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Bioinformatics
Table of Contents
Unit 2 Overview: Bioinformatics: Key Databases and Resources
2.1 Nucleotide sequence databases
2.2 Protein sequence databases
2.3 Structure databases
2.4 Genome browsers
2.5 Pathway databases
2.6 Literature databases
2.7 Data retrieval and submission
2.8 Database integration and interoperability

Nucleotide sequence databases are the backbone of bioinformatics research, storing vast amounts of genetic information. These digital repositories enable scientists to access, analyze, and share DNA and RNA sequences, facilitating studies on gene structure, function, and evolution across species.

Primary databases like GenBank, EMBL, and DDBJ collect raw experimental data, while secondary databases curate and annotate this information. Understanding the structure, features, and searching techniques of these databases is crucial for effective genomic analysis and research in modern biology.

Overview of nucleotide databases

  • Nucleotide databases serve as digital repositories for storing and organizing genetic sequence information crucial for bioinformatics research and analysis
  • These databases facilitate the sharing, retrieval, and analysis of DNA and RNA sequences, enabling researchers to study gene structure, function, and evolution across species

Types of sequence databases

Primary vs secondary databases

  • Primary databases contain raw experimental data submitted directly by researchers
  • Secondary databases curate and annotate data from primary sources, adding value through analysis and interpretation
  • Primary databases include GenBank, EMBL, and DDBJ, while secondary databases encompass RefSeq and UniProtKB
  • Primary databases focus on data collection, while secondary databases emphasize data refinement and integration

General vs specialized databases

  • General databases cover a wide range of organisms and sequence types (GenBank, EMBL)
  • Specialized databases focus on specific organisms, sequence types, or biological processes (TAIR for Arabidopsis, miRBase for microRNAs)
  • General databases provide broad coverage but may lack depth in specific areas
  • Specialized databases offer in-depth information and tailored tools for specific research domains

Major nucleotide databases

GenBank structure and features

  • Developed and maintained by the National Center for Biotechnology Information (NCBI)
  • Organized into divisions based on taxonomy, sequence type, and data source
  • Utilizes a flat file format with structured fields for sequence data and annotations
  • Incorporates features like CDS (coding sequences), gene names, and protein translations
  • Provides tools for sequence similarity searches (BLAST) and data visualization

EMBL-Bank organization

  • Managed by the European Bioinformatics Institute (EBI)
  • Employs a hierarchical structure with top-level entries and associated feature tables
  • Uses the EMBL flat file format, similar to GenBank but with some differences in field names
  • Includes cross-references to other databases and literature citations
  • Offers programmatic access through web services and RESTful APIs

DDBJ architecture

  • Maintained by the DNA Data Bank of Japan
  • Collaborates with GenBank and EMBL-Bank as part of the International Nucleotide Sequence Database Collaboration (INSDC)
  • Utilizes a flat file format compatible with GenBank and EMBL-Bank
  • Provides unique features like the Trace Archive for raw sequencing data
  • Offers analysis tools and submission systems tailored for Asian researchers

Database entries and records

Accession numbers and identifiers

  • Accession numbers serve as unique identifiers for database entries
  • Format varies between databases (GenBank: two letters followed by six digits, EMBL: two letters, six digits, one number)
  • Version numbers track updates to sequences (accession.version)
  • GI (GenInfo Identifier) numbers provide an additional layer of identification in NCBI databases
  • Accession numbers remain stable across database updates, ensuring consistent referencing

Sequence data representation

  • Nucleotide sequences stored using standard IUPAC codes (A, C, G, T, U, and ambiguity codes)
  • Sequences may be stored as raw strings or compressed formats for efficiency
  • Quality scores often accompany sequences, especially for next-generation sequencing data
  • Sequence length and topology (linear or circular) information included in database records
  • Some databases support storing modified nucleotides or non-standard bases

Annotation and metadata

  • Annotations provide biological context and functional information for sequences
  • Include features like gene names, protein products, and regulatory elements
  • Metadata encompasses information about the sequence source, experimental methods, and submitters
  • Controlled vocabularies and ontologies ensure consistency in annotations across entries
  • Cross-references link entries to related information in other databases or literature

Database searching techniques

BLAST vs FASTA algorithms

  • BLAST (Basic Local Alignment Search Tool) optimized for speed and sensitivity
  • BLAST uses a heuristic approach, breaking queries into short words for initial matches
  • FASTA (Fast Alignment Search Tool) performs slower but more thorough searches
  • FASTA algorithm suitable for longer sequences and more divergent homologs
  • BLAST provides statistical significance measures (E-values) for matches
  • FASTA offers flexibility in scoring matrices and gap penalties

