This is an overview of the organizational structure of miRPursuit. This is useful if you want to re-do the analysis of the pipeline with different parameters only from a specific stage onward. This way you can avoid unnecessary repeating of stages.

The workflow is divided in 4 main stages:
miRPursuit workflow schema

Image 1 - miRPursuit general schema.


There are multiple entry points depending on the form of the raw data. Some NGS sequencing service providers might ship your data already trimmed for adaptors, or you might want to use the raw data provided directly by the sequencing equipment, or you might want to use fasta files compiled from another source.

By using miRPursuit you can specify the type of input file you will use. The most simple is the - -fasta flag that searches the inserts_dir path ( see config files workdir.cfg ) for the target .fa/.fasta libraries and makes a copy to the project folder. In case no .fa/.fasta files are found the program will also search for compressed .fa.gz/.fasta.gz files and proceed to uncompress them.

In case the libraries are still in the fastq format the - -fastq flag should be given. This method does a quality control (fastqc not yet but soon) and then converts the fastq libraries to .fasta, analogously to what is done with “fasta” files, compressed fq.gz/fastq.gz files will be uncompressed if no .fq/.fastq file is found.

Additionally, the - -trim flag can be set to remove adaptor sequences. This requires the adaptor sequence to be stored in the adaptor var (see config files workdir.cfg ).


Filtering Databases
The fasta sequences are filtered based on their length, abundance, low complexity and t/r RNA are removed. These parameters can be set in the wbench_filter.cfg configuration file.
Genome and miRBase
The reads are further filtered by mapping them to the setup genome file with ‘0’ mismatches using patman. These parameters can be set in the patman_genome.cfg configuration file.


Identification of conserved miRNAs (miRBase)
The mapped reads are then aligned to the miRBase⁺ (ref) database using miRProf with the parameter set in the wbench_mirprof.cfg configuration file. The genome mapped reads are separated into two files per library those that mapped with miRBase (conserved reads) and those that did not (non conserved reads).


Adding previously known sequences to the miRBase set, requires following some rules. The fasta headers of the known sequences must respect the following rules:

Plants: Three letter code of sepecies “-” miR + identification of the source with letters + serialization



tasiRNA prediction
The non conserved reads are run through the ta-si predictor to identify trans acting siRNA (tasi-RNA) using the parameters in the wbench_tasi.cfg
Novel miRNA
The non conserved reads are also used to predict novel miRNA with miRCat by searching the genome for their respective precursor nucleotide sequences in the setup genome file. The parameters used by miRCat are set in the wbench_mircat.cfg configuration file and the genome file is set in the workdirs.cfg . If memory (RAM) restrictions apply, the genome can be split into several parts and miRCat will be run once for each part. The various parts should all be held in the same directory with a common name which includes the word part and the sequential number. Afterwards the resulting files will be merged and filtered to remove miRNAs that paired with more genome sites than those specified in the configuration file wbench_mircat.cfg.


Merging results and stepwise stats
The number of sequences kept in each step are given for each library, both total numbers and distinct numbers of sequences. The identified sequences and their respective absolute count are stored in a tab separate value file (.tsv). This provides easy exportation to most statistical softwares as well as MS Excel. The precursor predictions are agregated in the /counts/global-miRCat.csv file, Showing all the non-redundant precursores predicted for each miRNA.


Validation of targets
Target validation is done based on the supplied degradome and transcriptome information, which are both necessary to perform this analysis. The file paths are stored in the workdirs.cfg configuration file and the parameters are stored in wbench_paresnip.cfg configuration file.