EmExplorer: a database for exploring time activation of gene expression in mammalian embryos

Deciphering Sequence Determinants of Zygotic Genome Activation Genes: Insights From Machine Learning and the ZGAExplorer Platform

The mammalian life cycle initiates with the transition of genetic control from the maternal to the embryonic genome during zygotic genome activation (ZGA), which becomes pivotal for development. Nevertheless, understanding the conservation of genes and transcription factors (TFs) that underlie mammalian ZGA remains limited. Here, we compiled a comprehensive set of ZGA genes from mice, humans, pigs, bovines and goats, including Nr5a2 and TPRX1/2. The identification of 111 homologous genes through comparative analyses was followed by the discovery of a conserved genetic coding region, suggesting potential sequence preferences for ZGA genes. Notably, an interpretable machine learning model based on k-mer core features showed excellent performance in predicting ZGA genes (area under the ROC curve [AUC] > 0.81), revealing abundant and intricate 6-base sequence specific patterns and potential binding TFs, including motifs from NR5A2 and TPRX1/2. Further analysis demonstrated that gene sequence features and epigenetic modification features play equally important roles in regulating ZGA genes. Ultimately, we developed the ZGAExplorer platform to provide an invaluable resource for screening ZGA genes. Our study unravels the sequence determinants of ZGA genes across species through multi-omics data integration and machine learning, yielding insights into ZGA regulatory mechanisms and embryonic developmental arrest.

DNA sequence analysis landscape: a comprehensive review of DNA sequence analysis task types, databases, datasets, word embedding methods, and language models

Deoxyribonucleic acid (DNA) serves as fundamental genetic blueprint that governs development, functioning, growth, and reproduction of all living organisms. DNA can be altered through germline and somatic mutations. Germline mutations underlie hereditary conditions, while somatic mutations can be induced by various factors including environmental influences, chemicals, lifestyle choices, and errors in DNA replication and repair mechanisms which can lead to cancer. DNA sequence analysis plays a pivotal role in uncovering the intricate information embedded within an organism's genetic blueprint and understanding the factors that can modify it. This analysis helps in early detection of genetic diseases and the design of targeted therapies. Traditional wet-lab experimental DNA sequence analysis through traditional wet-lab experimental methods is costly, time-consuming, and prone to errors. To accelerate large-scale DNA sequence analysis, researchers are developing AI applications that complement wet-lab experimental methods. These AI approaches can help generate hypotheses, prioritize experiments, and interpret results by identifying patterns in large genomic datasets. Effective integration of AI methods with experimental validation requires scientists to understand both fields. Considering the need of a comprehensive literature that bridges the gap between both fields, contributions of this paper are manifold: It presents diverse range of DNA sequence analysis tasks and AI methodologies. It equips AI researchers with essential biological knowledge of 44 distinct DNA sequence analysis tasks and aligns these tasks with 3 distinct AI-paradigms, namely, classification, regression, and clustering. It streamlines the integration of AI into DNA sequence analysis tasks by consolidating information of 36 diverse biological databases that can be used to develop benchmark datasets for 44 different DNA sequence analysis tasks. To ensure performance comparisons between new and existing AI predictors, it provides insights into 140 benchmark datasets related to 44 distinct DNA sequence analysis tasks. It presents word embeddings and language models applications across 44 distinct DNA sequence analysis tasks. It streamlines the development of new predictors by providing a comprehensive survey of 39 word embeddings and 67 language models based predictive pipeline performance values as well as top performing traditional sequence encoding-based predictors and their performances across 44 DNA sequence analysis tasks.

Alternative splicing dynamics during gastrulation in mouse embryo

Alternative splicing (AS) plays an essential role in development, differentiation and carcinogenesis. However, the mechanisms underlying splicing regulation during mouse embryo gastrulation remain unclear. Based on spatial-temporal transcriptome and epigenome data, we detected the dynamics of AS and revealed its regulatory mechanisms across primary germ layers during mouse gastrulation, spanning developmental stages from E6.5 to E7.5. Subsequently, the dynamic expression of splicing factors (SFs) during gastrulation was characterized, while the expression patterns and functions of germ layer-specific SFs were identified. The results indicate that AS and differential alternative splicing events (DASEs) exhibit dynamic changes and are significantly abundant during the late stage of gastrulation. Similarly, SFs demonstrate stage-specific expression, with elevated levels observed during the middle and late stages of gastrulation. Epigenetic signals associated with SFs and AS sites demonstrate significant enrichment and undergo dynamic changes throughout gastrulation. Overall, this study offers a systematic analysis of AS during mouse gastrulation, identifies primary germ layer-specific AS events, and characterizes the expression patterns of SFs and the associated epigenetic signals. These findings enhance the understanding of the mechanisms underlying the formation of the three germ layers during mammalian gastrulation, with a focus on pre-mRNA AS.

