DOI: 10.5061/dryad.73n5tb37z
title: A novel approach for pollen identification and quantification using hybrid capture-based DNA metabarcoding

description: Efforts to explore optimal molecular methods for identifying plant
 mixtures, particularly pollen, are increasing. Pollen identification (ID)
 and quantification is important in many fields, including pollination
 ecology and agricultural sciences, but quantifying mixture proportions
 remains challenging. Traditional pollen ID using microscopy is
 time-consuming, requires expertise, and has limited accuracy and
 throughput. Molecular barcoding approaches being explored offer improved
 accuracy and throughput. The common approach, amplicon sequencing, employs
 PCR amplification to isolate DNA barcodes, but introduces significant
 bias, impairing downstream quantification. We apply a novel molecular
 hybridisation capture approach to artificial pollen mixtures, to improve
 upon current taxon ID and quantification methods. The method randomly
 fragments DNA, and uses RNA baits to capture DNA barcodes, which allows
 for PCR duplicate removal, reducing downstream quantification bias.
 Metabarcoding was tested using two reference libraries constructed from
 publicly available sequences; the matK plastid barcode, and RefSeq
 complete chloroplast references. Single barcode-based taxon ID did not
 consistently resolve to species or genus level. The RefSeq chloroplast
 database performed better qualitatively but had limited taxon coverage
 (relative to species used here) and introduced ID issues. At family level,
 both databases yielded comparable qualitative results, but the RefSeq
 database performed better quantitatively. A restricted matK database
 containing only mixture species yielded sequence proportions highly
 correlated with input pollen proportions, demonstrating that hybridization
 capture usefulness for metabarcoding and quantifying pollen mixtures. The
 choice of reference database remains one of the most important factors
 affecting qualitative and quantitative accuracy.
description: # A novel approach for pollen identification and quantification using
 hybrid capture-based DNA metabarcoding Dataset DOI:
 [10.5061/dryad.73n5tb37z](10.5061/dryad.73n5tb37z) ## Description of the
 data and file structure This dataset contains raw Illumina sequencing
 reads (FASTQ format) derived from artificial pollen mixtures composed of
 three plant species: *Eucalyptus baxteri*, *Echinacea arctotheca*, and
 *Prunus dulcis*. The purpose of the experiment was to evaluate the
 accuracy of DNA-based taxonomic identification and quantification using
 targeted chloroplast hybrid capture followed by high-throughput
 sequencing. The pollen was combined in varying proportions to create 14
 artificial mixes, each with three replicates, resulting in 42 DNA
 libraries plus  negative controls. Each sample was assigned a unique
 internal barcode and dual index combination for multiplex sequencing.
 Libraries were prepared using the NEBNext Ultra II kit with custom stubby
 Y-adapters and enriched using the OZBaits_CP V1.0 chloroplast bait set
 targeting 19 conserved chloroplast genes. The final library pool was
 sequenced on an Illumina HiSeq X Ten platform (2 × 150 bp reads). ## Files
 and variables ### File description: Each sample is represented by two
 compressed FASTQ files: a forward read file (R1) and a reverse read file
 (R2), containing raw Illumina sequencing data. These files have already
 been demultiplexed based on the internal in-line barcodes used during
 library preparation. The filenames follow a naming convention that
 includes: * the mix ID (e.g., `M07`, `M14`) corresponding to a specific
 artificial pollen mixture, and * the replicate ID (`a`, `b`, or `c`)
 indicating one of the three replicates per mix. For example:
 `Poll_713_M7a_R1.fastq.gz` and `Poll_713_M7a_R2.fastq.gz` represent the
 forward and reverse reads for replicate *a* of Mix *M7*. Blank (negative
 control) samples follow a similar naming convention
 (e.g., `Poll_760_EBC2_Blank_R1.fastq.gz`). ### Files: File:
 Poll_716_M8a_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_716_M8a_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_715_M7c_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_715_M7c_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_713_M7a_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_714_M7b_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_713_M7a_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_760_EBC2_Blank_R1_001.fastq.gz File:
 Poll_760_EBC2_Blank_R2_001.fastq.gz File:
 Poll_718_M8c_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_714_M7b_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_718_M8c_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_722_M10a_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_720_M9b_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_723_M10b_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_723_M10b_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_721_M9c_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_720_M9b_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz  File:
