ncRNA Annotation Service

What is ncRNA Annotation?

NcRNA (non-coding RNA) annotation is an essential aspect of genomic analysis that involves identifying and characterizing non-coding RNA sequences within the genome. Unlike protein-coding genes, ncRNAs do not encode proteins but are vital in regulating gene expression, guiding cellular processes, and maintaining genome integrity. Through accurate annotation of ncRNAs, researchers can uncover their diverse roles, interactions, and contributions to cellular functions and disease mechanisms, providing deeper insights into the complexities of gene regulation and the non-coding genome.

Classification of ncRNAs

1. Small ncRNAs:

MicroRNAs (miRNAs): Short RNA molecules (about 22 nucleotides) that regulate gene expression by binding to mRNA, leading to its degradation or translation inhibition.

Small Interfering RNAs (siRNAs): Short double-stranded RNA molecules (21-23 nucleotides) involved in RNA interference (RNAi), silencing gene expression by degrading target mRNAs.

Piwi-interacting RNAs (piRNAs): Single-stranded RNAs (24-32 nucleotides) associated with Piwi proteins, primarily involved in transposon silencing and maintenance of genome integrity in germ cells.

2. Long non-coding RNAs (lncRNAs):

Classical lncRNAs: Long non-coding RNAs (over 200 nucleotides) that regulate gene expression through chromatin modification, transcriptional interference, and post-transcriptional mechanisms.

Enhancer RNAs (eRNAs): lncRNAs transcribed from enhancer regions that influence gene expression by interacting with transcriptional machinery and chromatin modifiers.

Long Intergenic ncRNAs (lincRNAs): lncRNAs located between coding genes with roles in gene regulation, chromatin organization, and cellular differentiation.

3. Other ncRNAs:

Ribosomal RNAs (rRNAs): Major components of the ribosome, crucial for protein synthesis. Includes 18S, 5.8S, 28S, and 5S rRNAs.

Transfer RNAs (tRNAs): Small RNA molecules that transport amino acids to the ribosome during translation, ensuring accurate protein synthesis.

Small Nuclear RNAs (snRNAs): Components of the spliceosome involved in pre-mRNA splicing and modification of nuclear RNA.

Application of ncRNA Annotation

Functional Genomics: NcRNA annotation is critical for elucidating the diverse roles of non-coding RNAs in gene regulation and cellular processes. Through the identification and characterization of miRNAs, siRNAs, and lncRNAs, researchers can uncover the mechanisms of action, such as how miRNAs mediate post-transcriptional gene silencing or how lncRNAs modulate chromatin structure and gene expression. This analysis enhances the understanding of gene regulatory networks and cellular pathways (Peng, 2016).

Disease Mechanism Research: The annotation of ncRNAs is instrumental in studying their involvement in various diseases. Aberrant expression of specific miRNAs has been associated with cancer progression, neurodegenerative disorders, and cardiovascular diseases. By identifying dysregulated ncRNAs and their target genes, insights into disease mechanisms can be gained, potential biomarkers for early diagnosis identified, and novel therapeutic targets explored (Nelson, 2008).

Developmental Biology: NcRNAs are key regulators of developmental processes and cellular differentiation. Annotating ncRNAs during different developmental stages or in various tissue types aid in understanding their roles in embryonic development, organogenesis, and tissue-specific gene expression. eRNAs are vital for regulating developmental genes, and their annotation can reveal mechanisms underlying developmental disorders and congenital diseases.

Evolutionary Studies: Comparative analysis of ncRNAs across species provides insights into their evolutionary conservation and divergence. Annotating ncRNAs in different organisms allows researchers to identify conserved regulatory elements and evolutionary innovations. This understanding contributes to the knowledge of the functional significance of ncRNAs and their impact on species-specific traits and evolutionary adaptations (Friedman, 2009).

Regulatory Network Analysis: NcRNA annotation facilitates the construction of comprehensive regulatory networks by integrating ncRNA data with gene expression profiles and epigenetic modifications. This approach enables the identification of key regulatory ncRNAs and their interactions with target genes, transcription factors, and chromatin modifiers. Such networks provide a deeper understanding of the regulatory mechanisms governing cellular functions and responses to environmental stimuli.

