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• W2964120802 abstract "Metagenomic sequencing techniques produce large data sets of DNA fragments (e.g. reads or contigs) from environmental samples. To understand the microbial communities and functional structures within the samples, metagenomic sequence fragments need to be first assigned to their taxonomic origins from which they were derived (also called “binning”) to facilitate downstream analyses.Arguably the most popular metagenomic binning approaches are alignment-based methods. A sequence fragment is searched against a reference database consisting of full genomes of organisms, and the highest scoring organism is assigned as the taxonomic origin. Although efficient sequence alignment algorithms, including BWA-MEM [1], Bowtie2 [2] and (mega)BLAST [3], can readily be used for this purpose, the computational cost of alignment-based methods becomes prohibitive as the size of the sequence dataset grows dramatically, which is often the case in recent studies.Another completely different binning approach is based on genomic sequence composition, which exploits the sequence characteristics of metagenomic fragments and applies machine learning classification algorithms to assign putative taxnomic origins to all fragments. Since classifiers, such as support vector machines, are trained on whole reference genome sequences beforehand, compositional methods normally are substantially faster than alignment-based methods on large datasets. The rationale behind compositional-based binning methods is based on the fact that different genomes have different conserved sequence composition patterns, such as GC content, codon usage or a particular abundance distribution of consecutive nucleotide k-mers. To design a good compositional-based algorithm, we need to extract informative and discriminative features from the reference genomes. Most existing methods, including PhyloPythia(S) [4, 5], use k-mer frequencies to represent sequence fragments, where k is typically small (e.g. 6 to 10). While longer k-mers, which capture compositional dependency within larger contexts, could potentially lead to higher binning accuracy, they are more prone to noise and errors if used in the supervised setting. Moreover, incorporating long k-mers as features increases computational cost exponentially and requires significantly larger training datasets." @default.
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• W2964120802 date "2016-04-01" @default.
• W2964120802 modified "2023-09-26" @default.
• W2964120802 title "Low-Density Locality-Sensitive Hashing Boosts Metagenomic Binning." @default.
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