DNA motif is the pattern shared by similar fragments in DNA sequences, which plays a key role in regulating gene expression, and DNA motif discovery has become a key research topic. Exact planted ( l,d )-motif search (PMS) is one of the motif discovery approaches, which aims to find from t sequences all the ( l,d )-motifs that are motifs of l length appearing in at least qt sequences with at most d mismatches. The existing exact PMS algorithms are only suitable for small datasets of DNA sequences. The development of high-throughput sequencing technology generates vast amount of DNA sequence data, which brings challenges to solving exact PMS problems efficiently. Therefore, we propose an efficient exact PMS algorithm called PMmotif for large datasetsof DNA sequences, after analyzing the time complexity of the existing exact PMS algorithms. PMmotif finds ( l,d ) -motifs with strategy by searching the branches on the pattern tree that may contain ( l,d ) -motifs. It is verified by experiments that the running time ratio of the existing excellentPMS algorithmstoPMmotif isbetween14.83and 58.94. In addition, for the first time, PMmotif can solve the (15,5) and(17,6) challenge problem instances on large DNA sequence datasets within 24 hours.

Copy number variation (CNV) has been increasingly recognized as a type of genomic/genetic variation that plays a critical role in driving human diseases and genomic diversity. CNV detection and analysis from cancer genomes could provide crucial information for cancer diagnosis and treatment. There still remain considerable challenges in the control-free calling of CNVs accurately in cancer analysis, although advances in next-generation sequencing (NGS) technology have been inspiring the development of various computational methods. Herein, we propose a new read-depth (RD)-based approach, called CNVbd, to explore CNVs from single tumor samples of NGS data. CNVbd assembles three statistics drawn from the density peak clustering algorithm and isolation forest algorithm based on the denoised RD profile and establishes a back propagation neural network model to predict CNV bins. In addition, we designed a revision process and a boundary search algorithm to correct the false-negative predictions and refine the CNV boundaries. The performance of the proposed method is assessed on both simulation data and real sequencing datasets. The analysis shows that CNVbd is a very competitive method and can become a robust and reliable tool for analyzing CNVs in the tumor genome.

Copy number variations (CNVs) are a type of structural variation in the genome that impact gene dosage, with significant implications for both normal phenotypic variability and susceptibility to disease. The existing copy number variation detection methods have unstable sensitivity in data with different coverage depths, and cannot identify shorter copy number variation fragments. In this context, we introduce a new method called LMADCNV, specifically designed for detecting CNVs in single-sample data from next-generation sequencing (NGS). LMADCNV employs local features constructed via a cluster partitioning strategy, in conjunction with an anomaly scoring mechanism predicated on median absolute deviation, to facilitate the detection of CNVs. This innovative approach prudently leverages the positional correlation inherent in read depth (RD) data to achieve increased sensitivity without a significant loss in precision. Empirical validation through simulation and real-sample experiments confirms the superiority of LMADCNV over other seven CNV detection methods. LMADCNV not only offers a novel perspective for extracting local features but also shows promise as a robust and effective tool for CNV detection.