Lysine-specific demethylase 1 (LSD1) participate in porcine early embryonic development by regulating cell autophagy and apoptosis through the mTOR signaling pathway

Lysine-specific demethylase 1 (LSD1) stands as the pioneering histone demethylase uncovered, proficient in demethylating H3K4me1/2 and H3K9me1/2, thereby governing transcription and participating in cell apoptosis, proliferation, or differentiation. Nevertheless, the complete understanding of LSD1 during porcine early embryonic development and the underlying molecular mechanism remains unclear. Thus, we investigated the mechanism by which LSD1 plays a regulatory role in porcine early embryos. This study revealed that LSD1 inhibition resulted in parthenogenetic activation (PA) and in vitro fertilization (IVF) embryo arrested the development, and decreased blastocyst quality. Meanwhile, H3K4me1/2 and H3K9me1/2 methylase activity was increased at the 4-cell embryo stage. RNA-seq results revealed that autophagy related biological processes were highly enriched through GO and KEGG pathway analyses when LSD1 inhibition. Further studies showed that LSD1 depletion in porcine early embryos resulted in low mTOR and p-mTOR levels and high autophagy and apoptosis levels. The LSD1 deletion-induced increases in autophagy and apoptosis could be reversed by addition of mTOR activators. We further demonstrated that LSD1 inhibition induced mitochondrial dysfunction and mitophagy. In summary, our research results indicate that LSD1 may regulate autophagy and apoptosis through the mTOR pathway and affect early embryonic development of pigs.

Extraction of Innate Immune Genes in Dairy Cattle and the Regulation of Their Expression in Early Embryos

As more and more of the available genomic data have been published, several databases have been developed for deciphering early mammalian embryogenesis; however, less research has been conducted on the regulation of the expression of natural immunity genes during early embryonic development in dairy cows. To this end, we explored the regulatory mechanism of innate immunity genes at the whole-genome level. Based on comparative genomics, 1473 innate immunity genes in cattle were obtained by collecting the latest reports on human innate immunity genes and updated bovine genome data for comparison, and a preliminary database of bovine innate immunity genes was constructed. In order to determine the regulatory mechanism of innate immune genes in dairy cattle early embryos, we conducted weighted co-expression network analysis of the innate immune genes at different developmental stages of dairy cattle early embryos. The results showed that specific module-related genes were significantly enriched in the MAPK signaling pathway. Protein-protein interaction (PPI) analysis showed gene interactions in each specific module, and 10 of the highest connectivity genes were chosen as potential hub genes. Finally, combined with the results for differential expressed genes (DEGs), ATF3, IL6, CD8A, CD69, CD86, HCK, ERBB3, LCK, ITGB2, LYN, and ERBB2 were identified as the key genes of innate immunity in dairy cattle early embryos. In conclusion, the bovine innate immunity gene set was determined and the co-expression network of innate immunity genes in the early embryonic stage of dairy cattle was constructed by comparing and analyzing the whole genome of bovines and humans. The findings in this study provide the basis for exploring the involvement and regulation of innate immune genes in the early embryonic development of dairy cattle.

scapGNN: A graph neural network-based framework for active pathway and gene module inference from single-cell multi-omics data

Although advances in single-cell technologies have enabled the characterization of multiple omics profiles in individual cells, extracting functional and mechanistic insights from such information remains a major challenge. Here, we present scapGNN, a graph neural network (GNN)-based framework that creatively transforms sparse single-cell profile data into the stable gene-cell association network for inferring single-cell pathway activity scores and identifying cell phenotype-associated gene modules from single-cell multi-omics data. Systematic benchmarking demonstrated that scapGNN was more accurate, robust, and scalable than state-of-the-art methods in various downstream single-cell analyses such as cell denoising, batch effect removal, cell clustering, cell trajectory inference, and pathway or gene module identification. scapGNN was developed as a systematic R package that can be flexibly extended and enhanced for existing analysis processes. It provides a new analytical platform for studying single cells at the pathway and network levels.

EmAtlas: a comprehensive atlas for exploring spatiotemporal activation in mammalian embryogenesis

The emerging importance of embryonic development research rapidly increases the volume for a professional resource related to multi-omics data. However, the lack of global embryogenesis repository and systematic analysis tools limits the preceding in stem cell research, human congenital diseases and assisted reproduction. Here, we developed the EmAtlas, which collects the most comprehensive multi-omics data and provides multi-scale tools to explore spatiotemporal activation during mammalian embryogenesis. EmAtlas contains data on multiple types of gene expression, chromatin accessibility, DNA methylation, nucleosome occupancy, histone modifications, and transcription factors, which displays the complete spatiotemporal landscape in mouse and human across several time points, involving gametogenesis, preimplantation, even fetus and neonate, and each tissue involves various cell types. To characterize signatures involved in the tissue, cell, genome, gene and protein levels during mammalian embryogenesis, analysis tools on these five scales were developed. Additionally, we proposed EmRanger to deliver extensive development-related biological background annotations. Users can utilize these tools to analyze, browse, visualize, and download data owing to the user-friendly interface. EmAtlas is freely accessible at http://bioinfor.imu.edu.cn/ematlas.

dbEmbryo multi-omics database for analyses of synergistic regulation in early mammalian embryo development

