The discrimination of driver from passenger mutations have been a hot topic in the field of cancer biology. While recent advances in identification of drivers in cancer genomic research, there is no computational method focusing on the cancer frameshift indels (insertions and deletions) yet.

PredCID (v1.0) is a web application developed to discriminate cancer driver from passenger frameshift indels. More specifically, we developed an XGBoost classifier with a series of biological features. The results on the independent test set demonstrated that this method outperforms other widely used non-cancer-specific methods in distinguishing known cancer driver frameshift indels from passengers.

You may also be interested in the database of Cancer driver InDels, dbCID.

PredCID Locally Running Version
As input, the PredCID software requires the position information of indels in cDNA and genome which can be annotated by the tool TransVar.

Here is a Tutorial that describes how to prepare the input file, and provides an interpretation with regard to the prediction results.

Zhenyu Yue, Xinlu Chu and Junfeng Xia. PredCID: prediction of driver frameshift indels in human cancer, Briefings in Bioinformatics, 2020, bbaa119.

If you have any problem with the website, please contact: Junfeng Xia  jfxia@ahu.edu.cn