Learning Objective:
This tutorial walks you through small Raptor applications. It is intended to give you a short overview of what to expect in the other tutorials and how to use this documentation.
| Difficulty | Easy |
|---|---|
| Duration | 30 min |
| Prerequisite tutorials | Setup |
| Recommended reading |
Every page in the tutorials begins with this section. It is recommended that you do the "prerequisite tutorials" before the current one. You should also have a look at the links provided in "recommended reading" and maybe keep them open in separate tabs/windows for reference.
Raptor is a fast and space-efficient pre-filter for querying very large collections of nucleotide sequences. Before we go into the various applications and possibilities of Raptor in the next tutorials, we want to run Raptor in this tutorial with a first minimal example and give a first overview of some possibilities.
A toy data set (124 MiB compressed, 983 MiB decompressed) can be found here.
After extraction, the example_data will look like:
The bins folder contains a FASTA file for each bin and the reads directory contains a FASTQ file for each bin containing reads from the respective bin (with 2 errors). Additionally, mini.fastq (5 reads of all bins), all.fastq (concatenation of all FASTQ files) and all10.fastq (all.fastq repeated 10 times) are provided in the reads folder.
In the following, we will use the 64 data set. To build an index over all bins, we first prepare a file that contains one file path per line (a line corresponds to a bin) and use this file as input:
You may be prompted to enable or disable automatic update notifications. For questions, please consult the SeqAn documentation.
Afterwards, we can search for some reads:
The output starts with metainformation on how the output file was produced (lines starting with ##). Following this, there is a header section (lines starting with #) that maps a number to each input file. After the header section, each line of the output consists of the read ID (in the toy example these are numbers) and the corresponding bins in which they were found:
We offer the option to precompute the minimisers of the input files. This is useful to build indices of big datasets (in the range of several TiB). Following above example, we would change the build step as follows:
First we precompute the minimisers and store them in a directory:
Then we run the build step again and use the computed minimisers as input:
The preprocessing may apply the same cutoffs as used in Mantis (Pandey et al., 2018). This means that only minimisers that occur more often than the cutoff specifies are included in the output. If you wish to do so, you can use --use-filesize-dependent-cutoff.
By default, --kmer-count-cutoff 1 is used, which will include all minimisers in the output.
To reduce the overall memory consumption, the index can be divided into multiple (a power of two) parts. This can be done by passing --parts n to raptor build, where n is the number of parts you want to create. This will create n files, each representing one part of the index. This will reduce the memory consumption of raptor build and raptor search by roughly n, since there will only be one part in memory at any given time. raptor search will automatically detect the parts, and does not need any special parameters.
An old index can be upgraded by running raptor upgrade and providing some information about how the index was constructed.
1.10.0