During early mammalian embryo development, different epigenetic marks undergo reprogramming and play crucial roles in the mediation of gene expression. Currently, several databases provide multi-omics information on early embryos. However, how interconnected epigenetic markers function together to coordinate the expression of the genetic code in a spatiotemporal manner remains difficult to analyze, markedly limiting scientific and clinical research. Here, we present dbEmbryo, an integrated and interactive multi-omics database for human and mouse early embryos. dbEmbryo integrates data on gene expression, DNA methylation, histone modifications, chromatin accessibility, and higher-order chromatin structure profiles for human and mouse early embryos. It incorporates customized analysis tools, such as "multi-omics visualization," "Gene&Peak annotation," "ZGA gene cluster," "cis-regulation," "synergistic regulation," "promoter signal enrichment," and "3D genome." Users can retrieve gene expression and epigenetic profile patterns to analyze synergistic changes across different early embryo developmental stages. We showed the uniqueness of dbEmbryo among extant databases containing data on early embryo development and provided an overview. Using dbEmbryo, we obtained a phase-separated model of transcriptional control during early embryo development. dbEmbryo offers web-based analytical tools and a comprehensive resource for biologists and clinicians to decipher molecular regulatory mechanisms of human and mouse early embryo development.

Change point detection for clustered expression data

BACKGROUND

To detect changes in biological processes, samples are often studied at several time points. We examined expression data measured at different developmental stages, or more broadly, historical data. Hence, the main assumption of our proposed methodology was the independence between the examined samples over time. In addition, however, the examinations were clustered at each time point by measuring littermates from relatively few mother mice at each developmental stage. As each examination was lethal, we had an independent data structure over the entire history, but a dependent data structure at a particular time point. Over the course of these historical data, we wanted to identify abrupt changes in the parameter of interest - change points.

RESULTS

In this study, we demonstrated the application of generalized hypothesis testing using a linear mixed effects model as a possible method to detect change points. The coefficients from the linear mixed model were used in multiple contrast tests and the effect estimates were visualized with their respective simultaneous confidence intervals. The latter were used to determine the change point(s). In small simulation studies, we modelled different courses with abrupt changes and compared the influence of different contrast matrices. We found two contrasts, both capable of answering different research questions in change point detection: The Sequen contrast to detect individual change points and the McDermott contrast to find change points due to overall progression. We provide the R code for direct use with provided examples. The applicability of those tests for real experimental data was shown with in-vivo data from a preclinical study.

CONCLUSION

Simultaneous confidence intervals estimated by multiple contrast tests using the model fit from a linear mixed model were capable to determine change points in clustered expression data. The confidence intervals directly delivered interpretable effect estimates representing the strength of the potential change point. Hence, scientists can define biologically relevant threshold of effect strength depending on their research question. We found two rarely used contrasts best fitted for detection of a possible change point: the Sequen and McDermott contrasts.

Network Approaches for Charting the Transcriptomic and Epigenetic Landscape of the Developmental Origins of Health and Disease

The early developmental phase is of critical importance for human health and disease later in life. To decipher the molecular mechanisms at play, current biomedical research is increasingly relying on large quantities of diverse omics data. The integration and interpretation of the different datasets pose a critical challenge towards the holistic understanding of the complex biological processes that are involved in early development. In this review, we outline the major transcriptomic and epigenetic processes and the respective datasets that are most relevant for studying the periconceptional period. We cover both basic data processing and analysis steps, as well as more advanced data integration methods. A particular focus is given to network-based methods. Finally, we review the medical applications of such integrative analyses.

Network cartographs for interpretable visualizations

Networks offer an intuitive visual representation of complex systems. Important network characteristics can often be recognized by eye and, in turn, patterns that stand out visually often have a meaningful interpretation. In conventional network layout algorithms, however, the precise determinants of a node's position within a layout are difficult to decipher and to control. Here we propose an approach for directly encoding arbitrary structural or functional network characteristics into node positions. We introduce a series of two- and three-dimensional layouts, benchmark their efficiency for model networks, and demonstrate their power for elucidating structure-to-function relationships in large-scale biological networks.

Cross-species meta-analysis of transcriptome changes during the morula-to-blastocyst transition: metabolic and physiological changes take center stage

The morula-to-blastocyst transition (MBT) culminates with formation of inner cell mass (ICM) and trophectoderm (TE) lineages. Recent studies identified signaling pathways driving lineage specification, but some features of these pathways display significant species divergence. To better understand evolutionary conservation of the MBT, we completed a meta-analysis of RNA sequencing data from five model species and ICMTE differences from four species. Although many genes change in expression during the MBT within any given species, the number of shared differentially expressed genes (DEGs) is comparatively small, and the number of shared ICMTE DEGs is even smaller. DEGs related to known lineage determining pathways (e.g., POU5F1) are seen, but the most prominent pathways and functions associated with shared DEGs or shared across individual species DEG lists impact basic physiological and metabolic activities, such as TCA cycle, unfolded protein response, oxidative phosphorylation, sirtuin signaling, mitotic roles of polo-like kinases, NRF2-mediated oxidative stress, estrogen receptor signaling, apoptosis, necrosis, lipid and fatty acid metabolism, cholesterol biosynthesis, endocytosis, AMPK signaling, homeostasis, transcription, and cell death. We also observed prominent differences in transcriptome regulation between ungulates and nonungulates, particularly for ICM- and TE-enhanced mRNAs. These results extend our understanding of shared mechanisms of the MBT and formation of the ICM and TE and should better inform the selection of model species for particular applications.

