Structural Annotation Service

Introduction of Structural Annotation

Structural annotation is a critical aspect of genomic analysis that focuses on identifying and characterizing key genomic features that contribute to the biological function of genes. This process involves the precise annotation of various structural elements, including promoters, transcription start sites (TSS), 5' untranslated regions (5' UTR), start codons, exons, introns, stop codons, 3' untranslated regions (3'UTR), and poly-A tails.

Promoters are DNA regions that initiate gene transcription, playing a crucial role in regulating gene expression. The transcription start site marks the location where gene transcription begins, while the 5'UTR, a non-coding region upstream of the coding sequence, influences translation regulation. Start codons signal the beginning of the protein-coding sequence, whereas exons represent the coding sequences transcribed and translated into proteins. Introns, the non-coding sequences between exons, are spliced out during RNA processing. Stop codons mark the end of the protein-coding sequence, and the 3'UTR, located downstream of the coding sequence, affects mRNA stability and translation. Finally, the poly-A tail, a stretch of adenine nucleotides added to the mRNA's 3' end, is crucial for mRNA stability and export. Accurate annotation of these features is essential for understanding gene function, regulation, and the overall organization of the genome.

Application of Structural Annotation

• Gene Function Elucidation: Accurate structural annotation of genomic elements, including promoters, exons, and introns, facilitates a comprehensive understanding of gene functionality and regulation. This process enables researchers to identify and characterize alternative splicing events, which produce diverse protein isoforms. It also aids in delineating the functional domains within proteins and correlating specific genetic variants with observed phenotypic traits. Through detailed annotation, researchers can better elucidate the roles of genes in various biological processes and their contributions to phenotypic diversity.
• Regulatory Mechanism Insights: Structural annotation allows for the precise identification of regulatory regions, such as enhancers, silencers, and promoter elements, that control the expression of genes. This is critical for understanding how genes are turned on or off in different tissues, developmental stages, or in response to environmental stimuli. By integrating epigenomic data, such as DNA methylation and histone modification profiles, structural annotation can also reveal the complex layers of gene regulation that contribute to cellular identity and function.
• Transcript Characterization: Through the detailed annotation of exons, introns, and UTRs, researchers can characterize the diversity of transcripts produced by a gene. This includes identifying non-coding RNAs, alternative polyadenylation sites, and regulatory motifs within UTRs that influence mRNA stability, localization, and translation efficiency. Such insights are essential for understanding the post-transcriptional regulation of gene expression and its impact on cellular processes.
• Functional Genomics: Structural annotation provides a foundation for linking genomic features with functional outcomes. By mapping structural elements to gene expression profiles, protein interactions, and phenotypic data, researchers can uncover the functional roles of specific genes and regulatory elements. This is particularly important in the context of disease research, where structural variants such as deletions, duplications, and inversions can disrupt gene function and contribute to disease pathogenesis.
• Comparative Genomics: By comparing structural annotations across different species, researchers can identify conserved elements that are critical for maintaining essential biological functions. This can reveal evolutionary pressures acting on specific genes or regulatory regions, providing insights into the mechanisms of speciation, adaptation, and evolutionary innovation. Comparative analysis can also help identify lineage-specific expansions or contractions of gene families, shedding light on the genetic basis of species-specific traits.
• Genomic Variation and Disease Mechanisms: Structural annotation plays a critical role in detecting structural variants such as copy number variations (CNVs), insertions, deletions, and translocations, which are frequently associated with repetitive elements in the genome. These structural changes can significantly impact gene function, leading to genomic instability and contributing to a range of diseases, including cancer, neurodevelopmental disorders, and hereditary conditions. By precisely mapping these variants to specific genomic regions, researchers can gain insights into the mechanisms driving these diseases and identify potential targets for therapeutic intervention.
• Agricultural Genomics: In agricultural genomics, structural annotation is essential for identifying and characterizing genomic regions associated with key agronomic traits such as yield, disease resistance, and environmental adaptability. By annotating genes and regulatory elements that influence these traits, researchers can develop molecular markers for use in breeding programs, which aids in selecting favorable traits in crops and livestock. Furthermore, understanding the structural organization of crop genomes enhances strategies for improving crop resilience to biotic and abiotic stresses, thereby advancing global food security.

CD Genomics Structural Annotation Workflow

Sample Submission Guidelines

Bioinformatics Analysis Content

What Are the Advantages of Our Services?

Comprehensive Structural Annotation Analysis:

Our services offer a thorough analysis of genomic features, including promoters, exons, introns, UTRs, and poly-A sites. Utilizing a combination of both homology-based and de novo prediction approaches, we ensure that even the most complex and novel genomic elements are accurately annotated. This detailed annotation allows for the identification of alternative splicing events, regulatory motifs, and structural variants that are crucial for understanding gene function and regulation. Furthermore, our pipelines are optimized to handle large and complex genomes, ensuring consistent results across diverse species, from model organisms to non-model species.

