BIO4BI3 Genome Annotation Lecture 7
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Questions and Answers

What is the primary purpose of scoring function in exon selection?

  • To maximize a scoring function dependent on individual exons (correct)
  • To select random segments of DNA
  • To minimize the number of introns
  • To assign equal weights to all exons

    • Methionine is coded by multiple triplets in DNA.

    False (B)

    Name the four basic signals involved in defining an exon.

    Translational start site, 5’ donor splice site, 3’ donor splice site, Translational stop codon

    An ___ is defined as an open reading frame (ORF) delimited by a 3’ acceptor site and a stop codon.

    <p>terminal exon</p> Signup and view all the answers

    Match the type of exon with its definition:

    <p>Initial exon = ORFs delimited by a start site and a 5’ donor site Internal exon = ORFs delimited by a 3’ acceptor site and a 5’ donor site Terminal exon = ORFs delimited by a 3’ acceptor site and stop codon</p> Signup and view all the answers

    Which of the following is a composition bias observed in organisms?

    <p>A preference for codons coding for a particular amino acid (C)</p> Signup and view all the answers

    The frequency of nucleotide pairs can help differentiate between integers and exons.

    <p>True (A)</p> Signup and view all the answers

    What upstream elements should be considered when scoring exons?

    <p>TATA box elements</p> Signup and view all the answers

    What is one significant advantage of using deep learning over traditional rule-based methods in gene prediction?

    <p>It automatically discovers relevant patterns (B)</p> Signup and view all the answers

    Deep learning models can accurately predict splice sites in eukaryotes.

    <p>True (A)</p> Signup and view all the answers

    Name one example of software used for alternative splicing detection.

    <p>SpliceAI</p> Signup and view all the answers

    Deep learning models require large, labeled datasets for effective _____.

    <p>training</p> Signup and view all the answers

    Which technique allows pre-trained models on one genome to be adapted for other species?

    <p>Transfer Learning (D)</p> Signup and view all the answers

    Match the following applications of deep learning with their corresponding focus:

    <p>DeepGene = Gene Prediction SpliceAI = Alternative Splicing Detection DeepEnhancer = Enhancer Identification DeepCAPE = Promoter Identification</p> Signup and view all the answers

    What process is used to automatically detect features during the training of a neural network?

    <p>Feature Extraction</p> Signup and view all the answers

    Computational resources are not a challenge for training deep learning models.

    <p>False (B)</p> Signup and view all the answers

    Which of the following is NOT a category in the Gene Ontology classification?

    <p>Cellular interaction (A)</p> Signup and view all the answers

    Gene Ontology was initiated in 1999 with a focus on plant species.

    <p>False (B)</p> Signup and view all the answers

    What is a slower but effective method for similarity-based gene prediction?

    <p>Exonerate (C)</p> Signup and view all the answers

    What does the 'Biological process' category in Gene Ontology describe?

    <p>The function of the gene from a cell’s point of view.</p> Signup and view all the answers

    Gene Ontology provides a common vocabulary to identify genes in __________.

    <p>pathways</p> Signup and view all the answers

    NGS allows for the capture of alternative splicing events in a single run.

    <p>True (A)</p> Signup and view all the answers

    What is one method used for identifying conserved regions across species?

    <p>Conservation of miRNA</p> Signup and view all the answers

    Match the following databases with their focus:

    <p>KEGG = Molecular interactions and reaction networks Reactome = Reactions and pathways in human biology BLAST2GO = Functional annotation of genes AMIGO = Gene ontology data access</p> Signup and view all the answers

    The human genome contains approximately ______ miRNA that control tens of thousands of genes.

    <p>1900</p> Signup and view all the answers

    Which of the following is a reason to use Gene Ontology?

    <p>It identifies trends in differential gene expression. (C)</p> Signup and view all the answers

    A gene can exist in only one category within Gene Ontology.

    <p>False (B)</p> Signup and view all the answers

    Match the sequencing types to their advantages:

    <p>NGS = Captures the near transcriptional state of tissues Long-Read Sequencing = Improves gene model accuracy by capturing full-length transcripts Short-Read Sequencing = May miss complex regions and alternative splicing cDNA = Requires tremendous effort and can be expensive</p> Signup and view all the answers

    What main benefit does Long-Read Sequencing provide over Short-Read Sequencing?