Recent development of machine learning methods in sumoylation sites prediction

Sumoylation of proteins is an important reversible post-translational modification of proteins and mediates a variety of cellular processes. Sumo-modified proteins can change their subcellular localization, activity and stability. In addition, it also plays an important role in various cellular processes such as transcriptional regulation and signal transduction. The abnormal sumoylation is involved in many diseases, including neurodegeneration and immune-related diseases, as well as the development of cancer. Therefore, identification of the sumoylation site (SUMO site) is fundamental to understanding their molecular mechanisms and regulatory roles. In contrast to labor-intensive and costly experimental approaches, computational prediction of sumoylation sites in silico also attracted much attention for its accuracy, convenience and speed. At present, many computational prediction models have been used to identify SUMO sites, but these contents have not been comprehensively summarized and reviewed. Therefore, the research progress of relevant models is summarized and discussed in this paper. We will briefly summarize the development of bioinformatics methods on sumoylation site prediction. We will mainly focus on the benchmark dataset construction, feature extraction, machine learning method, published results and online tools. We hope the review will provide more help for wet-experimental scholars.

Non-coding RNAs as Novel Biomarkers in Cancer Drug Resistance

Chemotherapy is often the primary and most effective anticancer treatment; however, drug resistance remains a major obstacle to it being curative. Recent studies have demonstrated that non-coding RNAs (ncRNAs), especially microRNAs and long non-coding RNAs, are involved in drug resistance of tumor cells in many ways, such as modulation of apoptosis, drug efflux and metabolism, epithelial-to-mesenchymal transition, DNA repair, and cell cycle progression. Exploring the relationships between ncRNAs and drug resistance will not only contribute to our understanding of the mechanisms of drug resistance and provide ncRNA biomarkers of chemoresistance, but will also help realize personalized anticancer treatment regimens. Due to the high cost and low efficiency of biological experimentation, many researchers have opted to use computational methods to identify ncRNA biomarkers associated with drug resistance. In this review, we summarize recent discoveries related to ncRNA-mediated drug resistance and highlight the computational methods and resources available for ncRNA biomarkers involved in chemoresistance.

Sequence-Based Deep Learning Frameworks on Enhancer-Promoter Interactions Prediction

Enhancer-promoter interactions (EPIs) in the human genome are of great significance to transcriptional regulation, which tightly controls gene expression. Identification of EPIs can help us better decipher gene regulation and understand disease mechanisms. However, experimental methods to identify EPIs are constrained by funds, time, and manpower, while computational methods using DNA sequences and genomic features are viable alternatives. Deep learning methods have shown promising prospects in classification and efforts that have been utilized to identify EPIs. In this survey, we specifically focus on sequence-based deep learning methods and conduct a comprehensive review of the literature. First, we briefly introduce existing sequence- based frameworks on EPIs prediction and their technique details. After that, we elaborate on the dataset, pre-processing means, and evaluation strategies. Finally, we concluded with the challenges these methods are confronted with and suggest several future opportunities. We hope this review will provide a useful reference for further studies on enhancer-promoter interactions.

DevOmics: an integrated multi-omics database of human and mouse early embryo

Transcriptomic and epigenetic alterations during early embryo development have been proven to play essential roles in regulating the cell fate. Nowadays, advances in single-cell transcriptomics and epigenomics profiling techniques provide large volumes of data for understanding the molecular regulatory mechanisms in early embryos and facilitate the investigation of assisted reproductive technology as well as preimplantation genetic testing. However, the lack of integrated data collection and unified analytic procedures greatly limits their usage in scientific research and clinical application. Hence, it is necessary to establish a database integrating the regulatory information of human and mouse early embryos with unified analytic procedures. Here, we introduce DevOmics (http://devomics.cn/), which contains normalized gene expression, DNA methylation, histone modifications (H3K4me3, H3K9me3, H3K27me3, H3K27ac), chromatin accessibility and 3D chromatin architecture profiles of human and mouse early embryos spanning six developmental stages (zygote, 2cell, 4cell, 8cell, morula and blastocyst (ICM, TE)). The current version of DevOmics provides Search and Advanced Search for retrieving genes a researcher is interested in, Analysis Tools including the differentially expressed genes (DEGs) analysis for acquiring DEGs between different types of samples, allelic explorer for displaying allele-specific gene expression as well as epigenetic modifications and correlation analysis for showing the dynamic changes in different layers of data across developmental stages, as well as Genome Browser and Ortholog for visualization. DevOmics offers a user-friendly website for biologists and clinicians to decipher molecular regulatory mechanisms of human and mouse early embryos.

