Computational methods for detecting cancer hotspots.
Computational methods for detecting cancer hotspots.
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DOI：
10.1016/j.csbj.2020.11.020
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发表时间：
2020
影响因子：
6
通讯作者：
Trevino V
中科院分区：
文献类型：
--
作者：
Martinez-Ledesma E;Flores D;Trevino V
在患者中反复观察到的癌症突变被称为热点。热点是高度相关的，因为它们可能是功能性的。BRAF、PIK3CA、TP53、KRAS、IDH1中的已知热点支持这一观点。然而，数百个热点从未被实验验证。然而，热点的检测是具有挑战性的，因为背景突变模糊了它们的统计和计算识别。虽然有几种算法已被应用于确定热点，他们还没有被审查之前。因此，在这篇简短的综述中，我们总结了40多种用于检测编码和非编码DNA中癌症热点的计算方法。我们首先将这些方法组织成基于集群的、3D的、特定于位置的和杂项的，以提供一个总体概述。然后，我们描述了它们的嵌入过程，实现，变化和差异。最后，我们讨论了一些优势，为未来的发展提供了一些想法，并提到了应用于病毒整合，易位和表观遗传学的机会。
Cancer mutations that are recurrently observed among patients are known as hotspots. Hotspots are highly relevant because they are, presumably, likely functional. Known hotspots in BRAF, PIK3CA, TP53, KRAS, IDH1 support this idea. However, hundreds of hotspots have never been validated experimentally. The detection of hotspots nevertheless is challenging because background mutations obscure their statistical and computational identification. Although several algorithms have been applied to identify hotspots, they have not been reviewed before. Thus, in this mini-review, we summarize more than 40 computational methods applied to detect cancer hotspots in coding and non-coding DNA. We first organize the methods in cluster-based, 3D, position-specific, and miscellaneous to provide a general overview. Then, we describe their embed procedures, implementations, variations, and differences. Finally, we discuss some advantages, provide some ideas for future developments, and mention opportunities such as application to viral integrations, translocations, and epigenetics.
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影响因子：
14.9
作者：
Gonzalez-Perez A;Lopez-Bigas N
通讯作者：
Lopez-Bigas N
影响因子：
64.5
作者：
Cancer Genome Atlas Research Network
通讯作者：
Cancer Genome Atlas Research Network
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Linding R
影响因子：
28.2
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通讯作者：
Taylor BS