Efficient detection and assembly of non-reference DNA sequences with synthetic long reads.
Efficient detection and assembly of non-reference DNA sequences with synthetic long reads.
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DOI：
10.1093/nar/gkac653
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发表时间：
2022-10-14
影响因子：
14.9
通讯作者：
Hajirasouliha, Iman
中科院分区：
文献类型：
--
作者：
Meleshko, Dmitry;Yang, Rui;Marks, Patrick;Williams, Stephen;Hajirasouliha, Iman
最近的泛基因组研究揭示了人类基因组中大量的DNA序列，这些序列在参考基因组中不存在。这些非参考序列（NRS）的大部分不能可靠地组装或放置在参考基因组上。长读段和合成长读段（又名链接读段）技术的改进对NRS的表征具有巨大的潜力。虽然合成长读段需要比长读段数据集更少的输入DNA，但它们在算法上使用起来更具挑战性。除了计算昂贵的全基因组组装方法外，没有用于NRS检测的合成长读方法。我们提出了一种新的集成的基于标记和基于局部组装的算法，Novel-X，它使用编码在合成长读段中的条形码信息来提高对此类事件的检测，而无需全基因组从头组装。我们的评估表明，Novel-X发现了许多非参考序列，这些序列无法通过最先进的短读方法找到。我们将Novel-X应用于来自Polaris HiSeq 4000 PGx队列的68个样本的不同集合。Novel-X发现了16691个长度> 300 bp的NRS插入片段（总长度为18.2 Mb）。其中许多是针对特定人群的，或可能具有功能性影响。
Recent pan-genome studies have revealed an abundance of DNA sequences in human genomes that are not present in the reference genome. A lion’s share of these non-reference sequences (NRSs) cannot be reliably assembled or placed on the reference genome. Improvements in long-read and synthetic long-read (aka linked-read) technologies have great potential for the characterization of NRSs. While synthetic long reads require less input DNA than long-read datasets, they are algorithmically more challenging to use. Except for computationally expensive whole-genome assembly methods, there is no synthetic long-read method for NRS detection. We propose a novel integrated alignment-based and local assembly-based algorithm, Novel-X, that uses the barcode information encoded in synthetic long reads to improve the detection of such events without a whole-genome de novo assembly. Our evaluations demonstrate that Novel-X finds many non-reference sequences that cannot be found by state-of-the-art short-read methods. We applied Novel-X to a diverse set of 68 samples from the Polaris HiSeq 4000 PGx cohort. Novel-X discovered 16 691 NRS insertions of size > 300 bp (total length 18.2 Mb). Many of them are population specific or may have a functional impact.
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影响因子：
6
作者：
Luo R;Sedlazeck FJ;Darby CA;Kelly SM;Schatz MC
通讯作者：
Schatz MC
影响因子：
7
作者：
Huddleston J;Chaisson MJP;Steinberg KM;Warren W;Hoekzema K;Gordon D;Graves-Lindsay TA;Munson KM;Kronenberg ZN;Vives L;Peluso P;Boitano M;Chin CS;Korlach J;Wilson RK;Eichler EE
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Eichler EE
影响因子：
48
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Kidd, Jeffrey M.;Sampas, Nick;Antonacci, Francesca;Graves, Tina;Fulton, Robert;Hayden, Hillary S.;Alkan, Can;Malig, Maika;Ventura, Mario;Giannuzzi, Giuliana;Kallicki, Joelle;Anderson, Paige;Tsalenko, Anya;Yamada, N. Alice;Tsang, Peter;Kaul, Rajinder;Wilson, Richard K.;Bruhn, Laurakay;Eichler, Evan E.
通讯作者：
Eichler, Evan E.
影响因子：
7
作者：
Harrow J;Frankish A;Gonzalez JM;Tapanari E;Diekhans M;Kokocinski F;Aken BL;Barrell D;Zadissa A;Searle S;Barnes I;Bignell A;Boychenko V;Hunt T;Kay M;Mukherjee G;Rajan J;Despacio-Reyes G;Saunders G;Steward C;Harte R;Lin M;Howald C;Tanzer A;Derrien T;Chrast J;Walters N;Balasubramanian S;Pei B;Tress M;Rodriguez JM;Ezkurdia I;van Baren J;Brent M;Haussler D;Kellis M;Valencia A;Reymond A;Gerstein M;Guigó R;Hubbard TJ
通讯作者：
Hubbard TJ
影响因子：
1.2
作者：
Cingolani, Pablo;Platts, Adrian;Ruden, Douglas M.
通讯作者：
Ruden, Douglas M.