Capture and metabolomic analysis of the human endometrial epithelial organoid secretome

Suboptimal uterine fluid (UF) composition can lead to pregnancy loss and likely contributes to offspring susceptibility to chronic adult-onset disorders. However, our understanding of the biochemical composition and mechanisms underpinning UF formation and regulation remain elusive, particularly in humans. To address this challenge, we developed a high-throughput method for intraorganoid fluid (IOF) isolation from human endometrial epithelial organoids. The IOF is biochemically distinct to the extraorganoid fluid (EOF) and cell culture medium as evidenced by the exclusive presence of 17 metabolites in IOF. Similarly, 69 metabolites were unique to EOF, showing asymmetrical apical and basolateral secretion by the in vitro endometrial epithelium, in a manner resembling that observed in vivo. Contrasting the quantitative metabolomic profiles of IOF and EOF revealed donor-specific biochemical signatures of organoids. Subsequent RNA sequencing of these organoids from which IOF and EOF were derived established the capacity to readily perform organoid multiomics in tandem, and suggests that transcriptomic regulation underpins the observed secretory asymmetry. In summary, these data provided by modeling uterine luminal and basolateral fluid formation in vitro offer scope to better understand UF composition and regulation with potential impacts on female fertility and offspring well-being.

6mA-Pred: identifying DNA N6-methyladenine sites based on deep learning

With the accumulation of data on 6mA modification sites, an increasing number of scholars have begun to focus on the identification of 6mA sites. Despite the recognized importance of 6mA sites, methods for their identification remain lacking, with most existing methods being aimed at their identification in individual species. In the present study, we aimed to develop an identification method suitable for multiple species. Based on previous research, we propose a method for 6mA site recognition. Our experiments prove that the proposed 6mA-Pred method is effective for identifying 6mA sites in genes from taxa such as rice, Mus musculus, and human. A series of experimental results show that 6mA-Pred is an excellent method. We provide the source code used in the study, which can be obtained from http://39.100.246.211:5004/6mA_Pred/.

Analysis of Oncogene Protein Structure Using Small World Network Concept

Background:

The basic building block of a body is protein which is a complex system whose structure plays a key role in activation, catalysis, messaging and disease states. Therefore, careful investigation of protein structure is necessary for the diagnosis of diseases and for the drug designing. Protein structures are described at their different levels of complexity: primary (chain), secondary (helical), tertiary (3D), and quaternary structure. Analyzing complex 3D structure of protein is a difficult task but it can be analyzed as a network of interconnection between its component, where amino acids are considered as nodes and interconnection between them are edges.

Objective:

Many literature works have proven that the small world network concept provides many new opportunities to investigate network of biological systems. The objective of this paper is analyzing the protein structure using small world concept.

Methods:

Protein is analyzed using small world network concept, specifically where extreme condition is having a degree distribution which follows power law. For the correct verification of the proposed approach, dataset of the Oncogene protein structure is analyzed using Python programming.

Results:

Protein structure is plotted as network of amino acids (Residue Interaction Graph (RIG)) using distance matrix of nodes with given threshold, then various centrality measures (i.e., degree distribution, Degree-Betweenness correlation, and Betweenness-Closeness correlation) are calculated for 1323 nodes and graphs are plotted.

Conclusion:

Ultimately, it is concluded that there exist hubs with higher centrality degree but less in number, and they are expected to be robust toward harmful effects of mutations with new functions.

Development and Validation of a Novel 11-Gene Prognostic Model for Serous Ovarian Carcinomas Based on Lipid Metabolism Expression Profile

(1) Background: Biomarkers might play a significant role in predicting the clinical outcomes of patients with ovarian cancer. By analyzing lipid metabolism genes, future perspectives may be uncovered; (2) Methods: RNA-seq data for serous ovarian cancer were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. The non-negative matrix factorization package in programming language R was used to classify molecular subtypes of lipid metabolism genes and the limma package in R was performed for functional enrichment analysis. Through lasso regression, we constructed a multi-gene prognosis model; (3) Results: Two molecular subtypes were obtained and an 11-gene signature was constructed (PI3, RGS, ADORA3, CH25H, CCDC80, PTGER3, MATK, KLRB1, CCL19, CXCL9 and CXCL10). Our prognostic model shows a good independent prognostic ability in ovarian cancer. In a nomogram, the predictive efficiency was notably superior to that of traditional clinical features. Related to known models in ovarian cancer with a comparable amount of genes, ours has the highest concordance index; (4) Conclusions: We propose an 11-gene signature prognosis prediction model based on lipid metabolism genes in serous ovarian cancer.

