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Introduction

Kraken is a taxonomic sequence classifier that assigns taxonomic labels to DNA sequences. Kraken examines the $k$-mers within a query sequence and uses the information within those $k$-mers to query a database. That database maps $k$-mers to the lowest common ancestor (LCA) of all genomes known to contain a given $k$-mer.

The first version of Kraken used a large indexed and sorted list of $k$-mer/LCA pairs as its database. While fast, the large memory requirements posed some problems for users, and so Kraken 2 was created to provide a solution to those problems.

Kraken 2 differs from Kraken 1 in several important ways:

  1. Only minimizers of the $k$-mers in the query sequences are used as database queries. Similarly, only minimizers of the $k$-mers in the reference sequences in the database's genomic library are stored in the database. We will also refer to the minimizers as $\ell$-mers, where $\ell \leq k$. All $k$-mers are considered to have the same LCA as their minimizer's database LCA value.
  2. Kraken 2 uses a compact hash table that is a probabilistic data structure. This means that occasionally, database queries will fail by either returning the wrong LCA, or by not resulting in a search failure when a queried minimizer was never actually stored in the database. By incurring the risk of these false positives in the data structure, Kraken 2 is able to achieve faster speeds and lower memory requirements. Users should be aware that database false positive errors occur in less than 1% of queries, and can be compensated for by use of confidence scoring thresholds.
  3. Kraken 2 has the ability to build a database from amino acid sequences and perform a translated search of the query sequences against that database.
  4. Kraken 2 utilizes spaced seeds in the storage and querying of minimizers to improve classification accuracy.
  5. Kraken 2 provides support for "special" databases that are not based on NCBI's taxonomy. These are currently limited to three popular 16S databases.

Because Kraken 2 only stores minimizers in its hash table, and $k$ can be much larger than $\ell$, only a small percentage of the possible $\ell$-mers in a genomic library are actually deposited in the database. This creates a situation similar to the Kraken 1 "MiniKraken" databases; however, preliminary testing has shown the accuracy of a reduced Kraken 2 database to be quite similar to the full-sized Kraken 2 database, while Kraken 1's MiniKraken databases often resulted in a substantial loss of per-read sensitivity.

If you use Kraken 2 in your own work, please cite either the Kraken 2 paper and/or the original Kraken paper as appropriate. Thank you!

System Requirements

  • Disk space: Construction of a Kraken 2 standard database requires approximately 100 GB of disk space. A test on 01 Jan 2018 of the default installation showed 42 GB of disk space was used to store the genomic library files, 26 GB was used to store the taxonomy information from NCBI, and 29 GB was used to store the Kraken 2 compact hash table.

    Like in Kraken 1, we strongly suggest against using NFS storage to store the Kraken 2 database if at all possible.

  • Memory: To run efficiently, Kraken 2 requires enough free memory to hold the database (primarily the hash table) in RAM. While this can be accomplished with a ramdisk, Kraken 2 will by default load the database into process-local RAM; the --memory-mapping switch to kraken2 will avoid doing so. The default database size is 29 GB (as of Jan. 2018), and you will need slightly more than that in RAM if you want to build the default database.

  • Dependencies: Kraken 2 currently makes extensive use of Linux utilities such as sed, find, and wget. Many scripts are written using the Bash shell, and the main scripts are written using Perl. Core programs needed to build the database and run the classifier are written in C++11, and need to be compiled using a somewhat recent version of g++ that will support C++11. Multithreading is handled using OpenMP. Downloads of NCBI data are performed by wget and rsync. Most Linux systems will have all of the above listed programs and development libraries available either by default or via package download.

    Unlike Kraken 1, Kraken 2 does not use an external $k$-mer counter. However, by default, Kraken 2 will attempt to use the dustmasker or segmasker programs provided as part of NCBI's BLAST suite to mask low-complexity regions (see [Masking of Low-complexity Sequences]).

