4.3 Evidence-based gene prediction
9 min read•august 20, 2024
Gene prediction is a crucial step in understanding genomes. It involves identifying potential genes within DNA sequences using computational methods. These methods range from ab initio approaches that rely solely on sequence features to similarity-based methods that leverage known genes from other organisms.
Evidence-based gene prediction combines multiple lines of evidence to improve accuracy. This includes integrating data from RNA sequencing, protein alignments, and comparative genomics. By combining different approaches, researchers can better identify complex gene structures and validate predictions experimentally.
Ab initio gene prediction
- methods rely solely on the genomic sequence itself to identify potential gene structures
- These approaches utilize various sequence features and statistical models to distinguish coding regions from non-coding regions
- Ab initio methods are particularly useful for identifying novel genes in poorly characterized genomes
Sequence composition features
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- Coding regions exhibit distinct nucleotide composition compared to non-coding regions
- Higher GC content in coding regions
- Biased distribution of nucleotides at different codon positions
- Markov models capture the statistical properties of coding and non-coding regions
- Higher-order Markov models (5th or 6th order) are commonly used
- Compositional bias helps discriminate between potential coding and non-coding regions
Codon usage bias
- Codon usage refers to the preferential use of certain codons over synonymous codons for the same amino acid
- Codon usage bias varies across species and can be species-specific or gene-specific
- Highly expressed genes tend to have stronger codon usage bias
- Incorporating codon usage information improves the accuracy of gene prediction
- (CAI) measures the degree of codon usage bias
- Comparative analysis of codon usage patterns across related species aids in gene identification
Splice site signals
- Splice sites are critical signals for defining intron-exon boundaries in eukaryotic genes
- Canonical splice site motifs: GT-AG (donor-acceptor) for introns
- Non-canonical splice sites (GC-AG, AT-AC) are rare but biologically relevant
- Splice site prediction relies on identifying conserved motifs and sequence patterns
- (PWMs) capture the nucleotide frequencies at each position
- Incorporating splice site information significantly improves the accuracy of eukaryotic gene prediction
Promoter & terminator sequences
- upstream of genes contain regulatory elements for transcription initiation
- TATA box, CAAT box, and GC box are common promoter elements
- Terminator sequences downstream of genes signal transcription termination
- Polyadenylation signal (AATAAA) is a key terminator sequence
- Identifying promoter and terminator sequences helps define gene boundaries and directionality
- approaches (Support Vector Machines, ) are used to predict promoter and terminator regions
Similarity-based gene prediction
- Similarity-based methods utilize sequence homology to known proteins or transcripts to identify genes
- These approaches leverage the conservation of gene sequences across species or within a species
- Similarity-based methods are particularly effective for identifying well-conserved genes and homologs
Protein sequence alignment
- Protein identifies regions of similarity between a query sequence and a database of known proteins
- BLAST () is widely used for protein sequence alignment
- Identifies statistically significant local alignments
- Protein alignments help identify coding regions, exon-intron boundaries, and functional domains
- Limitations: may miss novel or rapidly evolving genes
Expressed sequence tags (ESTs)
- ESTs are short cDNA sequences derived from mRNA transcripts
- ESTs provide direct evidence of gene expression and help identify transcribed regions
- EST alignment to the genome aids in gene structure prediction
- Spliced alignments reveal intron-exon boundaries
- EST-based gene prediction is particularly useful for species with limited genomic resources
- Limitations: biased towards highly expressed genes, incomplete coverage
RNA-seq data integration
- RNA-seq provides high-throughput sequencing of the transcriptome
- RNA-seq data can be aligned to the reference genome to identify transcribed regions and splice junctions
- Integrating RNA-seq data improves the accuracy of gene prediction
- Helps identify novel transcripts and alternative splicing events
- Tools like and use RNA-seq alignments for transcript assembly and quantification
- Challenges: handling noise, biases, and variable coverage in RNA-seq data
Combined ab initio & similarity approaches
- Combining ab initio and similarity-based methods leverages the strengths of both approaches
- Integration of multiple lines of evidence improves the overall accuracy of gene prediction