Characterization of DNA Methylation Patterns and Mining of Epigenetic Markers During Genomic Reprogramming in SCNT Embryos

Somatic cell nuclear transfer (SCNT), also known as somatic cell cloning, is a commonly used technique to study epigenetic reprogramming. Although SCNT has the advantages of being safe and able to obtain pluripotent cells, early developmental arrest happens in most SCNT embryos. Overcoming epigenetic barriers is currently the primary strategy for improving reprogramming efficiency and improving developmental rate in SCNT embryos. In this study, we analyzed DNA methylation profiles of in vivo fertilized embryos and SCNT embryos with different developmental fates. Overall DNA methylation level was higher in SCNT embryos during global de-methylation process compared to in vivo fertilized embryos. In addition, promoter region, first intron and 3′UTR were found to be the major genomic regions that were hyper-methylated in SCNT embryos. Surprisingly, we found the length of re-methylated region was directly related to the change of methylation level. Furthermore, a number of genes including Dppa2 and Dppa4 which are important for early zygotic genome activation (ZGA) were not properly activated in SCNT embryos. This study comprehensively analyzed genome-wide DNA methylation patterns in SCNT embryos and provided candidate target genes for improving efficiency of genomic reprogramming in SCNT embryos. Since SCNT technology has been widely used in agricultural and pastoral production, protection of endangered animals, and therapeutic cloning, the findings of this study have significant importance for all these fields.

A Brief Survey for MicroRNA Precursor Identification Using Machine Learning Methods

MicroRNAs, a group of short non-coding RNA molecules, could regulate gene expression. Many diseases are associated with abnormal expression of miRNAs. Therefore, accurate identification of miRNA precursors is necessary. In the past 10 years, experimental methods, comparative genomics methods, and artificial intelligence methods have been used to identify pre-miRNAs. However, experimental methods and comparative genomics methods have their disadvantages, such as time-consuming. In contrast, machine learning-based method is a better choice. Therefore, the review summarizes the current advances in pre-miRNA recognition based on computational methods, including the construction of benchmark datasets, feature extraction methods, prediction algorithms, and the results of the models. And we also provide valid information about the predictors currently available. Finally, we give the future perspectives on the identification of pre-miRNAs. The review provides scholars with a whole background of pre-miRNA identification by using machine learning methods, which can help researchers have a clear understanding of progress of the research in this field.

Machine Learning of Single-Cell Transcriptome Highly Identifies mRNA Signature by Comparing F-Score Selection with DGE Analysis

Human preimplantation development is a complex process involving dramatic changes in transcriptional architecture. For a better understanding of their time-spatial development, it is indispensable to identify key genes. Although the single-cell RNA sequencing (RNA-seq) techniques could provide detailed clustering signatures, the identification of decisive factors remains difficult. Additionally, it requires high experimental cost and a long experimental period. Thus, it is highly desired to develop computational methods for identifying effective genes of development signature. In this study, we first developed a predictor called EmPredictor to identify developmental stages of human preimplantation embryogenesis. First, we compared the F-score of feature selection algorithms with differential gene expression (DGE) analysis to find specific signatures of the development stage. In addition, by training the support vector machine (SVM), four types of signature subsets were comprehensively discussed. The prediction results showed that a feature subset with 1,881 genes from the F-score algorithm obtained the best predictive performance, which achieved the highest accuracy of 93.3% on the cross-validation set. Further function enrichment demonstrated that the gene set selected by the feature selection method was involved in more development-related pathways and cell fate determination biomarkers. This indicates that the F-score algorithm should be preferentially proposed for detecting key genes of multi-period data in mammalian early development.

RAACBook: a web server of reduced amino acid alphabet for sequence-dependent inference by using Chou's five-step rule

By reducing amino acid alphabet, the protein complexity can be significantly simplified, which could improve computational efficiency, decrease information redundancy and reduce chance of overfitting. Although some reduced alphabets have been proposed, different classification rules could produce distinctive results for protein sequence analysis. Thus, it is urgent to construct a systematical frame for reduced alphabets. In this work, we constructed a comprehensive web server called RAACBook for protein sequence analysis and machine learning application by integrating reduction alphabets. The web server contains three parts: (i) 74 types of reduced amino acid alphabet were manually extracted to generate 673 reduced amino acid clusters (RAACs) for dealing with unique protein problems. It is easy for users to select desired RAACs from a multilayer browser tool. (ii) An online tool was developed to analyze primary sequence of protein. The tool could produce K-tuple reduced amino acid composition by defining three correlation parameters (K-tuple, g-gap, λ-correlation). The results are visualized as sequence alignment, mergence of RAA composition, feature distribution and logo of reduced sequence. (iii) The machine learning server is provided to train the model of protein classification based on K-tuple RAAC. The optimal model could be selected according to the evaluation indexes (ROC, AUC, MCC, etc.). In conclusion, RAACBook presents a powerful and user-friendly service in protein sequence analysis and computational proteomics. RAACBook can be freely available at http://bioinfor.imu.edu.cn/raacbook.