    MacOS NOTE: MacOS and other non-Linux operating systems are not explicitly supported by the developers, and MacOS users should refer to the Kraken-users group for support in installing the appropriate utilities to allow for full operation of Kraken 2. We will attempt to use MacOS-compliant code when possible, but development and testing time is at a premium and we cannot guarantee that Kraken 2 will install and work to its full potential on a default installation of MacOS.

    In particular, we note that the default MacOS X installation of GCC does not have support for OpenMP. Without OpenMP, Kraken 2 is limited to single-threaded operation, resulting in slower build and classification runtimes.

  • Network connectivity: Kraken 2's standard database build and download commands expect unfettered FTP and rsync access to the NCBI FTP server. If you're working behind a proxy, you may need to set certain environment variables (such as ftp_proxy or RSYNC_PROXY) in order to get these commands to work properly.

    Kraken 2's scripts default to using rsync for most downloads; however, you may find that your network situation prevents use of rsync. In such cases, you can try the --use-ftp option to kraken2-build to force the downloads to occur via FTP.

  • MiniKraken: At present, users with low-memory computing environments can replicate the "MiniKraken" functionality of Kraken 1 in two ways: first, by increasing the value of $k$ with respect to $\ell$ (using the --kmer-len and --minimizer-len options to kraken2-build); and secondly, through downsampling of minimizers (from both the database and query sequences) using a hash function. This second option is performed if the --max-db-size option to kraken2-build is used; however, the two options are not mutually exclusive. In a difference from Kraken 1, Kraken 2 does not require building a full database and then shrinking it to obtain a reduced database.

Installation

To begin using Kraken 2, you will first need to install it, and then either download or create a database.

Kraken 2 consists of two main scripts (kraken2 and kraken2-build), along with several programs and smaller scripts. As part of the installation process, all scripts and programs are installed in the same directory. After installation, you can move the main scripts elsewhere, but moving the other scripts and programs requires editing the scripts and changing the $KRAKEN2_DIR variables in the main scripts.

Once an install directory is selected, you need to run the following command in the directory where you extracted the Kraken 2 source:

./install_kraken2.sh $KRAKEN2_DIR

(Replace $KRAKEN2_DIR above with the directory where you want to install Kraken 2's programs/scripts.)

The install_kraken2.sh script should compile all of Kraken 2's code and setup your Kraken 2 program directory. Installation is successful if you see the message "Kraken 2 installation complete."

Once installation is complete, you may want to copy the main Kraken 2 scripts into a directory found in your PATH variable (e.g., "$HOME/bin"):

cp $KRAKEN2_DIR/kraken2{,-build,-inspect} $HOME/bin

After installation, you're ready to either create or download a database.

Kraken 2 Databases

A Kraken 2 database is a directory containing at least 3 files:

  • hash.k2d: Contains the minimizer to taxon mappings
  • opts.k2d: Contains information about the options used to build the database
  • taxo.k2d: Contains taxonomy information used to build the database

None of these three files are in a human-readable format. Other files may also be present as part of the database build process, and can, if desired, be removed after a successful build of the database.

In interacting with Kraken 2, you should not have to directly reference any of these files, but rather simply provide the name of the directory in which they are stored. Kraken 2 allows both the use of a standard database as well as custom databases; these are described in the sections [Standard Kraken 2 Database] and [Custom Databases] below, respectively.

Standard Kraken 2 Database

To create the standard Kraken 2 database, you can use the following command:

kraken2-build --standard --db $DBNAME

(Replace "$DBNAME" above with your preferred database name/location. Please note that the database will use approximately 100 GB of disk space during creation, with the majority of that being reference sequences or taxonomy mapping information that can be removed after the build.)

This will download NCBI taxonomic information, as well as the complete genomes in RefSeq for the bacterial, archaeal, and viral domains, along with the human genome and a collection of known vectors (UniVec_Core). After downloading all this data, the build process begins; this can be the most time-consuming step. If you have multiple processing cores, you can run this process with multiple threads, e.g.:

kraken2-build --standard --threads 24 --db $DBNAME

Using 32 threads on an AWS EC2 r4.8xlarge instance with 16 dual-core hyperthreaded 2.30 GHz CPUs and 244 GB of RAM, the build process took approximately 35 minutes in Jan. 2018.

