ArticleFrontiers in genetics2019
MAC: Merging Assemblies by Using Adjacency Algebraic Model and Classification.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Article
- Autocycler: long-read consensus assembly for bacterial genomes.Bioinformatics (Oxford, England) · 2025Article
- First draft reference genome and annotation of the alternative oil species Physaria fendleri.G3 (Bethesda, Md.) · 2024Article
- De novo assembly and annotation of Popillia japonica's genome with initial clues to its potential as an invasive pest.BMC genomics · 2024Article
- Structural insights into coordinating 5S RNP rotation with ITS2 pre-RNA processing during ribosome formation.EMBO reports · 2023Article
- Rapid transcriptomic and physiological changes in the freshwater pennate diatom Mayamaea pseudoterrestris in response to copper exposure.DNA research : an international journal for rapid publication of reports on genes and genomes · 2022Article
- Assemblies of the genomes of parasitic wasps using meta-assembly and scaffolding with genetic linkage.G3 (Bethesda, Md.) · 2022Article
- TahcoRoll: fast genomic signature profiling via thinned automaton and rolling hash.Medical review (2021) · 2021Article
- Draft Genome Sequences ofMicrobiology resource announcements · 2021Article
- Draft Genome Sequences ofMicrobiology resource announcements · 2021Article
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Authors and funding
5 authors.
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Abstract
With the generation of a large amount of sequencing data, different assemblers have emerged to perform de novo genome assembly. As a single strategy is hard to fit various biases of datasets, none of these tools outperforms the others on all species. The process of assembly reconciliation is to merge multiple assemblies and generate a high-quality consensus assembly. Several assembly reconciliation tools have been proposed. However, the existing reconciliation tools cannot produce a merged assembly which has better contiguity and contains less errors simultaneously, and the results of these tools usually depend on the ranking of input assemblies. In this study, we propose a novel assembly reconciliation tool MAC, which merges assemblies by using the adjacency algebraic model and classification. In order to solve the problem of uneven sequencing depth and sequencing errors, MAC identifies consensus blocks between contig sets to construct an adjacency graph. To solve the problem of repetitive region, MAC employs classification to optimize the adjacency algebraic model. What's more, MAC designs an overall scoring function to solve the problem of unknown ranking of input assembly sets. The experimental results from four species of GAGE-B demonstrate that MAC outperforms other assembly reconciliation tools.
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