Database URL: http://bioinfor.imu.edu.cn/raacbook

Characterization of the relationship between FLI1 and immune infiltrate level in tumour immune microenvironment for breast cancer

Breast cancer is the most common cancer and the leading cause of cancer death among women in the world. Tumour‐infiltrating lymphocytes were defined as the white blood cells left in the vasculature and localized in tumours. Recently, tumour‐infiltrating lymphocytes were found to be associated with good prognosis and response to immunotherapy in tumours. In this study, to examine the influence of FLI1 in immune system in breast cancer, we interrogated the relationship between the FLI1 expression levels with infiltration levels of 28 immune cell types. By splitting the breast cancer samples into high and low expression FLI1 subtypes, we found that the high expression FLI1 subtype was enriched in many immune cell types, and the up‐regulated differentially expressed genes between them were enriched in immune system processes, immune‐related KEGG pathways and biological processes. In addition, many important immune‐related features were found to be positively correlated with the FLI1 expression level. Furthermore, we found that the FLI1 was correlated with the immune‐related genes. Our findings may provide useful help for recognizing the relationship between tumour immune microenvironment and FLI1, and may unravel clinical outcomes and immunotherapy utility for FLI1 in breast cancer.

Predicting Bacteriophage Enzymes and Hydrolases by Using Combined Features

Bacteriophage is a type of virus that could infect the host bacteria. They have been applied in the treatment of pathogenic bacterial infection. Phage enzymes and hydrolases play the most important role in the destruction of bacterial cells. Correctly identifying the hydrolases coded by phage is not only beneficial to their function study, but also conducive to antibacteria drug discovery. Thus, this work aims to recognize the enzymes and hydrolases in phage. A combination of different features was used to represent samples of phage and hydrolase. A feature selection technique called analysis of variance was developed to optimize features. The classification was performed by using support vector machine (SVM). The prediction process includes two steps. The first step is to identify phage enzymes. The second step is to determine whether a phage enzyme is hydrolase or not. The jackknife cross-validated results showed that our method could produce overall accuracies of 85.1 and 94.3%, respectively, for the two predictions, demonstrating that the proposed method is promising.

A Brief Survey of Machine Learning Methods in Identification of Mitochondria Proteins in Malaria Parasite

The number of human deaths caused by malaria is increasing day-by-day. In fact, the mitochondrial proteins of the malaria parasite play vital roles in the organism. For developing effective drugs and vaccines against infection, it is necessary to accurately identify mitochondrial proteins of the malaria parasite. Although precise details for the mitochondrial proteins can be provided by biochemical experiments, they are expensive and time-consuming. In this review, we summarized the machine learning-based methods for mitochondrial proteins identification in the malaria parasite and compared the construction strategies of these computational methods. Finally, we also discussed the future development of mitochondrial proteins recognition with algorithms.

Identification of immune-enhanced molecular subtype associated with BRCA1 mutations, immune checkpoints and clinical outcome in ovarian carcinoma

Ovarian carcinoma has the highest mortality among the malignant tumours in gynaecology, and new treatment strategies are urgently needed to improve the clinical status of ovarian carcinoma patients. The Cancer Genome Atlas (TCGA) cohort were performed to explore the immune function of the internal environment of tumours and its clinical correlation with ovarian carcinoma. Finally, four molecular subtypes were obtained based on the global immune‐related genes. The correlation analysis and clinical characteristics showed that four subtypes were all significantly related to clinical stage; the immune scoring results indicated that most immune signatures were upregulated in C3 subtype, and the majority of tumour‐infiltrating immune cells were upregulated in both C3 and C4 subtypes. Compared with other subtypes, C3 subtype had a higher BRCA1 mutation, higher expression of immune checkpoints, and optimal survival prognosis. These findings of the immunological microenvironment in tumours may provide new ideas for developing immunotherapeutic strategies for ovarian carcinoma.

Identification three LncRNA prognostic signature of ovarian cancer based on genome-wide copy number variation

PURPOSE

Ovarian cancer is one of the most common malignant tumors of the female reproductive system, which seriously threatens the health of patients. It is of great significance to identify biomarkers to improve the clinical status of ovarian cancer patients.

METHODS

Methylation, RNA- sequencing, Copy number variation (CNV), mutation and clinical characteristics of ovarian cancer and control samples were downloaded from The Cancer Genome Atlas database (TCGA). The "iClusterPlus". R package was used to cluster the molecular subtypes. The copy number variation of the entire lncRNA genome was analyzed using GISTIC. The prognosis-associated lncRNA related to CNV was screened as potential targets for ovarian cancer.

RESULTS

Six molecular subtypes were identified based on multi-omics analysis and DElncRNAs are significantly enriched in specific molecular subtypes. The deletion or amplification of lncRNA copy number affects the occurrence and development of ovarian cancer to some extent. Three prognostic-associated lncRNA including LOC101927151, LINC00861 and LEMD1-AS1 were selected. These lncRNAs can be used as biomarkers to predict survival in patients with ovarian cancer. The accuracy of results were verified using the Gene Expression Omnibus (GEO) dataset.

CONCLUSION

Based on genome-wide copy number variation, prognostic-associated lncRNAs were identified as new biomolecular markers for ovarian cancer.