    <p>Captures full-length transcripts (C)</p> Signup and view all the answers

    What is the main advantage of using pathway databases like KEGG?

    <p>They help to visualize how genes function in biological pathways.</p> Signup and view all the answers

    Many important chromosome regions are considered genic.

    <p>False (B)</p> Signup and view all the answers

    What is one challenge of Short-Read Sequencing?

    <p>Miss large, complex regions</p> Signup and view all the answers

    What is the primary use of Hidden Markov Models (HMM) in gene finding?

    <p>To predict base positions in exons, introns, or intergenic regions (A)</p> Signup and view all the answers

    The frequency of codons is consistently the same across all organisms.

    <p>False (B)</p> Signup and view all the answers

    What does the log odds ratio (LP(S)) represent in codon usage?

    <p>It compares the observed codon usage probability to the expected probability under a random model.</p> Signup and view all the answers

    The frequency of codon c in a non-coding sequence is represented as P0(C) = F0(C1)F0(C2)...F0(Cm). Assuming the random model, F0(C) equals _____ .

    <p>1/64</p> Signup and view all the answers

    Match the following gene elements with their descriptions:

    <p>Promoter region = Region where transcription begins Exons = Coding sequences in a gene Introns = Non-coding sequences that are removed during RNA processing Stop codons = Signal to terminate protein synthesis</p> Signup and view all the answers

    Which of the following programs uses Hidden Markov Models for gene prediction?

    <p>GENESCAN (B)</p> Signup and view all the answers

    Codon TCT and ACG occur more frequently than expected in coding sequences.

    <p>False (B)</p> Signup and view all the answers

    What is a limitation of using BLAST programs for gene finding?

    <p>They do not define intron/exon boundaries well.</p> Signup and view all the answers

    What is genome annotation?

    <p>Identifying locations of genes and assigning functions (B)</p> Signup and view all the answers

    Functional annotation involves the identification of precise locations of genes and regulatory elements.

    <p>False (B)</p> Signup and view all the answers

    Name the two types of genome annotation.

    <p>Structural Annotation and Functional Annotation</p> Signup and view all the answers

    The system used for functional annotation that includes gene roles is called _____.

    <p>Gene Ontology</p> Signup and view all the answers

    Match the following classes of gene prediction with their examples:

    <p>Ab initio = Using algorithms to predict gene locations from sequences Homology-based = Finding genes based on similarities to known genes RNA-seq = Identifying genes by analyzing RNA transcripts Evidence-based = Utilizing experimental data to confirm gene predictions</p> Signup and view all the answers

    Which of the following statements is true about the significance of genome annotation?

    <p>It acts as a translator for genomic sequences. (C)</p> Signup and view all the answers

    MAKER is a tool used for structural annotation of genes.

    <p>True (A)</p> Signup and view all the answers

    What role does SNPEff play in genome annotation?

    <p>It is used for SNP functional impact assessment.</p> Signup and view all the answers

    Flashcards

    Genome Annotation

    The process of identifying genes and features in a genome and assigning functions to them.

    Structural Annotation

    Identifying the precise locations of genes, regulatory elements, and repetitive sequences.

    Functional Annotation

    Assigning biological functions to identified genes using databases (like Gene Ontology or KEGG).

    Gene Identification

    Finding genes using DNA sequences and RNA-seq data.

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    Gene Ontology (GO)

    A database of gene functions.

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    KEGG Pathways

    Databases for understanding biological pathways.

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    Genome Annotation Importance

    Connects genotype to phenotype and makes the genome's functions understandable.

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    Annotation translates genome to useful information

    Converts DNA codes into the meaningful context of cellular functions.

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    Exon Definition

    A segment of DNA that codes for a protein, found within a gene.

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    Exon Signals

    The four signals that define an exon: translational start site, 5' donor splice site, 3' acceptor splice site, and translational stop codon.

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    Exon Scoring

    A process that assigns a score based on the strength of exon signals and other features, such as the presence of 'start' and 'stop' codons and coding sequences.

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    Initial Exon

    An exon starting with a translational start site and ending with a 5' donor splice site.

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    Internal Exon

    An exon starting with a 3' acceptor site and ending with a 5' donor site, located within a gene.

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    Terminal Exon

    An exon starting with a 3' acceptor site and ending with a stop codon, marking the end of a coding sequence.

