DiNuP: a systematic approach to identify regions of differential nucleosome positioning

Motivation: With the rapid development of high-throughput sequencing technologies, the genome-wide profiling of nucleosome positioning has become increasingly affordable. Many future studies will investigate the dynamic behaviour of nucleosome positioning in cells that have different states or that are exposed to different conditions. However, a robust method to effectively identify the regions of differential nucleosome positioning (RDNPs) has not been previously available. Results:: We describe a novel computational approach, DiNuP, that compares nucleosome profiles generated by high-throughput sequencing under various conditions. DiNuP provides a statistical P-value for each identified RDNP based on the difference of read distributions. DiNuP also empirically estimates the false discovery rate as a cutoff when two samples have different sequencing depths and differentiate reliable RDNPs from the background noise. Evaluation of DiNuP showed it to be both sensitive and specific for the detection of changes in nucleosome location, occupancy and fuzziness. RDNPs that were identified using publicly available datasets revealed that nucleosome positioning dynamics are closely related to the epigenetic regulation of transcription. Availability and implementation: DiNuP is implemented in Python and is freely available at http://www.tongji.edu.cn/~zhanglab/DiNuP

Identification of exosome-like nanoparticle-derived microRNAs from 11 edible fruits and vegetables

Edible plant-derived exosome-like nanoparticles (EPDELNs) are novel naturally occurring plant ultrastructures that are structurally similar to exosomes. Many EPDELNs have anti-inflammatory properties. MicroRNAs (miRNAs) play a critical role in mediating physiological and pathological processes in animals and plants. Although miRNAs can be selectively encapsulated in extracellular vesicles, little is known about their expression and function in EPDELNs. In this study, we isolated nanovesicles from 11 edible fruits and vegetables and subjected the corresponding EPDELN small RNA libraries to Illumina sequencing. We identified a total of 418 miRNAs—32 to 127 per species—from the 11 EPDELN samples. Target prediction and functional analyses revealed that highly expressed miRNAs were closely associated with the inflammatory response and cancer-related pathways. The 418 miRNAs could be divided into three classes according to their EPDELN distributions: 26 “frequent” miRNAs (FMs), 39 “moderately present” miRNAs (MPMs), and 353 “rare” miRNAs (RMs). FMs were represented by fewer miRNA species than RMs but had a significantly higher cumulative expression level. Taken together, our in vitro results indicate that miRNAs in EPDELNs have the potential to regulate human mRNA

Genomic data for 78 chickens from 14 populations

Background: Since the domestication of the red jungle fowls (Gallus gallus; dating back to~10 000 B.P.) in Asia, domestic chickens (Gallus gallus domesticus) have been subjected to the combined effects of natural selection and human-driven artificial selection; this has resulted in marked phenotypic diversity in a number of traits, including behavior, body composition, egg production, and skin color. Population genomic variations through diversifying selection have not been fully investigated. Findings: The whole genomes of 78 domestic chickens were sequenced to an average of 18-fold coverage for each bird. By combining this data with publicly available genomes of five wild red jungle fowls and eight Xishuangbanna game fowls, we conducted a comprehensive comparative genomics analysis of 91 chickens from 17 populations. After aligning ~21.30 gigabases (Gb) of high-quality data from each individual to the reference chicken genome, we identified ~6.44 million (M) single nucleotide polymorphisms (SNPs) for each population. These SNPs included 1.10 M novel SNPs in 17 populations that were absent in the current chicken dbSNP (Build 145) entries. Conclusions: The current data is important for population genetics and further studies in chickens and will serve as a valuable resource for investigating diversifying selection and candidate genes for selective breeding in chickens.Peer reviewedAnimal Scienc

Comparative transcriptomics of 5 high-altitude vertebrates and their low-altitude relatives