The build process itself has two main steps, each of which requires passing over the contents of the reference library:

  1. Estimation of the capacity needed in the Kraken 2 compact hash table. This uses a low-memory method to reliably estimate the number of minimizers present in the reference library given the selected parameters $k$ and $\ell$.
  2. Population of the hash table (and conversion of the taxonomy to an internal format). This step is a second pass over the reference library to find minimizers and then place them in the database.

(There is one other preliminary step where sequence IDs are mapped to taxonomy IDs, but this is usually a rather quick process and is mostly handled during library downloading.)

Unlike Kraken 1's build process, Kraken 2 does not perform checkpointing after the estimation step. This is because the estimation step is dependent on the selected $k$ and $\ell$ values, and if the population step fails, it is likely because $k$ needs to be increased (reducing the overall memory requirements).

Classification

To classify a set of sequences, use the kraken2 command:

kraken2 --db $DBNAME seqs.fa

Output will be sent to standard output by default. The files containing the sequences to be classified should be specified on the command line. Sequences can also be provided through standard input using the special filename /dev/fd/0.

The kraken2 program allows several different options:

  • Multithreading: Use the --threads NUM switch to use multiple threads.

  • Quick operation: Rather than searching all $\ell$-mers in a sequence, stop classification after the first database hit; use --quick to enable this mode.

  • Hit group threshold: The option --minimum-hit-groups will allow you to require multiple hit groups (a group of overlapping k-mers that share a common minimizer that is found in the hash table) be found before declaring a sequence classified, which can be especially useful with custom databases when testing to see if sequences either do or do not belong to a particular genome.

  • Sequence filtering: Classified or unclassified sequences can be sent to a file for later processing, using the --classified-out and --unclassified-out switches, respectively.

  • Output redirection: Output can be directed using standard shell redirection (| or >), or using the --output switch.

  • Compressed input: Kraken 2 can handle gzip and bzip2 compressed files as input by specifying the proper switch of --gzip-compressed or --bzip2-compressed.

  • Input format auto-detection: If regular files (i.e., not pipes or device files) are specified on the command line as input, Kraken 2 will attempt to determine the format of your input prior to classification. You can disable this by explicitly specifying --gzip-compressed or --bzip2-compressed as appropriate. Note that use of the character device file /dev/fd/0 to read from standard input (aka stdin) will not allow auto-detection.

  • Paired reads: Kraken 2 provides an enhancement over Kraken 1 in its handling of paired read data. Rather than needing to concatenate the pairs together with an N character between the reads, Kraken 2 is able to process the mates individually while still recognizing the pairing information. Using the --paired option to kraken2 will indicate to kraken2 that the input files provided are paired read data, and data will be read from the pairs of files concurrently.

    Usage of --paired also affects the --classified-out and --unclassified-out options; users should provide a # character in the filenames provided to those options, which will be replaced by kraken2 with "_1" and "_2" with mates spread across the two files appropriately. For example:

      kraken2 --paired --classified-out cseqs#.fq seqs_1.fq seqs_2.fq

    will put the first reads from classified pairs in cseqs_1.fq, and the second reads from those pairs in cseqs_2.fq.

To get a full list of options, use kraken2 --help.

Output Formats

Standard Kraken Output Format

Each sequence (or sequence pair, in the case of paired reads) classified by Kraken 2 results in a single line of output. Kraken 2's output lines contain five tab-delimited fields; from left to right, they are:

  1. "C"/"U": a one letter code indicating that the sequence was either classified or unclassified.

  2. The sequence ID, obtained from the FASTA/FASTQ header.

  3. The taxonomy ID Kraken 2 used to label the sequence; this is 0 if the sequence is unclassified.

  4. The length of the sequence in bp. In the case of paired read data, this will be a string containing the lengths of the two sequences in bp, separated by a pipe character, e.g. "98|94".