Query optimization strategies

  • Use appropriate database selection to narrow search space (nucleotide vs protein databases)
  • Employ sequence masking to filter out low-complexity regions and repeats
  • Adjust word size and scoring parameters based on query length and expected similarity
  • Utilize position-specific scoring matrices (PSSMs) for increased sensitivity in protein searches
  • Implement iterative search strategies (PSI-BLAST) for detecting remote homologs

Data submission process

Sequence submission guidelines

  • Follow database-specific formats and requirements for sequence submissions
  • Provide accurate and complete metadata, including organism, strain, and isolation source
  • Adhere to naming conventions for genes, proteins, and other biological features
  • Include relevant experimental details and methodologies used to obtain the sequence
  • Specify release date preferences (immediate or hold until publication)

Quality control measures

  • Automated checks for sequence integrity, vector contamination, and chimeric sequences
  • Manual curation by database staff to ensure adherence to submission guidelines
  • Validation of taxonomic classifications and organism names
  • Verification of annotation consistency and compliance with controlled vocabularies
  • Implementation of error reporting and correction mechanisms for submitters and users

Database integration and cross-referencing

RefSeq and UniProt connections

  • RefSeq provides curated, non-redundant sequence standards for genomic, transcript, and protein records
  • UniProt offers comprehensive protein sequence and functional information
  • Cross-references between RefSeq and UniProt enable seamless navigation between nucleotide and protein data
  • Mapping services link GenBank/EMBL/DDBJ accessions to RefSeq and UniProt identifiers
  • Integration facilitates comprehensive analysis of gene-protein relationships and functional annotations

Ontology and controlled vocabularies

  • Gene Ontology (GO) provides standardized terms for gene functions, processes, and cellular components
  • Sequence Ontology (SO) defines terms for sequence features and attributes
  • Evidence ontology (ECO) standardizes evidence codes for functional annotations
  • Controlled vocabularies ensure consistency in describing experimental methods and biological concepts
  • Ontologies enable advanced querying and comparative analysis across different databases

Challenges in nucleotide databases

Data redundancy issues

  • Multiple submissions of identical or near-identical sequences clutter databases
  • Redundancy complicates searches and increases computational requirements
  • Non-uniform naming conventions lead to difficulties in identifying duplicate entries
  • Strategies to address redundancy include clustering algorithms and representative sequence selection
  • Trade-offs between maintaining comprehensive archives and providing non-redundant datasets

Annotation inconsistencies

  • Variations in annotation quality and completeness across different submitters and databases
  • Outdated annotations persist due to infrequent updates of older entries
  • Conflicting annotations for homologous sequences create confusion in functional assignments
  • Automated annotation pipelines may propagate errors across multiple entries
  • Efforts to improve annotation consistency include community curation initiatives and machine learning approaches

Big data in genomics

  • Exponential growth in sequence data generation driven by next-generation sequencing technologies
  • Development of scalable storage solutions and distributed computing frameworks
  • Implementation of compression algorithms specifically designed for genomic data
  • Integration of heterogeneous data types (genomic, transcriptomic, epigenomic) in unified databases
  • Adoption of graph-based data models to represent complex genomic relationships

Cloud-based sequence repositories

  • Shift towards cloud storage and computing for large-scale genomic datasets
  • Development of APIs and web services for seamless data access and analysis in the cloud
  • Implementation of data security and privacy measures for sensitive genomic information
  • Collaborative platforms enabling real-time data sharing and analysis across research teams
  • Integration of cloud-based repositories with workflow management systems for reproducible analyses

Practical applications

Genomic analysis workflows

  • Sequence assembly pipelines for reconstructing genomes from raw sequencing reads
  • Comparative genomics approaches for identifying conserved regions and evolutionary relationships
  • Functional annotation workflows combining sequence similarity searches and machine learning predictions
  • Variant calling and genotyping pipelines for identifying genetic variations in populations
  • Metagenomics analysis workflows for studying complex microbial communities

Comparative genomics approaches

  • Whole genome alignment techniques for identifying syntenic regions across species
  • Orthology and paralogy detection methods for studying gene family evolution
  • Phylogenomic analyses using multiple sequence alignments of conserved genes
  • Identification of species-specific genes and genomic islands through comparative approaches
  • Pan-genome analysis for characterizing core and accessory genomes within a species