🧠 Database Fundamentals

Q: What is a biological database?

A biological database is an organized collection of biological data (sequences, structures, pathways, expression) that is stored, managed, and made accessible for querying and analysis. Examples include NCBI, UniProt, Ensembl, PDB, and KEGG.

Q: What is the difference between primary and secondary databases?

Primary databases store raw experimental data (e.g., GenBank, PDB, GEO). Secondary databases store derived, curated, or integrated data (e.g., UniProt, Pfam, KEGG, Reactome). Primary data is submitted by researchers; secondary data is curated and annotated.

Q: Which biological database is best for protein sequences?

UniProt is the most comprehensive protein sequence database, with Swiss-Prot (curated) and TrEMBL (unreviewed). For protein structures, use PDB. For protein families and domains, use Pfam and InterPro.

Q: How do I access biological databases programmatically?

Most databases offer APIs: NCBI Entrez (E-utilities), UniProt REST API, Ensembl REST API, PDB API, and KEGG REST API. You can access them from Python using Biopython (Entrez), requests, or dedicated libraries.

Q: What is the difference between GenBank and RefSeq?

GenBank is a primary database containing all submitted sequences (may include redundancy and errors). RefSeq is a secondary, curated database of reference sequences for genomes, transcripts, and proteins, with non-redundant, well-annotated entries.

🎯 What is a Biological Database?

A biological database is an organized collection of biological data (sequences, structures, pathways, expression) that is stored, managed, and made accessible for querying and analysis. Biological databases are essential for storing the massive amounts of data generated by genomics, proteomics, and structural biology. They enable researchers to retrieve, compare, and analyze biological information.

Why Databases Matter

Storage: Massive data. Retrieval: Fast access. Integration: Cross-reference. Annotation: Curated knowledge. Analysis: Comparative. Reproducibility: Standardized.

📌 Storage📌 Retrieval📌 Integration📌 Annotation

# Biological Databases — the big picture
# Organized collections of biological data.

print("""
=== Biological Databases ===

What they are:
  - Organized collections of biological data
  - Sequences, structures, pathways, expression
  - Stored, managed, accessible
  - Enable retrieval and analysis

Why they matter:
  - Storage of massive data
  - Fast retrieval
  - Cross-referencing
  - Curated annotation
  - Comparative analysis
  - Reproducibility

Major databases:
  - NCBI (GenBank, RefSeq, PubMed)
  - UniProt (protein sequences)
  - Ensembl (genomes)
  - PDB (protein structures)
  - KEGG (pathways)
  - Reactome (pathways)
  - GEO (expression)
  - Pfam (protein families)
  - InterPro (protein domains)

Applications:
  - Sequence retrieval
  - BLAST search
  - Phylogenetics
  - Structure analysis
  - Pathway analysis
  - Expression analysis
""")

print("=== Historical Context ===")
print("""
1960s: First protein sequence databases (Atlas of Protein Sequence)
1970s: PDB (1971), GenBank (1982)
1980s: EMBL, Swiss-Prot
1990s: NCBI, Entrez, KEGG
2000s: Ensembl, UniProt, GEO
2010s: Big data, cloud, APIs
2020s: AI, knowledge graphs, integration
""")

print("=== Why It's Hard ===")
print("""
1. DATA VOLUME
   - Petabytes of data
   - Growing exponentially

2. DATA HETEROGENEITY
   - Different formats
   - Different standards

3. DATA QUALITY
   - Errors
   - Redundancy
   - Incomplete annotation

4. INTEGRATION
   - Cross-referencing
   - Identifiers
   - Ontologies

5. ACCESS
   - APIs
   - Rate limits
   - Authentication
""")

🎯 Why Databases Matter

🎯 The Value of Biological Databases

Biological databases are the backbone of modern biology. They store, organize, and make accessible the data that drives discovery.

Key Benefits

Storage: Petabytes. Retrieval: Fast. Integration: Cross-ref. Annotation: Curated. Analysis: Comparative. Sharing: Standardized.

🎯 Storage🎯 Retrieval🎯 Integration🎯 Annotation

# Why Databases Matter
print("=== 1. Data Storage ===")
print("""
Storage:
  - GenBank: > 3 billion sequences
  - UniProt: > 200 million proteins
  - PDB: > 200,000 structures
  - GEO: > 5 million samples

Growth:
  - Exponential
  - Doubling every ~2 years
  - Big data challenge
""")

print("\n=== 2. Fast Retrieval ===")
print("""
Retrieval:
  - Query by ID
  - Query by sequence
  - Query by keyword
  - BLAST search

Example:
  Entrez: search NCBI
  UniProt: search proteins
  PDB: search structures
""")

print("\n=== 3. Cross-Referencing ===")
print("""
Cross-referencing:
  - Link databases
  - Same entity, different DBs
  - Integration

