RESUMO
BACKGROUND: The problem of de-novo assembly for metagenomes using only long reads is gaining attention. We study whether post-processing metagenomic assemblies with the original input long reads can result in quality improvement. Previous approaches have focused on pre-processing reads and optimizing assemblers. BIGMAC takes an alternative perspective to focus on the post-processing step. RESULTS: Using both the assembled contigs and original long reads as input, BIGMAC first breaks the contigs at potentially mis-assembled locations and subsequently scaffolds contigs. Our experiments on metagenomes assembled from long reads show that BIGMAC can improve assembly quality by reducing the number of mis-assemblies while maintaining or increasing N50 and N75. Moreover, BIGMAC shows the largest N75 to number of mis-assemblies ratio on all tested datasets when compared to other post-processing tools. CONCLUSIONS: BIGMAC demonstrates the effectiveness of the post-processing approach in improving the quality of metagenomic assemblies.
Assuntos
Metagenoma , Metagenômica/métodos , Software , Algoritmos , Simulação por Computador , Estatística como AssuntoRESUMO
UNLABELLED: We introduce FinisherSC, a repeat-aware and scalable tool for upgrading de novo assembly using long reads. Experiments with real data suggest that FinisherSC can provide longer and higher quality contigs than existing tools while maintaining high concordance. AVAILABILITY AND IMPLEMENTATION: The tool and data are available and will be maintained at http://kakitone.github.io/finishingTool/ CONTACT: : dntse@stanford.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Assuntos
Algoritmos , Biologia Computacional/métodos , Genômica/métodos , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Sequências Repetitivas de Ácido Nucleico/genética , Análise de Sequência de DNA/métodos , Software , Animais , Caenorhabditis elegans/genética , Drosophila/genética , Modelos Estatísticos , Saccharomyces cerevisiae/genéticaRESUMO
Recent work identified the fundamental limits on the information requirements in terms of read length and coverage depth required for successful de novo genome reconstruction from shotgun sequencing data, based on the idealistic assumption of no errors in the reads (noiseless reads). In this work, we show that even when there is noise in the reads, one can successfully reconstruct with information requirements close to the noiseless fundamental limit. A new assembly algorithm, X-phased Multibridging, is designed based on a probabilistic model of the genome. It is shown through analysis to perform well on the model, and through simulations to perform well on real genomes.
