Creating a universal SNP and small indel variant caller with deep neural networks理解
一、deepvariant研究背景 1.NGS数据的测序过程存在测序错误，错误率~0.1-10% 不等，并且不同测序设备的错误类型存在不同 read1第一个碱基T 对应的质量字符为/，其质量值为ASCII（/）-33，为14，代表这个碱基被正确测序的概率为91% 第二个碱基C 对应的质量字符为A，其质量值为ASCII（A）-33，为32，代表这个碱基被正确测序的概率为99.9% 2.肿瘤样本的异质
相关文章
- 1. Neural Networks and Deep Learning
- 2. Neural Networks and Deep Learning -- Class 4: Deep Neural Networks
- 3. Neural Networks and Deep Learning_week2练习
- 4. COURSE 1 Neural Networks and Deep Learning
- 5. Neural Networks and Deep Learning -- Class 3: Shallow neural networks
- 6. Improving Deep Neural Networks
- 7. Neural Networks and Deep Learning-引论
- 8. neural networks and deep learning-chapter1-3
- 9. Neural Networks and Deep Learning_week3练习
- 10. ImageNet Classification with Deep Convolutional Neural Networks -- 解读
- 更多相关文章...
- • XSL-FO table-and-caption 对象 - XSL-FO 教程
- • W3C RDF and OWL 活动 - W3C 教程
- • RxJava操作符（一）Creating Observables
- • RxJava操作符（七）Conditional and Boolean
相关标签/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
欢迎关注本站公众号,获取更多信息
相关文章
- 1. Neural Networks and Deep Learning
- 2. Neural Networks and Deep Learning -- Class 4: Deep Neural Networks
- 3. Neural Networks and Deep Learning_week2练习
- 4. COURSE 1 Neural Networks and Deep Learning
- 5. Neural Networks and Deep Learning -- Class 3: Shallow neural networks
- 6. Improving Deep Neural Networks
- 7. Neural Networks and Deep Learning-引论
- 8. neural networks and deep learning-chapter1-3
- 9. Neural Networks and Deep Learning_week3练习
- 10. ImageNet Classification with Deep Convolutional Neural Networks -- 解读