SeqWho: reliable, rapid determination of sequence file identity using k-mer frequencies in Random Forest classifiers.
SeqWho: reliable, rapid determination of sequence file identity using k-mer frequencies in Random Forest classifiers.
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SeqWho：使用随机森林分类器中的 k-mer 频率可靠、快速地确定序列文件身份。
DOI：
10.1093/bioinformatics/btac050
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Kim,Daehwan
中科院分区：
文献类型：
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作者：
Bennett,Christopher;Thornton,Micah;Park,Chanhee;Henry,Gervaise;Zhang,Yun;Malladi,Venkat;Kim,Daehwan
随着测序技术的巨大进步和测序方案的增加，测序正被用于回答复杂的生物学问题。随后，分析管道变得更加耗时和复杂，通常需要非常广泛的预验证步骤。在这里，我们提出了SeqWho，这是一个程序，旨在启发式评估测序文件的质量，并通过使用随机森林分类器对偏差、天然油墨频率和重复序列身份进行训练，可靠地对生物体和协议类型进行分类。结果使用我们的一个主要模型，我们的方法能够准确和快速地对来自9个不同测序文库的人类和小鼠序列进行分类，分别为98.32%、97.86%和96.38%。最后，我们证明了SeqWho是一种强大的方法，可以可靠地验证任何管道中使用的测序文件的质量和身份。可用性和实现https://github.com/DaehwanKimLab/seqwho.Supplementary information补充数据可在bioinformaticsonline获取。
MotivationWith the vast improvements in sequencing technologies and increased number of protocols, sequencing is being used to answer complex biological problems. Subsequently, analysis pipelines have become more time consuming and complicated, usually requiring highly extensive prevalidation steps. Here, we present SeqWho, a program designed to assess heuristically the quality of sequencing files and reliably classify the organism and protocol type by using Random Forest classifiers trained on biases native ink-mer frequencies and repeat sequence identities.ResultsUsing one of our primary models, we show that our method accurately and rapidly classifies human and mouse sequences from nine different sequencing libraries by species, library and both together, 98.32%, 97.86% and 96.38% of the time, respectively. Ultimately, we demonstrate that SeqWho is a powerful method for reliably validating the quality and identity of the sequencing files used in any pipeline.Availability and implementationhttps://github.com/DaehwanKimLab/seqwho.Supplementary informationSupplementary data are available atBioinformaticsonline.