Package: kraken2 (2.1.1-1)

Taksonomisk klassifikationssystem der bruger præcise k-mer-træffere

Kraken 2 er den nyeste version af Kraken, et taksonomisk klassifikationssystem, der bruger præcise k-mer-træffere til at opnå høj præcision og hurtige klassifikationshastigheder. Dette program matcher hver k-mer i en forespørgelsessekvens til den laveste fælles forfader (LCA) for alle genomer indeholdende den angivne k-mer. K-mer- opgaverne informerer klassifikationsalgoritmen. (se: Kraken 1's internetside for detaljer).

Kraken 2 tilbyder signifikante forbedringer til Kraken 1, med hurtigere databasebyggetider, mindre databasestørrelser og hurtigere klassifikationshastigheder. Disse forbedringer blev opnået af de følgende opdateringer til klassifikationsprogrammet Kraken:

 1. Storage of Minimizers: Instead of storing/querying entire k-mers,
    Kraken 2 stores minimizers (l-mers) of each k-mer. The length of
    each l-mer must be ≤ the k-mer length. Each k-mer is treated by
    Kraken 2 as if its LCA is the same as its minimizer's LCA.
 2. Introduction of Spaced Seeds: Kraken 2 also uses spaced seeds to
    store and query minimizers to improve classification accuracy.
 3. Database Structure: While Kraken 1 saved an indexed and sorted list
    of k-mer/LCA pairs, Kraken 2 uses a compact hash table. This hash
    table is a probabilistic data structure that allows for faster
    queries and lower memory requirements. However, this data structure
    does have a <1% chance of returning the incorrect LCA or returning
    an LCA for a non-inserted minimizer. Users can compensate for this
    possibility by using Kraken's confidence scoring thresholds.
 4. Protein Databases: Kraken 2 allows for databases built from amino
    acid sequences. When queried, Kraken 2 performs a six-frame
    translated search of the query sequences against the database.
 5. 16S Databases: Kraken 2 also provides support for databases not
    based on NCBI's taxonomy. Currently, these include the 16S
    databases: Greengenes, SILVA, and RDP.

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