Gene Prediction: Methods and Importance

Importance of Gene Prediction

• Understanding gene function and regulation reveals how genes contribute to traits and their role in disease.
• Annotating genomes uses gene prediction to create detailed maps of genes and their locations in the DNA sequence.
• Studying evolutionary relationships uses comparing gene sequences across species to understand evolutionary history.

Gene Prediction Methods

• Ab Initio predicts genes based on the DNA sequence without prior knowledge of gene locations.
• Homology-Based relies on known gene sequences from related organisms to identify genes in a new genome.
• Evidence-Based combines multiple sources of evidence, including experimental data, to improve prediction accuracy.

Pairwise Alignment vs Multiple Sequence Alignment (MSA)

• Pairwise alignment compares two biological sequences to find regions of similarity.
  • Pairwise alignment uses relatively simple algorithms.
  • Needleman-Wunsch (global) and Smith-Waterman (local) are used in pairwise alignment.
• Multiple Sequence Alignment aligns three or more sequences to identify conserved regions and infer evolutionary relationships.
  • MSA is more complex and computationally intensive and may require cloud computing for large datasets.
• Applications for Pairwise alignment:
  • Detecting similarity between two sequences.
  • Global alignment uses Needleman-Wunsch, while local alignment uses Smith-Waterman.
• Applications for MSA:
  • Phylogenetic analysis to find conserved regions in protein families.
  • Predict protein structure.
  • Demonstrate homology in multi-gene families.
  • Progressive methods like Clustal Omega, MUSCLE, and MAFFT are used.

MAKER

• MAKER integrates ab initio predictions with homology data and RNA-Seq evidence to annotate genomes.
• MAKER uses Genomic sequence, ESTs, proteins, and RNA-Seq data as inputs.
• MAKER generates annotated genomes as output.
• MAKER can annotate genomes for both prokaryotes and eukaryotes.

Cufflinks

• Cufflinks assembles transcripts from RNA-Seq data and estimates gene expression levels.
• Cufflinks takes RNA-Seq reads (FASTQ files) as input.
• Cufflinks produces assembled transcripts (GTF file) along with estimated expression levels.
• Cufflinks can perform differential gene expression analysis across conditions or samples using RNA-Seq data.

Key Differences Between Ab Initio and Homology-Based Gene Prediction

• Ab Initio predicts genes based solely on the DNA sequence.
  • Requires no prior knowledge and uses intrinsic sequence features.
  • Statistical models are utilized to identify coding potential.
  • Struggles with sensitivity and specificity, especially in non-model organisms.
  • Useful for initial predictions in newly sequenced genomes.
  • Tools used include GeneMark, AUGUSTUS, and FGENESH.
• Homology-Based relies on known gene sequences from related organisms.
  • Requires annotated sequences from similar organisms.
  • Sequence alignment techniques are used to find similarities.
  • Used for comparative genomics in well-studied species.
  • More accurate for conserved genes but may miss novel genes without homologs.
  • Tools include BLAST, Exonerate, and GeneWise.

Prokaryotic vs. Eukaryotic Genomes

• Prokaryotic Genomes:
  • Have a single, circular chromosome and may contain plasmids.
  • Genes are often organized into operons, allowing coordinated expression.
  • Have minimal non-coding regions and lack introns.
  • Histone-like proteins are used.
• Eukaryotic Genomes:
  • Contain linear DNA organized into multiple chromosomes.
  • Genes contain coding (exons) and non-coding (introns) regions with complex organization.
  • Have a significant presence of introns and other non-coding sequences.
  • Chromosomes are associated with histone.

Types of Prokaryotic genes

• Operons: Clusters of genes transcribed together.
• Structural Genes: Encode proteins with specific cellular functions.
• Regulatory Genes: Control the expression of other genes.
• Resistance Genes: Provide bacteria with resistance to antibiotics.
• Pathogenicity Genes: Genes that contribute to the virulence of pathogenic bacteria.
• Non-coding Genes: Genes that do not encode proteins but have regulatory functions.
• Pseudogenes: Non-functional gene sequences that resemble functional genes.

Prokaryotic Gene Prediction Criteria

• Open Reading Frame: A continuous stretch of codons without a stop codon.
• Start Codon: The initiation codon that signals the beginning of translation.
• Stop Codon: A codon that terminates translation.
• Ribosome Binding Site: Facilitates the binding of ribosomes to mRNA for translation initiation.
• Promoter Regions: Recognized by specific sequences upstream of the gene.
• Regulatory Elements: Control gene expression.

