b1ae336 Feb 20, 2017
350 lines (286 sloc) 16.1 KB


Peter Menzel
Anders Krogh

Kaiju is a program for the taxonomic classification of high-throughput sequencing reads, e.g., Illumina or Roche/454, from whole-genome sequencing of metagenomic DNA. Reads are directly assigned to taxa using the NCBI taxonomy and a reference database of protein sequences from microbial and viral genomes.

The program is described in Menzel, P. et al. (2016) Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat. Commun. 7:11257 (open access).

Kaiju can be installed locally (see below) or used via a web server.

See the release notes for all releases here.


Copyright (c) 2015, 2016, 2017 Peter Menzel and Anders Krogh

Kaiju is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

Kaiju is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
See the file LICENSE for more details.

You should have received a copy of the GNU General Public License along with the source code. If not, see

Downloading and compiling Kaiju

Kaiju can be downloaded directly from GitHub either as a compressed archive or using the git command line client:

git clone

This will create the directory kaiju in the current directory.

Kaiju is written in C/C++11 for Linux and does not depend on additional libraries. For compiling Kaiju and its associated programs, type:

cd kaiju/src

Afterwards, Kaiju's executable files are available in the kaiju/bin directory. You can add this directory to your shell's $PATH variable or copy the files to a directory in your PATH.

Creating the reference database and index

Before classification of reads, Kaiju's database index needs to be built from the reference protein database. You can either create a local index based on the currently available data from GenBank, or download one of the indexes used by the Kaiju web server.

For creating a local index, the program in the bin/ directory will download the reference genomes and taxonomy files from the NCBI FTP server, convert them into a protein database and construct Kaiju's index (the Burrows-Wheeler transform and the FM-index) in one go.

It is recommended to create a new directory for downloading the files and database construction, for example:

mkdir kaijudb
cd kaijudb [-r|-p|-n|-e]

The downloaded files are several GB in size. Therefore, the program should be run in a directory having at least 80 GB of free space.

There are several options for creating the reference database with protein sequences from different source databases:

1. Complete Reference Genomes from NCBI RefSeq -r
Download only completely assembled and annotated reference genomes of Archaea and Bacteria from the NCBI RefSeq database.

Additionally, viral genomes from NCBI RefSeq can be added by using the option -v.

As of October 2016, this database contains ca. 20M protein sequences, which amounts to a requirement of 14GB RAM for running Kaiju.

2. Representative genomes from proGenomes -p
Download the protein sequences belonging to the representative set of genomes from the proGenomes database. This dataset generally covers a broader phylogenenic range compared to the RefSeq dataset, and is therefore recommended, especially for environmental samples.

Additionally, viral genomes from NCBI RefSeq can be added by using the option -v.

As of October 2016, this database contains ca. 19M protein sequences, which amounts to a requirement of 13GB RAM for running Kaiju.

3. Non-redundant protein database nr -n
Download the nr database that is used by NCBI BLAST and extract proteins belonging to Archaea, Bacteria and Viruses. -e
Download the nr database as above, but additionally include proteins from fungi and microbial eukaryotes. The complete taxon list for this option is in the file bin/taxonlist.tsv.

Because the nr database contains more proteins, more RAM is needed for index construction and for running Kaiju. As of October 2016, the nr database with option -e contains ca. 80M protein sequences, which amounts to a requirement of 43GB RAM for running Kaiju.

Index construction

When using option -r, downloads and extracts 5 genomes from the NCBI FTP server in parallel. This number can be changed by modifying the appropriate variables at the beginning of the script.

By default, uses 5 parallel threads for constructing the index, which can be changed by using the option -t. Note that a higher number of threads increases the memory usage during index construction, while reducing the number of threads decreases memory usage.

After is finished, only the files kaiju_db.fmi (or kaiju_db_nr.fmi / kaiju_db_nr_euk.fmi), nodes.dmp, and names.dmp are needed to run Kaiju. The remaining files and the genomes/ directory containing the downloaded genomes can be deleted.