  5. A space-delimited list indicating the LCA mapping of each $k$-mer in the sequence(s). For example, "562:13 561:4 A:31 0:1 562:3" would indicate that:

    • the first 13 $k$-mers mapped to taxonomy ID #562
    • the next 4 $k$-mers mapped to taxonomy ID #561
    • the next 31 $k$-mers contained an ambiguous nucleotide
    • the next $k$-mer was not in the database
    • the last 3 $k$-mers mapped to taxonomy ID #562

    Note that paired read data will contain a "|:|" token in this list to indicate the end of one read and the beginning of another.

    When Kraken 2 is run against a protein database (see [Translated Search]), the LCA hitlist will contain the results of querying all six frames of each sequence. Reading frame data is separated by a "-:-" token.

Kraken 1 offered a kraken-translate and kraken-report script to change the output into different formats. Through the use of kraken2 --use-names, Kraken 2 will replace the taxonomy ID column with the scientific name and the taxonomy ID in parenthesis (e.g., "Bacteria (taxid 2)" instead of "2"), yielding similar functionality to Kraken 1's kraken-translate script. The sample report functionality now exists as part of the kraken2 script, with the use of the --report option; the sample report formats are described below.

Sample Report Output Format

Like Kraken 1, Kraken 2 offers two formats of sample-wide results. Kraken 2's standard sample report format is tab-delimited with one line per taxon. The fields of the output, from left-to-right, are as follows:

  1. Percentage of fragments covered by the clade rooted at this taxon
  2. Number of fragments covered by the clade rooted at this taxon
  3. Number of fragments assigned directly to this taxon
  4. A rank code, indicating (U)nclassified, (R)oot, (D)omain, (K)ingdom, (P)hylum, (C)lass, (O)rder, (F)amily, (G)enus, or (S)pecies. Taxa that are not at any of these 10 ranks have a rank code that is formed by using the rank code of the closest ancestor rank with a number indicating the distance from that rank. E.g., "G2" is a rank code indicating a taxon is between genus and species and the grandparent taxon is at the genus rank.
  5. NCBI taxonomic ID number
  6. Indented scientific name

The scientific names are indented using space, according to the tree structure specified by the taxonomy.

By default, taxa with no reads assigned to (or under) them will not have any output produced. However, if you wish to have all taxa displayed, you can use the --report-zero-counts switch to do so. This can be useful if you are looking to do further downstream analysis of the reports, and want to compare samples. Sorting by the taxonomy ID (using sort -k5,5n) can provide a consistent line ordering between reports.

In addition, we also provide the option --use-mpa-style that can be used in conjunction with --report. This option provides output in a format similar to MetaPhlAn's output. The output with this option provides one taxon per line, with a lowercase version of the rank codes in Kraken 2's standard sample report format (except for 'U' and 'R'), two underscores, and the scientific name of the taxon (e.g., "d__Viruses"). The full taxonomy of each taxon (at the eight ranks considered) is given, with each rank's name separated by a pipe character (e.g., "d__Viruses|o_Caudovirales"). Following this version of the taxon's scientific name is a tab and the number of fragments assigned to the clade rooted at that taxon.

Translated Search

Kraken 2 allows users to perform a six-frame translated search, similar to the well-known BLASTX program. To do this, Kraken 2 uses a reduced 15 amino acid alphabet and stores amino acid minimizers in its database. LCA results from all 6 frames are combined to yield a set of LCA hits, which is then resolved in the same manner as in Kraken's normal operation.

To build a protein database, the --protein option should be given to kraken2-build (either along with --standard, or with all steps if building a custom database).

Custom Databases

We realize the standard database may not suit everyone's needs. Kraken 2 also allows creation of customized databases.

To build a custom database:

  1. Install a taxonomy. Usually, you will just use the NCBI taxonomy, which you can easily download using:

    kraken2-build --download-taxonomy --db $DBNAME

    This will download the accession number to taxon maps, as well as the taxonomic name and tree information from NCBI. These files can be found in $DBNAME/taxonomy/ . If you need to modify the taxonomy, edits can be made to the names.dmp and nodes.dmp files in this directory; you may also need to modify the *.accession2taxid files appropriately.

