[Frontiers in Microbiology, 6 October 2023]

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/cch...

…and the training and testing data sets are available at:

https://github.com/fmf...

Identification of plasmids and plasmid genes from sequencing data is an important question regarding antimicrobial resistance spread and other One-Health issues. PlASgraph2 is a deep-learning tool that classifies contigs from a short-read assembly as originating either from a plasmid, the chromosome or being ambiguous (i.e. could originate from both, e.g. in the case of a shared repeated contig).

PlASgraph2 is built on a graph neural network (GNN) and analysis the assembly graph (provided in GFA format) generated by an assembler such as Unicycler or SKESA.

[Bioinformatics, 25 January 2024]

Motivation: Transcriptomic long-read (LR) sequencing is an increasingly cost-effective technology for probing various RNA features. Numerous tools have been developed to tackle various transcriptomic sequencing tasks (e.g. isoform and gene fusion detection). However, the lack of abundant gold-standard datasets hinders the benchmarking of such tools. Therefore, the simulation of LR sequencing is an important and practical alternative. While the existing LR simulators aim to imitate the sequencing machine noise and to target specific library protocols, they lack some important library preparation steps (e.g. PCR) and are difficult to modify to new and changing library preparation techniques (e.g. single-cell LRs).

Results: We present TKSM, a modular and scalable LR simulator, designed so that each RNA modification step is targeted explicitly by a specific module. This allows the user to assemble a simulation pipeline as a combination of TKSM modules to emulate a specific sequencing design. Additionally, the input/output of all the core modules of TKSM follows the same simple format (Molecule Description Format) allowing the user to easily extend TKSM with new modules targeting new library preparation steps.

Availability and implementation: TKSM is available as an open source software at:

https://github.com/vpc...