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The existing methods for taxonomic profiling can be divided into homology-based and model-based approaches.
There are also hybrid methods for taxonomic classification that combine the result of alignment-based and composition-based approaches in a complementary way.
As do all other methods for taxonomic profiling of whole metagenome sequences, our method crucially depends on the range of microbial reference genomes available in current databases.
This assumes significance given that metagenomic sequence data sets typically contain millions of sequences and using similarity-based methods for taxonomic assignment will require enormous time and compute power for analysis.
Because existing methods for taxonomic profiling of metagenomes are all based on the assignment of fragmentary sequences to phylogenetic categories, the accuracy of results largely depends on fragment length.
As for the second part of the analysis, the methods for taxonomic classification are mostly conservative as they show a high proportion of true negatives, low numbers of true positives and high specificity scores for all the analyses.
Similar(53)
An alternative method for taxonomic assignment using SPs has been proposed in [ 8] in terms of taxa-specific SPs (TSPs).
We propose a method for taxonomic assignment of short reads motivated and inspired by LCA (Huson et al., 2007).
We here present a novel method for taxonomic profiling of metagenomes that is based on mixture modeling.
Results: We here introduce a new method for taxonomic profiling based on mixture modeling of the overall oligonucleotide distribution of a sample.
Our new method for taxonomic profiling combines the detection of protein domains with a mixture model reconstruction of the resulting domain frequencies by means of taxonomically labeled protein reference signatures.
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