Kaiju

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).

See the release notes for all releases here.

Authors

Peter Menzel pmenzel@gmail.com
Anders Krogh krogh@binf.ku.dk

License

Copyright (c) 2015-2024 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 http://www.gnu.org/licenses/.

Installation

Compiling Kaiju from source

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

git clone https://github.com/bioinformatics-centre/kaiju.git

This will create the directory kaiju in the current directory.

Kaiju is written in C/C++11 for Linux. It uses the zlib library for reading gzip-compressed files. If not already installed, it is necessary to install the zlib development library, e.g. on Ubuntu using:

sudo apt install libz-dev

For compiling Kaiju and its associated programs, type:

cd kaiju/src
make

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

Installation via Bioconda

Kaiju is also available via the bioconda channel and can be installed via

conda install -c bioconda kaiju
# or
mamba install -c bioconda kaiju

Creating the Kaiju 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 reference databases, or download a pre-built index.

For creating a local index, the program kaiju-makedb in the bin/ directory will download a source database and the 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.

kaiju-makedb needs curl and wget for downloading the reference databases.

The downloaded files can be very large, depending on the selected reference database. It is therefore recommended to run kaiju-makedb in a directory with at least 500 GB of free space.

Example usage:

mkdir kaijudb
cd kaijudb
kaiju-makedb -s <DB>

The table below lists the available source databases. Use the database name shown in the first column as argument to option -s in kaiju-makedb. The last column denotes the required memory for running Kaiju with the respective index and for creating the index (in brackets).

* as of Mai 2023.

Pre-built indexes for each reference database can be downloaded.

By default, kaiju-makedb 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 kaiju-makedb is finished, only the files kaiju_db_*.fmi, nodes.dmp, and names.dmp are needed to run Kaiju.

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:

>1_1358
MAQQRRGGFKRRKKVDFIAANKIEVVDYKDTELLKRFISERGKILPRRVTGTSAKNQRKVVNAIKRARVMALLPFVAEDQN
>2_44689
MASTQNIVEEVQKMLDTYDTNKDGEITKAEAVEYFKGKKAFNPERSAIYLFQVYDKDNDGKITIKELAGDIDFDKALKEYKEKQAKSKQQEAEVEEDIEAFILRHNKDDNTDITKDELIQGFKETGAKDPEKSANFILTEMDTNKDGTITVKELRVYYQKVQKLLNPDQ
>3_352472
MKTKSSNNIKKIYYISSILVGIYLCWQIIIQIIFLMDNSIAILEAIGMVVFISVYSLAVAINGWILVGRMKKSSKKAQYEDFYKKMILKSKILLSTIIIVIIVVVVQDIVINFILPQNPQPYVYMIISNFIVGIADSFQMIMVIFVMGELSFKNYFKFKRIEKQKNHIVIGGSSLNSLPVSLPTVKSNESNESNTISINSENNNSKVSTDDTINNVM
>4_91061
MTNPFENDNYTYKVLKNEEGQYSLWPAFLDVPIGWNVVHKEASRNDCLQYVENNWEDLNPKSNQVGKKILVGKR
...

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 kaiju-mkbwt and kaiju-mkfmi. For example, if the database FASTA file is called proteins.faa, then run:

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

Replace kaiju_db_*.fmi by the actual .fmi file depending on the selected database. For example, when running kaiju-makedb -s refseq, the corresponding index file is refseq/kaiju_db_refseq.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, which may also be gzip-compressed.

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

kaiju-multi

While kaiju can only process one input, kaiju-multi can take a comma-separated list of input files (and optionally output files) for processing multiple samples at once:

kaiju-multi -z 25 -t nodes.dmp -f kaiju_db.fmi -i sample1_R1.fastq,sample2_R1.fastq,sample3_R1.fastq -j sample1_R2.fastq,sample2_R2.fastq,sample3_R2.fastq  -o sample1.out,sample2.out,sample3.out

These lists must have the same length. It's also possible to merge all outputs into one file using output redirection:

kaiju-multi -z 25 -t nodes.dmp -f kaiju_db.fmi -i sample1_R1.fastq,sample2_R1.fastq,sample3_R1.fastq -j sample1_R2.fastq,sample2_R2.fastq,sample3_R2.fastq > all_samples.out

Run modes

The default run mode is Greedy with three allowed mismatches. The number of allowed mismatches can be changed using option -e.

In Greedy mode, matches are filtered by a minimum length and score, but also by their E-value (similar to blastp), which can be adjusted with the option -E. The default value is 0.01. The cutoffs for minimum required match length and match score can be changed using the options -m (default: 11) and -s (default: 65).

The run mode can be changed to MEM using option -a:

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

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

Option -x enables filtering of query sequences containing low-complexity regions by using the SEG algorithm from the blast+ package. It is enabled by default and can be disabled by the -X option. SEG filtering is always recommended in order to avoid false positive taxon assignments that are 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 in the nr source file is kept in Kaiju's database and therefore also in the output file.

The number of taxon identifiers (column 5) and accession numbers (column 5) is limited to 20 entries each in order to reduce large outputs produced by highly abundant protein sequences in nr, e.g. from HIV.

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.

Greedy run mode 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 kaiju2table converts Kaiju's output file(s) into a summary table for a given taxonomic rank, e.g., genus. It requires the nodes.dmp and names.dmp files for mapping the taxon identifiers from the third column in the Kaiju output to the corresponding taxon names.

Basic usage:

kaiju2table -t nodes.dmp -n names.dmp -r genus -o kaiju_summary.tsv kaiju.out [kaiju2.out, ...]

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:

kaiju2table -t nodes.dmp -n names.dmp -r genus -m 1.0 -o kaiju_summary.tsv kaiju.out [kaiju2.out, ...]

Similarly, option -c can be used to specify the threshold by absolute read count.

Option -u disables counting unclassified reads towards the total number of reads when calculating percentages.

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

Instead of printing the full taxon path, option -l can be used to specify the ranks to be printed by supplying a comma-separated list, for example: -l superkingdom,phylum,class,order,family,genus,species.

Adding taxa names to output file

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

kaiju-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 kaiju-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:

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

The shell's process substitution can be used for sorting without creating intermediate files:

kaiju-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, kaiju-mergeOutputs will use the identifier found in the first input file (specified by -i). This behavior can be changed using the -c option, which can take four possible values:

• 1: use taxon identifier from the first input file.
• 2: use taxon identifier from the second input file.
• lca: use the least common ancestor of the taxon identifiers from both files (default).
• lowest: use the lowest ranking of the two taxon identifiers if 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.

When the two tab-separated output files contain the classification score in the 4th column (by running kaiju -v), then option -s can be used to give precedence to the classification result with the higher score.

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 kaiju-mkbwt and kaiju-mkfmi:

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

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

The option -n for kaiju-mkbwt specifies the number of parallel threads. The more threads are used, the higher the memory consumption becomes. The option -e for kaiju-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.