MERCon - 2022

Permanent URI for this collectionhttp://192.248.9.226/handle/123/18494

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Browsing MERCon - 2022 by Author "Alahakoon, S"

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    Metagraph: plasmid/chromosome classification enhancement using graph neural networks
    (IEEE, 2022-07) Alahakoon, S; Dassanayake, G; Nandasiri, C; Wickramarachchi, A; Perera, I; Rathnayake, M; Adhikariwatte, V; Hemachandra, K
    Chromosomes and plasmids are the main sites of genetic information in microorganisms. Separately identifying plasmids and chromosomes is essential for further metagenomic analysis. Computational tools have achieved significant results in classifying DNA into plasmids and chromosomes. However, there is often a trade-off between recall and precision in the currently available tools. Several graph-based tools have been proposed to improve the prediction precision and recall simultaneously by improving upon the results produced by existing tools. We propose MetaGraph, a Graph Neural Network (GNN) based tool for plasmid/chromosome classification enhancement. It uses the high confidence predictions of existing plasmid/chromosome prediction tools and improves the prediction accuracy of low confidence predictions using plasmid probabilities as features for the GNN. We evaluated MetaGraph for a set of simulated DNA sequences. The results significantly improved over stateof-the-art tools like PlasFlow and PlasClass. The results were increased up to 20% from the initial PlasClass predictions. The source code for MetaGraph is freely available at: https://github.com/MetaGSC/MetaGraph
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    Metapcbin: plasmid/chromosome classification for metagenomic contigs using machine learning techniques
    (IEEE, 2022-07) Nandasiri, C; Alahakoon, S; Dassanayake, G; Wickramarachchi, A; Perera, I; Rathnayake, M; Adhikariwatte, V; Hemachandra, K
    Chromosomes and plasmids are the major carriers of genetic material in microorganisms such as bacteria. Separating chromosomal and plasmid DNA from large datasets is important as plasmids and chromosomes affect functions and other environmental adaptations. Bioinformatics methodologies have been developed for plasmid classification with the advancements in sequencing technologies. The usage of normalized short k-mer counts with machine learning models has been popular in the characterization of plasmids and chromosomes. Furthermore, bio-markers from DNA sequences as features have also been studied in plasmid classification. However, both approaches suffer from the trade-off between precision and recall. MetaPCbin is a plasmid detection tool that combines computational and genetic approaches into a hybrid method of plasmid prediction. MetaPCbin uses an artificial neural network that uses k-mer counts as features and a random forest model that uses biomarkers. MetaPCbin evaluates the precision and the recall of the classification of real-world DNA sequences from the RefSeq database and simulated sequences. The results show that it is capable of performing plasmid classification while maintaining high precision and recall compared to the state of the art. MetaPCbin is freely available at: https://github.com/MetaGSC/MetaPCbin