions with less than 95% identity to any other H. perforatum sequence) [ 20 , 21 ]. Tightly clustering groups of sequences unique to individual species (particularly vouchered specimens from a variety of sources) were collected and aligned.…”

“…This increased the confidence that the informative sites stipulated in the reference barcode were representative and characteristic of the species as a whole, and also allowed unreliable sequences to be identified and ignored. The flowchart emphasizes the value of checking the sources of database sequences, particularly those on GenBank [ 20 , 21 ], and where possible matching sequences against conspecific accessions.…”

Alignment-free classification of COI DNA barcode data with the Python package Alfie

“…Using other publicly available data (i.e. Pruesse et al 2007;Banchi et al 2020), the same custom model construction and training tools in Alfie can be used to construct binary or multiclass alignment-free classification tools for other DNA barcodes or genes. Although the Alfie package is an effective alignment-free classification framework at high taxonomic levels, traditional alignments are likely more effective for lower-level classification tasks (i.e.…”

RESCRIPt: Reproducible sequence taxonomy reference database management for the masses

“…NCBI RefSeq releases databases for both fungal SSU and LSU [25]. Macroorganism identification, for both diet metabarcoding and eDNA surveys, is commonly accomplished using the mitochondrial cytochrome oxidase subunit 1 (COI) gene for metazoa [33][34][35], ITS2 and chloroplast trnL (UAA) intron [36][37][38] for plants, 12S rRNA for fish [39,40], and a variety of other clade-specific marker genes. For some of these marker genes, curated reference databases exist, such as BOLD for COI [33] and PLANiTS for plant ITS2 [38], but for others the process of generating custom reference databases poses a research bottleneck.…”