将深度学习模型DeepVariant集成进DeepChem，提升基因变异检测精度。
A Modular Open Source Framework for Genomic Variant Calling
- 用CNN和修改版Inception v3模型实现基因变异检测
- 通过重对齐、图像生成等流程，提高SNP与小片段变异识别准确率
- 为药物研发与生物信息学融合提供可扩展的开源框架
基因变异检测是基因组研究的核心任务，对发现单核苷酸多态性（SNPs）和插入/缺失（indels）至关重要。本文在广泛使用的开源药物发现框架DeepChem基础上，集成DeepVariant，构建了一个模块化基因变异检测流程。该流程包括测序读段重对齐、候选变异识别、堆叠图像生成，再利用改进的Inception v3模型进行变异分类。本工作为DeepChem增加了可扩展的变异检测框架，推动其药物发现体系与生物信息学流程更紧密融合。
原文摘要 · Abstract (English)
Variant calling is a fundamental task in genomic research, essential for detecting genetic variations such as single nucleotide polymorphisms (SNPs) and insertions or deletions (indels). This paper presents an enhancement to DeepChem, a widely used open-source drug discovery framework, through the integration of DeepVariant. In particular, we introduce a variant calling pipeline that leverages DeepVariant's convolutional neural network (CNN) architecture to improve the accuracy and reliability of variant detection. The implemented pipeline includes stages for realignment of sequencing reads, candidate variant detection, and pileup image generation, followed by variant classification using a modified Inception v3 model. Our work adds a modular and extensible variant calling framework to the DeepChem framework and enables future work integrating DeepChem's drug discovery infrastructure more tightly with bioinformatics pipelines.
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