Example:
  UniProt → PDB (structure)
  UniProt → Ensembl (genome)
  UniProt → KEGG (pathway)
  GenBank → PubMed (literature)
""")

print("\n=== 4. Curated Annotation ===")
print("""
Annotation:
  - Manual curation
  - Automated annotation
  - Quality control

Example:
  Swiss-Prot (curated)
  RefSeq (curated)
  Pfam (curated)
""")

print("\n=== 5. Comparative Analysis ===")
print("""
Comparative:
  - BLAST
  - Multiple alignment
  - Phylogenetics
  - Structure comparison

Example:
  BLAST against GenBank
  → Find homologs
""")

print("\n=== 6. Reproducibility ===")
print("""
Reproducibility:
  - Standardized data
  - Versioning
  - Accession numbers
  - Citation

Example:
  GenBank accession: NC_000001
  → Same sequence everywhere
""")

print("\n=== 7. Knowledge Discovery ===")
print("""
Discovery:
  - Data mining
  - Machine learning
  - Knowledge graphs
  - Drug discovery

Example:
  Drug target identification
  → Database mining
""")

print("\n=== 8. Clinical Applications ===")
print("""
Clinical:
  - Variant interpretation
  - Disease genes
  - Pharmacogenomics

Example:
  ClinVar
  → Clinical variants
  → Diagnosis
""")

print("\n=== 9. Education ===")
print("""
Education:
  - Teaching
  - Training
  - Resources

Example:
  NCBI tutorials
  UniProt tutorials
  PDB tutorials
""")

print("\n=== 10. Research ===")
print("""
Research:
  - Hypothesis generation
  - Data analysis
  - Publication

Example:
  Database search
  → New hypothesis
  → Experiment
  → Publication
""")

📊 Types of Databases

🎯 Database Classification

Biological databases can be classified in several ways: by data type, by curation level, by scope, and by access method.

Types of Databases

Primary: Raw data. Secondary: Curated. Composite: Integrated. Specialized: Specific. Sequence: DNA/protein. Structure: 3D.

📊 Primary📊 Secondary📊 Composite📊 Specialized

# Types of Databases
print("=== 1. Primary Databases ===")
print("""
Primary:
  - Raw experimental data
  - Submitted by researchers
  - May be redundant
  - May have errors

Examples:
  - GenBank (DNA)
  - PDB (structures)
  - GEO (expression)
  - SRA (reads)
  - PRIDE (proteomics)

Characteristics:
  - Direct submission
  - No curation
  - Redundant
""")

print("\n=== 2. Secondary Databases ===")
print("""
Secondary:
  - Derived from primary
  - Curated
  - Annotated
  - Non-redundant

Examples:
  - UniProt (proteins)
  - RefSeq (reference)
  - Pfam (families)
  - InterPro (domains)
  - KEGG (pathways)

Characteristics:
  - Curated
  - Annotated
  - Integrated
""")

print("\n=== 3. Composite Databases ===")
print("""
Composite:
  - Integrate multiple sources
  - Unified interface
  - Cross-referenced

Examples:
  - Entrez (NCBI)
  - Ensembl (genomes)
  - UCSC (genomes)
  - InterPro (domains)

Characteristics:
  - Integration
  - Cross-referencing
  - Unified query
""")

print("\n=== 4. Specialized Databases ===")
print("""
Specialized:
  - Specific data type
  - Specific organism
  - Specific disease

Examples:
  - FlyBase (Drosophila)
  - TAIR (Arabidopsis)
  - SGD (yeast)
  - OMIM (human disease)
  - ClinVar (clinical variants)

Characteristics:
  - Focused
  - Deep annotation
  - Community-specific
""")

print("\n=== 5. Sequence Databases ===")
print("""
Sequence:
  - DNA, RNA, protein
  - Primary + secondary

Examples:
  - GenBank (DNA)
  - RefSeq (reference)
  - UniProt (protein)
  - EMBL (DNA)
  - DDBJ (DNA)

Types:
  - Nucleotide
  - Protein
  - Genome
""")

print("\n=== 6. Structure Databases ===")
print("""
Structure:
  - 3D structures
  - Experimental + predicted

Examples:
  - PDB (experimental)
  - AlphaFold DB (predicted)
  - CATH (classification)
  - SCOP (classification)
  - EMDB (EM)

Types:
  - X-ray
  - NMR
  - Cryo-EM
  - Predicted
""")

print("\n=== 7. Pathway Databases ===")
print("""
Pathway:
  - Metabolic pathways
  - Signaling pathways
  - Regulatory networks

Examples:
  - KEGG
  - Reactome
  - WikiPathways
  - BioCyc
  - MetaCyc

Types:
  - Metabolic
  - Signaling
  - Regulatory
""")

print("\n=== 8. Expression Databases ===")
print("""
Expression:
  - Gene expression
  - Transcriptomics
  - Proteomics

