Biomedical subjects

Huaiqiu Zhu

Publications and source records attributed to Huaiqiu Zhu.

3 recordsLinked to original sources

PubMed2026

A novel deep learning-driven framework for improving lncRNA comprehensive annotation with LncADeep 2.0.

MOTIVATION: Long non-coding RNAs (lncRNAs) have emerged as crucial players in diverse physiological and pathological processes, yet the biological mechanisms of the vast majority of lncRNAs remain elusive. To fill this gap, it is necessary to improve the accuracy of lncRNA identification and functional annotation. RESULTS: Here, we introduce LncADeep 2.0, an integrated deep learning framework designed to meet these needs. In the identification module, LncADeep 2.0 incorporated novel peptide features along with sequence and structural information, demonstrating superior performance over our previous LncADeep and other existing tools on both annotated transcripts from GENCODE and RNA-seq data. For functional annotation, LncADeep 2.0 leveraged lncRNA-centric interaction networks and gene ontology terms through the transfer learning strategy to achieve robust annotation performance with limited functional data. Compared to LncADeep, LncADeep 2.0 could accurately elucidate the general functions of given lncRNA sequences, predict tissue- or cell-type-specific functions from bulk and single-cell RNA-seq data, and establish connections between tumor-associated lncRNAs and genomic markers. Overall, LncADeep 2.0 stands out as an efficient and reliable tool for lncRNA identification and functional annotation across a wide spectrum of biological processes. AVAILABILITY AND IMPLEMENTATION: LncADeep 2.0 is available for use at https://github.com/Jefferson-Chou/LncADeep2 and https://doi.org/10.5281/zenodo.17164767.

RNA, Long Noncoding↗

PubMed2004

Enrichment of transcriptional regulatory sites in non-coding genomic region.

MOTIVATION: Over-represented k-mers in non-coding genomic regions often lead to identification of potential transcriptional regulatory sites (TRS). This phenomenon has been employed by many algorithms to predict TRS in silico. Yet, the improvement of these algorithms should be based on deeper understanding of the enrichment feature. To obtain a general distributional profile of TRS in different regions of genomes as well as in different genomes, we here performed a systematic analysis on the over-representation of TRS in intergenic regions and gene upstream regions of yeasts and viral genomes, and the distributional pattern of TRS in intergenic and intron regions of the Drosophila genome. We also explored the way to evaluate the accuracy of TRS consensus sequences by measuring their enrichment. RESULTS: To measure enrichment, a statistical background model was introduced by comparing TRS frequency in certain regions of genome to either the frequency in the whole genome or the frequency in exon region. This model was applied to different classes of non-coding genomic regions in four genomes. Most of the TRS were observed to be over-represented in the intergenic regions of the Saccharomyces cerevisiae, Schizosaccharomyces pombe and Epstein-Barr virus (EBV) genomes. The enrichment of S.cerevisiae TRS in the 600 bp upstream region of genes was also significant. In Drosophila genome, TRS did not show enrichment in intergenic and intron regions when TRS frequency in the whole genome was taken as background, as we did in other genomes. However, when we took TRS frequency in exon region as background, over 70% TRS are over-represented in those two classes of non-coding regions. This fact indicates the existence of transcriptional regulatory signals in introns. The analysis of some S.cerevisiae TRS, which have inconsistent consensus sequences with different levels of enrichment in intergenic region, suggests the possibility of evaluating the accuracy of experimentally determined TRS by measuring their enrichment in non-coding genomic regions.

Algorithms↗

PubMed2004

Multivariate entropy distance method for prokaryotic gene identification.

A new simple method is found for efficient and accurate identification of coding sequences in prokaryotic genome. The method employs a Shannon description of artificial language for DNA sequences. It consists in translating a DNA sequence into a pseudo-amino acid sequence with 20 fundamental words according to the universal genetic code. With an entropy-density profile (EDP), the method maps a sequence of finite length to a vector and then analyzes its position in the 20-dimensional phase space depending on its nature. It is found that the ratio of the relative distance to an averaged coding and non-coding EDP over a small number (up to one) of open reading frames (ORFs) can serve as a good coding potential. An iterative algorithm is designed for finding a set of "root" sequences using this coding potential. A multivariate entropy distance (MED) algorithm is then proposed for the identification of prokaryotic genes; it has a feature to combine the use of a coding potential and an EDP-based sequence similarity analysis. The current version of MED is unsupervised, parameter-free and simple to implement. It is demonstrated to be able to detect 95-99% genes with 10-30% of additional genes when tested against the RefSeq database of NCBI and to detect 97.5-99.8% of confirmed genes with known functions. It is also shown to be able to find a set of (functionally known) genes that are missed by other well-known gene finding algorithms. All measurements show that the MED algorithm reaches a similar performance level as the algorithms like GeneMark and Glimmer for prokaryotic gene prediction.

Algorithms↗