Abstract Background: Species living at high altitude are subject to strong selective pressures due to inhospitable environments (e.g., hypoxia, low temperature, high solar radiation, and lack of biological production), making these species valuable models for comparative analyses of local adaptation. Studies that have examined high-altitude adaptation have identified a vast array of rapidly evolving genes that characterize the dramatic phenotypic changes in high-altitude animals. However, how high-altitude environment shapes gene expression programs remains largely unknown. Findings: We generated a total of 910 Gb of high-quality RNA-seq data for 180 samples derived from 6 tissues of 5 agriculturally important high-altitude vertebrates (Tibetan chicken, Tibetan pig, Tibetan sheep, Tibetan goat, and yak) and their cross-fertile relatives living in geographically neighboring low-altitude regions. Of these, ∼75% reads could be aligned to their respective reference genomes, and on average ∼60% of annotated protein coding genes in each organism showed FPKM expression values greater than 0.5. We observed a general concordance in topological relationships between the nucleotide alignments and gene expression–based trees. Tissue and species accounted for markedly more variance than altitude based on either the expression or the alternative splicing patterns. Cross-species clustering analyses showed a tissue-dominated pattern of gene expression and a species-dominated pattern for alternative splicing. We also identified numerous differentially expressed genes that could potentially be involved in phenotypic divergence shaped by high-altitude adaptation. Conclusions: These data serve as a valuable resource for examining the convergence and divergence of gene expression changes between species as they adapt or acclimatize to high-altitude environments

Current Advances on the Important Roles of Enhancer RNAs in Molecular Pathways of Cancer

Enhancers are critical genomic elements that can cooperate with promoters to regulate gene transcription in both normal and cancer cells. Recent studies reveal that enhancer regions are transcribed to produce a class of noncoding RNAs referred to as enhancer RNAs (eRNAs). Emerging evidence shows that eRNAs play important roles in enhancer activation and enhancer-driven gene regulation, and the expression of eRNAs may be a critical factor in tumorigenesis. The important roles of eRNAs in cancer signaling pathways are also gradually unveiled, providing a new insight into cancer therapy. Here, we review the roles of eRNAs in regulating cancer signaling pathways and discuss the potential of eRNA-targeted therapy for human cancers

Revisiting Assessment of Computational Methods for Hi-C Data Analysis

The performances of algorithms for Hi-C data preprocessing, the identification of topologically associating domains, and the detection of chromatin interactions and promoter–enhancer interactions have been mostly evaluated using semi-quantitative or synthetic data approaches, without utilizing the most recent methods, since 2017. In this study, we comprehensively evaluated 24 popular state-of-the-art methods for the complete end-to-end pipeline of Hi-C data analysis, using manually curated or experimentally validated benchmark datasets, including a CRISPR dataset for promoter–enhancer interaction validation. Our results indicate that, although no single method exhibited superior performance in all situations, HiC-Pro, DomainCaller, and Fit-Hi-C2 showed relatively balanced performances of most evaluation metrics for preprocessing, topologically associating domain identification, and chromatin interaction/promoter–enhancer interaction detection, respectively. The comprehensive comparison presented in this manuscript provides a reference for researchers to choose Hi-C analysis tools that best suit their needs

Global Long Noncoding RNA and mRNA Expression Changes between Prenatal and Neonatal Lung Tissue in Pigs

Lung tissue plays an important role in the respiratory system of mammals after birth. Early lung development includes six key stages, of which the saccular stage spans the pre- and neonatal periods and prepares the distal lung for alveolarization and gas-exchange. However, little is known about the changes in gene expression between fetal and neonatal lungs. In this study, we performed transcriptomic analysis of messenger RNA (mRNA) and long noncoding RNA (lncRNA) expressed in the lung tissue of fetal and neonatal piglets. A total of 19,310 lncRNAs and 14,579 mRNAs were identified and substantially expressed. Furthermore, 3248 mRNAs were significantly (FDR-adjusted p value ≤ 0.05, FDR: False Discovery Rate) differentially expressed and were mainly enriched in categories related to cell proliferation, immune response, hypoxia response, and mitochondrial activation. For example, CCNA2, an important gene involved in the cell cycle and DNA replication, was upregulated in neonatal lungs. We also identified 452 significantly (FDR-adjusted p value ≤ 0.05) differentially expressed lncRNAs, which might function in cell proliferation, mitochondrial activation, and immune response, similar to the differentially expressed mRNAs. These results suggest that differentially expressed mRNAs and lncRNAs might co-regulate lung development in early postnatal pigs. Notably, the TU64359 lncRNA might promote distal lung development by up-regulating the heparin-binding epidermal growth factor-like (HB-EGF) expression. Our research provides basic lung development datasets and will accelerate clinical researches of newborn lung diseases with pig models