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Mikado website  

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.

Environment Modules

Run module spider Mikado to find out what environment modules are available for this application.

System Variables

  • HPC_MIKADO_DIR - installation directory
  • HPC_MIKADO_BIN - executable directory


If you publish research that uses Mikado you have to cite it as follows:

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. GigaScience, Volume 7, Issue 8, 1 August 2018, giy093, doi:10.1093/gigascience/giy093

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. GigaScience, Volume 7, Issue 12, 12 December 2018, giy131, doi:10.1093/gigascience/giy131