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Fernandes M, Mario de Andrade E, Reis da Silva SG, Romagnoli VDS, Ortega JM, Antônio de Oliveira Mendes T. Geneapp: A web application for visualizing alternative splicing for biomedicine. Comput Biol Med 2024; 178:108789. [PMID: 38936077 DOI: 10.1016/j.compbiomed.2024.108789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 06/17/2024] [Accepted: 06/18/2024] [Indexed: 06/29/2024]
Abstract
Alternative Splicing (AS) is an essential mechanism for eukaryotes. However, the consequences of deleting a single exon can be dramatic for the organism and can lead to cancer in humans. Additionally, alternative 5' and 3' splice sites, which define the boundaries of exons, also play key roles to human disorders. Therefore, Investigating AS events is crucial for understanding the molecular basis of human diseases and developing therapeutic strategies. Workflow for AS event analysis can be sampling followed by data analysis with bioinformatics to identify the different AS events in the control and case samples, data visualization for curation, and selection of relevant targets for experimental validation. The raw output of the analysis software does not favor the inspection of events by bioinformaticians requiring custom scripts for data visualization. In this work, we propose the Geneapp application with three modules: GeneappScript, GeneappServer, and GeneappExplorer. GeneappScript is a wrapper that assists in identifying AS in samples compared in two different approaches, while GeneappServer integrates data from AS analysis already performed by the user. In GeneappExplorer, the user visualizes the previous dataset by exploring AS events in genes with functional annotation. This targeted screens that Geneapp allows to perform helps in the identification of targets for experimental validation to confirm the hypotheses under study. The Geneapp is freely available for non-commercial use at https://geneapp.net to advance research on AS for bioinformatics.
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Affiliation(s)
- Miquéias Fernandes
- Postgraduation Program in Bioinformatics, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil; Department of Biochemistry and Molecular Biology, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil; Institute of Applied Biotechnology to Agriculture (BIOAGRO), Universidade Federal de Viçosa, Minas Gerais, Brazil.
| | - Edson Mario de Andrade
- Postgraduation Program in Bioinformatics, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil; Department of Biochemistry and Molecular Biology, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil; Institute of Applied Biotechnology to Agriculture (BIOAGRO), Universidade Federal de Viçosa, Minas Gerais, Brazil
| | - Saymon Gazolla Reis da Silva
- Department of Biochemistry and Molecular Biology, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil; Institute of Applied Biotechnology to Agriculture (BIOAGRO), Universidade Federal de Viçosa, Minas Gerais, Brazil
| | - Vinícius Dos Santos Romagnoli
- Department of Biochemistry and Molecular Biology, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil; Institute of Applied Biotechnology to Agriculture (BIOAGRO), Universidade Federal de Viçosa, Minas Gerais, Brazil
| | - José Miguel Ortega
- Postgraduation Program in Bioinformatics, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Tiago Antônio de Oliveira Mendes
- Postgraduation Program in Bioinformatics, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil; Department of Biochemistry and Molecular Biology, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil; Institute of Applied Biotechnology to Agriculture (BIOAGRO), Universidade Federal de Viçosa, Minas Gerais, Brazil.
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Lio CT, Düz T, Hoffmann M, Willruth LL, Baumbach J, List M, Tsoy O. Comprehensive benchmark of differential transcript usage analysis for static and dynamic conditions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.14.575548. [PMID: 38313260 PMCID: PMC10836064 DOI: 10.1101/2024.01.14.575548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
RNA sequencing offers unique insights into transcriptome diversity, and a plethora of tools have been developed to analyze alternative splicing. One important task is to detect changes in the relative transcript abundance in differential transcript usage (DTU) analysis. The choice of the right analysis tool is non-trivial and depends on experimental factors such as the availability of single- or paired-end and bulk or single-cell data. To help users select the most promising tool for their task, we performed a comprehensive benchmark of DTU detection tools. We cover a wide array of experimental settings, using simulated bulk and single-cell RNA-seq data as well as real transcriptomics datasets, including time-series data. Our results suggest that DEXSeq, edgeR, and LimmaDS are better choices for paired-end data, while DSGseq and DEXSeq can be used for single-end data. In single-cell simulation settings, we showed that satuRn performs better than DTUrtle. In addition, we showed that Spycone is optimal for time series DTU/IS analysis based on the evidence provided using GO terms enrichment analysis.
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Affiliation(s)
- Chit Tong Lio
- Data Science in Systems Biology, Technical University of Munich, 85354 Freising, Germany
| | - Tolga Düz
- Chair of Computational Systems Biology, University of Hamburg, Notkestrasse 9, 22607 Hamburg, Germany
| | - Markus Hoffmann
- Data Science in Systems Biology, Technical University of Munich, 85354 Freising, Germany
- Institute for Advanced Study, Technical University of Munich, Garching D-85748, Germany
- National Institute of Diabetes, Digestive, and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Lina-Liv Willruth
- Data Science in Systems Biology, Technical University of Munich, 85354 Freising, Germany
| | - Jan Baumbach
- Chair of Computational Systems Biology, University of Hamburg, Notkestrasse 9, 22607 Hamburg, Germany
- Institute of Mathematics and Computer Science, University of Southern Denmark, Campusvej 55, 5000 Odense, Denmark
| | - Markus List
- Data Science in Systems Biology, Technical University of Munich, 85354 Freising, Germany
| | - Olga Tsoy
- Chair of Computational Systems Biology, University of Hamburg, Notkestrasse 9, 22607 Hamburg, Germany
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