Background: Trans-acting factors are of special importance in transcription regulation, which is a group of proteins that can directly or indirectly recognize or bind to the 8-12 bp core sequence of cis-acting element...
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Background: Trans-acting factors are of special importance in transcription regulation, which is a group of proteins that can directly or indirectly recognize or bind to the 8-12 bp core sequence of cis-acting elements and regulate the transcription efficiency of target genes. The progressive development in high-throughput chromatin capture technology (e.g., Hi-C) enables the identification of chromatin-interacting sequence groups where transacting DNA motif groups can be discovered. The problem difficulty lies in the combinatorial nature of DNA sequence pattern matching and its underlying sequence pattern search ***: Here, we propose to develop MotifHub for trans-acting DNA motif group discovery on grouped sequences. Specifically, the main approach is to develop probabilisticmodeling for accommodating the stochastic nature of DNA motif ***: Based on the modeling, we develop global sampling techniques based on EM and Gibbs sampling to address the global optimization challenge for model fitting with latent variables. The results reflect that our proposed approaches demonstrate promising performance with linear time ***: MotifHub is a novel algorithm considering the identification of both DNA co-binding motif groups and trans-acting TFs. Our study paves the way for identifying hub TFs of stem cell development (OCT4 and SOX2) and determining potential therapeutic targets of prostate cancer (FOXA1 and MYC). To ensure scientific reproducibility and long-term impact, its matrix-algebra-optimized source code is released at http://*** ***/MotifHub.
The convergence of fast probabilistic modeling algorithms (G-algorithms) is analyzed. A G-algorithm is modified based on a new probabilistic approach, used to reject points in the neighborhood of the current solution....
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The convergence of fast probabilistic modeling algorithms (G-algorithms) is analyzed. A G-algorithm is modified based on a new probabilistic approach, used to reject points in the neighborhood of the current solution. A theoretically justified estimate of the rate of convergence, independent of the initial approximation, is obtained for this modification. A computational experiment is conducted to compare the performance of the modified G-algorithm with that of the classical one.
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