Abstract
A comprehensive understanding of transcription factor binding sites (TFBSs) is a key problem in contemporary biology, which is a critical issue in gene regulation. Identifying a pattern of TFBSs in every DNA sequence, motif discovery reveals the basic regulatory relationship and compassionate the evolutionary system of every species. In this case, recognizing the high-quality (ℓ, d) motif is a great challenge. This problem is addressed in motif discovery and motif finding, using the proposed algorithms, such as Segmentation to Filtration (S2F) and Firefly with FREEZE (FFF), respectively. In this study, the whole DNA sequences are divided into two segments. Segment 1 involves motif discovery and is sliced by base and sub k-mers applying an iterative approach, followed by filtration 1 and 2 techniques, respectively. This approach obtains the top five percent of the best motifs (TOPbk_mer) based on accuracy. In segment 2, the motifs recognized in segment 1 are given as input to the FFF algorithm to identify the TFBs locations. The standard firefly algorithm with two freezing techniques, local and global, is employed to recognize the final motif. The performance of these algorithms is evaluated on the simulated datasets and real datasets such as the Escherichia coli cyclic AMP receptor protein (CRP) dataset, mouse Embryonic Stem Cell (mESC) dataset, and human species ChIP-seq (Chromatin Immuno Precipitation Sequences) dataset. All of these datasets have a running time of the experiment within 3 min, and the sequence numbers (t) hold ranges up to 39,601. It is evident from the results that the two proposed algorithms, S2F and FFF, can identify the high-quality motif, and it is faster than the state-of-the-art PMS and QPMS algorithms.
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Data availability
1. The mESC data was downloaded from https://lgsun.grc.nia.nih.gov/CisFinder/, the web version of CisFinder. 2. For the ENCODE TF ChIP-seq data, homo sapiens (hg19) datasets were utilized and retrieve them with the following steps: a. Download the datasets of the narrow Peak format from ucsc http://genome.ucsc.edu/ENCODE/downloads.html. b. Convert the narrow peak format to the FASTA format. c. Find the web logo of the TFBSs from the JASPAR database. http://compbio.mit.edu/encode-motif.
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Theepalakshmi, P., Reddy, U.S. A new efficient quorum planted (ℓ, d) motif search on ChIP-seq dataset using segmentation to filtration and freezing firefly algorithms. Soft Comput 28, 3049–3070 (2024). https://doi.org/10.1007/s00500-023-09236-z
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DOI: https://doi.org/10.1007/s00500-023-09236-z