Motivation: In this work, we aim to develop a computational approach for predicting DNA-binding sites in proteins from amino acid sequences. To avoid overfitting with this method, all available DNA-binding proteins from the Protein Data Bank (PDB) are used to construct the models. The random forest (RF) algorithm is used because it is fast and has robust performance for different parameter values. A novel hybrid feature is presented which incorporates evolutionary information of the amino acid sequence, secondary structure (SS) information and orthogonal binary vector (OBV) information which reflects the characteristics of 20 kinds of amino acids for two physical–chemical properties (dipoles and volumes of the side chains). The numbers of binding and non-binding residues in proteins are highly unbalanced, so a novel scheme is proposed to deal with the problem of imbalanced datasets by downsizing the majority class. Results: The results show that the RF model achieves 91.41% overall accuracy with Matthew's correlation coefficient of 0.70 and an area under the receiver operating characteristic curve (AUC) of 0.913. To our knowledge, the RF method using the hybrid feature is currently the computationally optimal approach for predicting DNA-binding sites in proteins from amino acid sequences without using three-dimensional (3D) structural information. We have demonstrated that the prediction results are useful for understanding protein–DNA interactions. Availability: DBindR web server implementation is freely available at http://www.cbi.seu.edu.cn/DBindR/DBindR.htm. Contact:xsun@seu.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.

DNA molecules are highly compacted in the eukaryotic nucleus where distal regulatory elements reach their targets through three-dimensional chromosomal interactions. G-quadruplexes, stable four-stranded non-canonical DNA structures, can change local chromatin organization through the exclusion of nucleosomes. However, the relationship between G-quadruplexes and higher-order genome organization remains unknown. Here, we found that G-quadruplexes are significantly enriched at boundaries of topological associated domains (TADs). Architectural protein occupancy, which plays critical roles in the formation of TADs, was highly correlated with the content of G-quadruplexes at TAD boundaries. Moreover, adjacent boundaries containing G-quadruplexes frequently interacted with each other because of the high enrichment of architectural protein binding sites. Similar to CCCTC-binding factor (CTCF) binding sites, G-quadruplexes also showed strong insulation ability in the separation of adjacent regions. Additionally, the insulation ability of CTCF binding sites and TAD boundaries was significantly reinforced by G-quadruplexes. Furthermore, G-quadruplex motifs on different strands were associated with the orientation of CTCF binding sites. These findings suggest a potential role for G-quadruplexes in loop extrusion. The enrichment of transcription factor binding sites (TFBSs) around regulatory elements containing G-quadruplexes led to frequent interactions between regulatory elements containing G-quadruplexes. Intriguingly, more than 99% of G-quadruplexes overlapped with TFBSs. The binding sites of CTCF and cohesin proteins were preferentially located surrounding G-quadruplexes. Accordingly, we proposed a new mechanism of long-distance gene regulation in which G-quadruplexes are involved in distal interactions between enhancers and promoters.

Single-cell RNA sequencing (scRNA-seq) has been rapidly developing and widely applied in biological and medical research. Identification of cell types in scRNA-seq data sets is an essential step before in-depth investigations of their functional and pathological roles. However, the conventional workflow based on clustering and marker genes is not scalable for an increasingly large number of scRNA-seq data sets due to complicated procedures and manual annotation. Therefore, a number of tools have been developed recently to predict cell types in new data sets using reference data sets. These methods have not been generally adapted due to a lack of tool benchmarking and user guidance. In this article, we performed a comprehensive and impartial evaluation of nine classification software tools specifically designed for scRNA-seq data sets. Results showed that Seurat based on random forest, SingleR based on correlation analysis and CaSTLe based on XGBoost performed better than others. A simple ensemble voting of all tools can improve the predictive accuracy. Under nonideal situations, such as small-sized and class-imbalanced reference data sets, tools based on cluster-level similarities have superior performance. However, even with the function of assigning ‘unassigned’ labels, it is still challenging to catch novel cell types by solely using any of the single-cell classifiers. This article provides a guideline for researchers to select and apply suitable classification tools in their analysis workflows and sheds some lights on potential direction of future improvement on classification tools.