- Different tools have varying strategies for combining ab initio and similarity information
GenScan vs GeneMark
- combines ab initio predictions with protein and EST alignments
- Uses a probabilistic model (Generalized Hidden Markov Model) for gene structure prediction
- relies primarily on ab initio predictions using Markov models
- Incorporates protein and EST alignments as additional evidence
- GenScan tends to have higher , while GeneMark has higher specificity
Augustus vs GeneID
- uses a generalized Hidden Markov Model for ab initio prediction
- Incorporates extrinsic evidence from protein and EST alignments
- Allows for the integration of RNA-seq data and comparative genomics information
- combines ab initio predictions with protein and EST alignments
- Uses a hierarchical approach with different levels of evidence
- Incorporates splice site and start/stop codon predictions
- Augustus generally outperforms GeneID, particularly in eukaryotic gene prediction
GLIMMER vs Prodigal
- (Gene Locator and Interpolated Markov ModelER) is designed for prokaryotic gene prediction
- Uses interpolated Markov models for ab initio prediction
- Incorporates sequence composition bias and codon usage information
- is a more recent tool specifically developed for prokaryotic gene prediction
- Combines ab initio predictions with protein sequence alignments
- Handles overlapping genes and alternative start codons
- Prodigal has been shown to outperform GLIMMER in terms of accuracy and speed
Evaluating gene prediction accuracy
- Assessing the accuracy of gene prediction methods is crucial for understanding their performance and limitations
- Different evaluation metrics capture various aspects of gene prediction accuracy
- Comparison to high-quality, manually curated gene annotations serves as a gold standard
Sensitivity vs specificity
- Sensitivity (recall) measures the proportion of true genes that are correctly predicted
- Sensitivity = True Positives / (True Positives + False Negatives)
- Specificity measures the proportion of predicted genes that are true genes
- Specificity = True Negatives / (True Negatives + False Positives)
- Trade-off between sensitivity and specificity: increasing one may decrease the other
- Balanced measures like combine sensitivity and specificity
Exon-, transcript-, & gene-level accuracy
- Exon-level accuracy evaluates the correctness of predicted exon boundaries
- Exon sensitivity: proportion of true correctly predicted
- Exon specificity: proportion of predicted exons that are true exons
- Transcript-level accuracy assesses the correctness of predicted transcript structures
- Transcript sensitivity: proportion of true transcripts correctly predicted
- Transcript specificity: proportion of predicted transcripts that are true transcripts
- Gene-level accuracy evaluates the overall correctness of predicted gene structures
- Gene sensitivity: proportion of true genes correctly predicted
- Gene specificity: proportion of predicted genes that are true genes
Comparison to gold standard annotations
- High-quality, manually curated gene annotations serve as a gold standard for evaluation
- for human and mouse genomes
- for Drosophila genome
- Comparing predicted gene structures to gold standard annotations provides a reliable assessment of accuracy
- Limitations: gold standard annotations may be incomplete or contain errors
- Continuous refinement of gold standard annotations based on new evidence and experimental validation
Challenges of eukaryotic gene prediction
- Eukaryotic genomes present unique challenges for accurate gene prediction
- Complex gene structures, alternative splicing, and non-coding elements complicate the prediction process
- Addressing these challenges requires specialized approaches and integration of multiple data sources
Alternative splicing complexity
- Alternative splicing generates multiple transcript isoforms from a single gene
- Exon skipping, intron retention, alternative 5' or 3' splice sites
- Predicting alternative splicing events is computationally challenging
- Requires extensive training data and incorporation of RNA-seq evidence
- Tools like AStalavista and SpliceGrapher are designed to handle
Overlapping & nested genes
- Eukaryotic genomes contain overlapping and nested gene structures
- Genes on opposite strands can overlap
- Genes can be nested within introns of other genes
- Overlapping and nested genes complicate gene boundary prediction and transcript assembly
- Specialized algorithms and data structures (e.g., splice graphs) are used to handle these complex cases
Pseudogenes & transposable elements
- Pseudogenes are non-functional gene copies that have lost their protein-coding ability
- Processed pseudogenes lack introns and originate from mRNA retrotransposition
- Unprocessed pseudogenes arise from gene duplication and accumulate mutations
