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Generally, Kaiju’s MEM mode classifies between 13.1% (Human vagina) and 48.8% (Bioreactor sediment) more reads than Kraken, which is further increased to 17.8 and 56.6% in Kaiju… To find the best method for identifying fungal species from sequence reads in metagenomics datasets, we first compared the search algorithms BLAST, DIAMOND, Kaiju and Kraken [21, 24–26].BLAST and DIAMOND both align full reads, whereas Kaiju and Kraken use exact k-mer matches. In Vars.databases.kaiju, you will have to make sure the value of fmi fits the database you decide to use.

Kaiju is a program for sensitive taxonomic classification of high-throughput sequencing reads from metagenomic whole genome sequencing or metatranscriptomics experiments. Recent years have seen several approaches to accomplish this task in a time-efficient manner [1,2,3].One such tool, Kraken [], uses a memory-intensive algorithm that associates short genomic substrings (k-mers) with the lowest common ancestor (LCA) … Each sequencing read is assigned to a taxon in the NCBI taxonomy by comparing it to a reference database containing microbial and viral protein sequences . Assigning taxonomic labels to sequencing reads is an important part of many computational genomics pipelines for metagenomics projects. Kraken is an ultrafast and highly accurate program for assigning taxonomic labels to metagenomic DNA sequences. Pipeline construction and testing. Previous programs designed for this task have been relatively slow and computationally expensive, forcing researchers to use faster abundance estimation programs, which only classify small subsets of metagenomic data. The equivalent fmi value for the progenomes database is commented out.. Go over the redirects sections in the parameter file and make sure they are set according to your requirements.. In the provided parameter file, the nr_euk is set.