Drug Discovery and Development: NcRNAs are increasingly recognized as targets for drug discovery. By annotating ncRNAs associated with disease pathways, researchers can identify new drug targets and therapeutic strategies. For instance, targeting specific miRNAs or lncRNAs with small molecules or antisense oligonucleotides holds promise for treating a range of diseases. Comprehensive ncRNA annotation aids in designing and assessing these potential therapeutic interventions (Saito, 2015).

Agricultural Genomics: In agricultural genomics, annotating ncRNAs in crop genomes can enhance breeding programs by identifying ncRNA-related traits such as stress responses, disease resistance, and growth regulation. Understanding the role of ncRNAs in these traits aids in developing improved crop varieties with enhanced yield, resilience, and adaptation to environmental challenges.

Functional Annotation of Genomic Elements: Annotating ncRNAs enhances the functional annotation of genomic elements by identifying regulatory regions, transcriptional enhancers, and other functional components within the genome. This thorough annotation helps map the functional landscape of genomes, improving the understanding of gene regulation and the potential effects of genetic variations on phenotypes.

Integration with Epigenomics: Integrating ncRNA annotation with epigenomic data, such as DNA methylation and histone modification profiles, provides insights into the epigenetic regulation of ncRNAs and their impact on gene expression. This integrated approach helps in understanding how ncRNAs influence chromatin dynamics and gene expression regulation, offering a more holistic view of epigenetic control mechanisms.

CD Genomics ncRNA Annotation Workflow

Sample Submission Guidelines

Bioinformatics Analysis Content

What Are the Advantages of Our Services?

Comprehensive ncRNA Annotation Analysis

Our ncRNA annotation services deliver an in-depth analysis of various non-coding RNA types, such as miRNAs, siRNAs, piRNAs, and lncRNAs. By leveraging advanced tools like RNA-Seq and specialized databases such as miRBase and lncRNA repositories, we ensure precise identification and classification of ncRNAs. This meticulous approach provides valuable insights into the functional roles of these RNAs, elucidating their contributions to genomic architecture and regulatory networks.

Highly Specialized Analytical Pipelines

Our analytical pipelines are meticulously designed to tackle the complexities inherent in ncRNA analysis. They incorporate both homology-based and de novo methodologies, allowing for the detection of novel ncRNAs and their interactions. These pipelines are optimized for high-throughput environments, facilitating the efficient analysis of extensive datasets derived from transcriptome sequencing projects. This ensures that both known and previously uncharacterized ncRNAs are accurately identified and analyzed.

Integration with Multi-Omics Data: Our ncRNA annotation services are augmented by integrating multi-omics data, such as genomics, transcriptomics, and proteomics. This comprehensive approach enables us to investigate the regulatory functions of ncRNAs across various biological layers, including gene expression, protein interactions, and epigenetic modifications. By combining these data types, we gain a deeper insight into the functional roles of ncRNAs, revealing their extensive influence on cellular processes and molecular pathways.

Advanced Bioinformatics Techniques

Our services are underpinned by state-of-the-art bioinformatics methodologies. We employ Hidden Markov Models (HMMs) for precise ncRNA classification and use clustering algorithms to identify novel ncRNA families. Additionally, our comparative genomics approaches track the evolution and diversification of ncRNAs across species, offering insights into their functional evolution and conservation.

Customizable and Scalable Solutions

Our ncRNA annotation services are both flexible and scalable, tailored to meet the specific requirements of each project. Whether the focus is on comprehensive genome-wide analysis or detailed investigation of specific ncRNA types, our solutions are designed to align with your research goals. This adaptability ensures that clients receive either broad surveys or detailed annotations, depending on their needs.

Data Security and Compliance: Our genomic research services prioritize the protection of client data through stringent security measures. We employ advanced encryption techniques and secure data storage solutions to maintain data confidentiality and integrity. Our protocols are designed to meet rigorous data protection standards, ensuring that all client information is handled with the utmost care throughout our collaborative efforts.

What Does ncRNA Annotation Show?