- Transposable elements are repetitive sequences that can move within the genome
- Can be misidentified as genes due to their protein-coding potential
- Distinguishing pseudogenes and transposable elements from functional genes is crucial for accurate gene prediction
- Comparative genomics and sequence similarity analysis help identify and filter out these elements
Comparative genomics for gene prediction
- Comparative genomics leverages the conservation of gene sequences across related species
- Analyzing sequence conservation patterns and evolutionary signatures aids in gene identification and structure prediction
- Comparative approaches are particularly useful for identifying conserved genes and refining gene boundaries
Sequence conservation patterns
- Coding regions tend to exhibit higher sequence conservation compared to non-coding regions
- Selective pressure to maintain protein function constrains sequence divergence
- Pairwise or multiple sequence alignments reveal conserved regions potentially corresponding to genes
- Tools like BLAST, LASTZ, and MULTIZ are used for sequence alignment
- Conserved splice site dinucleotides (GT-AG) and codon usage patterns provide additional evidence for gene identification
Synonymous vs non-synonymous substitution rates
- Synonymous substitutions (Ks) are nucleotide changes that do not alter the amino acid sequence
- Non-synonymous substitutions (Ka) are nucleotide changes that result in amino acid changes
- is used to assess the selective pressure acting on a gene
- Ka/Ks < 1 indicates purifying selection (conserved genes)
- Ka/Ks > 1 suggests positive selection (rapidly evolving genes)
- Analyzing Ka/Ks ratios helps distinguish functional genes from pseudogenes and non-coding regions
Phylogenetic shadowing approach
- Phylogenetic shadowing uses closely related species to identify conserved functional elements
- Captures conservation patterns in a phylogenetic context
- Multiple sequence alignments of orthologous regions are analyzed to detect conserved sequences
- Conserved regions are more likely to correspond to functional elements, including genes
- Phylogenetic shadowing is particularly effective for identifying regulatory elements and non-coding RNAs
- Tools like phastCons and phyloP are used to quantify conservation scores and identify conserved elements
Emerging technologies for gene validation
- Experimental validation is crucial for confirming the accuracy of gene predictions
- Emerging technologies provide new opportunities for gene validation and refinement
- Integration of these technologies with computational predictions enhances the reliability of gene annotations
Long-read sequencing
- Long-read sequencing technologies (, ) generate reads spanning entire transcripts
- Helps resolve complex gene structures and identify full-length isoforms
- Isoform sequencing () protocol captures full-length cDNA sequences
- Enables the identification of novel isoforms and improves transcript annotation
- Long-read sequencing aids in the validation of splice junctions and untranslated regions (UTRs)
- Challenges: higher error rates compared to short-read sequencing, computational complexity of data analysis
Full-length cDNA sequencing
- Full-length cDNA sequencing captures the complete transcript sequence from 5' to 3' end
- Helps identify transcription start sites (TSS) and polyadenylation sites
- (CAGE) and 3' end sequencing (3'-seq) techniques are used for full-length cDNA analysis
- CAGE identifies the 5' ends of transcripts and maps TSS
- 3'-seq captures the 3' ends of transcripts and polyadenylation sites
- Full-length cDNA sequencing validates predicted gene structures and identifies novel transcripts
- Challenges: library preparation biases, limited coverage compared to RNA-seq
Proteogenomics & mass spectrometry
- Proteogenomics integrates genomic and proteomic data to validate and refine gene annotations
- Uses mass spectrometry to identify peptides and map them back to the genome
- Peptide identification provides direct evidence of protein-coding potential
- Helps validate predicted genes and identify novel protein-coding regions
- Proteogenomics aids in the identification of alternative translation start sites and frame-shifts
- Refines gene boundaries and improves the accuracy of gene models
- Challenges: limited sensitivity, dependence on protein abundance, computational complexity of data integration
Key Terms to Review (37)
Ab initio gene prediction: Ab initio gene prediction refers to the computational methods used to identify genes in a genome based solely on the DNA sequence without relying on prior knowledge of gene locations. These methods utilize statistical models and algorithms that analyze features of the DNA sequence, such as coding potential and sequence motifs, to predict where genes are likely to be found. This approach contrasts with evidence-based methods that incorporate data from known genes, such as cDNA or protein sequences.
