Manatee: detection and quantification of small non-coding RNAs from next-generation sequencing data

Extracellular vesicles, RNA sequencing, and bioinformatic analyses: Challenges, solutions, and recommendations

Extracellular vesicles (EVs) are heterogeneous entities secreted by cells into their microenvironment and systemic circulation. Circulating EVs carry functional small RNAs and other molecular footprints from their cell of origin, and thus have evident applications in liquid biopsy, therapeutics, and intercellular communication. Yet, the complete transcriptomic landscape of EVs is poorly characterized due to critical limitations including variable protocols used for EV-RNA extraction, quality control, cDNA library preparation, sequencing technologies, and bioinformatic analyses. Consequently, there is a gap in knowledge and the need for a standardized approach in delineating EV-RNAs. Here, we address these gaps by describing the following points by (1) focusing on the large canopy of the EVs and particles (EVPs), which includes, but not limited to - exosomes and other large and small EVs, lipoproteins, exomeres/supermeres, mitochondrial-derived vesicles, RNA binding proteins, and cell-free DNA/RNA/proteins; (2) examining the potential functional roles and biogenesis of EVPs; (3) discussing various transcriptomic methods and technologies used in uncovering the cargoes of EVPs; (4) presenting a comprehensive list of RNA subtypes reported in EVPs; (5) describing different EV-RNA databases and resources specific to EV-RNA species; (6) reviewing established bioinformatics pipelines and novel strategies for reproducible EV transcriptomics analyses; (7) emphasizing the significant need for a gold standard approach in identifying EV-RNAs across studies; (8) and finally, we highlight current challenges, discuss possible solutions, and present recommendations for robust and reproducible analyses of EVP-associated small RNAs. Overall, we seek to provide clarity on the transcriptomics landscape, sequencing technologies, and bioinformatic analyses of EVP-RNAs. Detailed portrayal of the current state of EVP transcriptomics will lead to a better understanding of how the RNA cargo of EVPs can be used in modern and targeted diagnostics and therapeutics. For the inclusion of different particles discussed in this article, we use the terms large/small EVs, non-vesicular extracellular particles (NVEPs), EPs and EVPs as defined in MISEV guidelines by the International Society of Extracellular Vesicles (ISEV).

Small RNA signatures of acute ischemic stroke in L1CAM positive extracellular vesicles

L1CAM-positive extracellular vesicles (L1EV) are an emerging biomarker that may better reflect ongoing neuronal damage than other blood-based biomarkers. The physiological roles and regulation of L1EVs and their small RNA cargoes following stroke is unknown. We sought to characterize L1EV small RNAs following stroke and assess L1EV RNA signatures for diagnosing stroke using weighted gene co-expression network analysis and random forest (RF) machine learning algorithms. Interestingly, small RNA sequencing of plasma L1EVs from patients with stroke and control patients (n = 28) identified micro(mi)RNAs known to be enriched in the brain. Weighted gene co-expression network analysis (WGCNA) revealed small RNA transcript modules correlated to diagnosis, initial NIH stroke scale, and age. L1EV RNA signatures associated with the diagnosis of AIS were derived from WGCNA and RF classification. These small RNA signatures demonstrated a high degree of accuracy in the diagnosis of AIS with an area under the curve (AUC) of the signatures ranging from 0.833 to 0.932. Further work is necessary to understand the role of small RNA L1EV cargoes in the response to brain injury, however, this study supports the utility of L1EV small RNA signatures as a biomarker of stroke.

A highly contiguous genome assembly for the pocket mouse Perognathus longimembris longimembris

The little pocket mouse, Perognathus longimembris, and its nine congeners are small heteromyid rodents found in arid and seasonally arid regions of Western North America. The genus is characterized by behavioral and physiological adaptations to dry and often harsh environments, including nocturnality, seasonal torpor, food caching, enhanced osmoregulation, and a well-developed sense of hearing. Here we present a genome assembly of Perognathus longimembris longimembris generated from PacBio HiFi long read and Omni-C chromatin-proximity sequencing as part of the California Conservation Genomics Project. The assembly has a length of 2.35 Gb, contig N50 of 11.6 Mb, scaffold N50 of 73.2 Mb, and includes 93.8% of the BUSCO Glires genes. Interspersed repetitive elements constitute 41.2% of the genome. A comparison with the highly endangered Pacific pocket mouse, P. l. pacificus, reveals broad synteny. These new resources will enable studies of local adaptation, genetic diversity, and conservation of threatened taxa.