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    Codon Bias

    A preference for using certain codons over others to code for the same amino acid.

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    Nucleotide Pair Frequency

    The frequency of specific nucleotide pairs in exons and introns. Exon pairs tend to occur more frequently over certain distances reflecting the coding nature of exons.

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    Codon Usage Table

    A table that lists the frequency of each codon in a given organism's coding sequences. Helps determine the likelihood of a DNA sequence being coding or non-coding.

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    Log-Likelihood

    A statistical measure used to compare the likelihood of a DNA sequence being coding versus non-coding. It compares the observed codon frequencies to the expected frequencies in coding regions.

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    What's the importance of codon bias?

    Codon bias is the non-random preference for certain codons over others, even when they encode the same amino acid. This influences the likelihood of a DNA sequence being protein-coding.

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    Markov Models

    Statistical models used to analyze sequences where the probability of an event depends on a limited number of preceding events. Used in gene prediction.

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    HMM for Genes

    Hidden Markov Models (HMMs) trained to recognize the patterns of genes in DNA sequences.

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    HMM States

    The different functional regions in a gene that an HMM identifies. These regions include promoters, exons, introns, and more.

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    Transition Probability

    The probability of moving between different HMM states in a gene prediction process. Shows the likelihood of switching from one gene region to another.

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    Homology-Based Gene Prediction

    A method of gene prediction that identifies genes by finding similar sequences in other organisms. Relies on the idea that evolutionarily related organisms often have similar genes.

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    Exonerate

    A slower but very effective similarity-based prediction tool used for identifying conserved coding regions in genomes. It utilizes models like est2genome or protein2genome to assist in the process.

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    NGS impact on gene annotation

    Next-Generation Sequencing (NGS) has fundamentally changed gene annotation, making ab initio methods secondary. This is because NGS captures the near transcriptional state of a tissue under various conditions, including alternative splicing events, in a single run, providing much richer data.

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    Comparative genome analysis

    Comparing genomes of different species or ecotypes can reveal regions under evolutionary pressure and therefore less diverged than others. This helps in identifying conserved genes and regions.

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    microRNA identification

    microRNAs (miRNAs) are small non-transcribed elements that can be challenging to identify due to their location far from other genes. Conservation of miRNA across species is one method of identification.

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    Human genome miRNA count

    The human genome has approximately 1900 miRNAs, which are thought to control tens of thousands of genes.

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    Long-read sequencing advantage

    Long-read sequencing overcomes the limitations of short-read sequencing by capturing full-length transcripts, improving gene model accuracy and enabling the analysis of complex genomic regions.

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    Long-read applications

    Long-read sequencing allows for the discovery of new gene isoforms, lncRNAs, and transposable elements, and improves functional annotation by revealing novel regulatory regions.

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    Deep Learning for Annotation

    Deep learning uses neural networks to automatically discover patterns in DNA sequences, improving annotation accuracy.

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    Gene Prediction with Deep Learning

    Deep learning models can predict genes by analyzing DNA sequences, identifying coding regions and learning from existing annotations.

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    Alternative Splicing Detection using Deep Learning

    Deep learning models can identify splice sites, the junctions between exons and introns, with high accuracy, helping to understand complex gene regulation.

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    Regulatory Region Identification with Deep Learning

    Deep learning methods can classify non-coding regulatory regions, such as enhancers and promoters, by analyzing sequence data and chromatin states.

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    Deep Learning Training Process

    Deep learning models are trained on labeled data, learning patterns from known genes or regulatory elements, to improve their prediction accuracy

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    Challenges of Deep Learning in Annotation

    Deep learning requires vast labeled datasets and significant computational power, limiting its widespread application.

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    Transfer Learning in Annotation

    Pre-trained deep learning models can be adapted for annotating different species, reducing the need for massive datasets.

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    Multi-Omics Integration in Annotation

    Deep learning models that combine information from DNA, RNA, epigenetic, and proteomic data can produce more accurate annotations.

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    GO Categories

    The Gene Ontology database categorizes genes into three main categories: Molecular Function, Biological Process, and Cellular Component. Each category describes a gene's function from a different perspective.

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    Molecular Function

    This GO category describes the specific biochemical activity of a gene product. Examples include 'zinc ion binding' or 'ubiquitin-protein ligase activity'.