Open Reading Frame (ORF) Criteria

• A continuous stretch of codons without a stop codon.
• ORFs must be of sufficient length.
  • At least 100-150 base pairs long.
• Absence of premature stop codons before the expected termination point.
• The reading frame starts with a start codon (AUG) and ends with a stop codon (UAA, UAG, UGA).

Prokaryotic Gene Prediction Methods

• Ab Initio Prediction: Predicts genes based solely on sequence features without external data.
  • Key Features: Start Codons, Stop Codons, Ribosome Binding Sites.
• Homology-Based Prediction: Uses known sequences from related organisms to identify potential genes.
  • Align new sequences with databases of annotated genes and identify conserved regions.
• Machine Learning Approaches: Utilize algorithms trained on known gene sequences to predict new gene locations.
  • Techniques include Hidden Markov Models (HMMs) and Support Vector Machines (SVMs).

Eukaryotic Gene Prediction Methods

• Ab Initio Prediction: Accurately predict intron-exon boundaries and account for alternative splicing.
  • Tools include AUGUSTUS, GeneMark, and FGENESH.
• Homology-Based Prediction: Align genomic sequences with known eukaryotic gene databases and identify conserved sequences and functional motifs.
• Expression Data Utilization: RNA-Seq Analysis.
  • Provides information about actively expressed genes and helps identify splice variants and novel transcripts.
• Machine Learning Approaches: Complex Models such as;
  • More sophisticated than prokaryotic models due to the complexity of eukaryotic genes.
  • They incorporate features like splicing signals and regulatory motifs.

Tools for prokaryotic gene prediction

• GeneMark - Probabilistic model for gene prediction.
• Glimmer - Statistical and machine learning methods.
• Prodigal- Rapid and accurate gene prediction tool.

Tools for eukaryotic gene prediction

• AUGUSTUS - Ab initio tool incorporating RNA-Seq data.
• GeneID - Combines ab initio and homology-based methods.
• FGENESH - Predicts genes based on known structures.

Importance of Egpred in gene prediction

• Egpred is used for prediction of eukaryotic genes through a link.
• Similarity Search: First BLASTX against RefSeq database, then against sequences from first BLAST.
  • Detection of significant exons from BLASTX output and BLASTN against Introns.
• Prediction uses ab-initio programs like NNSPLICE to compute splice sites.

Multiple choice questions

• The virulence of pathogenic bacteria is what pathogenicity genes contribute to.
• Resistance to environmental stresses is what Beta-lactamase genes provide bacteria with.
• Promoter regions initiate the expression of genes.
• RNA-Seq helps estimate gene expression levels.
• Hidden Markov Models (HMMs) is a statistical model is commonly used in ab initio gene prediction.
• Alternative splicing is a key challenge in eukaryotic gene prediction.
• Cost-effectiveness is NOT typically used to evaluate gene prediction methods

Matching question answers:

1. GeneMark - A tool designed for predicting genes in prokaryotic genomes.
2. AUGUSTUS - A tool used for gene prediction in eukaryotic genomes that incorporates evidence from RNA-Seq data.
3. Operon - A cluster of genes transcribed together under a single promoter.
4. Hidden Markov Models (HMMs) - A statistical model that uses states to represent different parts of genes.
5. RNA-Seq - A technique that provides information about actively expressed genes and helps identify splice variants.
6. Ab Initio Prediction - A method that predicts genes based solely on sequence features without external data.
7. Homology-Based Prediction - A method that relies on known gene sequences from related organisms to identify genes in a new genome.
8. Alternative Splicing - A process that allows a single gene to produce multiple protein variants.

Challenges in Gene Prediction

• Alternative Splicing: Genes can produce multiple protein products, increasing complexity.
• Non-coding RNAs: Functional non-coding RNAs pose a challenge to traditional methods.
• Incomplete Genomic Data: Poor-quality genomes can hinder accurate gene prediction.

Future of Gene Prediction

• Improved Algorithms using deep learning techniques will enable more sophisticated models.
• Integration of Data: Combining various data types, including genomic sequences, RNA-Seq, and epigenetic modifications, will enhance prediction accuracy.
• Machine Learning Advancements will capture intricate relationships within genomic data.
• Personalized Genomics will enable tailored diagnoses and treatments based on individual genetic profiles.