RESUMO
k-SLAM is a highly efficient algorithm for the characterization of metagenomic data. Unlike other ultra-fast metagenomic classifiers, full sequence alignment is performed allowing for gene identification and variant calling in addition to accurate taxonomic classification. A k-mer based method provides greater taxonomic accuracy than other classifiers and a three orders of magnitude speed increase over alignment based approaches. The use of alignments to find variants and genes along with their taxonomic origins enables novel strains to be characterized. k-SLAM's speed allows a full taxonomic classification and gene identification to be tractable on modern large data sets. A pseudo-assembly method is used to increase classification accuracy by up to 40% for species which have high sequence homology within their genus.
Assuntos
Biologia Computacional/métodos , Código de Barras de DNA Taxonômico/métodos , Metagenoma , Metagenômica/métodos , Algoritmos , Estudos de Casos e Controles , Biologia Computacional/normas , Código de Barras de DNA Taxonômico/normas , Microbioma Gastrointestinal , Genoma Bacteriano , Humanos , Cirrose Hepática/microbiologia , Metagenômica/normas , Reprodutibilidade dos Testes , Escherichia coli Shiga Toxigênica/classificação , Escherichia coli Shiga Toxigênica/genéticaRESUMO
SUMMARY: An ultrafast DNA sequence aligner (Isaac Genome Alignment Software) that takes advantage of high-memory hardware (>48 GB) and variant caller (Isaac Variant Caller) have been developed. We demonstrate that our combined pipeline (Isaac) is four to five times faster than BWA + GATK on equivalent hardware, with comparable accuracy as measured by trio conflict rates and sensitivity. We further show that Isaac is effective in the detection of disease-causing variants and can easily/economically be run on commodity hardware. AVAILABILITY: Isaac has an open source license and can be obtained at https://github.com/sequencing.