Custom database

It is also possible to make a custom database from a collection of protein sequences. The format needs to be a FASTA file in which the headers are the numeric NCBI taxon identifiers of the protein sequences, which can optionally be prefixed by another identifier (e.g. a counter) followed by an underscore, for example:


The taxon identifiers must be contained in the NCBI taxonomy files nodes.dmp and names.dmp. Then, Kaiju's index is created using the programs mkbwt and mkfmi. For example, if the database FASTA file is called proteins.faa, then run:

mkbwt -n 5 -a ACDEFGHIKLMNPQRSTVWY -o proteins proteins.faa
mkfmi proteins

which creates the file proteins.fmi that is used by Kaiju. Note that the protein sequences may only contain the uppercase characters of the standard 20 amino acids, all other characters need to be removed.

Running Kaiju

Kaiju requires at least three arguments:

kaiju -t nodes.dmp -f kaiju_db.fmi -i inputfile.fastq

If you chose options -n or -e in, then use -f kaiju_db_nr.fmi or -f kaiju_db_nr_euk.fmi.

For paired-end reads use -i firstfile.fastq and -j secondfile.fastq.

The reads must be in the same order in both files. Kaiju will strip suffixes from the read names by deleting all characters after a / or space. The read names are then compared between the first and second file and an error is issued if they are not identical.

Kaiju can read input files in FASTQ and FASTA format. If the files are compressed, the shell's process substitution can be used to decompress them on the fly. For example for GZIP compressed files:

kaiju -i <(gunzip -c firstfile.fastq.gz) -j <(gunzip -c secondfile.fastq.gz) ...

By default, Kaiju will print the output to the terminal (STDOUT). The output can also be written to a file using the -o option:

kaiju -t nodes.dmp -f kaiju_db.fmi -i inputfile.fastq -o kaiju.out

Kaiju can use multiple parallel threads, which can be specified with the -z option, e.g. for using 25 parallel threads:

kaiju -z 25 -t nodes.dmp -f kaiju_db.fmi -i inputfile.fastq -o kaiju.out

Run modes

The default run mode is MEM, which only considers exact matches. For using the Greedy mode, which allows mismatches, set the mode via the option -a and the number of allowed substitutions using option -e:

kaiju -t nodes.dmp -f kaiju_db.fmi -i inputfile.fastq -a greedy -e 5

The cutoffs for minimum required match length and match score can be changed using the options -m (default: 11) and -s (default: 65).

If the input sequences are already protein sequences, use option -p to disable translation of the input.

Option -x can be used to enable filtering of query sequences containing low-complexity regions by using the SEG algorithm from the blast+ package. Enabling this option is always recommended in order to avoid false positive matches caused by spurious matches due to simple repeat patterns or other sequencing noise.

Output format

Kaiju will print one line for each read or read pair. The default output format contains three columns separated by tabs. Using the option -v enables the verbose output, which will print additional columns:

  1. either C or U, indicating whether the read is classified or unclassified.
  2. name of the read
  3. NCBI taxon identifier of the assigned taxon
  4. the length or score of the best match used for classification
  5. the taxon identifiers of all database sequences with the best match
  6. the accession numbers of all database sequences with the best match
  7. matching fragment sequence(s)

NB: Since the nr database aggregates multiple genes of identical sequences, only the first accession number for each sequence is kept in Kaiju's database and therefore also in the output file.

Classification accuracy

The accuracy of the classification depends both on the choice of the reference database and the chosen options when running Kaiju. These choices also affect the speed and memory usage of Kaiju.

For highest sensitivity, it is recommended to use the nr database (+eukaryotes) as a reference database because it is the most comprehensive set of protein sequences. Alternatively, use proGenomes over Refseq for increased sensitivity.

Additionally, Greedy run mode, for example, with 5 allowed mismatches, yields a higher sensitivity compared with MEM mode.

For fastest classification, use MEM mode and multiple parallel threads (-z); and for lowest memory usage use the proGenomes reference database. The number of parallel threads has only little impact on memory usage.

Further, the choice of the minimum required match length (-m) in MEM mode or match score (-s) in Greedy mode governs the trade-off between sensitivity and precision of the classification. Please refer to the paper for a discussion on this topic.