A Comparative Analysis of Single-Cell Transcriptome Identifies Reprogramming Driver Factors for Efficiency Improvement

Terminally differentiated somatic cells can be reprogrammed into a totipotent state through somatic cell nuclear transfer (SCNT). The incomplete reprogramming is the major reason for developmental arrest of SCNT embryos at early stages. In our studies, we found that pathways for autophagy, endocytosis, and apoptosis were incompletely activated in nuclear transfer (NT) 2-cell arrest embryos, whereas extensively inhibited pathways for stem cell pluripotency maintenance, DNA repair, cell cycle, and autophagy may result in NT 4-cell embryos arrest. As for NT normal embryos, a significant shift in expression of developmental transcription factors (TFs) Id1, Pou6f1, Cited1, and Zscan4c was observed. Compared with pluripotent gene Ascl2 being activated only in NT 2-cell, Nanog, Dppa2, and Sall4 had major expression waves in normal development of both NT 2-cell and 4-cell embryos. Additionally, Kdm4b/4d and Kdm5b had been confirmed as key markers in NT 2-cell and 4-cell embryos, respectively. Histone acetylases Kat8, Elp6, and Eid1 were co-activated in NT 2-cell and 4-cell embryos to facilitate normal development. Gadd45a as a key driver functions with Tet1 and Tet2 to improve the efficiency of NT reprogramming. Taken together, our findings provided an important theoretical basis for elucidating the potential molecular mechanisms and identified reprogramming driver factor to improve the efficiency of SCNT reprogramming.

Remarks on Computational Method for Identifying Acid and Alkaline Enzymes

The catalytic efficiency of the enzyme is thousands of times higher than that of ordinary catalysts. Thus, they are widely used in industrial and medical fields. However, enzymes with protein structure can be destroyed and inactivated in high temperature, over acid or over alkali environment. It is well known that most of enzymes work well in an environment with pH of 6-8, while some special enzymes remain active only in an alkaline environment with pH > 8 or an acidic environment with pH < 6. Therefore, the identification of acidic and alkaline enzymes has become a key task for industrial production. Because of the wide varieties of enzymes, it is hard work to determine the acidity and alkalinity of the enzyme by experimental methods, and even this task cannot be achieved. Converting protein sequences into digital features and building computational models can efficiently and accurately identify the acidity and alkalinity of enzymes. This review summarized the progress of the digital features to express proteins and computational methods to identify acidic and alkaline enzymes. We hope that this paper will provide more convenience, ideas, and guides for computationally classifying acid and alkaline enzymes.

lncRNA DGCR5 Up-Regulates TGF-β1, Increases Cancer Cell Stemness and Predicts Survival of Prostate Cancer Patients

Background

Long non-coding RNA (lncRNA) DiGeorge syndrome critical region gene 5 (DGCR5) plays different roles in different types of human cancer, but its role in prostate cancer (PC) has not been reported.

Methods

DGCR5 and TGF-β1 expression in paired tumor and adjacent healthy tissues from 64 PC patients was analyzed by performing RT-qPCR. A 5-year follow-up study was performed to analyze the prognostic value of DGCR5 for PC. The interaction between DGCR5 and TGF-β1 was analyzed by overexpression experiments. Cell stemness was analyzed by cell stemness assay.

Results

In our study, we found that DGCR5 was down-regulated in tumor tissues than in adjacent healthy tissues of PC patients, but TGF-β1 was up-regulated in the tumor tissues. DGCR5 expression was not affected by clinical stages, but low DGCR5 level in the tumor was correlated with poor survival. DGCR5 and TGF-β1 were inversely correlated in tumor tissues but not in adjacent healthy tissues. DGCR5 over-expression resulted in down-regulation of TGF-β1, while TGF-β1 treatment did not significantly affect DGCR5 expression. DGCR5 over-expression led to decreased stemness of PC cells, but TGF-β1 treatment played a reverse role and attenuated the effects of DGCR5 over-expression. DGCR5 may decrease the stemness of PC cells by down-regulating TGF-β1.

iDNA6mA-Rice: A Computational Tool for Detecting N6-Methyladenine Sites in Rice

DNA N6-methyladenine (6mA) is a dominant DNA modification form and involved in many biological functions. The accurate genome-wide identification of 6mA sites may increase understanding of its biological functions. Experimental methods for 6mA detection in eukaryotes genome are laborious and expensive. Therefore, it is necessary to develop computational methods to identify 6mA sites on a genomic scale, especially for plant genomes. Based on this consideration, the study aims to develop a machine learning-based method of predicting 6mA sites in the rice genome. We initially used mono-nucleotide binary encoding to formulate positive and negative samples. Subsequently, the machine learning algorithm named Random Forest was utilized to perform the classification for identifying 6mA sites. Our proposed method could produce an area under the receiver operating characteristic curve of 0.964 with an overall accuracy of 0.917, as indicated by the fivefold cross-validation test. Furthermore, an independent dataset was established to assess the generalization ability of our method. Finally, an area under the receiver operating characteristic curve of 0.981 was obtained, suggesting that the proposed method had good performance of predicting 6mA sites in the rice genome. For the convenience of retrieving 6mA sites, on the basis of the computational method, we built a freely accessible web server named iDNA6mA-Rice at http://lin-group.cn/server/iDNA6mA-Rice.