G-quadruplexes (G4s) are non-canonical secondary nucleic acid structures. Sequences with the potential to form G4s are abundant in regulatory regions of the genome including telomeres, promoters and 5′ non-coding regions, indicating they fulfill important genome regulatory functions. Generally, G4s perform various biological functions by interacting with proteins. In recent years, an increasing number of G-quadruplex-binding proteins have been identified with biochemical experiments. G4-binding proteins are involved in vital cellular processes such as telomere maintenance, DNA replication, gene transcription, mRNA processing. Therefore, G4-binding proteins are also associated with various human diseases. An intensive study of G4-protein interactions provides an attractive approach for potential therapeutics and these proteins can be considered as drug targets for novel medical treatment. In this review, we present biological functions and structural properties of G4-binding proteins, and discuss how to exploit G4-protein interactions to develop new therapeutic targets.

MicroRNAs (miRNAs) are a class of small noncoding RNAs that regulate gene expression by targeting mRNAs for translation repression or mRNA degradation. Many miRNAs are being discovered and studied, but in most cases their origin, evolution and function remain unclear. Here, we characterized miRNAs derived from repetitive elements and miRNA families expanded by segmental duplication events in the human, rhesus and mouse genomes. We applied a comparative genomics approach combined with identifying miRNA paralogs in segmental duplication pair data in a genome-wide study to identify new homologs of human miRNAs in the rhesus and mouse genomes. Interestingly, using segmental duplication pair data, we provided credible computational evidence that two miRNA genes are located in the pseudoautosomal region of the human Y chromosome. We characterized all the miRNAs whether they were derived from repetitive elements or not and identified significant differences between the repeat-related miRNAs (RrmiRs) and non-repeat-derived miRNAs in (1) their location in protein-coding and intergenic regions in genomes, (2) the minimum free energy of their hairpin structures, and (3) their conservation in vertebrate genomes. We found some lineage-specific RrmiR families and three lineage-specific expansion families, and provided evidence indicating that some RrmiR families formed and expanded during evolutionary segmental duplication events. We also provided computational and experimental evidence for the functions of the conservative RrmiR families in the three species. Together, our results indicate that repetitive elements contribute to the origin of miRNAs, and large segmental duplication events could prompt the expansion of some miRNA families, including RrmiR families. Our study is a valuable contribution to the knowledge of evolution and function of non-coding region in genome.

More than 98% of the human genome does not encode proteins, and the vast majority of the noncoding regions have not been well studied. Some of these regions contain enhancers and functional non-coding RNAs. Previous research suggested that enhancer transcripts could be potent independent indicators of enhancer activity, and some enhancer lncRNAs (elncRNAs) have been proven to play critical roles in gene regulation. Here, we identified enhancer–promoter interactions from high-throughput chromosome conformation capture (Hi-C) data. We found that elncRNAs were highly enriched surrounding chromatin loop anchors. Additionally, the interaction frequency of elncRNA-associated enhancer–promoter pairs was significantly higher than the interaction frequency of other enhancer–promoter pairs, suggesting that elncRNAs may reinforce the interactions between enhancers and promoters. We also found that elncRNA expression levels were positively correlated with the interaction frequency of enhancer–promoter pairs. The promoters interacting with elncRNA-associated enhancers were rich in RNA polymerase II and YY1 transcription factor binding sites. We clustered enhancer–promoter pairs into different groups to reflect the different ways in which elncRNAs could influence enhancer–promoter pairs. Interestingly, G-quadruplexes were found to potentially mediate some enhancer–promoter interaction pairs, and the interaction frequency of these pairs was significantly higher than that of other enhancer–promoter pairs. We also found that the G-quadruplexes on enhancers were highly related to the expression of elncRNAs. G-quadruplexes located in the promoters of elncRNAs led to high expression of elncRNAs, whereas G-quadruplexes located in the gene bodies of elncRNAs generally resulted in low expression of elncRNAs.