XomAnnotate: Analysis of Heterogeneous and Complex Exome- A Step towards Translational Medicine

In translational cancer medicine, implicated pathways and the relevant master genes are of focus. Exome's specificity, processing-time, and cost advantage makes it a compelling tool for this purpose. However, analysis of exome lacks reliable combinatory analysis tools and techniques. In this paper we present XomAnnotate – a meta- and functional-analysis software for exome. We compared UnifiedGenotyper, Freebayes, Delly, and Lumpy algorithms that were designed for whole-genome and combined their strengths in XomAnnotate for exome data through meta-analysis to identify comprehensive mutation profile (SNPs/SNVs, short inserts/deletes, and SVs) of patients. The mutation profile is annotated followed by functional analysis through pathway enrichment and network analysis to identify most critical genes and pathways implicated in the disease genesis. The efficacy of the software is verified through MDS and clustering and tested with available 11 familial non-BRCA1/BRCA2 breast cancer exome data. The results showed that the most significantly affected pathways across all samples are cell communication and antigen processing and presentation. ESCO1, HYAL1, RAF1 and PRKCA emerged as the key genes. Network analysis further showed the purine and propanotate metabolism pathways along with RAF1 and PRKCA genes to be master regulators in these patients. Therefore, XomAnnotate is able to use exome data to identify entire mutation landscape, pathways, and the master genes accurately with wide concordance from earlier microarray and whole-genome studies -- making it a suitable biomedical software for using exome in next-generation translational medicine.Availabilityhttp://www.iomics.in/research/XomAnnotate在转化癌症医学领域，与疾病相关的信号通路及关键主控基因始终是研究焦点。外显子组（exome）凭借其特异性、处理时长优势与成本效益，成为该研究方向极具吸引力的工具。然而当前外显子组分析领域却缺乏可靠的整合分析工具与技术手段。本文提出了一款面向外显子组的整合元分析与功能分析软件——XomAnnotate。我们针对为全基因组开发的UnifiedGenotyper、Freebayes、Delly与Lumpy算法开展对比，并通过元分析整合其优势至XomAnnotate中，用于外显子组数据的分析，以获取患者的全面突变图谱（SNPs/SNVs、短插入/缺失片段及结构变异（SVs））。随后对突变图谱进行注释，并通过通路富集分析与网络分析开展功能解析，以鉴定与疾病发生密切相关的核心基因及信号通路。本研究通过多维尺度分析（MDS）与聚类分析验证了该软件的有效性，并利用公开的11例非BRCA1/BRCA2家族性乳腺癌外显子组数据对其进行测试。分析结果显示，所有样本中受影响最显著的通路为细胞通信通路与抗原加工呈递通路。ESCO1、HYAL1、RAF1与PRKCA被鉴定为核心关键基因。网络分析进一步揭示，嘌呤代谢通路与丙酸代谢通路，以及RAF1、PRKCA基因是该类患者的核心调控因子。综上，XomAnnotate可通过外显子组数据精准鉴定完整的突变全景、信号通路及主控基因，且与此前的微阵列及全基因组研究结果具有高度一致性，使其成为下一代转化医学领域中外显子组分析的适配性生物医学软件。可获取地址：http://www.iomics.in/research/XomAnnotate