2014
DOI: 10.1142/s0219720014500139
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MPI-blastn and NCBI-TaxCollector: Improving metagenomic analysis with high performance classification and wide taxonomic attachment

Abstract: Metagenomic sequencing technologies are advancing rapidly and the size of output data from high-throughput genetic sequencing has increased substantially over the years. This brings us to a scenario where advanced computational optimizations are requested to perform a metagenomic analysis. In this paper, we describe a new parallel implementation of nucleotide BLAST (MPI-blastn) and a new tool for taxonomic attachment of Basic Local Alignment Search Tool (BLAST) results that supports the NCBI taxonomy (NCBI-Tax… Show more

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Cited by 10 publications
(12 citation statements)
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References 24 publications
(39 reference statements)
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“…This tool consists of an adapted version of the algorithm introduced in NCBI-taxcollector [29], which includes an additional step before the taxonomic assignment. In this additional step, all sequence accession numbers from SILVA database were mapped with taxonomic identification numbers (TAXID) from NCBI taxonomy database [30].Sequence matches were classified at an 80% identity level for domain and phylum; 90% identity for class, order, and family; 95% identity for genus; and a 99% identity level for species. The total numbers of 16S rRNA classified sequences were converted into an OTU abundance matrix for each taxonomy level across the samples.…”
Section: Illumina High-throughput Sequencingmentioning
confidence: 99%
“…This tool consists of an adapted version of the algorithm introduced in NCBI-taxcollector [29], which includes an additional step before the taxonomic assignment. In this additional step, all sequence accession numbers from SILVA database were mapped with taxonomic identification numbers (TAXID) from NCBI taxonomy database [30].Sequence matches were classified at an 80% identity level for domain and phylum; 90% identity for class, order, and family; 95% identity for genus; and a 99% identity level for species. The total numbers of 16S rRNA classified sequences were converted into an OTU abundance matrix for each taxonomy level across the samples.…”
Section: Illumina High-throughput Sequencingmentioning
confidence: 99%
“…Affinity is commonly measured using the dissociation constant [Kd or pKd = −log(Kd)], which is the ligand concentration at which half the protein in solution is bound to ligand at equilibrium . Predicting molecular binding affinity from structural complexes has been investigated for decades, due to its fundamental importance in biochemistry and applications to structure‐based drug development . Most approaches attempt to produce a quantitative mapping to binding affinity from features that can be derived from a protein−ligand structure .…”
Section: Introductionmentioning
confidence: 99%
“…Mapping atomic interactions to binding affinity is not trivial, and a variety of methods have been developed. These methods can be broadly classified into approaches that attempt to directly model the physical forces contributing to molecular binding and those that rely on statistical associations between combinations of atom−atom interactions and ligand affinity . Robust physics‐based methods such as molecular dynamics are able to infer information about changes in system energy and other factors and can produce highly accurate affinity prediction, albeit at the cost of increased computation time .…”
Section: Introductionmentioning
confidence: 99%
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