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What is the primary purpose of scoring function in exon selection?
What is the primary purpose of scoring function in exon selection?
Methionine is coded by multiple triplets in DNA.
Methionine is coded by multiple triplets in DNA.
False
Name the four basic signals involved in defining an exon.
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.
An ___ is defined as an open reading frame (ORF) delimited by a 3’ acceptor site and a stop codon.
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Match the type of exon with its definition:
Match the type of exon with its definition:
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Which of the following is a composition bias observed in organisms?
Which of the following is a composition bias observed in organisms?
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The frequency of nucleotide pairs can help differentiate between integers and exons.
The frequency of nucleotide pairs can help differentiate between integers and exons.
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What upstream elements should be considered when scoring exons?
What upstream elements should be considered when scoring exons?
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What is one significant advantage of using deep learning over traditional rule-based methods in gene prediction?
What is one significant advantage of using deep learning over traditional rule-based methods in gene prediction?
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Deep learning models can accurately predict splice sites in eukaryotes.
Deep learning models can accurately predict splice sites in eukaryotes.
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Name one example of software used for alternative splicing detection.
Name one example of software used for alternative splicing detection.
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Deep learning models require large, labeled datasets for effective _____.
Deep learning models require large, labeled datasets for effective _____.
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Which technique allows pre-trained models on one genome to be adapted for other species?
Which technique allows pre-trained models on one genome to be adapted for other species?
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Match the following applications of deep learning with their corresponding focus:
Match the following applications of deep learning with their corresponding focus:
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What process is used to automatically detect features during the training of a neural network?
What process is used to automatically detect features during the training of a neural network?
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Computational resources are not a challenge for training deep learning models.
Computational resources are not a challenge for training deep learning models.
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Which of the following is NOT a category in the Gene Ontology classification?
Which of the following is NOT a category in the Gene Ontology classification?
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Gene Ontology was initiated in 1999 with a focus on plant species.
Gene Ontology was initiated in 1999 with a focus on plant species.
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What is a slower but effective method for similarity-based gene prediction?
What is a slower but effective method for similarity-based gene prediction?
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What does the 'Biological process' category in Gene Ontology describe?
What does the 'Biological process' category in Gene Ontology describe?
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Gene Ontology provides a common vocabulary to identify genes in __________.
Gene Ontology provides a common vocabulary to identify genes in __________.
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NGS allows for the capture of alternative splicing events in a single run.
NGS allows for the capture of alternative splicing events in a single run.
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What is one method used for identifying conserved regions across species?
What is one method used for identifying conserved regions across species?
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Match the following databases with their focus:
Match the following databases with their focus:
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The human genome contains approximately ______ miRNA that control tens of thousands of genes.
The human genome contains approximately ______ miRNA that control tens of thousands of genes.
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Which of the following is a reason to use Gene Ontology?
Which of the following is a reason to use Gene Ontology?
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A gene can exist in only one category within Gene Ontology.
A gene can exist in only one category within Gene Ontology.
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Match the sequencing types to their advantages:
Match the sequencing types to their advantages:
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What main benefit does Long-Read Sequencing provide over Short-Read Sequencing?
What main benefit does Long-Read Sequencing provide over Short-Read Sequencing?
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What is the main advantage of using pathway databases like KEGG?
What is the main advantage of using pathway databases like KEGG?
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Many important chromosome regions are considered genic.
Many important chromosome regions are considered genic.
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What is one challenge of Short-Read Sequencing?
What is one challenge of Short-Read Sequencing?
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What is the primary use of Hidden Markov Models (HMM) in gene finding?
What is the primary use of Hidden Markov Models (HMM) in gene finding?
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The frequency of codons is consistently the same across all organisms.
The frequency of codons is consistently the same across all organisms.
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What does the log odds ratio (LP(S)) represent in codon usage?
What does the log odds ratio (LP(S)) represent in codon usage?
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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 _____ .
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 _____ .
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Match the following gene elements with their descriptions:
Match the following gene elements with their descriptions:
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Which of the following programs uses Hidden Markov Models for gene prediction?
Which of the following programs uses Hidden Markov Models for gene prediction?
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Codon TCT and ACG occur more frequently than expected in coding sequences.
Codon TCT and ACG occur more frequently than expected in coding sequences.
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What is a limitation of using BLAST programs for gene finding?
What is a limitation of using BLAST programs for gene finding?
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What is genome annotation?
What is genome annotation?
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Functional annotation involves the identification of precise locations of genes and regulatory elements.
Functional annotation involves the identification of precise locations of genes and regulatory elements.
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Name the two types of genome annotation.
Name the two types of genome annotation.
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The system used for functional annotation that includes gene roles is called _____.
The system used for functional annotation that includes gene roles is called _____.
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Match the following classes of gene prediction with their examples:
Match the following classes of gene prediction with their examples:
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Which of the following statements is true about the significance of genome annotation?
Which of the following statements is true about the significance of genome annotation?
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MAKER is a tool used for structural annotation of genes.
MAKER is a tool used for structural annotation of genes.
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What role does SNPEff play in genome annotation?
What role does SNPEff play in genome annotation?
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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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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.