    Some of the standard sets of genomic libraries have taxonomic information associated with them, and don't need the accession number to taxon maps to build the database successfully. These libraries include all those available through the --download-library option (see next point), except for the plasmid and non-redundant databases. If you are not using custom sequences (see the --add-to-library option) and are not using one of the plasmid or non-redundant database libraries, you may want to skip downloading of the accession number to taxon maps. This can be done by passing --skip-maps to the kraken2-build --download-taxonomy command.

  2. Install one or more reference libraries. Several sets of standard genomes/proteins are made easily available through kraken2-build:

    • archaea: RefSeq complete archaeal genomes/proteins
    • bacteria: RefSeq complete bacterial genomes/proteins
    • plasmid: RefSeq plasmid nucleotide/protein sequences
    • viral: RefSeq complete viral genomes/proteins
    • human: GRCh38 human genome/proteins
    • fungi: RefSeq complete fungal genomes/proteins
    • plant: RefSeq complete plant genomes/proteins
    • protozoa: RefSeq complete protozoan genomes/proteins
    • nr: NCBI non-redundant protein database
    • nt: NCBI non-redundant nucleotide database
    • UniVec: NCBI-supplied database of vector, adapter, linker, and primer sequences that may be contaminating sequencing projects and/or assemblies
    • UniVec_Core: A subset of UniVec chosen to minimize false positive hits to the vector database

    To download and install any one of these, use the --download-library switch, e.g.:

    kraken2-build --download-library bacteria --db $DBNAME

    Multiple libraries can be downloaded into a database prior to building by issuing multiple kraken2-build --download-library commands, e.g.:

    kraken2-build --download-library archaea --db $DBNAME
kraken2-build --download-library viral --db $DBNAME

    The above commands would prepare a database that would contain archaeal and viral genomes; the --build option (see below) will still need to be used after downloading these libraries to actually build the database, however.

    (Note that downloading nr requires use of the --protein option, and that UniVec and UniVec_Core are incompatible with the --protein option.)

    Other genomes can also be added, but such genomes must meet certain requirements:

    • Sequences must be in a FASTA file (multi-FASTA is allowed)
    • Each sequence's ID (the string between the > and the first whitespace character on the header line) must contain either an NCBI accession number to allow Kraken 2 to lookup the correct taxa, or an explicit assignment of the taxonomy ID using kraken:taxid (see below).

    Sequences not downloaded from NCBI may need their taxonomy information assigned explicitly. This can be done using the string kraken:taxid|XXX in the sequence ID, with XXX replaced by the desired taxon ID. For example, to put a known adapter sequence in taxon 32630 ("synthetic construct"), you could use the following:

    >sequence16|kraken:taxid|32630  Adapter sequence
CAAGCAGAAGACGGCATACGAGATCTTCGAGTGACTGGAGTTCCTTGGCACCCGAGAATTCCA

    The kraken:taxid string must begin the sequence ID or be immediately preceded by a pipe character (|). Explicit assignment of taxonomy IDs in this manner will override the accession number mapping provided by NCBI.

    If your genomes meet the requirements above, then you can add each sequence to your database's genomic library using the --add-to-library switch, e.g.:

    kraken2-build --add-to-library chr1.fa --db $DBNAME
kraken2-build --add-to-library chr2.fa --db $DBNAME

    Note that if you have a list of files to add, you can do something like this in bash:

    for file in chr*.fa
do
    kraken2-build --add-to-library $file --db $DBNAME
done

    Or even add all *.fa files found in the directory genomes:

    find genomes/ -name '*.fa' -print0 | xargs -0 -I{} -n1 kraken2-build --add-to-library {} --db $DBNAME

    (You may also find the -P option to xargs useful to add many files in parallel if you have multiple processors.)

  3. Once your library is finalized, you need to build the database. This can be done with the command:

     kraken2-build --build --db $DBNAME

    The --threads option is also helpful here to reduce build time.

    By default, the values of $k$ and $\ell$ are 35 and 31, respectively (or 15 and 12 for protein databases). These values can be explicitly set with the --kmer-len and --minimizer-len options, however. Note that the minimizer length must be no more than 31 for nucleotide databases, and 15 for protein databases. Additionally, the minimizer length $\ell$ must be no more than the $k$-mer length. There is no upper bound on the value of $k$, but sequences less than $k$ bp in length cannot be classified.