Alternative splicing complexity: Alternative splicing complexity refers to the diverse ways in which pre-mRNA can be spliced to produce multiple mature mRNA transcripts from a single gene. This process allows for the generation of different protein isoforms, which can have distinct functions and regulatory roles within the cell. The intricate regulation of alternative splicing contributes significantly to protein diversity, impacting gene expression and cellular function.
Augustus: Augustus refers to the first emperor of Rome, who ruled from 27 BC until his death in AD 14. He transformed the Roman Republic into a powerful empire and laid the foundations for a regime that would last for centuries. His political strategies and reforms shaped governance, military organization, and economic stability in Rome, influencing various aspects of political structures and leadership throughout history.
Basic local alignment search tool: The basic local alignment search tool, commonly known as BLAST, is a powerful algorithm used to compare an input biological sequence against a database of sequences to identify regions of similarity. It is particularly useful in gene prediction as it helps researchers locate homologous sequences, predict gene function, and infer evolutionary relationships by identifying conserved regions across different species.
Bayesian Networks: Bayesian networks are probabilistic graphical models that represent a set of variables and their conditional dependencies through a directed acyclic graph. They are widely used for inference and decision-making under uncertainty, providing a framework to model the relationships between variables in complex systems. In the context of gene prediction, Bayesian networks can effectively integrate various sources of evidence to improve the accuracy of identifying genes.
Cap Analysis of Gene Expression: Cap Analysis of Gene Expression (CAGE) is a technique used to analyze the transcription start sites of RNA molecules, providing insight into gene expression levels. By sequencing the 5' cap structure of mRNA, CAGE allows researchers to identify where genes are actively transcribed and how their expression varies across different conditions or tissues, linking it to evidence-based gene prediction.
Codon Adaptation Index: The Codon Adaptation Index (CAI) is a numerical value that measures the relative usage of codons in a particular gene compared to a reference set of highly expressed genes in a given organism. This index helps predict the efficiency of protein synthesis by reflecting how well a gene's codon usage aligns with the preferred codon usage of the host organism. A higher CAI indicates a greater likelihood of successful translation and efficient protein expression.
Cufflinks: Cufflinks are software tools designed for the analysis of RNA-Seq data, primarily used to assemble transcripts and estimate their abundance from high-throughput sequencing data. They play a crucial role in understanding gene expression levels and alternative splicing patterns, enabling researchers to predict gene structures based on empirical evidence from RNA-Seq datasets.
Data mining: Data mining is the process of discovering patterns, trends, and useful information from large sets of data using various techniques like statistical analysis, machine learning, and artificial intelligence. This method allows researchers to extract meaningful insights that can aid in making informed decisions or predictions, particularly in complex fields such as genomics, where large datasets are commonplace.
Exons: Exons are the segments of a gene that are retained in the final messenger RNA (mRNA) molecule after the splicing process. They contain the coding information that dictates the amino acid sequence of proteins and are crucial in gene expression and regulation. The presence of exons is essential for accurate gene prediction as they help differentiate between coding and non-coding regions within genomic DNA.
Expression data: Expression data refers to the information gathered about the levels of gene expression within a cell or organism at a specific time. This data is crucial for understanding how genes are turned on or off in response to various conditions, helping researchers decipher cellular functions and identify biomarkers related to diseases.
F1 Score: The F1 Score is a metric used to evaluate the performance of a classification model, particularly in situations where class distribution is imbalanced. It combines precision and recall into a single score by calculating their harmonic mean, providing a balanced measure that accounts for both false positives and false negatives. This metric is especially useful in gene prediction tasks, where accurately identifying genes can significantly impact downstream analyses and biological interpretations.
FlyBase Annotations: FlyBase annotations are detailed descriptions and classifications of genes, gene products, and their functions in the fruit fly, Drosophila melanogaster. These annotations provide crucial insights into the genetic makeup of the organism, facilitating evidence-based gene prediction and functional analysis through various experimental data, such as sequencing and phenotype information.
