Kraken is a taxonomic sequence classifier that assigns taxonomic labels to short DNA reads. It does this by examining the k -mers within a read and querying a database with those "Gronbyxa fyller 100" -mers. This database contains a mapping of every k -mer in Kraken 's genomic library to the lowest common ancestor LCA in a taxonomic tree of all genomes that contain that k -mer. The set of LCA taxa that correspond to the k -mers in a read are then analyzed to create a single taxonomic label for the read; this label can be any of the nodes in the taxonomic tree.
Kraken is designed to be rapid, sensitive, and highly precise. Our tests on various real and simulated data have shown Kraken to have sensitivity slightly lower than Megablast with precision being slightly higher.
On a set of simulated bp reads, Kraken processed over 1. The latest released version of Kraken will be available at the Kraken websiteand the latest updates to the Kraken source code are available at the Kraken GitHub repository. If you use Kraken in your research, please cite the Kraken paper. Users concerned about the disk or memory requirements should read the paragraph about MiniKraken, below. Construction of Kraken's standard Gronbyxa fyller 100 will require at least GB of disk space as of Oct.
Customized databases may require more or less space. After construction, the minimum required database files require approximately GB of disk space. Disk space used is linearly proportional to the number of distinct k -mers; as of Oct. In addition, the disk used to store the database should be locally-attached storage.
Storing the database on a network filesystem NFS partition can cause Kraken's operation to be very slow, or to be stopped completely. As NFS accesses are much slower than local disk accesses, both preloading and database building will be slowed by use of NFS. To run efficiently, Kraken requires enough free memory to hold the database in RAM.
While this can be accomplished using a ramdisk, Kraken supplies Gronbyxa fyller 100 utility for loading the database into RAM via the OS cache.
The default database size is GB as of Oct. Kraken currently makes extensive use of Linux utilities such as sed, find, and wget. Many scripts are "Gronbyxa fyller 100" using the Bash shell, and the main scripts are written using Perl. Multithreading is handled using OpenMP. Downloads of NCBI data are performed by wget and in some "Gronbyxa fyller 100," by rsync. Most Linux systems that have any sort of development package installed will have all of the above listed programs and libraries available.
Finally, if you want to build your own database, you will need to install the Jellyfish k -mer counter. Note that Kraken only supports use of Jellyfish version 1. Jellyfish version 2 is not Gronbyxa fyller 100 with Kraken.
To allow users with low-memory computing environments to use Kraken, we supply a reduced standard database that can be downloaded from the Kraken web site.
When Kraken is run with a reduced database, we call it MiniKraken. To begin using Kraken, you will first need to install it, and then either download or create a database.
Kraken consists of two main scripts " kraken " and " kraken-build "along with several programs and smaller scripts.
As part of the installation process, all scripts and programs are installed in the same directory. Once a directory is selected, you
Gronbyxa fyller 100 to run the following command in the directory where you extracted the Kraken source:. Installation is successful if you see the message " Kraken installation complete. Once installation is complete, you may want to copy the two main Kraken scripts into a directory found in your PATH variable e.
In interacting with Kraken, 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 allows both the use of a standard database as well as custom databases; these are described in the sections Standard Kraken and Custom Databases below, respectively.
Building the standard Kraken database downloads and uses all complete bacterial, archeal, and viral genomes in Refseq at the time of the build. The build process will then require approximately GB of additional disk space. After building the standard database, usage of the database will require users to keep only the database.
This will download NCBI taxonomic information, as well as the complete genomes in RefSeq for the bacterial, archaeal, and viral domains. After downloading all this data, the build process begins; Gronbyxa fyller 100 is the most time-consuming step. If you have multiple processing cores, you can run this process with multiple threads, e.
Using 24 threads on a computer with GB of RAM, the build process took approximately 5 hours steps with an asterisk have some multi-threading enabled in October Please note that the time required for Gronbyxa fyller 100 the database depends on the number of genomic sequences:.
Note that if any step including the initial downloads fails, the build process will abort. However, kraken-build will produce checkpoints throughout the installation process, and will restart the build at the last incomplete step if you attempt to run the same command again on a partially-built database.
After building the database, to remove any unnecessary files including the library files no longer neededrun the following:. To create a custom database, or to use a database from another source, see Custom Databases. If you encounter problems with Jellyfish not
Gronbyxa fyller 100 able to allocate enough memory on your system to run the build process, you can supply a smaller hash size to Jellyfish using kraken-build 's --jellyfish-hash-size switch.
