近几年，高通量的新一代测序技术彻底改变了遗传学研究，特别是 ChIP-Seq技术对DNA与蛋白质交互作用和RNA-Seq测序技术对转录组的分析的研究。在真核生物中多个蛋白质调节因子共同与其对应的顺式调控元件相互作用介导一个基因的转录调控。而ChIP-Seq实验提供了大量的转录因子结合位置数据。这些越来越多的新数据为识别真核生物顺式调控元件提供了前所未有的机遇，但挑战是我们如何高效使用这些 ChIP-Seq数据来识别基因组范围所有转录因子及其合作的辅调节因子的顺式调控元件。另外RNA-Seq实验提供了大量的转录组短序列，其拼接成的转录组被广泛用于比较样品基因水平表达差异。但现在迫切的问题是至今仍缺少令生物学家满意的短序列从头组装算法。因此本项目将整合ChIP-Seq数据进行大规模转录因子及辅因子的顺式调控元件模体预测的算法设计，及基于RNA-Seq的转录组短序列从头组装算法设计。

In recent years, the high-throughput next-generation sequencing (NGS) has revolutionized the study of genetics. Especially the ChIP-Seq and RNA-Seq technologies have been widely utilized in the study of DNA-protein interactions and the analysis of transcriptomes, respectively. In eukaryotes, the transcriptional regulation of a gene is usually mediated by the interactions of multiple collaborative protein regulators with their respective specific cis-regulatory elements (CRE). And the large number of transcription factor binding location data produced by ChIP-Seq technologies has provided an unprecedented opportunity to identify CREs in genomes. Nonetheless, how to effectively utilize the large volumes of ChIP-Seq data to identify the CREs of the transcription factors and co-factors in a whole genome is a challenging job. Besides, the RNA-Seq technologies produced a large number of short reads, which can be spliced into transcriptomes that are widely used to detect differential expressions of gene samples. The lack of algorithms satisfied by biologists for do novo assembling short reads is another urgent problem. Therefore, in this project, the novel algorithms will be proposed for prediction of CRE motifs of transcription factors and their cofactors by integrating large-scale ChIP-Seq datasets, and do novo assembly of RNA-Seq short reads, respectively.