Discovering misannotated lncRNAs using deep learning training dynamics

The potential regulatory role of the non-coding RNAs in regulating the exogenous estrogen-induced feminization in Takifugu rubripes gonad

Estrogen plays a pivotal role in the early stage of sex differentiation in teleost. However, the underlying mechanisms of estrogen-induced feminization process are still needed for further clarification. Here, the comparative analysis of whole-transcriptome RNA sequencing was conducted between 17beta-Estradiol induced feminized XY (E-XY) gonads and control gonads (C) in Takifugu rubripes. A total of 57 miRNAs, 65 lncRNAs, and 4 circRNAs were found to be expressed at lower levels in control-XY (C-XY) than that in control-XX (C-XX), and were up-regulated in XY during E2-induced feminization process. The expression levels of 24 miRNAs, and 55 lncRNAs were higher in C-XY than that in C-XX, and were down-regulated in E2-treated XY. Furthermore, a correlation analysis was performed between miRNA-seq and mRNA-seq data. In C-XX/C-XY, 114 differential expression (DE) miRNAs were predicted to target to 904 differential expression genes (DEGs), while in C-XY/E-XY, 226 DEmiRNAs were predicted to target to 2,048 DEGs. In C-XX/C-XY, and C-XY/E-XY, KEGG pathway enrichment analysis showed that those targeted genes were mainly enriched in MAPK signaling, calcium signaling, steroid hormone biosynthesis and ovarian steroidogenesis pathway. Additionally, the competitive endogenous RNA (ceRNA) regulatory network was constructed by 24 miRNAs, 21 lncRNAs, 4 circRNAs and 5 key sex-related genes. These findings suggested that the expression of critical genes in sex differentiation were altered in E2-treated XY T. rubripes may via the lncRNA-miRNA-mRNA regulation network to facilitate the differentiation and maintenance of ovaries. Our results provide a new insight into the comprehensive understanding of the effects of estrogen signaling pathways on sex differentiation in teleost gonads.

A comprehensive survey on deep learning-based identification and predicting the interaction mechanism of long non-coding RNAs

Long noncoding RNAs (lncRNAs) have been discovered to be extensively involved in eukaryotic epigenetic, transcriptional, and post-transcriptional regulatory processes with the advancements in sequencing technology and genomics research. Therefore, they play crucial roles in the body's normal physiology and various disease outcomes. Presently, numerous unknown lncRNA sequencing data require exploration. Establishing deep learning-based prediction models for lncRNAs provides valuable insights for researchers, substantially reducing time and costs associated with trial and error and facilitating the disease-relevant lncRNA identification for prognosis analysis and targeted drug development as the era of artificial intelligence progresses. However, most lncRNA-related researchers lack awareness of the latest advancements in deep learning models and model selection and application in functional research on lncRNAs. Thus, we elucidate the concept of deep learning models, explore several prevalent deep learning algorithms and their data preferences, conduct a comprehensive review of recent literature studies with exemplary predictive performance over the past 5 years in conjunction with diverse prediction functions, critically analyze and discuss the merits and limitations of current deep learning models and solutions, while also proposing prospects based on cutting-edge advancements in lncRNA research.

Micropeptides: potential treatment strategies for cancer

Some noncoding RNAs (ncRNAs) carry open reading frames (ORFs) that can be translated into micropeptides, although noncoding RNAs (ncRNAs) have been previously assumed to constitute a class of RNA transcripts without coding capacity. Furthermore, recent studies have revealed that ncRNA-derived micropeptides exhibit regulatory functions in the development of many tumours. Although some of these micropeptides inhibit tumour growth, others promote it. Understanding the role of ncRNA-encoded micropeptides in cancer poses new challenges for cancer research, but also offers promising prospects for cancer therapy. In this review, we summarize the types of ncRNAs that can encode micropeptides, highlighting recent technical developments that have made it easier to research micropeptides, such as ribosome analysis, mass spectrometry, bioinformatics methods, and CRISPR/Cas9. Furthermore, based on the distribution of micropeptides in different subcellular locations, we explain the biological functions of micropeptides in different human cancers and discuss their underestimated potential as diagnostic biomarkers and anticancer therapeutic targets in clinical applications, information that may contribute to the discovery and development of new micropeptide-based tools for early diagnosis and anticancer drug development.

Discovering microproteins: making the most of ribosome profiling data

Building a reference set of protein-coding open reading frames (ORFs) has revolutionized biological process discovery and understanding. Traditionally, gene models have been confirmed using cDNA sequencing and encoded translated regions inferred using sequence-based detection of start and stop combinations longer than 100 amino-acids to prevent false positives. This has led to small ORFs (smORFs) and their encoded proteins left un-annotated. Ribo-seq allows deciphering translated regions from untranslated irrespective of the length. In this review, we describe the power of Ribo-seq data in detection of smORFs while discussing the major challenge posed by data-quality, -depth and -sparseness in identifying the start and end of smORF translation. In particular, we outline smORF cataloguing efforts in humans and the large differences that have arisen due to variation in data, methods and assumptions. Although current versions of smORF reference sets can already be used as a powerful tool for hypothesis generation, we recommend that future editions should consider these data limitations and adopt unified processing for the community to establish a canonical catalogue of translated smORFs.