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    Biological Process

    This GO category describes a gene's role in larger cellular activities. Examples include 'cell division' or 'histone methylation'.

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    Cellular Component

    This GO category describes the physical location of a gene product within a cell. Examples include 'nucleus' or 'mitochondrion'.

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    Why use GO?

    Gene Ontology provides standardized terms to describe gene functions, enabling researchers to better understand genes, analyze expression patterns, identify trends, and characterize genomes.

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    Pathway Databases

    Databases like KEGG and Reactome map genes to known metabolic or signaling pathways, showing how they interact and contribute to cellular functions.

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    Reactome

    This pathway database primarily focuses on human biology but covers other species. It emphasizes reactions and pathways involved in cellular processes.

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    Study Notes

    Lecture 7 - Genome Annotation

    • Lecture is part of BIO4BI3 Bioinformatics course.
    • Genome annotation is the process of identifying locations of genes and other features in a genome, assigning functions to these elements.
    • Genome annotation involves structural and functional annotation.

    What is Annotation?

    • Structural Annotation: pinpointing precise locations of genes, regulatory elements, and repetitive sequences.
    • Functional Annotation: assigning biological functions to identified genes using databases like Gene Ontology or KEGG.
    • Genome annotation is essential for linking genotype to phenotype.
    • Without annotation, genome sequences are like an unread book in an unknown language. Annotation translates sequences into understandable form.

    Steps of Genome Annotation

    • Gene Identification (Structural Annotation): Identifying genes, regulatory elements, and other features based on DNA and RNA-seq data.
    • Functional Annotation: Assigning functional roles to identified genes via databases like Gene Ontology and KEGG pathways.

    RNA-based Annotation

    • RNA sequencing (RNA-seq) is the primary method for discovering gene-coding regions.
    • RNA-seq helps in identifying expressed genes and alternative splice isoforms, particularly when used with long-read sequencing techniques.
    • Gene expression varies across tissues and developmental stages, which is important to understand.
    • Long-read RNA-seq excels at capturing entire transcripts. Short-read sequencing often misses complete transcripts.

    Gene Prediction Approaches

    • Ab initio: Predicts genes based on inherent sequence features (start/stop codons, splice sites)
      • Tools: Augustus, GeneMark
    • Homology: Compares genome sequences to known genes in other species.
      • Tools: BLASTX, Exonerate
    • Comparative Genomics: Compares entire genomes to find conserved genes.
      • Tools: MUMmer, MAUVE
    • Machine Learning: Uses existing datasets for more accurate gene model predictions.
      • Tools: DNABERT2, DeepGene

    Defining an Exon

    • Four primary signals define exons:
      • Translational start site
      • 5' donor splice site
      • 3' donor splice site
      • Translation stop codon
    • These signals are derived from position weight matrices built from known functional signals.

    Scoring an Exon

    • Exon scoring considers both the signals used to identify the exon and the coding statistics of the exon itself.
    • Comparative and homology-based methods additionally factor the alignment's quality into the exon score.
    • Exons are assembled into predictive gene models to maximize the score, dependent on the exon structures and complete gene structural model.

    Composition Bias

    • Organisms prefer particular codons for specific amino acids, even though multiple codons can code for the same amino acid.
    • The frequency of nucleotide pairs in introns and exons can display this bias.

    Codon Usage Log-Likelihood

    • Likelihood calculation considers codon frequency in the analyzed species, and assumes independence between neighboring codons.
    • Log-likelihoods are used to measure the probability of codon sequences that occur within a protein-coding region.

    Hidden Markov Model in Gene Finding

    • Markov models characterize observations where the next observation's probability relies on a number of previous observations
    • Markov models of order 5 are frequently used in DNA analyses to assess codon biases and adjacency for intron-exon prediction.
    • HMMs predict whether a base resides within an intron, exon, or intergenic region, accounting for known gene structures.

    HMM in Gene Finding

    • HMMs use conserved sequence or compositional bias, to identify the structure of genes.
    • HMMs use controlled syntax, like occurrence of promoters before start codons
    • Transition probabilities in HMMs (exon-to-intron movement) are calculated from training sets of known genes and their structural elements.

