This repo contains the trained parameter advisor models for Scallop and StringTie. For details on these models, please read our manuscript "Adaptive, sample-specific parameter selection for more accurate transcript assembly" (https://www.biorxiv.org/content/10.1101/2024.01.25.577290v1.abstract).

Here we provide an example of generating a parameter advisor set for a new RNA-seq sample via our model.

Download fastq files of the sample (e.g. SRA accession number:SRR1023790) from https://sra-explorer.info.

1. Download Mash from https://mash.readthedocs.io/en/latest/.

2. Install the python dependencies: pip install -r requirements.txt, we use python 3.10.12.

3. download all the files from https://doi.org/10.1184/R1/25037156.v1, here we need to use the following files:

   (1) trained_scallop.pth: the trained parameter advising model for Scallop.

   (2) trained_stringtie.pth: the trained parameter advising model for StringTie.

   (3) train_features_withaug_scallop.npy: the set representations of all the samples in the representative set of Scallop, including all the samples from the data augmentation module.

   (4) train_features_withaug_stringtie.npy: the set representations of all the samples in the representative set of StringTie, including all the samples from the data augmentation module.

4. Put the files described above, all the files from the folder ./files/, and the scripts autoparadvisor_contrastive.py, MinHash.capnp, and advisorset_generator.py into the same folder, run the command: python advisorset_generator.py --name SRR1023790 --fastqs SRR1023790_1.fastq.gz SRR1023790_2.fastq.gz --assembler scallop --top 5 (or python advisorset_generator.py --name SRR1023790 --fastqs SRR1023790_1.fastq.gz SRR1023790_2.fastq.gz --assembler stringtie --top 5). Here the value of --top is the size of the advisor set. top>=5 is recommended.

5. Our script will output:

   (1) "SRR1023790.msh": Mash sketch of the sample.

   (2) a folder ./SRR1023790_scallop_advisorset/ (or ./SRR1023790_stringtie_advisorset/) and all the parameter candidates are stored there.

(1) representative_sample_scallop: accession numbers of 1263 representative samples for Scallop.

(2) representative_sample_stringtie: accession numbers of 1235 representative samples for StringTie.