Mikado is a lightweight Python3 pipeline whose purpose is to facilitate the identification of expressed loci from RNA-Seq data * and to select the best models in each locus.
Python

README.md

GitHub Downloads Release PyPI Build Status

Mikado - pick your transcript: a pipeline to determine and select the best RNA-Seq prediction

Mikado is a lightweight Python3 pipeline to identify the most useful or “best” set of transcripts from multiple transcript assemblies. Our approach leverages transcript assemblies generated by multiple methods to define expressed loci, assign a representative transcript and return a set of gene models that selects against transcripts that are chimeric, fragmented or with short or disrupted CDS. Loci are first defined based on overlap criteria and each transcript therein is scored based on up to 50 available metrics relating to ORF and cDNA size, relative position of the ORF within the transcript, UTR length and presence of multiple ORFs. Mikado can also utilize blast data to score transcripts based on proteins similarity and to identify and split chimeric transcripts. Optionally, junction confidence data as provided by Portcullis can be used to improve the assessment. The best-scoring transcripts are selected as the primary transcripts of their respective gene loci; additionally, Mikado can bring back other valid splice variants that are compatible with the primary isoform.

Mikado uses GTF or GFF files as mandatory input. Non-mandatory but highly recommended input data can be generated by obtaining a set of reliable splicing junctions with Portcullis_, by locating coding ORFs on the transcripts using either Transdecoder or Prodigal, and by obtaining homology information through either BLASTX or DIAMOND.

Our approach is amenable to include sequences generated by de novo Illumina assemblers or reads generated from long read technologies such as Pacbio.

Extended documentation is hosted on ReadTheDocs: http://mikado.readthedocs.org/

Installation

Mikado can be installed from PyPI with pip:

pip3.5 install mikado

Alternatively, you can clone the repository from source and install with:

python3 setup.py test;
python3 setup.py bdist_wheel;
pip3 install dist/*whl

You can verify the correctness of the installation with the unit tests:

python3 setup.py test

The steps above will ensure that any additional python dependencies will be installed correctly. A full list of library dependencies can be found in the file requirements.txt

Additional dependencies

Mikado by itself does require only the presence of a database solution, such as SQLite (although we do support MySQL and PostGRESQL as well). However, the Daijin pipeline requires additional programs to run.

For driving Mikado through Daijin, the following programs are required:

  • DIAMOND or Blast+ to provide protein homology. DIAMOND is preferred for its speed.
  • Prodigal or Transdecoder to calculate ORFs. The versions of Transdecoder that we tested scale poorly in terms of runtime and disk usage, depending on the size of the input dataset. Prodigal is much faster and lighter, however, the data on our paper has been generated through Transdecoder - not Prodigal. Currently we set Prodigal as default.
  • Mikado also makes use of a dataset of RNA-Seq high-quality junctions. We are using Portcullis to calculate this data alongside the alignments and assemblies.

If you plan to generate the alignment and assembly part as well through Daijin, the pipeline requires the following:

  • SAMTools
  • If you have short-read RNA-Seq data:
    • At least one short-read RNA-Seq aligner, choice between GSNAP, STAR, TopHat2, HISAT2
    • At least one RNA-Seq assembler, choice between StringTie, Trinity, [Cufflinks], CLASS2. Trinity additionally requires GMAP.
    • Portcullis is optional, but highly recommended to retrieve high-quality junctions from the data
  • If you have long-read RNA-Seq data:
    • At least one long-read RNA-Seq aligner, current choice between STAR and GMAP

Citing Mikado

If you use Mikado in your work, please consider to cite:

Venturini L., Caim S., Kaithakottil G., Mapleson D.L., Swarbreck D. Leveraging multiple transcriptome assembly methods for improved gene structure annotation. bioRxiv:216994

If you also use Portcullis to provide reliable junctions to Mikado, either independently or as part of the Daijin pipeline, please consider to cite:

Mapleson D.L., Venturini L., Kaithakottil G., Swarbreck D. Efficient and accurate detection of splice junctions from RNAseq with Portcullis bioRxiv:217620