👻Intro to Computational Biology Unit 4 – Genome Assembly & Annotation

Key Concepts

• Genome assembly involves piecing together short DNA sequences (reads) into longer contiguous sequences (contigs) and ultimately reconstructing the original genome
• Annotation is the process of identifying and labeling functional elements within an assembled genome such as genes, regulatory regions, and repetitive elements
• Sanger sequencing, a first-generation sequencing technology, produces long, high-quality reads but is relatively low-throughput and expensive
  • Involves DNA synthesis with chain-terminating dideoxynucleotides (ddNTPs) labeled with fluorescent dyes
• Next-generation sequencing (NGS) technologies (Illumina, 454, SOLiD) generate millions of short reads in parallel, enabling high-throughput and cost-effective sequencing
  • Illumina sequencing uses reversible terminator chemistry and bridge amplification on a flow cell
• Third-generation sequencing technologies (PacBio, Oxford Nanopore) produce long reads (>10kb) in real-time, facilitating the assembly of complex genomes and detection of structural variations
• Hybrid assembly approaches leverage the strengths of both short and long reads to improve assembly quality and completeness
• Comparative genomics involves analyzing and comparing genomes across different species to identify conserved and divergent features, providing insights into evolutionary relationships and functional elements

Sequencing Technologies

• Sanger sequencing, developed by Frederick Sanger in 1977, was the first widely adopted sequencing method and was used to sequence the human genome in the Human Genome Project
• Sanger sequencing is based on the chain termination method, which uses ddNTPs to randomly terminate DNA synthesis during sequencing reactions
  • Each ddNTP is labeled with a different fluorescent dye, allowing for the identification of the incorporated nucleotide
• NGS technologies emerged in the mid-2000s, revolutionizing genomics by enabling massively parallel sequencing and reducing sequencing costs
• Illumina sequencing, the most widely used NGS platform, utilizes reversible terminator chemistry and bridge amplification
  • DNA fragments are ligated to adapters and immobilized on a flow cell surface
  • Clonal amplification occurs through bridge PCR, forming clusters of identical DNA fragments
  • Sequencing proceeds through cycles of single-base extension using fluorescently labeled reversible terminators
• 454 sequencing (Roche) uses pyrosequencing, where light is emitted upon nucleotide incorporation, and SOLiD sequencing (Applied Biosystems) employs sequencing by ligation with color-coded probes
• Third-generation sequencing technologies, such as PacBio's Single Molecule Real-Time (SMRT) sequencing and Oxford Nanopore sequencing, generate long reads by directly sequencing single DNA molecules
  • SMRT sequencing uses zero-mode waveguides (ZMWs) to observe the incorporation of fluorescently labeled nucleotides by a single DNA polymerase molecule in real-time
  • Oxford Nanopore sequencing measures changes in electrical current as DNA molecules pass through a protein nanopore, allowing for the identification of nucleotides based on their unique current signatures

Assembly Algorithms

• Overlap-layout-consensus (OLC) algorithms (Celera Assembler, Arachne) are well-suited for assembling long reads with high error rates
  • Identify overlaps between reads, construct an overlap graph, and determine the most likely sequence (consensus) based on the graph
• De Bruijn graph (DBG) algorithms (Velvet, SOAPdenovo) are commonly used for assembling short reads from NGS platforms
  • Break reads into fixed-length k-mers, construct a DBG where nodes represent k-mers and edges represent overlaps between k-mers, and traverse the graph to assemble contigs
• Greedy algorithms (SSAKE, VCAKE) iteratively extend contigs by selecting the highest-scoring overlaps between reads and existing contigs
• Hybrid assembly approaches (MaSuRCA, DBG2OLC) combine short and long reads to improve assembly contiguity and accuracy
  • Use long reads to resolve repetitive regions and scaffold contigs generated from short reads
• Scaffolding algorithms (SSPACE, BESST) order and orient contigs into scaffolds using paired-end or mate-pair information, with gaps between contigs represented by 'N's
• Gap filling tools (GapCloser, GapFiller) attempt to close gaps within scaffolds using unassembled reads or by performing local assembly