NOCICEPTRA2.0 - A comprehensive ncRNA atlas of human native and iPSC-derived sensory neurons

Non-coding RNAs (ncRNAs) are pivotal in gene regulation during development and disease. MicroRNAs have been extensively studied in neurogenesis. However, limited knowledge exists about the developmental signatures of other ncRNA species in sensory neuron differentiation, and human dorsal root ganglia (DRG) ncRNA expression remains undocumented. To address this gap, we generated a comprehensive atlas of small ncRNA species during iPSC-derived sensory neuron differentiation. Utilizing iPSC-derived sensory neurons and human DRG RNA sequencing, we unveiled signatures describing developmental processes. Our analysis identified ncRNAs associated with various sensory neuron stages. Striking similarities in ncRNA expression signatures between human DRG and iPSC-derived neurons support the latter as a model to bridge the translational gap between preclinical findings and human disorders. In summary, our research sheds light on the role of ncRNA species in human nociceptors, and NOCICEPTRA2.0 offers a comprehensive ncRNA database for sensory neurons that researchers can use to explore ncRNA regulators in nociceptors thoroughly.

Disruption of the Aspergillus fumigatus RNA interference machinery alters the conidial transcriptome

The RNA interference (RNAi) pathway has evolved numerous functionalities in eukaryotes, with many on display in Kingdom Fungi. RNAi can regulate gene expression, facilitate drug resistance, or even be altogether lost to improve growth potential in some fungal pathogens. In the WHO fungal priority pathogen, Aspergillus fumigatus, the RNAi system is known to be intact and functional. To extend our limited understanding of A. fumigatus RNAi, we first investigated the genetic variation in RNAi-associated genes in a collection of 217 environmental and 83 clinical genomes, where we found that RNAi components are conserved even in clinical strains. Using endogenously expressed inverted-repeat transgenes complementary to a conditionally essential gene (pabA) or a nonessential gene (pksP), we determined that a subset of the RNAi componentry is active in inverted-repeat transgene silencing in conidia and mycelium. Analysis of mRNA-seq data from RNAi double-knockout strains linked the A. fumigatus dicer-like enzymes (DclA/B) and RNA-dependent RNA polymerases (RrpA/B) to regulation of conidial ribosome biogenesis genes; however, surprisingly few endogenous small RNAs were identified in conidia that could explain this broad change. Although RNAi was not clearly linked to growth or stress response defects in the RNAi knockouts, serial passaging of RNAi knockout strains for six generations resulted in lineages with diminished spore production over time, indicating that loss of RNAi can exert a fitness cost on the fungus. Cumulatively, A. fumigatus RNAi appears to play an active role in defense against double-stranded RNA species alongside a previously unappreciated housekeeping function in regulation of conidial ribosomal biogenesis genes.

Adapter dimer contamination in sRNA-sequencing datasets predicts sequencing failure and batch effects and hampers extracellular vesicle-sRNA analysis

Small RNA (sRNA) profiling of Extracellular Vesicles (EVs) by Next-Generation Sequencing (NGS) often delivers poor outcomes, independently of reagents, platforms or pipelines used, which contributes to poor reproducibility of studies. Here we analysed pre/post-sequencing quality controls (QC) to predict issues potentially biasing biological sRNA-sequencing results from purified human milk EVs, human and mouse EV-enriched plasma and human paraffin-embedded tissues. Although different RNA isolation protocols and NGS platforms were used in these experiments, all datasets had samples characterized by a marked removal of reads after pre-processing. The extent of read loss between individual samples within a dataset did not correlate with isolated RNA quantity or sequenced base quality. Rather, cDNA electropherograms revealed the presence of a constant peak whose intensity correlated with the degree of read loss and, remarkably, with the percentage of adapter dimers, which were found to be overrepresented sequences in high read-loss samples. The analysis through a QC pipeline, which allowed us to monitor quality parameters in a step-by-step manner, provided compelling evidence that adapter dimer contamination was the main factor causing batch effects. We concluded this study by summarising peer-reviewed published workflows that perform consistently well in avoiding adapter dimer contamination towards a greater likelihood of sequencing success.

