Lorenzo Arcioni, Manuel Arcieri, Jessica Di Martino, Franco Liberati, Paolo Bottoni, Tiziana Castrignanò
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引用次数: 0
摘要
背景:对于没有参考基因组的物种,转录组数据的可用性使得从 RNA-Seq 数据中构建全新的转录组集合作为替代参考资源成为可能。转录组提供了特定实验条件下物种蛋白质编码基因的直接信息。从头组装过程会产生一个 FASTA 格式的单基因文件,随后进行目标注释。基于同源性的注释是一种通过估计序列与参考数据库中其他序列的相似性来推断序列功能的方法,是一种计算要求很高的程序:为了减轻计算负担,我们推出了HPC-T-Annotator,这是一种在高性能计算(HPC)基础设施上进行全新转录组同源注释的工具,通过网络界面进行直接配置。一旦给出配置数据,整个用于注释的并行计算软件就会自动生成,并可通过简单的命令行在超级计算机上启动。然后,可以使用集成在拟议软件中的 Python 笔记本形式的后处理实用程序轻松查看输出数据:结论:HPC-T-Annotator 加快了全新转录组组装中基于同源性的注释工作。它在高性能计算基础设施上的高效并行化策略大大降低了计算负荷和执行时间,使大规模转录组分析和比较项目成为可能,而其直观的图形界面则使不具备信息技术技能的用户也能使用。
HPC-T-Annotator: an HPC tool for de novo transcriptome assembly annotation.
Background: The availability of transcriptomic data for species without a reference genome enables the construction of de novo transcriptome assemblies as alternative reference resources from RNA-Seq data. A transcriptome provides direct information about a species' protein-coding genes under specific experimental conditions. The de novo assembly process produces a unigenes file in FASTA format, subsequently targeted for the annotation. Homology-based annotation, a method to infer the function of sequences by estimating similarity with other sequences in a reference database, is a computationally demanding procedure.
Results: To mitigate the computational burden, we introduce HPC-T-Annotator, a tool for de novo transcriptome homology annotation on high performance computing (HPC) infrastructures, designed for straightforward configuration via a Web interface. Once the configuration data are given, the entire parallel computing software for annotation is automatically generated and can be launched on a supercomputer using a simple command line. The output data can then be easily viewed using post-processing utilities in the form of Python notebooks integrated in the proposed software.
Conclusions: HPC-T-Annotator expedites homology-based annotation in de novo transcriptome assemblies. Its efficient parallelization strategy on HPC infrastructures significantly reduces computational load and execution times, enabling large-scale transcriptome analysis and comparison projects, while its intuitive graphical interface extends accessibility to users without IT skills.
期刊介绍:
BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology.
BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.