Fast and Accurate Classification of Meta-Genomics Long Reads With deSAMBA

There is still a lack of fast and accurate classification tools to identify the taxonomies of noisy long reads, which is a bottleneck to the use of the promising long-read metagenomic sequencing technologies. Herein, we propose de Bruijn graph-based Sparse Approximate Match Block Analyzer (deSAMBA), a tailored long-read classification approach that uses a novel pseudo alignment algorithm based on sparse approximate match block (SAMB). Benchmarks on real sequencing datasets demonstrate that deSAMBA enables to achieve high yields and fast speed simultaneously, which outperforms state-of-the-art tools and has many potentials to cutting-edge metagenomics studies.

Counterfactuals and Closeness

The semantics for counterfactuals builds on Lewis’ theory and similarity weighting, with two principal aims. The first of these is to resist those who argue that appeal to counterfactuals in the analysis of causation is circular because their semantics must appeal to causal facts (specifically, in the similarity weighting for worlds). The second of these is to secure the semantics for counterfactuals for a possible account of causal non-symmetry in terms of counterfactual non-symmetry. As a result, weight is rightly placed upon perfect match as an important aspect of similarity although this is moderated to deal an indeterministic version of Fine’s Future Similarity Objection. A more precise understanding of the role of approximate match is developed appealing to a type of probabilistic independence.