Inferring gene-pathway associations from consolidated transcriptome datasets: an interactive gene network explorer for .
Fuente:
PubMed
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , |
|---|---|
| Format: | Artículo científico |
| Langue: | en |
| Publié: |
NAR genomics and bioinformatics
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1868266198717693953 |
|---|---|
| author | Bertagna, Michael A Bright, Lydia J Ye, Fei Jiang, Yu-Yang Sarkar, Debolina Pradhan, Ajay Kumar, Santosh Gao, Shan Turkewitz, Aaron P Tsypin, Lev M Z |
| author_facet | Bertagna, Michael A Bright, Lydia J Ye, Fei Jiang, Yu-Yang Sarkar, Debolina Pradhan, Ajay Kumar, Santosh Gao, Shan Turkewitz, Aaron P Tsypin, Lev M Z Bertagna, Michael A Bright, Lydia J Ye, Fei Jiang, Yu-Yang Sarkar, Debolina Pradhan, Ajay Kumar, Santosh Gao, Shan Turkewitz, Aaron P Tsypin, Lev M Z |
| collection | PubMed - marine biology |
| contents | Inferring gene-pathway associations from consolidated transcriptome datasets: an interactive gene network explorer for . Bertagna, Michael A Bright, Lydia J Ye, Fei Jiang, Yu-Yang Sarkar, Debolina Pradhan, Ajay Kumar, Santosh Gao, Shan Turkewitz, Aaron P Tsypin, Lev M Z Tetrahymena thermophila Gene Regulatory Networks Transcriptome Software Computational Biology Gene Expression Profiling Although an established model organism, remains comparatively inaccessible to high throughput screens, and alternative bioinformatic approaches still rely on unconnected datasets and outdated algorithms. Here, we report a new approach to consolidating RNA-seq and microarray data based on a systematic exploration of parameters and computational controls, enabling us to infer functional gene associations from their co-expression patterns. To illustrate the power of this approach, we took advantage of new data regarding a previously studied pathway, the biogenesis of a secretory organelle called the mucocyst. Our untargeted clustering approach recovered over 80% of the genes that were previously verified to play a role in mucocyst biogenesis. Furthermore, we tested four new genes that we predicted to be mucocyst-associated based on their co-expression and found that knocking out each of them results in mucocyst secretion defects. We also found that our approach succeeds in clustering genes associated with several other cellular pathways that we evaluated based on prior literature. We present the Gene Network Explorer (TGNE) as an interactive tool for genetic hypothesis generation and functional annotation in this organism and as a framework for building similar tools for other systems. |
| format | Artículo científico |
| id | pubmed_40432793 |
| institution | PubMed |
| language | en |
| publishDate | 2025 |
| publisher | NAR genomics and bioinformatics |
| record_format | pubmed |
| spellingShingle | Inferring gene-pathway associations from consolidated transcriptome datasets: an interactive gene network explorer for . Bertagna, Michael A Bright, Lydia J Ye, Fei Jiang, Yu-Yang Sarkar, Debolina Pradhan, Ajay Kumar, Santosh Gao, Shan Turkewitz, Aaron P Tsypin, Lev M Z Tetrahymena thermophila Gene Regulatory Networks Transcriptome Software Computational Biology Gene Expression Profiling Inferring gene-pathway associations from consolidated transcriptome datasets: an interactive gene network explorer for . Bertagna, Michael A Bright, Lydia J Ye, Fei Jiang, Yu-Yang Sarkar, Debolina Pradhan, Ajay Kumar, Santosh Gao, Shan Turkewitz, Aaron P Tsypin, Lev M Z Tetrahymena thermophila Gene Regulatory Networks Transcriptome Software Computational Biology Gene Expression Profiling Although an established model organism, remains comparatively inaccessible to high throughput screens, and alternative bioinformatic approaches still rely on unconnected datasets and outdated algorithms. Here, we report a new approach to consolidating RNA-seq and microarray data based on a systematic exploration of parameters and computational controls, enabling us to infer functional gene associations from their co-expression patterns. To illustrate the power of this approach, we took advantage of new data regarding a previously studied pathway, the biogenesis of a secretory organelle called the mucocyst. Our untargeted clustering approach recovered over 80% of the genes that were previously verified to play a role in mucocyst biogenesis. Furthermore, we tested four new genes that we predicted to be mucocyst-associated based on their co-expression and found that knocking out each of them results in mucocyst secretion defects. We also found that our approach succeeds in clustering genes associated with several other cellular pathways that we evaluated based on prior literature. We present the Gene Network Explorer (TGNE) as an interactive tool for genetic hypothesis generation and functional annotation in this organism and as a framework for building similar tools for other systems. |
| title | Inferring gene-pathway associations from consolidated transcriptome datasets: an interactive gene network explorer for . |
| topic | Tetrahymena thermophila Gene Regulatory Networks Transcriptome Software Computational Biology Gene Expression Profiling |
| url | https://pubmed.ncbi.nlm.nih.gov/40432793/ |