Petasearch

Petasearch enables searching through the largest sets of proteins.

Installation

Petasearch depends on block-aligner for fast computation of Smith-Waterman alignments in the blockalign module. Thus, the Rust Programming Langugage needs be installed on the user's machine.

Build from source

Clone this repository to your local machine. After cloning, navigate to the project folder and run the following commands.

mkdir build
cd build
cmake -DCMAKE_BUILD_TYPE=Release ..
make -j 16

Afterwards, the built binary srasearch can be found in ./build/src/. You can add the path to the binary to your $PATH in order to user it easily.

Usage

Create Petasearch databases

To achieve space efficiency, srasearch will store the target databases in a specific highly-compressed format. You can use convert2sradb to convert a FASTA/FASTQ file or a MMseqs2 database into a srasearch database.

Example usage:

srasearch convert2sradb target.fasta targetDB
srasearch convert2sradb mmseqsDB targetDB

Preindexing

srasearch requires pre-indexing the target database by calling the module createkmertable first. You can use the following command to generate the kmer table and ID table for a target database called targetDB.

srasearch createkmertable targetDB target_kmertable

Combined workflow

Petasearch provides a combined workflow that will produce only one output file alignments.m8 that contain all the search results from searching queryDB against all the target databases listed in targetlist.

srasearch petasearch queryDB targetlist resultlist alignments.m8 tmp

Easy workflow

Petasearch also provides an easy workflow that will accept fasta file as the input query dataset. The user also do not need to provide resultlist as an input.

sraserach easy-petasearch query.fasta targetlist alignments.m8 tmp

Use index table to prefilter query-target pairs

To search a MMseqs2 database against a list of Petasearch databases, simply run:

srasearch comparekmertables queryDB targetlist resultlist

targetlist should be a file containing all target databases and kmer tables. An example targetlist would look like this:

target_kmertable1   targetDB1
target_kmertable2   targetDB2

resultlist should be a file containing all file names for output files to store the prefiltering result. An example resultlist would look like this:

compkmer_res_1
compkmer_res_2

Compute Smith-Waterman alignment selectively

srasearch blockalign queryDB targetDB1 compkmer_res_1 compali_res_1

Print out alignment results

srasearch convertsraalis queryDB targetDB1 compali_res_1 alignments_1.m8

resultlist should be a file with the same amount of entries of targetlist. Those are the file names