    Kraken 2 also utilizes a simple spaced seed approach to increase accuracy. A number $s$ < $\ell$/4 can be chosen, and $s$ positions in the minimizer will be masked out during all comparisons. Masked positions are chosen to alternate from the second-to-last position in the minimizer; e.g., $s$ = 5 and $\ell$ = 31 will result in masking out the 0 positions shown here:

    111 1111 1111 1111 1111 1101 0101 0101

    By default, $s$ = 7 for nucleotide databases, and $s$ = 0 for protein databases. This can be changed using the --minimizer-spaces option along with the --build task of kraken2-build.

A full list of options for kraken2-build can be obtained using kraken2-build --help.

After building a database, if you want to reduce the disk usage of the database, you can use the --clean option for kraken2-build to remove intermediate files from the database directory.

Masking of Low-complexity Sequences

Low-complexity sequences, e.g. "ACACACACACACACACACACACACAC", are known to occur in many different organisms and are typically less informative for use in alignments; the BLAST programs often mask these sequences by default. Using this masking can help prevent false positives in Kraken 2's results, and so we have added this functionality as a default option to Kraken 2's library download/addition process.

Kraken 2 uses two programs to perform low-complexity sequence masking, both available from NCBI: dustmasker, for nucleotide sequences, and segmasker, for amino acid sequences. These programs are available as part of the NCBI BLAST+ suite. If these programs are not installed on the local system and in the user's PATH when trying to use kraken2-build, the database build will fail. Users who do not wish to install these programs can use the --no-masking option to kraken2-build in conjunction with any of the --download-library, --add-to-library, or --standard options; use of the --no-masking option will skip masking of low-complexity sequences during the build of the Kraken 2 database.

Special Databases

To support some common use cases, we provide the ability to build Kraken 2 databases using data from various external databases. These external databases may not follow the NCBI taxonomy, and so we've provided mechanisms to automatically create a taxonomy that will work with Kraken 2 (although such taxonomies may not be identical to NCBI's).

To build one of these "special" Kraken 2 databases, use the following command:

kraken2-build --db $DBNAME --special TYPE

where the TYPE string is one of the database names listed below.

At present, the "special" Kraken 2 database support we provide is limited to pre-packaged solutions for some public 16S sequence databases, but this may grow in the future.

16S Databases

For targeted 16S sequencing projects, a normal Kraken 2 database using whole genome data may use more resources than necessary. A Kraken 2 database created from a well-curated genomic library of just 16S data can provide both a more efficient solution as well as a more accurate set of predictions for such projects. We provide support for building Kraken 2 databases from three publicly available 16S databases:

  • Greengenes (Kraken 2 database name: greengenes), using all available 16S data.
  • RDP (Kraken 2 database name: rdp), using the bacterial and archaeal 16S data.
  • SILVA (Kraken 2 database name: silva), using the Small subunit NR99 sequence set.

Note that these databases may have licensing restrictions regarding their data, and it is your responsibility to ensure you are in compliance with those restrictions; please visit the databases' websites for further details. The kraken2-build script only uses publicly available URLs to download data and then converts that data into a form compatible for use with Kraken 2.

Furthermore, if you use one of these databases in your research, please visit the corresponding database's website to determine the appropriate and up-to-date citation.

Confidence Scoring

At present, we have not yet developed a confidence score with a probabilistic interpretation for Kraken 2. However, we have developed a simple scoring scheme that has yielded good results for us, and we've made that available in Kraken 2 through use of the --confidence option to kraken2. The approach we use allows a user to specify a threshold score in the [0,1] interval; the classifier then will adjust labels up the tree until the label's score (described below) meets or exceeds that threshold. If a label at the root of the taxonomic tree would not have a score exceeding the threshold, the sequence is called unclassified by Kraken 2 when this threshold is applied.

