Wir wollen dem Leben in den Rachen greifen. Translation ist die wirkungsvollste regulatorische Ebene des Lebens.
Translatomics is for all life, but our work is focused.
The video tutorials and the resources for the Bioinformatics course.
本实验室参与申报的科技部国家重点研发计划《医学生命组学数据质量控制关键技术研发与应用》项目立项。基因谷 2018.9.19. 头条
FANSe3 算法创世界纪录，一小时全基因组、一分钟全外显子组分析南方日报 2018.1.22.
张弓课题组联合承启生物在Nucleic Acids Research发表最强翻译组学数据库 TranslatomeDB暨南大学生命科学技术学院 2017.11.24.
暨大研发另类质谱鉴定算法策略, 零成本大幅提高蛋白质组鉴定能力生物通 2017.10.24.
TranslatomeDB provides collection and integrated analysis of published and user-generated translatome sequencing data (mRNA-seq, RNC-seq, Ribo-seq). All datasets are analyzed using a unified, robust, accurate and experimentally-verifiable FANSe3 algorithm for read mapping and edgeR for DGE analysis.
FANSe3 is the third generation of FANSe series mapping algorithm, which maps millions and billions of sequencing reads to reference sequences. Unlike the other NGS mapping algorithms, which prioritize the speed and then try to compensate the accuracy, the FANSe series algorithm is prioritized to guarantee the accuracy and robustness, and using computational optimization to speed up.
FANSe2 is a mapping algorithm which can map a billion reads in hours with ultimate and robust accuracy. Compared with FANSe, FANSe2 inherited the near-perfect and robust accuracy while making substantial strategic and technical improvements, increasing the running speed for more than 10x and thus is suitable for large reference genome sequences.
FANSe2splice is a mapping tool for spliced mapping for next-generation sequencing. It splits a read into two halves and map them to reference genome. This is particularly useful in case of RNA-seq, where RNA splicing may occur. FANSe2splice inherited accuracy and robustness from FANSe2. It detects junction from single-end reads. It detects junctions from a single read without the aid of any other reads, and thus ideal for low-throughput sequencing.
RiboTempo predicts the translation rate profile in E. coli and B. subtilis. The algorithm incorporates calculation of the rate of translation of each codon based on the tRNA concentration and codon selectivity of the cognate tRNA. Please refer to the citations.