Gencode annotations: Gencode annotations refer to a comprehensive collection of genomic features, including gene structures, transcripts, and protein-coding regions, which are derived from experimental evidence and computational predictions. These annotations play a crucial role in understanding the functional elements of the genome, facilitating the study of gene expression, regulation, and evolutionary biology.
Gene finding algorithms: Gene finding algorithms are computational methods designed to identify the locations of genes within a genomic sequence. These algorithms analyze patterns in the DNA sequences, such as coding regions, introns, and regulatory elements, to predict where genes are located. Their effectiveness is enhanced when they integrate evidence from various biological data sources, making them essential tools in the field of genomics.
Geneid: GeneID is a computational tool used for gene prediction in genomic sequences, particularly focused on identifying coding regions and gene structures. It integrates various forms of biological evidence, such as sequence similarity and known gene annotations, to provide more accurate predictions. This tool is essential in evidence-based gene prediction, helping researchers identify potential genes in both well-studied and newly sequenced genomes.
GeneMark: GeneMark is a software tool used for gene prediction, which plays a crucial role in computational genomics. It utilizes both ab initio and evidence-based approaches to identify potential genes within DNA sequences. By employing statistical models and machine learning techniques, GeneMark helps researchers accurately predict gene structures, making it a valuable resource in genome annotation and sequence assembly processes.
Genscan: Genscan is a computational tool used for ab initio gene prediction, which identifies potential coding regions in genomic DNA sequences based solely on the statistical properties of the sequence itself. This software employs models trained on known genes to predict gene structures, including exon-intron boundaries, without the need for prior experimental evidence. Its significance extends into evidence-based gene prediction by providing preliminary predictions that can be further refined using experimental data.
Glimmer: Glimmer is a software tool used for ab initio gene prediction, focusing on identifying genes in genomic sequences based solely on their intrinsic features without relying on prior experimental data. It uses hidden Markov models (HMMs) to effectively predict gene structures by analyzing patterns in the DNA sequence, such as coding regions and splice sites. Glimmer's ability to perform well even with limited training data makes it particularly valuable in computational genomics.
Hidden Markov Models: Hidden Markov Models (HMMs) are statistical models that represent systems with unobservable (hidden) states and observable outputs, where the state transitions follow a Markov process. HMMs are widely used in bioinformatics, particularly for gene prediction tasks, due to their ability to model biological sequences and capture the probabilistic relationships between hidden states and observed data. By leveraging HMMs, researchers can identify gene structures and functions based on patterns within the nucleotide sequences.
Iso-seq: Iso-seq, or isoform sequencing, is a technique used to capture full-length RNA transcripts and identify different isoforms of genes. This method provides insights into the complexity of gene expression by allowing researchers to see how genes can produce multiple variants, which may have different functions. Iso-seq is particularly useful in evidence-based gene prediction as it helps improve the annotation of genomes by accurately representing the diversity of transcript isoforms.
Ka/ks ratio: The ka/ks ratio is a measure used in molecular evolution to compare the rate of nonsynonymous mutations (ka) to synonymous mutations (ks) in a gene. This ratio helps to determine the selective pressures acting on a gene, indicating whether it is under positive selection, purifying selection, or neutral evolution. By analyzing these rates, researchers can infer the evolutionary dynamics of genes and understand how they contribute to function and adaptation.
Machine Learning: Machine learning is a branch of artificial intelligence that enables systems to learn from data and improve their performance over time without being explicitly programmed. This approach is crucial in genomic research as it helps identify patterns and make predictions based on vast amounts of biological data, ultimately aiding in tasks like gene prediction, RNA annotation, and understanding regulatory interactions.
Neural Networks: Neural networks are computational models inspired by the human brain that consist of interconnected nodes or neurons, designed to recognize patterns and make predictions based on input data. They play a crucial role in various fields, including gene prediction, where they can analyze complex biological data to identify gene structures and functions by learning from large datasets.
Orthologs: Orthologs are genes in different species that evolved from a common ancestral gene through speciation events, retaining similar functions. These genes provide critical insights into evolutionary relationships and functional conservation, making them essential for evidence-based gene prediction, understanding evolutionary processes, and analyzing genome alignment and synteny across different organisms.
