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Testing Efficacy of Assembly-Free and Alignment-Free Methods for Species Identification Using Genome Skims, with Patellogastropoda as a Test Case.

ABSTRACT: Most recently, species identification has leaped from DNA barcoding into shotgun sequencing-based "genome skimming" alternatives. Genome skims have mainly been used to assemble organelle genomes, which discards much of the nuclear genome. Recently, an alternative approach was proposed for sample identification, using unassembled genome skims, which can effectively improve phylogenetic signal and identification resolution. Studies have shown that the software Skmer and APPLES work well at estimating genomic distance and performing phylogenetic placement in birds and insects using low-coverage genome skims. In this study, we use Skmer and APPLES based on genome skims of 11 patellogastropods to perform assembly-free and alignment-free species identification and phylogenetic placement. Whether or not data corresponding to query species are present in the reference database, Skmer selects the best matching or closest species with COI barcodes under different sizes of genome skims except lacking species belonging to the same family as a query. APPLES cannot place patellogastropods in the correct phylogenetic position when the reference database is sparse. Our study represents the first attempt at assembly-free and alignment-free species identification of marine mollusks using genome skims, demonstrating its feasibility for patellogastropod species identification and flanking the necessity of establishing a database to share genome skims.

SUBMITTER: Xu T

PROVIDER: S-EPMC9318368 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:MotivationNetwork comparison is a computationally intractable problem with important applications in systems biology and other domains. A key challenge is to properly quantify similarity between wiring patterns of two networks in an alignment-free fashion. Also, alignment-based methods exist that aim to identify an actual node mapping between networks and as such serve a different purpose. Various alignment-free methods that use different global network properties (e.g. degree distribution) have been proposed. Methods based on small local subgraphs called graphlets perform the best in the alignment-free network comparison task, due to high level of topological detail that graphlets can capture. Among different graphlet-based methods, Graphlet Correlation Distance (GCD) was shown to be the most accurate for comparing networks. Recently, a new graphlet-based method called NetDis was proposed, which was claimed to be superior. We argue against this, as the performance of NetDis was not properly evaluated to position it correctly among the other alignment-free methods.ResultsWe evaluate the performance of available alignment-free network comparison methods, including GCD and NetDis. We do this by measuring accuracy of each method (in a systematic precision-recall framework) in terms of how well the method can group (cluster) topologically similar networks. By testing this on both synthetic and real-world networks from different domains, we show that GCD remains the most accurate, noise-tolerant and computationally efficient alignment-free method. That is, we show that NetDis does not outperform the other methods, as originally claimed, while it is also computationally more expensive. Furthermore, since NetDis is dependent on the choice of a network null model (unlike the other graphlet-based methods), we show that its performance is highly sensitive to the choice of this parameter. Finally, we find that its performance is not independent on network sizes and densities, as originally claimed.Contactnatasha@imperial.ac.ukSupplementary informationSupplementary data are available at Bioinformatics online.

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Testing Efficacy of Assembly-Free and Alignment-Free Methods for Species Identification Using Genome Skims, with Patellogastropoda as a Test Case.

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