4.2 Ab initio gene prediction
6 min read•august 20, 2024
Ab initio gene prediction is a crucial step in genome annotation. It uses statistical models to identify genes in genomic sequences without relying on external evidence. These methods analyze DNA signals, sequence composition, and gene structure to predict potential coding regions.
Markov models, particularly (HMMs) and generalized HMMs, form the backbone of ab initio prediction. These models are trained on known genes to learn patterns and probabilities. Tools like and apply these models to predict genes in eukaryotic and prokaryotic genomes.
Fundamentals of ab initio gene prediction
- Ab initio gene prediction aims to identify genes in genomic sequences without relying on external evidence such as cDNA or protein sequences
- It is a crucial step in genome annotation and understanding the genetic basis of organisms
- The methods rely on statistical models trained on known gene structures to predict potential coding regions and
Biological basis for gene prediction
Signals in DNA sequence
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- upstream of genes contain binding sites for transcription factors (TATA box, CAAT box)
- Translation start and stop codons (ATG, TAA, TAG, TGA) mark the beginning and end of coding regions
- Splice donor and acceptor sites (GT-AG) flank introns and are recognized by the spliceosome during pre-mRNA processing
- Polyadenylation signals (AATAAA) indicate the 3' end of transcripts and guide cleavage and polyadenylation
Sequence composition of genes
- Coding regions exhibit biased nucleotide composition compared to non-coding regions
- Codon usage bias reflects the preferential use of certain codons for amino acids due to tRNA abundance or translational efficiency
- CpG islands, regions with high CG content, are often associated with promoters and transcription start sites
- Repetitive elements (SINEs, LINEs) are less frequent in coding regions compared to intergenic regions
Markov models for gene prediction
Markov chains vs hidden Markov models
- Markov chains model the probability of a sequence of states based on the current state (nucleotide or codon)
- Hidden Markov models (HMMs) introduce hidden states (exon, intron, intergenic) that emit observable sequences with different probabilities
- HMMs allow for modeling the dependencies between adjacent states and the observed sequence
Training HMMs on known genes
- parameters (transition and emission probabilities) are estimated from a training set of annotated genes
- The Baum-Welch algorithm is used for unsupervised training, iteratively updating parameters to maximize the likelihood of the observed sequences
- Supervised training with labeled data (exon, intron, intergenic) can improve the accuracy of the model
Viterbi algorithm for optimal path
- The Viterbi algorithm finds the most probable sequence of hidden states given an observed sequence and trained HMM
- It uses to efficiently compute the maximum likelihood path through the state space
- The optimal path corresponds to the predicted gene structure, with transitions between exon, intron, and intergenic states
Generalized hidden Markov models
GHMMs vs HMMs
- Generalized hidden Markov models (GHMMs) extend HMMs by allowing states to emit variable-length sequences
- In GHMMs, each state can have a duration distribution that models the length of the emitted sequence
- GHMMs are more suitable for modeling biological features with variable lengths, such as exons and introns
Duration modeling in GHMMs
- Duration distributions (geometric, gamma, or explicit) capture the length variability of features like exons and introns
- Incorporating duration modeling improves the accuracy of gene structure prediction by favoring biologically plausible lengths
- The duration distribution parameters are estimated from the along with transition and emission probabilities
Gene structure modeling with GHMMs
- GHMMs can model the complex structure of eukaryotic genes with multiple exons and introns
- States represent different gene components (promoter, 5' UTR, exon, intron, 3' UTR, polyadenylation site)
- Transitions between states capture the order and dependencies of gene components (exon-intron boundaries, splice sites)
- The GHMM architecture is designed to reflect the biological constraints and patterns of gene structure
Ab initio gene prediction tools
GENSCAN for eukaryotic gene prediction
- GENSCAN is a widely used ab initio gene prediction tool for eukaryotic genomes
- It employs a GHMM with states for exons, introns, and intergenic regions, as well as signals like start codons and splice sites
- GENSCAN incorporates various biological features, such as codon usage, CpG islands, and promoter elements
- It can predict complete gene structures, including multiple exons and alternative splicing events
Glimmer for prokaryotic gene prediction
- Glimmer (Gene Locator and Interpolated Markov ModelER) is designed for gene prediction in prokaryotic genomes
- It uses interpolated Markov models (IMMs) to capture the variable-order dependencies in coding and non-coding regions
- Glimmer employs a two-phase approach: initial prediction of coding regions followed by a refinement step using IMMs
- It has been successfully applied to various bacterial and archaeal genomes and can handle short coding sequences
Comparison of ab initio tools
- Different ab initio tools have their strengths and weaknesses depending on the target genome and the specific biological features they model
- GENSCAN and Glimmer are optimized for eukaryotic and prokaryotic genomes, respectively, considering their distinct gene structures
- Some tools, like and , offer flexibility in training on specific datasets or incorporating external evidence
- Comparative evaluations help assess the performance and suitability of different tools for a given genome annotation task
Evaluating gene prediction performance
Sensitivity vs specificity
- (recall) measures the proportion of true positive predictions out of all actual positives (TP / (TP + FN))
- Specificity measures the proportion of true negative predictions out of all actual negatives (TN / (TN + FP))
- A balance between sensitivity and specificity is desired, as increasing one may come at the cost of the other
- The , the harmonic mean of and recall, provides a single metric for overall performance
Exon-, transcript-, and gene-level accuracy
- Exon-level accuracy assesses the correctness of predicted exon boundaries compared to the actual exon structures
- Transcript-level accuracy evaluates the predicted splicing patterns and the agreement with the true transcript variants
