BreakNet: detecting deletions using long reads and a deep learning approach.
BreakNet: detecting deletions using long reads and a deep learning approach.
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BreakNet：使用长读段和深度学习方法检测删除。
DOI：
10.1186/s12859-021-04499-5
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发表时间：
2021-12-02
影响因子：
3
通讯作者：
Luo H
中科院分区：
文献类型：
--
作者：
Luo J;Ding H;Shen J;Zhai H;Wu Z;Yan C;Luo H
结构变异在人类遗传多样性中占有重要地位，缺失是一种重要的结构变异类型，被认为与遗传疾病有关。虽然已经提出了各种基于长读段的删除调用方法，但仍然需要一种新的方法来挖掘长读段比对信息中的特征。最近，深度学习在基因组分析中引起了广泛关注，它是一种很有前途的调用SV的技术。在本文中，我们提出了BreakNet，这是一种深度学习方法，通过使用长读取来检测删除。BreakNet首先从长读段比对中提取特征矩阵。其次，它使用时间分布式卷积神经网络（CNN）来整合特征矩阵并将其映射到特征向量。第三，BreakNet采用双向长短期记忆（BLSTM）模型来分析产生的前向和后向连续特征向量集。最后，分类模块确定区域是否涉及删除。在真实的长读段测序数据集上，我们证明了BreakNet在F1得分方面优于Sniffles，SVIM和cuteSV。所提出的方法的源代码可从GitHub https://github.com/luojunwei/BreakNet获得。我们的工作表明，深度学习可以与长读取相结合，比现有方法更有效地调用删除。在线版本包含补充材料，可通过10.1186/s12859-021-04499-5获得。
Structural variations (SVs) occupy a prominent position in human genetic diversity, and deletions form an important type of SV that has been suggested to be associated with genetic diseases. Although various deletion calling methods based on long reads have been proposed, a new approach is still needed to mine features in long-read alignment information. Recently, deep learning has attracted much attention in genome analysis, and it is a promising technique for calling SVs. In this paper, we propose BreakNet, a deep learning method that detects deletions by using long reads. BreakNet first extracts feature matrices from long-read alignments. Second, it uses a time-distributed convolutional neural network (CNN) to integrate and map the feature matrices to feature vectors. Third, BreakNet employs a bidirectional long short-term memory (BLSTM) model to analyse the produced set of continuous feature vectors in both the forward and backward directions. Finally, a classification module determines whether a region refers to a deletion. On real long-read sequencing datasets, we demonstrate that BreakNet outperforms Sniffles, SVIM and cuteSV in terms of their F1 scores. The source code for the proposed method is available from GitHub at https://github.com/luojunwei/BreakNet. Our work shows that deep learning can be combined with long reads to call deletions more effectively than existing methods. The online version contains supplementary material available at 10.1186/s12859-021-04499-5.
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影响因子：
64.8
作者：
Sudmant PH;Rausch T;Gardner EJ;Handsaker RE;Abyzov A;Huddleston J;Zhang Y;Ye K;Jun G;Fritz MH;Konkel MK;Malhotra A;Stütz AM;Shi X;Casale FP;Chen J;Hormozdiari F;Dayama G;Chen K;Malig M;Chaisson MJP;Walter K;Meiers S;Kashin S;Garrison E;Auton A;Lam HYK;Mu XJ;Alkan C;Antaki D;Bae T;Cerveira E;Chines P;Chong Z;Clarke L;Dal E;Ding L;Emery S;Fan X;Gujral M;Kahveci F;Kidd JM;Kong Y;Lameijer EW;McCarthy S;Flicek P;Gibbs RA;Marth G;Mason CE;Menelaou A;Muzny DM;Nelson BJ;Noor A;Parrish NF;Pendleton M;Quitadamo A;Raeder B;Schadt EE;Romanovitch M;Schlattl A;Sebra R;Shabalin AA;Untergasser A;Walker JA;Wang M;Yu F;Zhang C;Zhang J;Zheng-Bradley X;Zhou W;Zichner T;Sebat J;Batzer MA;McCarroll SA;1000 Genomes Project Consortium;Mills RE;Gerstein MB;Bashir A;Stegle O;Devine SE;Lee C;Eichler EE;Korbel JO
通讯作者：
Korbel JO
影响因子：
48
作者：
Sedlazeck FJ;Rescheneder P;Smolka M;Fang H;Nattestad M;von Haeseler A;Schatz MC
通讯作者：
Schatz MC
影响因子：
158.5
作者：
Weiss, Lauren A.;Shen, Yiping;Daly, Mark J.
通讯作者：
Daly, Mark J.
影响因子：
46.9
作者：
Poplin, Ryan;Chang, Pi-Chuan;DePristo, Mark A.
通讯作者：
DePristo, Mark A.
DOI：
10.1093/bioinformatics/bts378
发表时间：
2012-09-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Rausch T;Zichner T;Schlattl A;Stütz AM;Benes V;Korbel JO
通讯作者：
Korbel JO