 Poll_721_M9c_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_722_M10a_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_724_M10c_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_717_M8b_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_730_M12c_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_724_M10c_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_719_M9a_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_719_M9a_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_717_M8b_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_729_M12b_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_728_M12a_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_730_M12c_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_729_M12b_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_728_M12a_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_738_M15b_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_738_M15b_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_739_M15c_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_727_M11c_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_726_M11b_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_727_M11c_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_739_M15c_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_726_M11b_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_737_M15a_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_737_M15a_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_741_M16b_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_742_M16c_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_741_M16b_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_743_M17a_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_742_M16c_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_735_M14b_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_743_M17a_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_744_M17b_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_735_M14b_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_744_M17b_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_745_M17c_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_745_M17c_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_740_M16a_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_740_M16a_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_725_M11a_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_725_M11a_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_746_M18a_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_746_M18a_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_747_M18b_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_747_M18b_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_750_M19b_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_750_M19b_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_748_M18c_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_748_M18c_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_753_M20b_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_753_M20b_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_752_M20a_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_751_M19c_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_752_M20a_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_751_M19c_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_731_M13a_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_731_M13a_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_734_M14a_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_732_M13b_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_734_M14a_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_732_M13b_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_754_M20c_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_733_M13c_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_754_M20c_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_733_M13c_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_749_M19a_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz File:
 Poll_749_M19a_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_736_M14c_Mix_E_baxteri_E_arctotheca_P_dulces_R2_001.fastq.gz File:
 Poll_736_M14c_Mix_E_baxteri_E_arctotheca_P_dulces_R1_001.fastq.gz ###
 Corresponding sample names in publication Note that in the publication,
 the Mix ID was modified from M7-M20, to M1-M14. | Sample name (file) |
 Corresponding sample name (publication) | | :----------------- |
 :-------------------------------------- | | M7 | M1 | | M8 | M2 | | M9 |
 M3 | | M10 | M4 | | M11 | M5 | | M12 | M6 | | M13 | M7 | | M14 | M8 | |
 M15 | M9 | | M16 | M10 | | M17 | M11 | | M18 | M12 | | M19 | M13 | | M20 |
 M14 | | EBC2 | Blank | ## Code/software ### How to Open and Analyze the
 Data The dataset consists of demultiplexed Illumina sequencing reads in
 compressed FASTQ format (`.fastq.gz`). These files can be opened in any
 text editor that supports gzip compression, but they are typically
 analyzed using command-line tools or within bioinformatics workflows. A
 range of free and open-source software is available to view,
 quality-check, and analyze these files. Below is an overview of the tools
 used in this study, organized by processing step. --- #### **1.
 Demultiplexing** *(already completed)* **Software:** *
 `bcl2fastq`(Illumina): Used to demultiplex reads based on Illumina index
 sequences. * `Sabre`: Used to demultiplex based on internal 8 bp barcodes.
 Note: Sabre was run allowing one mismatch, as all internal barcodes
 differed by at least two base pairs. --- #### **2. Quality Filtering and
 Trimming** **Software:** * `Clumpify` (BBTools suite): Used to remove PCR
 duplicates by grouping identical reads. * `AdapterRemoval`: Used for
 trimming adapter sequences, filtering short/low-quality reads, and
 removing trailing A-tails. Reads shorter than 30 bp or with Phred scores
 &lt;20 were removed. --- #### **3. Quality Assessment** **Software:** *
 `FastQC`: For checking per-base quality scores, GC content, and other
 quality metrics. * `MultiQC`: For summarizing FastQC reports across all
 samples. * `seqtk`, `zcat`, `less`: Command-line tools for quick
 inspection or subsampling of reads. --- #### **4. Taxonomic Classification
 and Quantification** **Software:** * `Kraken2`: Used for assigning
 taxonomic labels to reads via a k-mer-based approach. * `Bracken`: Used
 for estimating taxon abundance from Kraken2 output.