Number of OTUs, percentage of tubificids with or without hair setae and IOBS values of each sample obtained with Sanger-sequenced specimen data and NGS approach with uncorrected and corrected sequence abundances.

<p>Number of OTUs, percentage of tubificids with or without hair setae and IOBS values of each sample obtained with Sanger-sequenced specimen data and NGS approach with uncorrected and corrected sequence abundances.</p

Detection of each OTU in the mixed samples by NGS.

<p>A. The heatmap indicates if a taxon present in a mixed sample could be detected in the NGS data (green) or not (blue). The OTUs present in the NGS data of a given sample but not identified by Sanger sequencing are indicated in red. B. The boxplots display the proportions of specimen sequences (blue) and of NGS sequences (red) for each taxon sequenced in at least two mixed samples. OTUs designated by a letter followed by a number are known OTUs [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148644#pone.0148644.ref020" target="_blank">20</a>] and OTUs designated by a number in brackets are new; Indet = unidentified.</p

Numbers of specimens, of sequenced specimens and of OTUs (Sanger, NGS and total) per sample.

<p>Numbers of specimens, of sequenced specimens and of OTUs (Sanger, NGS and total) per sample.</p

R and P values (Pearson test) of the relationships between the OTU proportions obtained with Sanger-sequenced specimen data and with NGS approach per sample (n = 12–17) and for all samples (n = 82).

<p>R and P values (Pearson test) of the relationships between the OTU proportions obtained with Sanger-sequenced specimen data and with NGS approach per sample (n = 12–17) and for all samples (n = 82).</p

Relationships between the proportions of OTUs obtained with Sanger-sequenced specimen data and NGS approach without correction of sequence abundances (left) and after correction (right) per taxon and per sample, for all samples (1–6).

<p>Relationships between the proportions of OTUs obtained with Sanger-sequenced specimen data and NGS approach without correction of sequence abundances (left) and after correction (right) per taxon and per sample, for all samples (1–6).</p

normalized_OTUs_reads

For each OTU (lines) are indicated the number of reads found in each sample (columns). The taxonomic assignment of each OTU is indicated in the first column. The sample names are indicated in the first line

SFA114_OBAN_ISUs

The file is a fasta file containing the individual sequence units (ISUs) obtained after the stringent quality filtering, assembly, demultiplexing and dereplication steps described in the paper. The paired-end reads from large forward and reverse fastq files were treated using a custom C language program. The 5 fields of each underscore-separated ISU header represent from the first to the last field: the project alphanumeric ID ('SFA114'), the project name ('OBAN'), the type of molecule ('DNA' of 'RNA'), the sample ID number ('1' - '45') and the number of reads of the ISU

Morphological_counts

Number of foraminifera morphospecies counted in the 45 sequenced sediment samples. For each species identifed based on morphological characters (lines), the counts are detailed for each sample (column in spreadsheet "Species") or summed across samples (spreadsheet "final counts")

Predicting the Ecological Quality Status of Marine Environments from eDNA Metabarcoding Data Using Supervised Machine Learning

Monitoring biodiversity is essential to assess the impacts of increasing anthropogenic activities in marine environments. Traditionally, marine biomonitoring involves the sorting and morphological identification of benthic macro-invertebrates, which is time-consuming and taxonomic-expertise demanding. High-throughput amplicon sequencing of environmental DNA (eDNA metabarcoding) represents a promising alternative for benthic monitoring. However, an important fraction of eDNA sequences remains unassigned or belong to taxa of unknown ecology, which prevent their use for assessing the ecological quality status. Here, we show that supervised machine learning (SML) can be used to build robust predictive models for benthic monitoring, regardless of the taxonomic assignment of eDNA sequences. We tested three SML approaches to assess the environmental impact of marine aquaculture using benthic foraminifera eDNA, a group of unicellular eukaryotes known to be good bioindicators, as features to infer macro-invertebrates based biotic indices. We found similar ecological status as obtained from macro-invertebrates inventories. We argue that SML approaches could overcome and even bypass the cost and time-demanding morpho-taxonomic approaches in future biomonitoring

RSBL-2012-0942_DeepSea_aDNA_IlluminaSeqs

DNA sequences generated by cloning/Sanger sequencing and by Illumina sequencing (MiSeq instrument). In each sequence header, several '_'-separated fields that could be indexed indicate: [0] the technology ('cloning' or 'illumina'), [1] the sediment core of origin, [2] the sub-sample layer depth in centimetre, [3] a numeric identifier, and for the cloning sequences, [4] the lab of origin the DNA extract and [5] the primer combination