Assembly Challenges

• Repetitive sequences, such as transposable elements and segmental duplications, can lead to ambiguities in the assembly process and cause misassemblies or fragmented assemblies
• Heterozygosity in diploid organisms can result in the assembly of separate contigs for each allele, complicating the assembly process and potentially leading to a fragmented or incomplete assembly
• Sequencing errors, particularly in long reads with high error rates, can introduce noise and ambiguities in the assembly graph, making it difficult to determine the correct path and leading to misassemblies
• Uneven coverage across the genome due to biases in library preparation or sequencing can result in gaps or misassemblies in regions with low coverage
• Contamination from other organisms (bacteria, viruses) or sample cross-contamination can introduce foreign sequences into the assembly, leading to misassemblies or incorrect conclusions
• Large genome size and high complexity (polyploidy, high GC content) can increase the computational resources required for assembly and make it challenging to obtain a complete and accurate assembly
• Structural variations, such as insertions, deletions, and inversions, can be difficult to detect and represent accurately in the assembly, particularly when using short reads

Quality Assessment

• N50 is a commonly used metric to assess assembly contiguity, representing the length of the contig or scaffold at which 50% of the total assembly length is contained in contigs or scaffolds of that size or larger
• L50 is the number of contigs or scaffolds that, when sorted by length, contain 50% of the total assembly length
• Completeness can be assessed by aligning the assembly to a reference genome (if available) and calculating the percentage of the reference covered by the assembly
• BUSCO (Benchmarking Universal Single-Copy Orthologs) evaluates the completeness of an assembly by searching for the presence of conserved single-copy orthologs, which are expected to be present in all species within a given lineage
• Assembly accuracy can be assessed by comparing the assembly to a reference genome or by using paired-end or mate-pair reads to identify misassemblies and structural errors
  • Tools like QUAST and REAPR can identify misassemblies, compress homopolymer runs, and estimate the number of mismatches and indels
• Mapping quality metrics, such as the percentage of reads that align to the assembly and the average read depth, can provide insights into the overall quality and consistency of the assembly
• Visual inspection of the assembly using genome browsers (IGV, JBrowse) can help identify potential misassemblies, gaps, or other anomalies

Gene Prediction

• Ab initio gene prediction methods (AUGUSTUS, SNAP, GeneMark) use statistical models and machine learning algorithms to identify protein-coding genes based on sequence features such as codon usage, GC content, and splicing signals
  • These methods require training on a set of known genes from the target species or a closely related organism
• Evidence-based gene prediction methods (MAKER, EVidenceModeler) incorporate external evidence, such as protein alignments, ESTs, and RNA-seq data, to improve the accuracy of gene predictions
  • Protein alignments from related species can help identify conserved coding regions and improve the accuracy of exon-intron boundaries
  • RNA-seq data provides direct evidence of transcribed regions and can help identify alternative splicing events and non-coding RNAs
• Comparative genomics approaches (Projector, GeneWise) use sequence conservation between related species to identify protein-coding genes and improve the accuracy of gene predictions
• Combining multiple gene prediction methods and evidence sources through a consensus approach can improve the overall accuracy and completeness of gene annotations
• Manual curation by expert annotators is often necessary to refine gene models, resolve complex loci, and assign gene names and functions based on evidence from literature and databases

