Thomas Desvignes1, Phillipe Loher2, Karen Eilbeck3, Jeffery Ma2, Gianvito Urgese4, Bastian Fromm5, Jason Sydes1, Ernesto Aparicio-Puerta6, Victor Barrera7, Roderic Espín8, Florian Thibord9,10, Xavier Bofill-De Ros11, Eric Londin2, Aristeidis G Telonis2, Elisa Ficarra4, Marc R Friedländer5, John H Postlethwait1, Isidore Rigoutsos2, Michael Hackenberg6, Ioannis S Vlachos12, Marc K Halushka13, Lorena Pantano14. 1. Institute of Neuroscience, University of Oregon, Eugene, OR 97403, USA. 2. Computational Medicine Center, Thomas Jefferson University, Philadelphia, PA 19144, USA. 3. University of Utah, Biomedical Informatics, Salt Lake City, UT 84108, USA. 4. Department of Control and Computer Engineering, Politecnico di Torino, Torino 10129, Italy. 5. Science for Life Laboratory, Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm 114 18, Sweden. 6. Computational Epigenomics Laboratory, Genetics Department and Biotechnology Institute and Biosanitary Institute, University of Granada, Granada 18002, Spain. 7. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA. 8. Universitat Oberta de Catalunya, Barcelona 08018, Spain. 9. Sorbonne Université, Pierre Louis Doctoral School of Public Health, Paris 75006, France. 10. Institut National pour la Santé et la Recherche Médicale (INSERM) Unité Mixte de Recherche en Santé (UMR_S), University of Bordeaux, Bordeaux 33076, France. 11. RNA Biology Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA. 12. Non-coding Research Lab, Department of Pathology, Cancer Research Institute, Harvard Medical School Initiative for RNA Medicine, Beth Israel Deaconess Medical Center, Boston, MA 02115, USA. 13. Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA. 14. Bioinformatics Core, The Picower Institute for Learning and Memory, Cambridge, MA 02139, USA.
Abstract
MOTIVATION: MicroRNAs (miRNAs) are small RNA molecules (∼22 nucleotide long) involved in post-transcriptional gene regulation. Advances in high-throughput sequencing technologies led to the discovery of isomiRs, which are miRNA sequence variants. While many miRNA-seq analysis tools exist, the diversity of output formats hinders accurate comparisons between tools and precludes data sharing and the development of common downstream analysis methods. RESULTS: To overcome this situation, we present here a community-based project, miRNA Transcriptomic Open Project (miRTOP) working towards the optimization of miRNA analyses. The aim of miRTOP is to promote the development of downstream isomiR analysis tools that are compatible with existing detection and quantification tools. Based on the existing GFF3 format, we first created a new standard format, mirGFF3, for the output of miRNA/isomiR detection and quantification results from small RNA-seq data. Additionally, we developed a command line Python tool, mirtop, to create and manage the mirGFF3 format. Currently, mirtop can convert into mirGFF3 the outputs of commonly used pipelines, such as seqbuster, isomiR-SEA, sRNAbench, Prost! as well as BAM files. Some tools have also incorporated the mirGFF3 format directly into their code, such as, miRge2.0, IsoMIRmap and OptimiR. Its open architecture enables any tool or pipeline to output or convert results into mirGFF3. Collectively, this isomiR categorization system, along with the accompanying mirGFF3 and mirtop API, provide a comprehensive solution for the standardization of miRNA and isomiR annotation, enabling data sharing, reporting, comparative analyses and benchmarking, while promoting the development of common miRNA methods focusing on downstream steps of miRNA detection, annotation and quantification. AVAILABILITY AND IMPLEMENTATION: https://github.com/miRTop/mirGFF3/ and https://github.com/miRTop/mirtop. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: MicroRNAs (miRNAs) are small RNA molecules (∼22 nucleotide long) involved in post-transcriptional gene regulation. Advances in high-throughput sequencing technologies led to the discovery of isomiRs, which are miRNA sequence variants. While many miRNA-seq analysis tools exist, the diversity of output formats hinders accurate comparisons between tools and precludes data sharing and the development of common downstream analysis methods. RESULTS: To overcome this situation, we present here a community-based project, miRNA Transcriptomic Open Project (miRTOP) working towards the optimization of miRNA analyses. The aim of miRTOP is to promote the development of downstream isomiR analysis tools that are compatible with existing detection and quantification tools. Based on the existing GFF3 format, we first created a new standard format, mirGFF3, for the output of miRNA/isomiR detection and quantification results from small RNA-seq data. Additionally, we developed a command line Python tool, mirtop, to create and manage the mirGFF3 format. Currently, mirtop can convert into mirGFF3 the outputs of commonly used pipelines, such as seqbuster, isomiR-SEA, sRNAbench, Prost! as well as BAM files. Some tools have also incorporated the mirGFF3 format directly into their code, such as, miRge2.0, IsoMIRmap and OptimiR. Its open architecture enables any tool or pipeline to output or convert results into mirGFF3. Collectively, this isomiR categorization system, along with the accompanying mirGFF3 and mirtop API, provide a comprehensive solution for the standardization of miRNA and isomiR annotation, enabling data sharing, reporting, comparative analyses and benchmarking, while promoting the development of common miRNA methods focusing on downstream steps of miRNA detection, annotation and quantification. AVAILABILITY AND IMPLEMENTATION: https://github.com/miRTop/mirGFF3/ and https://github.com/miRTop/mirtop. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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