    Coding regions are important for understanding a gene

    Homology-based Gene Prediction

    • Utilizing closely related sequences to locate similar genes in a DNA space.
    • Programs like BLASTX are suited for comparisons.
    • A limitation is that defining intron/exon boundaries is not always accurate.
    • ESTs from other species can be effective for conserved coding regions.

    Annotation with Long-Reads

    • Short reads can struggle with large, complex, and alternative splicing regions.
    • Long-read sequencing captures entire transcript sequences and improves accuracy.

    SNPEff

    • SNPEff predicts the functional impact of polymorphisms in genomes.
    • Requires gene annotations and alignment of sequenced data.
    • Requires a file containing polymorphisms detected in the re-sequenced genome.

    MAKER Genome Annotation

    • A pipeline approach, comprising several steps:
    • Repeat Masking: identifying and masking repetitive sequences.
    • Evidence Alignment: uses known genes, proteins, RNA-seq to align with the genome.
    • Gene Prediction (ab initio): combining alignments and signal-based methods.
    • Final Gene Model Integration: combines evidence-based and ab initio prediction.
    • Functional Annotation: utilizes tools like BLAST for functional information assignments.

    Genome Browser Visualization

    • Visual tools illustrating various genomic features like transcripts, genes, repeats, and polymorphisms.

    Deep Learning and Genome Annotation

    • Deep learning is a subset of machine learning that uses neural networks to learn features from large datasets.
    • Deep learning is useful to analyze massive and complex genomic data because it can automatically uncover relevant patterns without the requirement of manual feature engineering.

    Applications of Deep Learning in Annotation

    • Deep learning can predict gene coding regions, improve alternative splicing detection, identify enhancers and promoters, etc.

    How Deep Learning Works in Annotation

    • Raw DNA sequences and relevant data sets are inputted into the model.
    • Neural networks perform feature extraction, identifying features like start/stop codons, splice sites, etc.
    • A labeled dataset is used to train the model.
    • The trained model can predict features in new, unlabeled genomes.

    The Future of Genome Annotation

    • Future efforts require more labeled data to improve models and computational resources for training.
    • Techniques like transfer learning, can refine models on one genome to utilize them in other species.
    • Combining multiple "omics" data can further enhance annotation accuracy.

    Gene Ontology Consortium

    • Provides a standardized classification system for gene functions using terms and standardized relationships.
    • The system has three categories for gene organization: molecular function, biological process, and cellular component.
    • Started in 1999 for the fruit fly project but expanded to cover many species (including mammals and plants),.

    GO Categories

    • Molecular Function: describing functions from a biochemical perspective. Can be general or specific (e.g., zinc ion binding, ubiquitin-protein ligase activity).
    • Biological Process: describing function from the cellular point of view (e.g., cell division, histone methylation).
    • Cellular Component: defining the regions where a gene functions (e.g., nucleus, mitochondrion).

    BLAST2GO

    • A bioinformatics tool for functional annotation of genes. It uses BLAST to identify genes in a genome and then use knowledge bases (e.g., GO, KEGG, PANTHER) to determine their functions.

    Functional Annotation and Pathway Databases

    • Gene Ontology (GO) helps classify gene function into molecular function, biological process, and cellular component.
    • Pathway databases like KEGG and Reactome show connections between genes and pathways, visualizing relationships and interactions, or providing context for interpreting results.

    Network Analysis

    • Network analysis is the study of interactions in biological networks.
    • Gene co-expression networks show related function between co-expressed genes.
    • Protein-protein interaction networks (PPI) analyze functional or physical relationships between proteins.
    • Pathway integration connects annotations to known pathways to understand how genes interact in larger biological processes, identify hub genes, or reveal bottleneck genes.

    Co-Expression Network Example

    • Visual representation of co-expression relationships in genes (example given with specific gene names).

    Why Use Pathway Databases?

    • Pathway databases provide a context to genes and proteins.
    • They link genes to pathways to understand interactions between gene products.
    • Pathway databases can discover potentially disrupted pathways in disease states.
    • Researchers can analyze network interactions to understand how genes work together in broader biological processes.

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    Lecture 7 - Genome Annotation PDF

    Description

    This quiz explores the essential concepts of genome annotation as part of the BIO4BI3 Bioinformatics course. It covers both structural and functional annotation processes, delving into gene identification and the assignment of biological functions. Understanding genome annotation is crucial for linking genotype to phenotype.

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