Helper programs

Creating input file for Krona

The program kaiju2krona can be used to convert Kaiju's tab-separated output file into a tab-separated text file, which can be imported into Krona. It requires the nodes.dmp and names.dmp files from the NCBI taxonomy for mapping the taxon identifiers from Kaiju's output to the corresponding taxon names.

kaiju2krona -t nodes.dmp -n names.dmp -i kaiju.out -o kaiju.out.krona

The file kaiju.out.krona can then be imported into Krona and converted into an HTML file using Krona's ktImportText program:

ktImportText -o kaiju.out.html kaiju.out.krona

Creating classification summary

The program kaijuReport can convert Kaiju's tab-separated output file into a summary report file for a given taxonomic rank, e.g., genus. It requires the nodes.dmp and names.dmp files for mapping the taxon identifiers from Kaiju's output to the corresponding taxon names.

kaijuReport -t nodes.dmp -n names.dmp -i kaiju.out -r genus -o kaiju.out.summary

The program can also filter out taxa with low abundances, e.g. for only showing genera that comprise at least 1 percent of the total reads:

kaijuReport -t nodes.dmp -n names.dmp -i kaiju.out -r genus -m 1 -o kaiju.out.summary

or for showing genera comprising at least 1 percent of all classified reads:

kaijuReport -t nodes.dmp -n names.dmp -i kaiju.out -r genus -m 1 -u -o kaiju.out.summary

Option -p will print the full taxon path instead of just the taxon name.

Adding taxa names

The program addTaxonNames appends the name that corresponds to the taxon id in Kaiju's output file as a last column to the output.

addTaxonNames -t nodes.dmp -n names.dmp -i kaiju.out -o kaiju.names.out

Option -u will omit unclassified reads.
Option -p will print the full taxon path instead of just the taxon name.
Option -r will print the path containing only to the specified ranks. For example, -r phylum,genus will append the names of phylum and genus to the end of each line.

Merging outputs

The program mergeOutputs can merge two tab-separated output files in the column format (see above) used by Kaiju and Kraken. Only the first three columns are used.

The files need to be sorted by the read name in the second column, for example by:

sort -k2,2 kaiju.out  >kaiju.out.sort
sort -k2,2 kraken.out >kraken.out.sort

Then both files can be merged:

mergeOutputs -i kaiju.out.sort -j kraken.out.sort -o combined.out -v

Again, process substitution can be used for sorting without creating intermediate files:

mergeOutputs -i <(sort -k2,2 kaiju.out) -j <(sort -k2,2 kraken.out) -o combined.out -v

The output file will be in the same column format as the input files (but only contain the first three columns) and it will have the same length as the input files (which also have to be of same length). In the case of conflicting taxon identifiers for a classified read in both input files, mergeOutputs will use the identifier found in the first input file (specified by -i). This behaviour can be changed using the -c option, which can take four possible values:

  • 1: use taxon identifier from the first input file (default)
  • 2: use taxon identifier from the second input file
  • lca: use the least common ancestor of the taxon identifiers from both files.
  • lowest: use the lowest ranking of the two taxon identifiers iff they are within the same lineage. Otherwise use the LCA.

Options lca and lowest require the path to the file nodes.dmp by using the -t option.

KaijuX and KaijuP

The programs kaijux and kaijup can be used for finding the best matching database sequence for each query sequence without taxonomic classification, i.e., they will just print the name of the database sequence. Thus, both programs do not use the nodes.dmp file containing the taxonomy, but only need the .fmi database file. While kaijux takes nucleotide sequences as input and translates them into the six reading frames like standard kaiju, kaijup takes protein sequences as input, which are directly searched in the database. All other parameters remain the same as in standard kaiju. In case of paired-end reads, both mates are searched independently.

To build an index for a custom database, all sequences need to be in a single FASTA file and may only contain the 20 letters from the standard protein alphabet ACDEFGHIKLMNPQRSTVWY.

For example, building the index (the Burrows-Wheeler transform and FM-index) from the file with the protein sequences proteins.faa is done in two steps by the programs mkbwt and mkfmi:

mkbwt -n 5 -a ACDEFGHIKLMNPQRSTVWY -o proteins proteins.faa
mkfmi proteins

This will create two intermediate files proteins.bwt and, and finally the file proteins.fmi, which is used by Kaiju.

The option -n for mkbwt specifies the number of parallel threads. The more threads are used, the higher the memory consumption becomes. The option -e for mkbwt specifies the exponent of the suffix array checkpoint distances and therefore determines the trade-off between the size of the suffix array and the speed of the search. The default value is 5.