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SFMetaDB: A Comprehensive Annotation of Mouse RNA Splicing Factor RNA-Seq Datasets

Jin Li, Ching-San Tseng, Antonio Federico, Franjo Ivankovic, Yi-Shuian Huang, Alfredo Ciccodicola, Maurice S. Swanson, Peng Yu
doi: https://doi.org/10.1101/177931
Jin Li
1Department of Electrical and Computer Engineering Texas A&M University, College Station, TX 77843, USA
2TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, TX 77843, USA
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Ching-San Tseng
3Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
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Antonio Federico
4Institute of Genetics and Biophysics “Adriano Buzzati Traverso”, CNR, Naples, Italy
5Department of Science and Technology, University of Naples “Parthenope”, Naples, Italy
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Franjo Ivankovic
6Department of Molecular Genetics and Microbiology, Center for NeuroGenetics, the Genetics Institute, and the Genetics & Genomics Graduate Program, University of Florida, College of Medicine, Gainesville, Florida, USA
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Yi-Shuian Huang
3Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
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Alfredo Ciccodicola
4Institute of Genetics and Biophysics “Adriano Buzzati Traverso”, CNR, Naples, Italy
5Department of Science and Technology, University of Naples “Parthenope”, Naples, Italy
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Maurice S. Swanson
6Department of Molecular Genetics and Microbiology, Center for NeuroGenetics, the Genetics Institute, and the Genetics & Genomics Graduate Program, University of Florida, College of Medicine, Gainesville, Florida, USA
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Peng Yu
1Department of Electrical and Computer Engineering Texas A&M University, College Station, TX 77843, USA
2TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, TX 77843, USA
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  • For correspondence: pengyu.bio@gmail.com
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Abstract

Although the number of RNA-Seq datasets deposited publicly has increased over the past few years, incomplete annotation of the associated metadata limits their potential use. Because of the importance of RNA splicing in diseases and biological processes, we constructed a database called SFMetaDB by curating datasets related with RNA splicing factors. Our effort focused on the RNA-Seq datasets in which splicing factors were knocked-down, knocked-out or over-expressed, leading to 75 datasets corresponding to 56 splicing factors. These datasets can be used in differential alternative splicing analysis for the identification of the potential targets of these splicing factors and other functional studies. Surprisingly, only ∼15% of all the splicing factors have been studied by loss- or gain-of-function experiments using RNA-Seq. In particular, splicing factors with domains from a few dominant Pfam domain families have not been studied. This suggests a significant gap that needs to be addressed to fully elucidate the splicing regulatory landscape. Indeed, there are already mouse models available for ∼20 of the unstudied splicing factors, and it can be a fruitful research direction to study these splicing factors in vitro and in vivo using RNA-Seq.

Database URL http://sfmetadb.ece.tamu.edu/

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted August 18, 2017.
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SFMetaDB: A Comprehensive Annotation of Mouse RNA Splicing Factor RNA-Seq Datasets
Jin Li, Ching-San Tseng, Antonio Federico, Franjo Ivankovic, Yi-Shuian Huang, Alfredo Ciccodicola, Maurice S. Swanson, Peng Yu
bioRxiv 177931; doi: https://doi.org/10.1101/177931
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SFMetaDB: A Comprehensive Annotation of Mouse RNA Splicing Factor RNA-Seq Datasets
Jin Li, Ching-San Tseng, Antonio Federico, Franjo Ivankovic, Yi-Shuian Huang, Alfredo Ciccodicola, Maurice S. Swanson, Peng Yu
bioRxiv 177931; doi: https://doi.org/10.1101/177931

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