[Ting-Ying Wu] TISCalling: leveraging machine learning to identify translational initiation sites in plants and viruses
POST:
TISCalling is a computational prediction model that analyzes mRNA sequences to pinpoint potential start sites along mRNAs.
The research group developed a new computational tool called TISCalling, which is designed to find and identify the start signals for protein production (translation initiation sites, or TISs) in plant and plant viral genomes.
Traditional methods for finding these sites were often limited. They struggled to identify TISs that don't begin with the usual "AUG" codon, and they couldn't locate protein-coding genes in genomes on a large and system-wide scale. They are also limited in showing us which parts of a messenger RNA (mRNA) sequence were most important for deciding where a protein starts. TISCalling uses machine learning techniques to analyze mRNA) sequences. It was trained using data from plants and animals, making it accurate for finding TISs in a variety of species, including plants and viruses. Besides just finding the TISs, the tool also figures out which features of the mRNA sequence are important for a TIS to be recognized. TISCalling is also user-friendly, with a web-based visualization tool and a downloadable package for scientists to use on their own data (https://predict.southerngenomics.org/TISCalling/). Overall, TISCalling is an important step forward in understanding how proteins are made and in improving the accuracy of genetic maps by uncovering previously unknown protein-coding regions. This study is a collaboration among the teams of Drs. Ting-Ying Wu in the Institute of Plant and Microbial Biology, Academia Sinica, Ming-Jung Liu in Agricultural Biotechnology Research Center (ABRC), Academia Sinica and Chia-Yi Cheng in National Taiwan University. The first author is Dr. Ming-Ren Yen, a postdoc from Ting-Ying Wu’s lab; the coauthor is Miss Ya-Ru Li, a research assistant from Ming-Jung Liu’s lab. This project was supported by the Academia Sinica Career Development Award Program and the National Science and Technology Council of Taiwan.