Publications by authors named "L-D Huan"
Article Synopsis
- Cigarette smoking leads to changes in DNA methylation that can potentially reverse after quitting smoking, as seen in studies over varying time frames.
- Participants who quit smoking showed specific hypermethylation patterns at certain CpG sites compared to those who continued smoking, indicating a distinct biological response to smoking cessation.
- The study suggests that the process of quitting smoking may prompt early restoration of DNA methylation towards levels similar to non-smokers, influencing gene expression related to immune responses and cell health.
Background: The planted (l, d) motif search (PMS) is an important yet challenging problem in computational biology. Pattern-driven PMS algorithms usually use k out of t input sequences as reference sequences to generate candidate motifs, and they can find all the (l, d) motifs in the input sequences. However, most of them simply take the first k sequences in the input as reference sequences without elaborate selection processes, and thus they may exhibit sharp fluctuations in running time, especially for large alphabets.
View Article and Find Full Text PDFAn Efficient Exact Algorithm for the Motif Stem Search Problem over Large Alphabets.
IEEE/ACM Trans Comput Biol Bioinform
June 2016
Article Synopsis
- Interest in planted (l, d) motif search (PMS) is growing, particularly for exploring significant segments in biological sequences, though large-alphabet applications have been under-discussed.
- The paper introduces motif stem search (MSS), aimed at finding l-length string "stems" with wildcards to represent a minimal superset of all (l, d) motifs in large-alphabet sequences.
- Key contributions include precise motif stem representation with regular expressions, a method to generate non-redundant stems, and the StemFinder algorithm that performs faster and with fewer stems than previous MSS methods, available at a specified link.
An Efficient Algorithm for Discovering Motifs in Large DNA Data Sets.
IEEE Trans Nanobioscience
July 2015
The planted (l,d) motif discovery has been successfully used to locate transcription factor binding sites in dozens of promoter sequences over the past decade. However, there has not been enough work done in identifying (l,d) motifs in the next-generation sequencing (ChIP-seq) data sets, which contain thousands of input sequences and thereby bring new challenge to make a good identification in reasonable time. To cater this need, we propose a new planted (l,d) motif discovery algorithm named MCES, which identifies motifs by mining and combining emerging substrings.
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