Title:
ANNOgesic: The Swiss Army Knife for RNA-Seq Based Annotation of Bacterial/Archaeal Genomes
ANNOgesic: The Swiss Army Knife for RNA-Seq Based Annotation of Bacterial/Archaeal Genomes
Speaker:
于松桓 (Sung-Huan Yu), PhD, Max Planck Institute of Biochemistry
于松桓 (Sung-Huan Yu), PhD, Max Planck Institute of Biochemistry
Time:
04/25 (Sat.) 1 pm PDT, 2 pm MDT, 3 pm CDT, 4 pm EDT
04/26 (Sun.) 4 am Taiwan
04/25 (Sat.) 1 pm PDT, 2 pm MDT, 3 pm CDT, 4 pm EDT
04/26 (Sun.) 4 am Taiwan
Keywords:
Biology, Informatics, Bioinformatics, Microbiology, RNA-Seq, Genome annotation, Non-coding RNA, Machine learning
Biology, Informatics, Bioinformatics, Microbiology, RNA-Seq, Genome annotation, Non-coding RNA, Machine learning
Abstract:
To understand the gene regulation of an organism of interest, a comprehensive genome annotation is essential. While some features, such as coding sequences, can be computationally predicted with high accuracy based purely on the genomic sequence, others, such as promoter elements or noncoding RNAs, are harder to detect. RNA sequencing (RNA-seq) has proven to be an efficient method to identify these genomic features and to improve genome annotations. However, processing and integrating RNA-seq data in order to generate high-resolution annotations is challenging, time consuming, and requires numerous steps. We have constructed a powerful and modular tool called ANNOgesic that provides the required analyses and simplifies RNA-seq-based bacterial and archaeal genome annotation. It can integrate data from conventional RNA-seq and differential RNA-seq and predicts and annotates numerous features, including small noncoding RNAs, with high precision. The software is available under an open source license (ISCL) at https://pypi.org/project/ANNOgesic/.
To understand the gene regulation of an organism of interest, a comprehensive genome annotation is essential. While some features, such as coding sequences, can be computationally predicted with high accuracy based purely on the genomic sequence, others, such as promoter elements or noncoding RNAs, are harder to detect. RNA sequencing (RNA-seq) has proven to be an efficient method to identify these genomic features and to improve genome annotations. However, processing and integrating RNA-seq data in order to generate high-resolution annotations is challenging, time consuming, and requires numerous steps. We have constructed a powerful and modular tool called ANNOgesic that provides the required analyses and simplifies RNA-seq-based bacterial and archaeal genome annotation. It can integrate data from conventional RNA-seq and differential RNA-seq and predicts and annotates numerous features, including small noncoding RNAs, with high precision. The software is available under an open source license (ISCL) at https://pypi.org/project/ANNOgesic/.
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