stepRNA: Identification of Dicer cleavage signatures and passenger strand lengths in small RNA sequences

The enzyme Dicer is a component of many small RNA (sRNA) pathways involved in RNA processing for post-transcriptional regulation, anti-viral response and control of transposable elements. Cleavage of double-stranded RNA by Dicer produces a signature overhanging sequence at the 3’ end of the sRNA sequence relative to a complementary passenger strand in a RNA duplex. There is a need for reliable tools to computationally search for Dicer cleavage signatures to help characterise families of sRNAs. This is increasingly important due to the rising popularity of sRNA sequencing, especially in non-model organisms. Here, we present stepRNA, a fast, local tool that identifies (i) overhang signatures strongly indicative of Dicer cleavage in RNA sequences, and (ii) the length of the passenger strand in sRNAs duplexes. We demonstrate the use of stepRNA with simulated and biological datasets to detect Dicer cleavage signatures in experimentally validated examples. Compared to currently available tools, stepRNA is more accurate, requires only sRNA sequence data rather than a reference genome, and provides information about other important features such as passenger strand length. stepRNA is freely available at https://github.com/Vicky-Hunt-Lab/stepRNA and is easily installable.

vsRNAfinder: a novel method for identifying high-confidence viral small RNAs from small RNA-Seq data

Virus-encoded small RNAs (vsRNA) have been reported to play an important role in viral infection. Unfortunately, there is still a lack of an effective method for vsRNA identification. Herein, we presented vsRNAfinder, a de novo method for identifying high-confidence vsRNAs from small RNA-Seq (sRNA-Seq) data based on peak calling and Poisson distribution and is publicly available at https://github.com/ZenaCai/vsRNAfinder. vsRNAfinder outperformed two widely used methods namely miRDeep2 and ShortStack in identifying viral miRNAs with a significantly improved sensitivity. It can also be used to identify sRNAs in animals and plants with similar performance to miRDeep2 and ShortStack. vsRNAfinder would greatly facilitate effective identification of vsRNAs from sRNA-Seq data.

Exosomes Derived from Epidermal Stem Cells Improve Diabetic Wound Healing

Diabetic foot ulceration is a major diabetic complication with unmet needs. We investigated the efficacy of epidermal stem cells (ESCs) and ESCs-derived exosomes (ESCs-Exo) in improving impaired diabetic wound healing and their mechanisms of action. In vitro experiments showed that ESCs-Exo enhanced the proliferation and migration of diabetic fibroblasts and macrophages (Mφ), and promoted alternative or M2 Mφ polarization. In wounds of db/db mice, treatment with both ESCs and ESCs-Exo, when compared to fibroblast exosomes (FB-Exo) and PBS control, accelerated wound healing by decreasing inflammation, augmenting wound cell proliferation, stimulating angiogenesis and inducing M2 Mφ polarization. Multiplex protein quantification of wound lysates revealed TGFβ signaling influenced by ESCs-Exo. High-throughput sequencing of small RNAs contained in the ESCs-Exo showed higher proportions of miRNAs when compared to FB-Exo. In silico functional analysis demonstrated that the ESCs-Exo-miRNAs target genes were primarily involved in homeostatic processes and cell differentiation and highlighted regulatory control of PI3K/AKT and TGFβ signaling pathways. This was also validated in vitro. Collectively, our results indicate that ESCs and ESCs-Exo are equally effective in promoting impaired diabetic wound healing and that ESCs-Exo treatment may be a promising and technically advantageous alternative to stem cell therapies.

Analyzing Association between Expression Quantitative Trait and CNV for Breast Cancer Based on Gene Interaction Network Clustering and Group Sparse Learning

Aims:

The occurrence and development of tumor is accompanied by the change of pathogenic gene expression. Tumor cells avoid the damage of immune cells by regulating the expression of immune related genes.