Highly Specialized Analytical Pipelines:

Our analytical pipelines are specifically tailored to address the unique challenges presented by structural features within large and repetitive genomes. We employ a combination of machine learning algorithms, hidden Markov models (HMMs), and comparative genomics techniques to annotate complex regions with high precision. Our pipelines are also capable of detecting novel structural elements that may not be captured by traditional annotation methods. This approach ensures that all relevant genomic features are annotated, providing a comprehensive view of the genome's architecture and its functional implications.

Integration with Multi-Omics Data:

To offer a comprehensive perspective on genome functionality, we integrate structural annotation with multi-omics datasets, including transcriptomics, proteomics, and epigenomics. This integration facilitates the correlation of structural features with gene expression profiles, protein interactions, and epigenetic modifications. By analyzing these relationships, we can reveal how specific genomic elements regulate chromatin structure, transcriptional activity, and post-transcriptional modifications. Additionally, this approach enables us to investigate the impact of structural variants on gene expression and associated phenotypic traits, enhancing our understanding of the molecular mechanisms driving complex diseases and biological processes.

Advanced Bioinformatics Techniques:

Our structural annotation services are underpinned by cutting-edge bioinformatics methodologies. We employ sophisticated tools, such as genome alignment algorithms for comparative analysis, clustering techniques for novel element discovery, and machine learning models for pattern recognition in complex genomic data. These techniques enable us to achieve a high level of accuracy in identifying and characterizing genomic features, even in highly repetitive or low-complexity regions. Moreover, our use of advanced visualization tools ensures that the results of our annotations are both accessible and interpretable, facilitating downstream analysis and decision-making.

Customizable and Scalable Solutions:

We recognize that every research project has unique requirements. As such, our structural annotation services are fully customizable, allowing researchers to tailor the analysis to their specific needs. Whether the focus is on a genome-wide survey or a deep annotation of specific regions, our scalable solutions are designed to accommodate projects of any size or complexity. We offer flexible options for data output formats, annotation depth, and integration with other genomic data, ensuring that our services align seamlessly with your research goals and workflow.

Data Security and Compliance:

Recognizing the sensitivity and confidentiality of genomic data, we implement rigorous security protocols to safeguard client information. Our practices include using secure methods for data transfer and enforcing strict access controls to mitigate unauthorized access. Additionally, we uphold a transparent compliance framework to ensure that data management adheres to the highest ethical standards, providing clients with confidence in the protection and integrity of their data.

What Does Structural Annotation Show?

Example of Structural Annotation Input Data

• Genome Sequence File: This FASTA file contains the complete DNA sequences of the organism's genome, serving as the primary reference for structural annotation.
  Example file: arabidopsis_genome.fasta
• Transcriptome Sequence File: This FASTQ file contains the RNA-seq reads, which provide evidence for gene structure annotation, including exon-intron boundaries and alternative splicing events.
  Example file: arabidopsis_rnaseq_reads.fastq
• Gene Annotation Database: A GTF or GFF3 file containing coordinates and descriptions of known genes, including exons, introns, and UTRs, used as a reference for structural annotation.
  Example file: known_gene_annotation.gtf
• Read Alignment File: This BAM file contains RNA-seq reads that have been aligned to the reference genome, helping to validate and refine gene structures.
  Example file: aligned_reads.bam
• Functional Annotation File: A GFF3 or BED file that includes functional information such as gene names, protein-coding regions, and regulatory elements, aiding in the interpretation of annotated gene structures.
  Example file: functional_annotation.bed

Accuracy of Gene Annotation

The accuracy of gene structure annotation was assessed using the Annotation Edit Distance (AED) metric, which measures the concordance of transcript models with underlying sequence alignments. The results reveal that the Araport11 annotation exhibits better consistency with supporting evidence compared to the previous TAIR10 models. The cumulative fraction of AED scores by TAIR10 ranking categories highlights the refinements made, particularly in low-confidence gene models. An example is the gene AT4G16890.1, where an erroneous intron was corrected, and novel isoforms were added.

Figure 1. (a) AED score distribution for TAIR10 and Araport11 gene models. (b) Cumulative fraction of AED scores by TAIR10 ranks. (c) Gene structure improvement in AT4G16890.1, showing corrected intron annotation and added isoforms. (Cheng, 2017)

Alternative Splicing Events in Araport11

Nearly 40% of protein-coding loci in Araport11 encode two or more splicing isoforms. Among these, 388 loci generate between 7 and 27 isoforms, which are linked to specific metabolic processes. Using SUPPA software, 19,915 splicing events were identified, with over 65% newly annotated in Araport11. Intron retention (IR) was the most common AS event. PSI values across tissues revealed tissue-specific splicing regulation.

