ArticleBioinformatics (Oxford, England)2024
Highly efficient clustering of long-read transcriptomic data with GeLuster.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- De novo clustering of large long-read transcriptome datasets with isONclust3.Bioinformatics (Oxford, England) · 2025Article
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6 authors.
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Abstract
motivationThe advancement of long-read RNA sequencing technologies leads to a bright future for transcriptome analysis, in which clustering long reads according to their gene family of origin is of great importance. However, existing de novo clustering algorithms require plenty of computing resources.
resultsWe developed a new algorithm GeLuster for clustering long RNA-seq reads. Based on our tests on one simulated dataset and nine real datasets, GeLuster exhibited superior performance. On the tested Nanopore datasets it ran 2.9-17.5 times as fast as the second-fastest method with less than one-seventh of memory consumption, while achieving higher clustering accuracy. And on the PacBio data, GeLuster also had a similar performance. It sets the stage for large-scale transcriptome study in future. AVAILABILITY AND IMPLEMENTATION: GeLuster is freely available at https://github.com/yutingsdu/GeLuster.
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