Functional Annotation

• Homology-based annotation involves searching for sequence similarities between predicted genes and known proteins in databases (UniProt, NCBI nr) using tools like BLAST or DIAMOND
  • Annotations are transferred from the best hits based on sequence identity, coverage, and e-value thresholds
• Domain-based annotation identifies conserved protein domains and motifs within predicted genes using databases like Pfam, SMART, and InterPro
  • Provides insights into the potential functions and evolutionary relationships of proteins
• Gene Ontology (GO) annotations assign standardized terms describing the molecular functions, biological processes, and cellular components associated with each gene product
  • GO annotations are often inferred based on homology or domain-based annotations and can be used for functional enrichment analyses
• Pathway annotations map genes to known biological pathways and processes using databases like KEGG, Reactome, and BioCyc
  • Helps understand the functional context and interactions of genes within the genome
• Non-coding RNA (ncRNA) annotation involves identifying and classifying various types of ncRNAs, such as tRNAs, rRNAs, miRNAs, and lncRNAs, using specialized tools (tRNAscan-SE, RNAmmer, Infernal) and databases (Rfam, miRBase)
• Pseudogene annotation identifies non-functional gene copies that have lost their protein-coding ability due to mutations or truncations
  • Pseudogenes can be identified based on sequence similarity to known genes and the presence of inactivating mutations or frameshifts
• Repeat annotation identifies and classifies repetitive elements, such as transposons and tandem repeats, using tools like RepeatMasker and databases like Repbase
  • Repetitive elements can play important roles in genome evolution and gene regulation

Tools and Databases

• Genome assembly tools:
  • Sanger: Phrap, CAP3
  • Short reads: Velvet, SOAPdenovo, ABySS, SPAdes
  • Long reads: Canu, Falcon, Flye, wtdbg2
  • Hybrid: MaSuRCA, DBG2OLC, Unicycler
• Quality assessment tools:
  • QUAST, BUSCO, REAPR, FRCbam
• Gene prediction tools:
  • Ab initio: AUGUSTUS, SNAP, GeneMark, GlimmerHMM
  • Evidence-based: MAKER, EVidenceModeler, BRAKER
  • Comparative: Projector, GeneWise, Exonerate
• Functional annotation tools:
  • BLAST, DIAMOND, InterProScan, Blast2GO, PANNZER
• Genome browsers:
  • UCSC Genome Browser, Ensembl, JBrowse, IGV
• Databases:
  • Sequence: GenBank, ENA, DDBJ
  • Protein: UniProt, NCBI Protein, Pfam, InterPro
  • Pathways: KEGG, Reactome, BioCyc
  • ncRNA: Rfam, miRBase
  • Repeats: Repbase
  • GO: Gene Ontology Consortium
  • Orthologs: OrthoMCL, EggNOG, OrthoDB

Applications and Future Directions

• Comparative genomics enables the identification of conserved and divergent features across species, providing insights into evolutionary relationships, gene function, and adaptation
  • Helps identify novel drug targets, disease-associated genes, and agriculturally important traits
• Metagenomics involves sequencing and analyzing microbial communities directly from environmental samples, allowing for the study of unculturable organisms and their roles in various ecosystems
  • Applications in human health (gut microbiome), environmental monitoring, and bioremediation
• Personalized medicine utilizes individual genomic information to tailor disease prevention, diagnosis, and treatment strategies
  • Identification of disease-associated variants, drug response predictions, and targeted therapies
• Agricultural genomics applies genome sequencing and annotation to crop and livestock improvement, enabling the development of disease-resistant, high-yielding, and stress-tolerant varieties
  • Marker-assisted selection and genome editing technologies (CRISPR-Cas9) accelerate breeding efforts
• Synthetic biology and genome engineering rely on accurate genome assembly and annotation to design and construct artificial biological systems or modify existing organisms for various applications
  • Production of biofuels, pharmaceuticals, and novel materials
• Advances in sequencing technologies, such as single-cell sequencing and spatial transcriptomics, provide higher resolution data for understanding cellular heterogeneity and spatial organization within tissues
• Integration of multi-omics data (transcriptomics, proteomics, metabolomics) with genome annotations enables a more comprehensive understanding of gene function and regulation
• Improved computational methods, such as deep learning and graph-based algorithms, are expected to enhance the accuracy and efficiency of genome assembly and annotation tasks
• Collaborative efforts and standardization initiatives, such as the Earth BioGenome Project and the Genomic Standards Consortium, aim to sequence and annotate the genomes of all known eukaryotic species and establish best practices for genomic data sharing and analysis