Supporting data for "Leveraging multiple transcriptome assembly methods for improved gene structure annotation"

The performance of RNA-Seq aligners and assemblers varies greatly across different organisms and experiments, and often the optimal approach is not known beforehand. Here we show that the accuracy of transcript reconstruction can be boosted by combining multiple methods, and we present a novel algorithm to integrate multiple RNA-Seq assemblies into a coherent transcript annotation. Our algorithm can remove redundancies and select the best transcript models according to user-specified metrics, while solving common artefacts such as erroneous transcript chimerisms. We have implemented this method in an open-source Python3 and Cython program, Mikado, available at https://github.com/lucventurini/Mikado.

Keywords:

• rna-seq
• transcriptome
• assembly
• genome annotation

Read the peer-reviewed publication(s):

• Venturini, L., Caim, S., Kaithakottil, G. G., Mapleson, D. L., & Swarbreck, D. (2018). Leveraging multiple transcriptome assembly methods for improved gene structure annotation. GigaScience, 7(8). https://doi.org/10.1093/gigascience/giy093 (PubMed:30052957)

Additional information:

https://github.com/lucventurini/mikado

https://github.com/lucventurini/mikado-analysis

Accessions (data included in GigaDB):

BioProject: PRJEB22606

Accessions (data not in GigaDB):

BioProject: PRJEB4208
BioProject: PRJEB7093