FilterLAP: Filtering Falsepositive Mutation Calls via a Label Propagation Framework Benefiting from the recent advantages of genomic sequencing, detecting genomic mutations becomes a routine work in precise diagnoses and treatments for cancers. In clinical practices, many factors, such as tumor purity, clonal structure, etc., interfere the performance of calling mutations. The computational pipelines prefer to sensitively report the candidate calls, while a filter is applied for removing the falsepositive calls. The existing filters rely on the whole genomeexome sequencing data, which can provide sufficient samples for training the filters. However, the genepanel sequencing is more popular in clinical practices, but there is no practical filter for limited training samples. In light of this, we develop a semilearning filter for genepanel sequencing data, FilterLAP, which implemented via a label propagation framework. Given few labeled samples with a set of unlabeled ones, its basic idea is to predict the label information of unlabeled nodes from the label information of labeled nodes, and establishes a complete graph model by using the relationship between samples, by combining transductive inference with label propagation algorithm. For each node in the network, tags are propagated to adjacent nodes according to similarity and the probability distribution of similar nodes tends to be similar and can be divided into a class. We perform multiple sets of experiments on genepanel sequencing data captured from Illumina platform. FilterLAP outperforms on both SNV and INDEL filtering, where the AUCs reach 0.900.97, and the average accuracies on overall mutation calls are over 90%. Comparing to GATK hard filters, FilterLAP present a 5% improvement on accuracy. These results demonstrate that the proposed method can better reduce the false positive mutation calls on genepanel sequencing data. In addition, it is stable and efficient, which can be used as a practical tool for mutation call filtering for genepanel sequencing data.

DNMFilter_Indel: Filtering de novo Indels in ParentOffspring Trios Identification of de novo indels from whole genome or exome sequencing data of parentoffspring trios is a challenging task in human disease studies and clinical practices. Existing computational approaches usually yield high false positive rate. In this study, we developed a gradient boosting approach for filtering de novo indels obtained by any computational approaches. Through application on the real genome sequencing data, our approach showed it could significantly reduce the false positive rate of de novo indels without a significant compromise on sensitivity. The software DNMFilter_Indel was written in a combination of Java and R and freely available from the website at https:github.comyongzhuangDNMFilter_Indel.