Schematics of a bidirectional recurrent neural network.

<p>Schematics of a bidirectional recurrent neural network.</p

Accuracy and running time on R9 data.

<p>The results of base calling were aligned to the reference using BWA-MEM [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0178751#pone.0178751.ref028" target="_blank">28</a>]. The first column reports the percentage of reads that aligned to the reference on at least 90% of their length. The accuracy was computed as the number of matches in the alignment divided by the length of the alignment. The speed is measured in events per second.</p

Abudances for repetitive 6-mers.

<p>Abudances for repetitive 6-mers.</p

Raw signal from MinION and its segmentation to events.

<p>The plot was generated from the <i>E. coli</i> data (<a href="http://www.ebi.ac.uk/ena/data/view/ERR1147230" target="_blank">http://www.ebi.ac.uk/ena/data/view/ERR1147230</a>).</p

DeepNano reduces bias in 6-mer composition.

<p>Comparison of 6-mer content in <i>Klebsiella</i> reference genome and base-called reads by Metrichor (left) and DeepNano (right). From top to bottom: template, complement, 2D.</p

Accuracy of base callers on two R7.3 testing data sets.

<p>The results of base calling were aligned to the reference using BWA-MEM [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0178751#pone.0178751.ref028" target="_blank">28</a>]. The accuracy was computed as the number of matches in the alignment divided by the length of the alignment.</p

Sizes of experimental data sets.

<p>The sizes differ between strands because only base calls mapping to the reference were used. Note that the counts of 2D events are based on the size of the alignment.</p

Data_Sheet_1_plASgraph2: using graph neural networks to detect plasmid contigs from an assembly graph.PDF

Identification of plasmids from sequencing data is an important and challenging problem related to antimicrobial resistance spread and other One-Health issues. We provide a new architecture for identifying plasmid contigs in fragmented genome assemblies built from short-read data. We employ graph neural networks (GNNs) and the assembly graph to propagate the information from nearby nodes, which leads to more accurate classification, especially for short contigs that are difficult to classify based on sequence features or database searches alone. We trained plASgraph2 on a data set of samples from the ESKAPEE group of pathogens. plASgraph2 either outperforms or performs on par with a wide range of state-of-the-art methods on testing sets of independent ESKAPEE samples and samples from related pathogens. On one hand, our study provides a new accurate and easy to use tool for contig classification in bacterial isolates; on the other hand, it serves as a proof-of-concept for the use of GNNs in genomics. Our software is available at https://github.com/cchauve/plasgraph2 and the training and testing data sets are available at https://github.com/fmfi-compbio/plasgraph2-datasets.</p

Additional file 1 of RNA motif search with data-driven element ordering

Supplementary online material. The file contains supplementary material with additional details on methods, file formats, and experiments. (PDF 617 kb