Oxford Nanopore: Oxford Nanopore is a technology developed for DNA and RNA sequencing that utilizes nanopore-based sensors to detect the sequence of nucleotides in real-time. This innovative approach allows for rapid and portable sequencing, making it especially valuable in genomics research and clinical applications, where timely data is crucial for evidence-based gene prediction.
PacBio: PacBio, short for Pacific Biosciences, is a biotechnology company known for its innovative DNA sequencing technology that utilizes Single Molecule, Real-Time (SMRT) sequencing. This method allows for the generation of long-read sequences, which are crucial for accurate genome assembly and gene prediction. The unique capabilities of PacBio sequencing make it an essential tool in genomics, particularly in understanding complex genomic structures and improving the precision of gene annotations.
Phylogenetic shadowing approach: The phylogenetic shadowing approach is a method used in computational genomics to predict genes by leveraging evolutionary relationships among species. This technique involves comparing the genomes of closely related organisms to identify conserved sequences that are likely to encode functional elements, such as genes. By highlighting these conserved regions, researchers can enhance the accuracy of gene prediction models and uncover previously unannotated genes.
Position Weight Matrices: Position weight matrices (PWMs) are a mathematical representation used to describe the binding preferences of transcription factors at specific DNA sequences. Each column of a PWM corresponds to a position in the DNA sequence, while each row represents the relative frequency of each nucleotide at that position, allowing researchers to identify conserved motifs in gene regulatory regions.
Precision: Precision refers to the measure of the consistency and reliability of results in gene prediction algorithms, indicating the proportion of true positive predictions to the total positive predictions made. In gene prediction, a high precision means that when a gene is predicted, it is likely to be correct, which is crucial for both ab initio and evidence-based methods. It helps in evaluating the accuracy of different models and impacts downstream analyses by ensuring that predicted genes are as reliable as possible.
Prodigal: In the context of evidence-based gene prediction, 'prodigal' refers to a software tool designed for the accurate identification of protein-coding genes in genomic sequences. It uses a combination of heuristic and statistical methods to enhance gene prediction, making it particularly valuable in analyzing bacterial genomes. The software is known for its efficiency and reliability in generating gene models, which are essential for understanding functional elements in DNA.
Promoter regions: Promoter regions are specific sequences of DNA located upstream of a gene that serve as critical sites for the initiation of transcription. These regions are recognized by RNA polymerase and transcription factors, which assemble at the promoter to start the process of converting DNA into RNA. Understanding promoter regions is essential for predicting gene expression, determining regulatory elements, and exploring non-coding RNA functionality.
Sensitivity: Sensitivity is a measure of a test's ability to correctly identify true positive results, specifically how well it can detect the presence of a feature, such as a gene or structural variant, when it is actually present. A high sensitivity means that the method or tool has a low rate of false negatives, ensuring that most true instances are captured. This characteristic is crucial when evaluating the performance of predictive models and detection methods in genomics.
Sequence Alignment: Sequence alignment is a method used to identify similarities and differences between biological sequences, such as DNA, RNA, or protein sequences. This technique is crucial in various areas of genomics and bioinformatics, as it helps researchers understand evolutionary relationships, functional similarities, and structural characteristics among sequences.
Stringtie: StringTie is a software tool used for the reconstruction of transcriptomes from RNA-Seq data, providing evidence-based gene prediction through its ability to assemble transcripts. It employs a novel algorithm that models the expression levels of genes and can also estimate their abundance, making it a powerful tool in genomics for understanding gene structures and their functions.
Synteny: Synteny refers to the conservation of gene order on chromosomes between different species. It plays a significant role in understanding evolutionary relationships and can provide insights into the functional conservation of genes across species. By studying synteny, researchers can identify conserved genomic regions that may be crucial for specific biological functions and can aid in gene prediction and annotation efforts.
UCSC Genome Browser: The UCSC Genome Browser is a web-based tool that provides access to genomic data and visualizes various biological annotations across multiple species. It serves as a crucial resource for researchers, enabling evidence-based gene prediction, evolutionary rate estimation, and the study of enhancer-promoter interactions through its extensive databases and interactive graphical interface.