Each space in the hash table uses approximately 6. Kraken's build process will normally attempt to minimize disk writing by allocating large blocks of RAM and operating within them until data needs to be written to disk. However, this extra RAM usage may exceed your capacity.
In such cases, you may want to use kraken-build 's --work-on-disk switch. This will minimize the amount of RAM usage and cause Kraken's build programs to perform most operations off of disk files. This switch can also be useful for people building on a ramdisk or solid state drive. Please note that working off of disk files can be quite slow on some computers, causing builds to
Gronbyxa fyller 100 several days if not weeks.
We realize the standard database may not suit everyone's needs. Kraken also allows creation of customized databases. Usually, you will just use the NCBI taxonomy, which you can easily download using:. If you need to modify the taxonomy, edits can be made to the names. Install a genomic library. Four sets of standard genomes are made easily available through kraken-build:. If downloaded from NCBI, the genomes can be added directly using the --add-to-library switch, Gronbyxa fyller 100. Once your library is finalized, you need to build the database.
Although D does increase as k increases, it is impossible to know exactly how many distinct k -mers will exist in a library for a given k without actually performing the count. The minimizers serve to keep k -mers that are adjacent in query sequences close to each other in the database, which allows Kraken to exploit the CPU cache.
Changing the value of M can significantly affect the speed of Kraken, and neither increasing or decreasing M will guarantee faster or slower speed. The "--shrink" task allows you to take an existing Kraken database and create a smaller MiniKraken database from it. The --shrink task is only meant to be run on a completed database.
However, if you know before you create a database that you will only be able Gronbyxa fyller 100 use a certain amount of memory, you can use the --max-db-size switch for the --build task to provide a maximum size in GB for the database. This allows you to create a Gronbyxa fyller 100 database without having to create a full Kraken database first.
A full list of options for kraken-build can Gronbyxa fyller 100 obtained using kraken-build --help. After building a database, if you want to reduce the disk usage of the database you can use kraken-build 's --clean switch to remove all intermediate files from the database directory. Output will be sent to standard output by default. The files containing the sequences to be classified Gronbyxa fyller 100 be specified on the command line.
Note that to obtain optimum speeds, Kraken's database should be loaded into RAM first. This can be done through use of a ramdisk, if you have superuser permissions. Failing that, you can use the --preload switch to krakene. The database files will be loaded before classification using this switch.
See Memory Usage and Efficiency for more information. Use the --threads NUM switch to use multiple threads. Rather than searching all k -mers in a sequence, stop classification after the first database hit; use --quick to enable this mode.
Note that --min-hits will allow you to require multiple hits 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.
Classified or unclassified sequences can be sent to a file for later processing, using the --classified-out and --unclassified-out switches, respectively.
Kraken can handle gzip and bzip2 compressed files as input by specifying the proper switch of --gzip-compressed or --bzip2-compressed. If regular files are specified on the command line as input, Kraken will attempt to determine the format of your input prior to classification. Kraken does not query k -mers containing ambiguous nucleotides non-ACGT. If you have paired reads, you can use this fact to your advantage and increase Kraken's accuracy by concatenating the pairs together with a single N between the sequences.
Using the --paired option when running kraken will automatically do this for you; simply specify the two mate pair files on the command line. We have found this to raise "Gronbyxa fyller 100" by about 3 percentage points over classifying the sequences as single-end reads.
Each sequence Gronbyxa fyller 100 by Kraken results in a single line of output. Output lines contain five tab-delimited fields; from left to right, they are:.
For users who want the full taxonomic name associated with each input sequence, we provide a script named kraken-translate that produces two different output formats for classified sequences. The script operates on the output of krakenlike so:. The same database used to run kraken should be used to translate the output; see Kraken Environment Variables below for ways to reduce redundancy on the command line.
The first column of kraken-translate 's output are the sequence IDs of the classified sequences, and the second column contains the "Gronbyxa fyller 100" of the sequence. For example, an output line from kraken of:. history that have shaped the area around them. More about the naturum · Over gravestones The Svalhögen. Rare insects · Organic and Gronbyxa fyller 100 Grown. On a set of simulated bp reads, Kraken processed over million reads per minute on a single core in normal operation, and over million reads per.