Background:

Tracing the causes of gene expression variation is helpful to understand tumor evolution and metastasis.

Objective:

Current gene expression variation explanation methods are confronted with several main challenges: low explanation power, insufficient prediction accuracy, and lack of biological meaning.

Method:

In this study, we propose a novel method to analyze the mRNA expression variations of breast cancers risk genes. Firstly, we collected some high-confidence risk genes related to breast cancer and then designed a rank-based method to preprocess the breast cancers copy number variation (CNV) and mRNA data. Secondly, to elevate the biological meaning and narrow down the combinatorial space, we introduced a prior gene interaction network and applied a network clustering algorithm to generate high density subnetworks. Lastly, to describe the interlinked structure within and between subnetworks and target genes mRNA expression, we proposed a group sparse learning model to identify CNVs for pathogenic genes expression variations.

Result:

The performance of the proposed method is evaluated by both significantly improved predication accuracy and biological meaning of pathway enrichment analysis.

Conclusion:

The experimental results show that our method has practical significance

MGcount: a total RNA-seq quantification tool to address multi-mapping and multi-overlapping alignments ambiguity in non-coding transcripts

BACKGROUND

Total-RNA sequencing (total-RNA-seq) allows the simultaneous study of both the coding and the non-coding transcriptome. Yet, computational pipelines have traditionally focused on particular biotypes, making assumptions that are not fullfilled by total-RNA-seq datasets. Transcripts from distinct RNA biotypes vary in length, biogenesis, and function, can overlap in a genomic region, and may be present in the genome with a high copy number. Consequently, reads from total-RNA-seq libraries may cause ambiguous genomic alignments, demanding for flexible quantification approaches.

RESULTS

Here we present Multi-Graph count (MGcount), a total-RNA-seq quantification tool combining two strategies for handling ambiguous alignments. First, MGcount assigns reads hierarchically to small-RNA and long-RNA features to account for length disparity when transcripts overlap in the same genomic position. Next, MGcount aggregates RNA products with similar sequences where reads systematically multi-map using a graph-based approach. MGcount outputs a transcriptomic count matrix compatible with RNA-sequencing downstream analysis pipelines, with both bulk and single-cell resolution, and the graphs that model repeated transcript structures for different biotypes. The software can be used as a python module or as a single-file executable program.

CONCLUSIONS

MGcount is a flexible total-RNA-seq quantification tool that successfully integrates reads that align to multiple genomic locations or that overlap with multiple gene features. Its approach is suitable for the simultaneous estimation of protein-coding, long non-coding and small non-coding transcript concentration, in both precursor and processed forms. Both source code and compiled software are available at https://github.com/hitaandrea/MGcount .

DANSR: A Tool for the Detection of Annotated and Novel Small RNAs

Existing small noncoding RNA analysis tools are optimized for processing short sequencing reads (17-35 nucleotides) to monitor microRNA expression. However, these strategies under-represent many biologically relevant classes of small noncoding RNAs in the 36-200 nucleotides length range (tRNAs, snoRNAs, etc.). To address this, we developed DANSR, a tool for the detection of annotated and novel small RNAs using sequencing reads with variable lengths (ranging from 17-200 nt). While DANSR is broadly applicable to any small RNA dataset, we applied it to a cohort of matched normal, primary, and distant metastatic colorectal cancer specimens to demonstrate its ability to quantify annotated small RNAs, discover novel genes, and calculate differential expression. DANSR is available as an open source tool.

Noncoding RNAs in apoptosis: identification and function

Apoptosis is a vital cellular process that is critical for the maintenance of homeostasis in health and disease. The derailment of apoptotic mechanisms has severe consequences such as abnormal development, cancer, and neurodegenerative diseases. Thus, there exist complex regulatory mechanisms in eukaryotes to preserve the balance between cell growth and cell death. Initially, protein-coding genes were prioritized in the search for such regulatory macromolecules involved in the regulation of apoptosis. However, recent genome annotations and transcriptomics studies have uncovered a plethora of regulatory noncoding RNAs that have the ability to modulate not only apoptosis but also many other biochemical processes in eukaryotes. In this review article, we will cover a brief summary of apoptosis and detection methods followed by an extensive discussion on microRNAs, circular RNAs, and long noncoding RNAs in apoptosis.