Example of ncRNA Annotation Input Data

• Genome Sequence File: This FASTA file contains the complete DNA sequences of the organism's genome, serving as the primary input for ncRNA annotation.
  Example file: organism_genome.fasta
• Known ncRNA Database: This file includes sequences of known ncRNAs used to identify and classify ncRNAs within the genome.
  Example file: Rfam14.2.fasta
• Annotation File: A GFF or BED file that provides coordinates and descriptions of known ncRNAs within the genome.
  Example file: ncRNA_annotation.gff3

Ribosomal RNA Annotation

A detailed examination of rRNA in Trichoplax was performed, focusing on the polycistronic rRNA operon and its processing. The study reconstructed the pre-rRNA cluster by integrating previously published sequences with genomic data from the Triad1 assembly. The rRNA operon consists of SSU (18S), 5.8S, and LSU (28S) rRNAs, along with internal and external spacers. Although the Triad1 assembly did not contain complete and uninterrupted pre-rRNA sequences, the consensus sequence was successfully constructed using available genomic loci. Additionally, the study identified nine 5S rRNA genes, including one pseudogene, and revealed three anti-parallel gene pairs in the genome.

Figure 1. Trichoplax pre-rRNA cluster reconstructed from previously published sequences, with blast hits of the pre-rRNA to the Triad1 genome assembly shown. (Hertel, 2009)

Signal Recognition Particle (SRP) RNA

The SRP plays a crucial role in directing signal peptide-bearing proteins to their proper cellular locations, either the prokaryotic plasma membrane or the eukaryotic endoplasmic reticulum membrane. The SRP RNA component, known as 7SL or SRP RNA, is highly conserved, facilitating its identification through blast comparisons with sequences from the SRPDB. In Trichoplax, the SRP RNA was identified and structurally aligned using the Rfam database.

Figure 2. Structural alignments of Trichoplax RNase P RNA, SRP RNA, and U3 snoRNA with corresponding Rfam alignments, annotated and computed by infernal. (Hertel, 2009)

Small Nucleolar RNAs (snoRNAs)

SnoRNAs are categorized into two classes: box H/ACA and box C/D, each directing different chemical modifications in target RNAs. Among these, U3 snoRNA, a box C/D class member, is distinct due to its role in early rRNA maturation and its highly conserved sequence. Although no Trichoplax homologs of U17 or other snoRNAs were identified via blast, alternative methods revealed 3 H/ACA and 4 C/D snoRNA candidates, confirmed through secondary structure analysis and sequence alignment.

Figure 3. Secondary structure model of a novel H/ACA snoRNA and its predicted rRNA target sites. Alignment of U18 snoRNA sequences across various Metazoa, highlighting conserved motifs. (Hertel, 2009)

Title: Functional annotation of structural ncRNAs within enhancer RNAs in the human genome: implications for human disease

Publication: Scientific Reports

Main Methods: ChIP-seq, RNA-seq, Gene Ontology analysis

Abstract: In this study, a genome-wide catalog of eRNA regions was established using ChIP-seq and RNA-seq data from 50 human cell and tissue types. The research focused on characterizing these eRNA regions, particularly exploring their genomic, epigenetic, transcriptomic, and chromatin interaction features. Through comprehensive analysis, the study identified numerous known and novel functional RNA structures within eRNA regions. These structures were further investigated for their association with genetic variants linked to inflammatory autoimmune diseases. The results demonstrated a disproportionate enrichment of these disease-associated variants in structural ncRNAs, with a notable bias towards immune-specific cell types. This work underscores the potential of eRNAs as critical regulators in gene expression and as promising targets for disease diagnosis and therapy.

Research Results:

Enrichment of Multi-Omic Signatures in eRNA Regions

The analysis highlights a pronounced enrichment of multi-omic signatures in eRNA regions compared to weakly-transcribed enhancers in H1-hESCs. Key components of these signatures include RNAPII and histone modifications like H3K4me2 and H3K4me3, which are essential for transcriptional regulation. The study also identifies a notable enrichment of critical transcription factors and chromatin regulators, such as Taf1 and Chd1, in eRNA regions. This enrichment points to the role of eRNAs in sustaining open chromatin and maintaining pluripotency. Furthermore, the increased presence of transcription factor binding motifs in eRNA regions underscores their potential importance in regulating gene transcription programs.