Figure 2. (A) Pie charts showing proportions of AS events in Araport11 and TAIR10 annotations. (B) Unsupervised clustering of splicing events across eleven tissues. (C) Comparison of PSI for retained introns between wild-type and skip-2 plants. (Cheng, 2017)

Exitrons and Their Impact on Coding Sequences

Exitrons, a specific subset of non-constitutive introns found within coding exons, significantly impact the coding sequences of transcripts. The majority of exitrons in the Araport11 annotation have lengths divisible by three, leading to extended coding sequences without altering the reading frame. However, for exitrons with lengths not divisible by three, retention can lead to variations in the coding sequence, including changes to the C-terminal region and positioning of the stop codon. Furthermore, ribosome occupancy analyses revealed that exitrons have higher ribosome engagement compared to introns, particularly in dark-grown seedlings, indicating their potential role in translation.

Figure 3. (a) Location of exitrons within coding regions. (b) Impact of splicing on coding sequence when exitron length is divisible by three. (c, d) Impact of splicing on coding sequence when exitron length is not divisible by three. (e, f) Ribosome occupancy of exitrons compared to introns under different light conditions. (Cheng, 2017)

Title: Integrative structural annotation of de novo RNA-Seq provides an accurate reference gene set of the enormous genome of the onion (Allium cepa L.)

Publication: DNA Research

Main Methods: De novo transcriptome assembly, Structural Annotation, Functional Annotation 

Abstract: The structural annotation of the onion (Allium cepa) transcriptome addresses the challenges posed by its vast and complex genome, which has historically limited genomic research and molecular breeding efforts for this important crop. Despite the onion's nutritional and medicinal significance, genomic resources have been scarce due to the genome's immense size of 16.3 Gb. To overcome these limitations, a high-quality de novo transcriptome assembly was generated, leveraging a combination of reference mapping and ab initio gene prediction models. This approach enabled the creation of an extensively annotated gene set, providing a reliable reference for future genomic studies in Allium species. The study also underscores the importance of precise transcriptome assembly in non-model organisms, where the presence of introns, transposable elements, and non-coding RNAs often complicates accurate gene annotation. By employing rigorous validation against pre-existing gene sets, this research delivers a robust structural annotation framework that facilitates deeper genetic and genomic exploration in A. cepa, setting a precedent for similar efforts in other non-model species.

Research Results:

Structural and Functional Annotation Workflow

Structural annotation of the onion transcriptome was conducted using the Integrated Structural Gene Annotation Pipeline (ISGAP). The process began with protein alignments based on reference gene annotations from several monocot plants. Gene structures identified were merged into consensus sequences, forming the initial gene models. Quality control measures, such as the removal of gene models with frameshifts or early stop codons, were applied. Subsequent steps involved extending partial gene models and refining them using the Augustus tool with a training set of 2,000 complete genes. Final gene models were further filtered through the NR database in GenBank, and their biological functions were annotated using InterProScan and other plant protein databases with stringent criteria.

Figure 4. ISGAP showing the combined use of reference-based and ab initio prediction methods, along with the six-frame translation process. (Kim, 2015)

Validation and Comparison of Gene Models

The validation of gene models predicted by ISGAP and six-frame translation involved aligning known onion proteins to the assembled transcripts. ISGAP demonstrated superior coverage, with 348 genes matching 351 onion proteins (88.2%), compared to 281 genes from six-frame translation matching 344 proteins (86.4%). Additionally, ISGAP covered 85.9% of the mapped regions, whereas six-frame translation covered 74.0%. Further validation using the RefSeq plant protein database showed that ISGAP predicted 8,324 genes, covering 90.5% of the mapped regions, compared to 6,526 genes from six-frame translation covering 73.7%. This indicates that ISGAP produced a higher number of precisely annotated genes.

Figure 5. Comparison of onion gene sets predicted by six-frame translation and ISGAP. The histogram shows the numbers of covered query sequences (left) and predicted genes (right). (A) Validation using 511 onion proteins. (B) Assessment against plant proteins in the RefSeq database. (Kim, 2015)

Assessment of Multi-Exon Genes and Annotated Gene Structures

The evaluation of gene annotations focused on multi-exon genes, comparing ISGAP and six-frame translation results. In validation using onion proteins, ISGAP accurately predicted 23 genes that corresponded to 78 multi-exon proteins, covering 84.8% of the onion proteins. Conversely, the six-frame translation method failed to represent any of these multi-exon proteins. When assessing the RefSeq proteins, ISGAP identified 12,904 proteins (89.3%) corresponding to 1,573 genes, whereas six-frame translation only matched 6 proteins with 3 genes, demonstrating a significant shortfall in accurately annotating multi-exon genes. The discrepancies in six-frame translation arose due to intron retention and incorrect translation of regions or strands. In contrast, ISGAP successfully annotated these regions by accurately identifying exon boundaries based on reference protein structures and ab initio models.