Bioinformatics and Machine Learning Approaches to Understand the Regulation of Mobile Genetic Elements

Simple Summary

Transposable elements (TEs) are DNA sequences that are, or were, able to move (transpose) within the genome of a single cell. They were first discovered by Barbara McClintock while working on maize, and they make up a large fraction of the genome. Transpositions can result in mutations and they can alter the genome size. Cells regulate the activity of TEs using a variety of mechanisms, such as chemical modifications of DNA and small RNAs. Machine learning (ML) is an interdisciplinary subject that studies computer algorithms that can improve through experience and by the use of data. ML has been successfully applied to a variety of problems in bioinformatics and has exhibited favorable precision and speed. Here, we provide a systematic and guided review on the ML and bioinformatic methods and tools that are used for the analysis of the regulation of TEs.

Abstract

Transposable elements (TEs, or mobile genetic elements, MGEs) are ubiquitous genetic elements that make up a substantial proportion of the genome of many species. The recent growing interest in understanding the evolution and function of TEs has revealed that TEs play a dual role in genome evolution, development, disease, and drug resistance. Cells regulate TE expression against uncontrolled activity that can lead to developmental defects and disease, using multiple strategies, such as DNA chemical modification, small RNA (sRNA) silencing, chromatin modification, as well as sequence-specific repressors. Advancements in bioinformatics and machine learning approaches are increasingly contributing to the analysis of the regulation mechanisms. A plethora of tools and machine learning approaches have been developed for prediction, annotation, and expression profiling of sRNAs, for methylation analysis of TEs, as well as for genome-wide methylation analysis through bisulfite sequencing data. In this review, we provide a guided overview of the bioinformatic and machine learning state of the art of fields closely associated with TE regulation and function.

DIANA-mAP: Analyzing miRNA from Raw NGS Data to Quantification

microRNAs (miRNAs) are small non-coding RNAs (~22 nts) that are considered central post-transcriptional regulators of gene expression and key components in many pathological conditions. Next-Generation Sequencing (NGS) technologies have led to inexpensive, massive data production, revolutionizing every research aspect in the fields of biology and medicine. Particularly, small RNA-Seq (sRNA-Seq) enables small non-coding RNA quantification on a high-throughput scale, providing a closer look into the expression profiles of these crucial regulators within the cell. Here, we present DIANA-microRNA-Analysis-Pipeline (DIANA-mAP), a fully automated computational pipeline that allows the user to perform miRNA NGS data analysis from raw sRNA-Seq libraries to quantification and Differential Expression Analysis in an easy, scalable, efficient, and intuitive way. Emphasis has been given to data pre-processing, an early, critical step in the analysis for the robustness of the final results and conclusions. Through modularity, parallelizability and customization, DIANA-mAP produces high quality expression results, reports and graphs for downstream data mining and statistical analysis. In an extended evaluation, the tool outperforms similar tools providing pre-processing without any adapter knowledge. Closing, DIANA-mAP is a freely available tool. It is available dockerized with no dependency installations or standalone, accompanied by an installation manual through Github.

Mobile genomics: tools and techniques for tackling transposons

Next-generation sequencing approaches have fundamentally changed the types of questions that can be asked about gene function and regulation. With the goal of approaching truly genome-wide quantifications of all the interaction partners and downstream effects of particular genes, these quantitative assays have allowed for an unprecedented level of detail in exploring biological interactions. However, many challenges remain in our ability to accurately describe and quantify the interactions that take place in those hard to reach and extremely repetitive regions of our genome comprised mostly of transposable elements (TEs). Tools dedicated to TE-derived sequences have lagged behind, making the inclusion of these sequences in genome-wide analyses difficult. Recent improvements, both computational and experimental, allow for the better inclusion of TE sequences in genomic assays and a renewed appreciation for the importance of TE biology. This review will discuss the recent improvements that have been made in the computational analysis of TE-derived sequences as well as the areas where such analysis still proves difficult. This article is part of a discussion meeting issue 'Crossroads between transposons and gene regulation'.