Figure 4. Identification and characterization of eRNA regions in hESCs. (A) UCSC genome browser view of a representative eRNA region (red) and weakly-transcribed regions (blue). (B) Metagene profiles showing the average signal of multi-omic signatures across eRNA regions (red) and weakly-transcribed enhancer regions (blue). (Ren, 2017)

Characterization of eRNAs Across Multiple Human Cell Types

A comprehensive identified 23,878 eRNA regions across 50 human cell and tissue types, representing 1.8% of the human genome. eRNA regions are more cell type-specific compared to weakly-transcribed enhancers. GO analysis shows that eRNA-associated genes are involved in specific cellular functions, such as T cell activation and metabolic processes. Additionally, eRNA expression levels strongly correlate with the expression of nearby genes, highlighting their role in mRNA synthesis.

Figure 5. eRNAs in many cell types. (A) Saturation curves of eRNA regions and genome coverage. (B) Cell specificity of eRNA regions versus weakly-transcribed enhancers. (C) GO terms for eRNA-associated genes across 14 cell types. (D) Correlation between eRNA and mRNA expression levels across cell type. (Ren, 2017)

Structural Diversity and Functional Prediction of Novel ncRNAs

The study utilized RNAz, REAPR, and EvoFold to explore the structural diversity of ncRNAs within lymphoid eRNA regions across ten human lymphoid cell types. Reliable predictions, identified by at least two methods, revealed an average of 116 novel structural ncRNAs per cell type, with 75.7% classified as novel. The predominant structure among these novel ncRNAs was the stem-loop. Functional predictions, based on clustering with known ncRNAs from the Rfam database using NoFold, suggested that a novel ncRNA in CD4 primary cells with a cloverleaf structure resembles tRNA, implying a similar functional role. Additionally, another novel ncRNA exhibited a stem-loop structure similar to miR-155, suggesting potential regulatory functions in haematopoietic cells.

Figure 6. Structural ncRNAs in Lymphoid eRNA Regions. (A) Genomic distribution of structural ncRNAs. (B) Venn diagram of novel ncRNAs in CD4 cells. (C) Novel ncRNAs confirmed by BLASTN across 10 cell types. (D) Example of ncRNA similar to tRNA. (E) Example of ncRNA similar to miRNA miR-155. (Ren, 2017)

Characterization and Expression of lncRNAs

The study quantified the expression of protein-coding genes, annotated lncRNAs, and novel lncRNAs within lymphoid eRNA regions, identifying novel lncRNAs not previously annotated in databases such as GENCODE V19 or LNCipedia. Comparison of the size distribution of these novel lncRNA transcripts with those from existing databases revealed distinct characteristics. Analysis of expression levels showed that novel lncRNA transcripts generally exhibited higher expression than annotated lncRNAs. Further examination of these novel lncRNAs in bone marrow, thymus samples, and ten samples from the Human BodyMap project underscored their significance in lymphoid tissues.

Figure 7. Expression of lncRNAs in Lymphoid eRNA Regions. (A) Quantification of protein-coding genes, annotated lncRNAs, and novel lncRNAs. (B) Size distribution of novel lncRNAs versus protein-coding and annotated lncRNAs. (C) Expression levels across gene categories, with novel lncRNAs showing higher expression. (D) Distribution of expression levels for novel and annotated lncRNAs. (E) Expression of novel lncRNAs in bone marrow, thymus, and Human BodyMap samples. (Ren, 2017)

Conclusion

The study reveals that structural ncRNAs within eRNA regions play a significant role in gene regulation and disease. By analyzing eRNA regions across various human cell types, the research identifies numerous novel structural ncRNAs, particularly in lymphoid cells. These novel ncRNAs often display structural similarities to known functional ncRNAs, suggesting they may have similar regulatory roles. The study also finds that eRNAs are enriched with disease-associated variants, emphasizing their potential as targets for diagnostics and therapeutic interventions. This work enhances the understanding of eRNA functions and their implications for human health.

1. What is ncRNA annotation and why is it important?

ncRNA annotation involves identifying and classifying ncRNAs within a genome, including miRNAs, small siRNAs, and lncRNAs. This process is crucial because ncRNAs play significant roles in gene regulation, cellular processes, and disease mechanisms. Accurate annotation helps researchers understand these functions, unravel complex regulatory networks, and uncover potential therapeutic targets.