- Gene-level accuracy measures the overall correctness of predicted gene structures, including the number and orientation of genes
- Different levels of accuracy provide insights into the strengths and weaknesses of gene prediction methods
Benchmarking on gold standard annotations
- Benchmarking datasets with high-quality, manually curated gene annotations serve as a gold standard for evaluation
- Datasets like ENCODE, RefSeq, and GENCODE provide trusted annotations for various model organisms
- Predicted gene structures are compared against the benchmark annotations to compute performance metrics
- Regularly updated benchmarking datasets incorporate new experimental evidence and improve the reliability of evaluations
Challenges and limitations
Pseudogenes and non-coding RNA genes
- Pseudogenes, non-functional gene copies, can be mistakenly predicted as protein-coding genes due to sequence similarity
- Non-coding RNA genes (microRNAs, lncRNAs) lack typical coding features and are often missed by ab initio gene predictors
- Distinguishing pseudogenes and non-coding RNA genes requires additional computational methods and experimental validation
- Incorporating RNA-seq data and comparative genomics can help identify and filter out pseudogenes and predict non-coding RNA genes
Alternative splicing and isoforms
- Alternative splicing generates multiple transcript isoforms from a single gene locus, increasing proteome diversity
- Ab initio gene predictors often struggle to accurately predict all possible isoforms and their relative abundances
- Isoform prediction requires the integration of RNA-seq data and machine learning approaches to model splicing patterns
- Challenges include identifying rare isoforms, predicting microexons, and resolving complex alternative splicing events
Improving predictions with homology
- Homology-based gene prediction leverages sequence conservation across related species to refine ab initio predictions
- Protein sequence alignments and synteny information can guide the identification of exon-intron boundaries and gene structures
- Integrating ab initio predictions with homology evidence can improve the accuracy and completeness of gene annotations
- Challenges include handling gene duplication events, lineage-specific gene losses, and divergent sequences with limited conservation
Key Terms to Review (18)
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.
BLAST: BLAST, which stands for Basic Local Alignment Search Tool, is a widely used algorithm in bioinformatics for comparing an input biological sequence against a database of sequences to find regions of similarity. It helps researchers identify homologous sequences and infers functional and evolutionary relationships, making it a crucial tool for various applications, including aligning sequences, assembling genomes, predicting genes, and annotating functions.
Dynamic Programming: Dynamic programming is a method used to solve complex problems by breaking them down into simpler subproblems and storing the results of these subproblems to avoid redundant calculations. This technique is particularly useful in optimization problems, where it helps to efficiently find the best solution among many possible solutions. It is widely applied in bioinformatics for tasks such as aligning sequences, assembling genomes, filling gaps in genome scaffolding, and predicting gene structures.
Exon-intron structure: The exon-intron structure refers to the arrangement of coding regions (exons) and non-coding regions (introns) within a gene. Exons are segments of DNA that are transcribed into mRNA and ultimately translated into proteins, while introns are removed during the RNA splicing process. This structural organization plays a critical role in gene expression and regulation, influencing how genes are processed and the diversity of proteins that can be produced.
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.
GenBank: GenBank is a comprehensive public database that collects and provides access to DNA sequences and their associated information. It serves as a vital resource for researchers by enabling the sharing of genomic data, facilitating gene prediction, and supporting various bioinformatics analyses including phylogenetic studies and evolutionary rate estimations.
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.
Gold standard annotations: Gold standard annotations refer to high-quality, meticulously verified genomic data that serve as a benchmark for evaluating the performance of gene prediction algorithms. These annotations provide a reliable reference point for the identification and classification of genes, helping researchers assess the accuracy and efficiency of computational models. By comparing predicted gene structures against gold standard annotations, scientists can fine-tune their methods and improve overall gene prediction accuracy.
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.
HMM: A Hidden Markov Model (HMM) is a statistical model used to represent systems that are assumed to follow a Markov process with hidden states. In the context of gene prediction, HMMs are particularly useful for identifying gene structures in sequences of DNA, as they can model the probabilistic relationships between observed sequences and the underlying biological states that generate them.
Open reading frame: An open reading frame (ORF) is a continuous stretch of nucleotides in a DNA or RNA sequence that can be translated into a protein, starting from a start codon and ending at a stop codon. ORFs are fundamental in gene prediction because they indicate potential protein-coding regions within a genome, which are critical for understanding gene function and regulation.
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.
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.
Splice sites: Splice sites are specific sequences in pre-mRNA where splicing occurs to remove introns and join exons together, forming the final mRNA molecule. These sites play a crucial role in gene expression by ensuring that only the necessary coding sequences are included in the mRNA, impacting protein synthesis. Understanding splice sites is essential for gene prediction as they provide vital clues about the structure of genes and the organization of coding regions.
Training data: Training data refers to a set of examples used to train machine learning models, enabling them to learn patterns and make predictions. This data is crucial for supervised learning methods, where the model learns from labeled examples to understand the relationship between input features and output labels. In gene prediction, high-quality training data is essential for building accurate models that can identify genes in DNA sequences.