A sequence label's score is a fraction $C$/$Q$, where $C$ is the number of $k$-mers mapped to LCA values in the clade rooted at the label, and $Q$ is the number of $k$-mers in the sequence that lack an ambiguous nucleotide (i.e., they were queried against the database). Consider the example of the LCA mappings in Kraken 2's output given earlier:

"562:13 561:4 A:31 0:1 562:3" would indicate that:

  • the first 13 $k$-mers mapped to taxonomy ID #562
  • the next 4 $k$-mers mapped to taxonomy ID #561
  • the next 31 $k$-mers contained an ambiguous nucleotide
  • the next $k$-mer was not in the database
  • the last 3 $k$-mers mapped to taxonomy ID #562

In this case, ID #561 is the parent node of #562. Here, a label of #562 for this sequence would have a score of $C$/$Q$ = (13+3)/(13+4+1+3) = 16/21. A label of #561 would have a score of $C$/$Q$ = (13+4+3)/(13+4+1+3) = 20/21. If a user specified a --confidence threshold over 16/21, the classifier would adjust the original label from #562 to #561; if the threshold was greater than 20/21, the sequence would become unclassified.

Inspecting a Kraken 2 Database's Contents

The kraken2-inspect script allows users to gain information about the content of a Kraken 2 database. The output format of kraken2-inspect is identical to the reports generated with the --report option to kraken2. Instead of reporting how many reads in input data classified to a given taxon or clade, as kraken2's --report option would, the kraken2-inspect script will report the number of minimizers in the database that are mapped to the various taxa/clades. For example, the first five lines of kraken2-inspect's output on an example database might look like this:

$ kraken2-inspect --db EXAMPLE_DB | head -5
100.00% 1770368409      1581179 R       1       root
 96.50% 1708407622      58003   R1      131567    cellular organisms
 91.28% 1615910070      985309  D       2           Bacteria
 43.89% 777062062       1312736 P       1224          Proteobacteria
 18.62% 329590216       555667  C       1236            Gammaproteobacteria

This output indicates that 555667 of the minimizers in the database map directly to the Gammaproteobacteria class (taxid #1236), and 329590216 (18.62%) of the database's minimizers map to a taxon in the clade rooted at Gammaproteobacteria. For more information on kraken2-inspect's options, use its --help option.

Distinct minimizer count information

The KrakenUniq project extended Kraken 1 by, among other things, reporting an estimate of the number of distinct k-mers associated with each taxon in the input sequencing data. This allows users to better determine if Kraken's classifications are due to reads distributed throughout a reference genome, or due to only a small segment of a reference genome (and therefore likely false positive).

Thanks to the generosity of KrakenUniq's developer Florian Breitwieser in allowing parts of the KrakenUniq source code to be licensed under Kraken 2's MIT license, this distinct counting estimation is now available in Kraken 2. Development work by Martin Steinegger and Ben Langmead helped bring this functionality to Kraken 2.

At present, this functionality is an optional experimental feature -- meaning that we may later alter it in a way that is not backwards compatible with previous versions of the feature.

To use this functionality, simply run the kraken2 script with the additional --report-minimizer-data flag along with --report, e.g.:

kraken2 --db $DBNAME --report k2_report.txt --report-minimizer-data \
    --output k2_output.txt sequence_data.fq

This will put the standard Kraken 2 output (formatted as described in [Standard Kraken Output Format]) in k2_output.txt and the report information in k2_report.txt. Within the report file, two additional columns will be present, e.g.:

normal report format:

36.40	182	182	S2	211044	                      Influenza A virus (A/Puerto Rico/8/1934(H1N1))

modified report format:

36.40	182	182	1688	18	S2	211044	                      Influenza A virus (A/Puerto Rico/8/1934(H1N1))

In this modified report format, the two new columns are the fourth and fifth, respectively representing the number of minimizers found to be associated with a taxon in the read sequences (1688), and the estimate of the number of distinct minimizers associated with a taxon in the read sequence data (18). This would indicate that although 182 reads were classified as belonging to H1N1 influenza, only 18 distinct minimizers led to those 182 classifications.

