The metagenomics classifier uses a k-mer based classification algorithm to classify each query sequence (usually a read) against a collection of reference sequences. There are two logical steps to this process: 1) reference sequences are indexed into a searchable database 2) reference sequence database is searched using query sequences and query sequences are classified to taxid(s) associated with the reference sequences. This guide explains how to run query sequences against a pre-existing reference sequence database (As of DRAGEN 4.3+, users can build their own custom reference sequence database).
| Option | Description |
|---|---|
Applies to: --kmer-classifier-input-read-file, --kmer-classifier-multiple-inputs
If the analysis is for a single FASTA/FASTQ read file, then that filename is input to --kmer-classifier-input-read-file and --kmer-classifier-multiple-inputs=false. However, many read files can be submitted to the Kmer Classifier at one time, minimizing the load time for a large reference sequence database. In this case, the input file must be a .tsv (tab-separated) file with two columns (optionally 3 columns). The first column is a unique ID, the second column is the path to the read file, and the optional third column is the path to the second read file in the case of paired-end reads. The ID is used to distinguish the output files. There is no header line. This .tsv file is the input file to --kmer-classifier-input-read-file and --kmer-classifier-multiple-inputs=true.
Applies to: --kmer-classifier-db-file, --kmer-classifier-db-to-taxid-json, --kmer-classifier-load-db-ram
A file of reference sequences (the "database") can be quite large. If the database file is stored on a normal file system, it is recommended that you set --kmer-classifier-load-db-ram=true. This will tell the Kmer Classifier to load the database file into memory for faster analysis. It is also allowable to store the database file on a RAM disk, which reduces load time over many Kmer Classifier runs. In this case, it is recommended to set --kmer-classifier-load-db-ram=false.
Applies to: --kmer-classifier-db-to-taxid-json
This input file is downloaded alongside the reference sequence database. It associates a taxid internal to the classifier database to an external source, like the NCBI taxonomy. This JSON file is a dictionary where the keys are internal taxids, and is mapped to an external taxid, name, and rank. Example:
The internal taxids are used in the output files. This JSON file can be used to map the results to taxids from the NCBI taxonomy.
The genome database includes NCBI RefSeq genomes for human, bacteria, archaea, viruses, and fungi. The December 3 2023 NCBI taxonomy was used to build the database, and the sequences were collected in December 2023.
To download the reference index file and the taxid mapping JSON:
This database includes the contents of the Genome database and all of the NCBI nucleotide (nt) database. The sequences from the NCBI nucleotide database were collected in July 2023, and the December 3 2023 NCBI taxonomy was used to build the database. Two versions of this database are available for download: One that requires a machine with >= 550GB RAM, and a compressed version that trades approximately 5-10% accuracy for a smaller RAM footprint and requires a machine with >= 225GB RAM.
To download the reference index file and the taxid mapping JSON:
To download the compressed reference index file and the taxid mapping JSON:
This database includes all protein sequences of the UniRef90 database. The sequences were collected in March 2024 and the March 28 2024 NCBI taxonomy was used to build the database.
To download the reference index file and the taxid mapping JSON:
This database includes full length bacterial 16S sequences from the NCBI. The sequences were collected in April 2024 and the March 28 2024 NCBI taxonomy was used to build the database.
To download the reference index file and the taxid mapping JSON:
There are two output files, one organized around the reads, and the other organized around the taxids.
Applies to: --kmer-classifier-output-taxid-seq, --kmer-classifier-output-read-seq The main output file is a .tsv file with the extension .read_classifications.tsv. It has no header line, has tab-separated columns, and can vary in the number of columns depending on command line options. It details the results for each read.
The second output file is a .tsv file with the extension .classifier.taxid_kmer_counts.tsv. It has a header line and has tab-separated columns. It summarizes the results for each taxid.
| Column | Description | Data Type |
|---|---|---|
| Header | Description | Data Type |
|---|---|---|
Required Inputs
--enable-kmer-classifier
Enables the Kmer Classifier. (Default=false).
--output-file-prefix
Prefix for all output files.
--output-directory
Directory for all output files.
--kmer-classifier-input-read-file
Input sequence file (zipped or unzipped) to the Kmer Classifier.
--kmer-classifier-db-file
Database of sequences to classify against.
Optional Inputs
--intermediate-results-dir
Area for temporary files. Size must be greater than size of all FASTQ files multiplied by 2.
--kmer-classifier-load-db-ram
Load the database onto RAM. Do not use if database is on ramdisk. (Default=false).
--kmer-classifier-multiple-inputs
Set to true to run with multiple inputs. The input read file is now a .tsv file that has three columns: Sample ID, Read1 file, (optional) Read 2 file. (Default=false).
--kmer-classifier-min-window
The minimum number of consecutive kmers to classify assignment at taxid. (Default=1).
--kmer-classifier-output-read-seq
Option to enable read sequence column in the output file. (Default=false).
--kmer-classifier-output-taxid-seq
Option to enable a taxid string column in the output file. (Default=false).
--kmer-classifier-db-to-taxid-json
Path to JSON file that maps database IDs to external taxids, names, and ranks.
--kmer-classifier-no-read-output
Option to not create individual read output. (Default=false).
--kmer-classifier-no-taxid-counts
Option to not write taxid count output file. (Default=false).
--kmer-classifier-protein-input
Option to indicate protein query sequences. To use this option, the reference sequence database MUST be of protein sequences. (Default=false).
--kmer-classifier-ncpus
Option to set the number of CPUs available for processing.
1
Read index
integer
2
Read name
string
3
Taxid the read classified to
integer
4
Maximum number of contiguous kmers that classified to this taxid
integer
5
Score assigned to the classification
integer
6
Number of kmers that classified to this taxid
integer
7
Read duplication count
integer
8
Name associated with taxid, if given with --kmer-classifier-db-to-taxid-json
string
9
Taxonomic rank associated with taxid, if given with --kmer-classifier-db-to-taxid-json
string
10
Taxid that each kmer classified to (is output when the --kmer-classifier-output-taxid-seq flag is set)
list of integers separated by commas
11
Read sequence (is output when the the --kmer-classifier-output-read-seq flag is set)
string
db_taxid
Identifier for this taxid used internally in the database
integer
duplicity
Ratio of total number of kmers from reads assigned to this taxid compared to the number of distinct kmers from reads assigned to this taxid
float
distinct_coverage
Percent of kmers in the database assigned to this taxid that are covered by kmers in the reads assigned to this taxid
integer
read_count
Number of reads that classified to this taxid
integer
total_kmer_count
Number of kmers that classified to this taxid
integer
distinct_kmer_count
Number of distinct kmers that classified to this taxid
integer
cumulative_read_count
Cumulative number of reads assigned to this taxid and its taxonomic descendants
integer
taxid
Taxid
integer
name
Name associated with the taxid, if given with --kmer-classifier-db-to-taxid-json
string
rank
Taxonomic rank of the taxid, if given with --kmer-classifier-db-to-taxid-json
string
taxid_distinct_kmer_count
Number of distinct kmers assigned to this taxid from the reference sequences
string
probability_present
Not in use
float