2. How does ncRNA annotation contribute to gene expression studies?

NcRNA annotation contributes to gene expression studies by identifying ncRNAs that regulate gene expression at various levels, including transcriptional and post-transcriptional. For example, miRNAs can silence target mRNAs, while lncRNAs can influence chromatin structure and gene accessibility. By annotating these ncRNAs, researchers can better understand their roles in gene regulation and how they impact overall gene expression profiles.

3. What tools are used for ncRNA annotation?

Several advanced tools are employed for ncRNA annotation, including RNA-Seq for transcriptome-wide profiling, miRBase for microRNA identification, and databases such as NONCODE and lncRNA.org for long non-coding RNA cataloging. These tools enable accurate identification, classification, and functional annotation of ncRNAs across different organisms.

4. Can ncRNA annotation help in disease research?

Yes, ncRNA annotation is instrumental in disease research. Non-coding RNAs, such as miRNAs and lncRNAs, are often dysregulated in various diseases, including cancer, cardiovascular conditions, and neurodegenerative disorders. By annotating these ncRNAs, researchers can identify disease-associated ncRNAs, understand their roles in disease mechanisms, and discover potential biomarkers or therapeutic targets for early diagnosis and treatment.

5. What are the benefits of integrating ncRNA annotation with multi-omics data?

Integrating ncRNA annotation with multi-omics data provides a holistic view of cellular processes. This approach allows for the exploration of ncRNA interactions with genes, proteins, and epigenetic modifications, offering insights into how ncRNAs regulate gene expression and influence cellular functions across different biological contexts.

6. How does ncRNA annotation aid in understanding gene regulatory networks?

ncRNA annotation aids in understanding gene regulatory networks by identifying ncRNAs that interact with specific genes, transcription factors, and chromatin modifiers. This information helps map out complex regulatory pathways and elucidate how ncRNAs contribute to gene regulation, providing a clearer picture of cellular mechanisms and network dynamics.

7. What challenges are involved in ncRNA annotation?

NcRNA annotation involves several challenges due to the complexity and diversity of non-coding RNAs. One major challenge is the accurate identification and classification of different ncRNA types, as they can vary greatly in size, function, and expression patterns. Additionally, distinguishing between functional ncRNAs and non-functional or redundant sequences requires sophisticated bioinformatics tools and comprehensive databases. Another difficulty is the dynamic and context-dependent nature of ncRNA expression, which may vary across tissues, developmental stages, and environmental conditions. Addressing these challenges necessitates advanced computational methods, extensive experimental validation, and integration with multi-omics data to ensure precise and meaningful annotation of ncRNAs.

8. How can ncRNA annotation improve agricultural genomics?

In agricultural genomics, ncRNA annotation can enhance breeding programs by identifying ncRNAs associated with important traits such as stress responses, disease resistance, and growth regulation. Understanding the roles of ncRNAs in these traits helps in developing crop varieties with improved yield, resilience, and adaptation to environmental challenges, ultimately contributing to more efficient and sustainable agriculture.

References

1. Peng, Y.; Croce, C. M. The role of MicroRNAs in human cancer. Signal Transduction and Targeted Therapy. 2016, 1(1), 1-9.
2. Nelson, P. T.; et al. W. MicroRNAs (miRNAs) in neurodegenerative diseases. Brain Pathology. 2008, 18(1), 130-138.
3. Friedman, R. C.; et al. Most mammalian mRNAs are conserved targets of microRNAs. Genome Research. 2009, 19(1), 92-105.
4. Saito, Y.; et al. microRNA-34a as a therapeutic agent against human cancer. Journal of Clinical Medicine. 2015, 4(11), 1951-1959.
5. Ren, C.; et al. Functional annotation of structural ncRNAs within enhancer RNAs in the human genome: implications for human disease. Scientific Reports. 2017, 7, 15518.
6. Hertel, J.; et al. Non-coding RNA annotation of the genome of Trichoplax adhaerens. Nucleic Acids Research. 2009, 37(5), 1602-1615.