The format with the --report-minimizer-data flag, then, is similar to that described in [Sample Report Output Format], but slightly different. The fields in this new format, from left-to-right, are:

  1. Percentage of fragments covered by the clade rooted at this taxon
  2. Number of fragments covered by the clade rooted at this taxon
  3. Number of fragments assigned directly to this taxon
  4. Number of minimizers in read data associated with this taxon (new)
  5. An estimate of the number of distinct minimizers in read data associated with this taxon (new)
  6. A rank code, indicating (U)nclassified, (R)oot, (D)omain, (K)ingdom, (P)hylum, (C)lass, (O)rder, (F)amily, (G)enus, or (S)pecies. Taxa that are not at any of these 10 ranks have a rank code that is formed by using the rank code of the closest ancestor rank with a number indicating the distance from that rank. E.g., "G2" is a rank code indicating a taxon is between genus and species and the grandparent taxon is at the genus rank.
  7. NCBI taxonomic ID number
  8. Indented scientific name

We decided to make this an optional feature so as not to break existing software that processes Kraken 2's standard report format. However, this new format can be converted to the standard report format with the command:

cut -f1-3,6-8 k2_new_report.txt > k2_std_report.txt

As noted above, this is an experimental feature. We intend to continue development on this feature, and may change the new format and/or its information if we determine it to be necessary.

For background on the data structures used in this feature and their interaction with Kraken, please read the KrakenUniq paper, and please cite that paper if you use this functionality as part of your work.

Kraken 2 Environment Variables

The kraken2 and kraken2-inspect scripts supports the use of some environment variables to help in reducing command line lengths:

  • KRAKEN2_NUM_THREADS: if the --threads option is not supplied to kraken2, then the value of this variable (if it is set) will be used as the number of threads to run kraken2. (This variable does not affect kraken2-inspect.)

  • KRAKEN2_DB_PATH: much like the PATH variable is used for executables by your shell, KRAKEN2_DB_PATH is a colon-separated list of directories that will be searched for the database you name if the named database does not have a slash (/) character. By default, Kraken 2 assumes the value of this variable is "." (i.e., the current working directory). This variable can be used to create one (or more) central repositories of Kraken databases in a multi-user system. Example usage in bash:

      export KRAKEN2_DB_PATH="/home/user/my_kraken2_dbs:/data/kraken2_dbs:"

    This will cause three directories to be searched, in this order:

    1. /home/user/my_kraken2_dbs
    2. /data/kraken2_dbs
    3. the current working directory (caused by the empty string as the third colon-separated field in the KRAKEN2_DB_PATH string)

    The search for a database will stop when a name match is found; if two directories in the KRAKEN2_DB_PATH have databases with the same name, the directory of the two that is searched first will have its database selected.

    If the above variable and value are used, and the databases /data/kraken2_dbs/mainDB and ./mainDB are present, then

      kraken2 --db mainDB sequences.fa

    will classify sequences.fa using /data/kraken_dbs/mainDB; if instead you wanted to use the mainDB present in the current directory, you would need to specify a directory path to that database in order to circumvent searching, e.g.:

      kraken2 --db ./mainDB sequences.fa

    Note that the KRAKEN2_DB_PATH directory list can be skipped by the use of any absolute (beginning with /) or relative pathname (including at least one /) as the database name.

  • KRAKEN2_DEFAULT_DB: if no database is supplied with the --db option, the database named in this variable will be used instead. Using this variable, you can avoid using --db if you only have a single database that you usually use, e.g. in bash:

      export KRAKEN2_DEFAULT_DB="/home/user/kraken2db"
  kraken2 sequences.fa > kraken2.output

    This will classify sequences.fa using the /home/user/kraken2db database.

    Note that the value of KRAKEN2_DEFAULT_DB will also be interpreted in the context of the value of KRAKEN2_DB_PATH if you don't set KRAKEN2_DEFAULT_DB to an absolute or relative pathname. Given the earlier example in this section, the following:

      export KRAKEN2_DEFAULT_DB="mainDB"
  kraken2 sequences.fa

    will use /data/kraken_dbs/mainDB to classify sequences.fa.