Figure 6. Examples of well-annotated genes by ISGAP compared to six-frame translation, showing correct annotations of multi-exon genes, appropriate regions, and strands. (A and B) Multi-exon gene cases; (C) Correct region annotation; (D) Correct strand annotation. (Kim, 2015)

Functional Annotation and Transcriptomic Variation

Using ISGAP, 27,421 onion genes (50.6%) were functionally annotated, with the protein kinase domain being the most common. BLAST analysis against the Uniprot and RefSeq databases identified 50,352 genes (93.0%) with assigned functions. Coverage analysis showed onion gene models aligned with 60.5-68.0% of monocot and 68.1-71.3% of dicot proteins. Comparative transcriptomics between H6 and SP3B identified 50,064 SNPs and 14,016 InDels, with 11,444 SNPs confirmed for marker development.

Figure 7. Distribution of top 20 domains and gene coverage across monocots and dicots. (Kim, 2015)

Conclusion

This study provides a comprehensive structural and functional annotation of the onion transcriptome, addressing the challenges posed by its large and complex genome. By employing ISGAP and leveraging a combination of reference mapping and ab initio gene prediction, the research achieved a high level of annotation accuracy, particularly for multi-exon genes. The study not only fills a significant gap in genomic resources for Allium species but also sets a methodological precedent for similar efforts in non-model organisms. The functional annotation and transcriptomic analysis offer valuable insights for future genomic studies and molecular breeding programs.

1. What is Structural Annotation in genomics?

Structural annotation in genomics refers to the process of identifying and labeling the key features of a genome, such as genes, exons, introns, promoters, and regulatory elements. It involves predicting the locations and functions of these elements to provide a detailed map of the genome's structure, which is crucial for understanding gene expression, regulation, and overall genome functionality.

2. Why is Structural Annotation important for genome analysis?

Structural annotation is vital for genome analysis because it helps researchers accurately identify coding regions and other functional elements within the genome. This information is essential for understanding the genetic basis of traits, diseases, and evolutionary processes. Accurate annotation also enables more effective gene prediction, variant detection, and comparative genomics studies.

3. How does Structural Annotation differ from Functional Annotation?

Structural annotation is crucial for genome analysis as it provides detailed insights into the organization and function of genetic elements within the genome. By identifying and characterizing coding regions, regulatory elements, and other key genomic features, structural annotation aids in deciphering the genetic basis of traits, diseases, and evolutionary changes. It enhances gene prediction accuracy, facilitates the detection of genetic variants, and supports comparative genomics by enabling precise comparisons between genomes.

4. What tools are used for Structural Annotation?

Several advanced tools are used for structural annotation, including but not limited to, GENSCAN for gene prediction, RepeatMasker for identifying repetitive elements, and AUGUSTUS for gene structure prediction. These tools are often integrated into specialized pipelines that combine homology-based and de novo methods to achieve high accuracy in annotation.

5. How can Structural Annotation benefit my research?

Structural annotation provides a detailed map of your genome, enabling you to identify key genetic elements, understand gene regulation, and explore structural variations. This information can significantly enhance the accuracy of downstream analyses, such as variant calling, evolutionary studies, and disease mechanism research, ultimately driving your research forward.

6. What challenges are associated with Structural Annotation?

Challenges in structural annotation include accurately identifying genes in highly repetitive or low-complexity regions, differentiating between functional and non-functional sequences, and predicting alternative splicing events. These challenges require advanced computational tools and algorithms to ensure that annotations are both comprehensive and accurate.

7. Can Structural Annotation be applied to non-model organisms?

Yes, structural annotation can be applied to non-model organisms. Although these organisms may lack extensive reference data, advances in de novo annotation methods and comparative genomics allow for effective annotation of non-model species. This is crucial for studying biodiversity, evolutionary biology, and species-specific traits.

8. How does Structural Annotation integrate with other omics data?

Structural annotation can be integrated with multi-omics data, such as transcriptomics and epigenomics, to provide a more comprehensive understanding of genome function. By correlating structural features with gene expression, protein interactions, and epigenetic modifications, researchers can gain insights into how these features regulate biological processes and contribute to phenotypic variation.

References

1. Cheng, C. Y.; et al. Araport11: a complete reannotation of the Arabidopsis thaliana reference genome. The Plant Journal. 2017, 89(4), 789-804.
2. Kim, S.; et al. Integrative structural annotation of de novo RNA-Seq provides an accurate reference gene set of the enormous genome of the onion (Allium cepa L.). DNA Research. 2015, 22(1), 19-27.