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A Faster Algorithm for Motif Finding in Sequences from ChIP-Seq Data

机译:芯片-SEQ数据中序列中的图案查找更快的算法

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Motif finding in nucleotide sequences for the discovery of over–represented transcription factor binding sites is a very challenging problem, both from the computational and the experimental points of view. Transcription factors in fact recognize very weakly conserved sequence elements, that in typical applications are very hard to discriminate against random sequence similarities. Recent advances in technology like ChIP-Seq can generate better datasets to be investigated, in which the degree of conservation of binding sites is higher: on the other hand, the size itself of the datasets has posed new challenges for the design of efficient algorithms able to produce results in reasonable time. In this work we present an updated version of our algorithm Weeder, in which time and space requirements are significantly reduced and, moreover, also the accuracy of the results is notably improved.
机译:在发现过度代表的转录因子结合位点发现的核苷酸序列是一个非常具有挑战性的问题,无论是来自计算和实验的观点。事实上的转录因子认识到非常弱的序列元素,在典型的应用中非常难以区分随机序列相似之处。芯片SEQ等技术的最新进步可以生成更好的数据集进行调查,其中绑定站点的守恒程度更高:另一方面,数据集的大小本身对能够设计的设计的新挑战构成了新的挑战在合理的时间内产生结果。在这项工作中,我们提出了一个更新版本的我们的算法杂草,其中时间和空间要求显着降低,而且也显着提高了结果的准确性。

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