Examples:
  - GEO (microarray/RNA-seq)
  - ArrayExpress
  - GTEx (human tissues)
  - TCGA (cancer)
  - Human Protein Atlas

Types:
  - Microarray
  - RNA-seq
  - Proteomics
""")

print("\n=== 9. Literature Databases ===")
print("""
Literature:
  - Scientific papers
  - Abstracts
  - Citations

Examples:
  - PubMed
  - PMC
  - arXiv
  - bioRxiv
  - Google Scholar

Types:
  - Abstracts
  - Full text
  - Preprints
""")

print("\n=== 10. Classification ===")
print(f"{'Type':<20} {'Description':<30} {'Examples':<30}")
print("-" * 80)
print(f"{'Primary':<20} {'Raw data':<30} {'GenBank, PDB':<30}")
print(f"{'Secondary':<20} {'Curated':<30} {'UniProt, RefSeq':<30}")
print(f"{'Composite':<20} {'Integrated':<30} {'Entrez, Ensembl':<30}")
print(f"{'Specialized':<20} {'Organism/disease':<30} {'FlyBase, OMIM':<30}")
print(f"{'Sequence':<20} {'DNA/protein':<30} {'GenBank, UniProt':<30}")
print(f"{'Structure':<20} {'3D structures':<30} {'PDB, AlphaFold':<30}")
print(f"{'Pathway':<20} {'Pathways':<30} {'KEGG, Reactome':<30}")
print(f"{'Expression':<20} {'Expression':<30} {'GEO, GTEx':<30}")
print(f"{'Literature':<20} {'Papers':<30} {'PubMed, PMC':<30}")

🧬 Nucleotide Databases

🎯 DNA and RNA Databases

Nucleotide databases store DNA and RNA sequences. They are essential for genomics, transcriptomics, and molecular biology.

Nucleotide Databases

GenBank: Primary. RefSeq: Curated. EMBL: Europe. DDBJ: Japan. Ensembl: Genomes. UCSC: Genomes.

🧬 GenBank🧬 RefSeq🧬 EMBL🧬 Ensembl

# Nucleotide Databases
print("=== 1. GenBank ===")
print("""
GenBank:
  - NCBI
  - Primary database
  - All submitted sequences
  - Redundant
  - > 3 billion sequences

Access:
  - Web: ncbi.nlm.nih.gov/genbank
  - Entrez
  - FTP

Formats:
  - GenBank
  - FASTA
  - XML

Example:
  Accession: NC_000001
  → Human chromosome 1
""")

print("\n=== 2. RefSeq ===")
print("""
RefSeq:
  - NCBI
  - Secondary database
  - Curated
  - Non-redundant
  - Reference sequences

Access:
  - Web: ncbi.nlm.nih.gov/refseq
  - Entrez

Prefixes:
  - NC_ (genome)
  - NM_ (mRNA)
  - NP_ (protein)
  - NR_ (non-coding)

Example:
  NM_000546
  → TP53 mRNA
""")

print("\n=== 3. EMBL ===")
print("""
EMBL:
  - EBI (Europe)
  - Primary database
  - Part of INSDC
  - Nucleotide sequences

Access:
  - Web: ebi.ac.uk/ena
  - API

Partners:
  - GenBank (NCBI)
  - DDBJ (Japan)
  - INSDC (international)
""")

print("\n=== 4. DDBJ ===")
print("""
DDBJ:
  - Japan
  - Primary database
  - Part of INSDC
  - Nucleotide sequences

Access:
  - Web: ddbj.nig.ac.jp
  - API
""")

print("\n=== 5. Ensembl ===")
print("""
Ensembl:
  - EBI + Wellcome Sanger
  - Genome database
  - Annotation
  - Comparative genomics

Access:
  - Web: ensembl.org
  - REST API
  - FTP

Features:
  - Genomes
  - Genes
  - Variants
  - Comparative
  - Regulation

Example:
  ENSG00000141510
  → TP53 gene
""")

print("\n=== 6. UCSC Genome Browser ===")
print("""
UCSC:
  - University of California
  - Genome browser
  - Annotation
  - Comparative

Access:
  - Web: genome.ucsc.edu
  - API

Features:
  - Genomes
  - Tracks
  - Annotation
  - Comparative
""")

print("\n=== 7. Other Nucleotide Databases ===")
print("""
1. SILVA
   - rRNA
   - Microbial

2. Rfam
   - RNA families
   - Non-coding

3. miRBase
   - microRNA

4. GtRNAdb
   - tRNA

5. PlasmoDB
   - Plasmodium

6. VectorBase
   - Vectors
""")

print("\n=== 8. Accession Numbers ===")
print("""
Accession:
  - Unique identifier
  - Stable
  - Versioned

Examples:
  - NC_000001.11 (chromosome)
  - NM_000546.6 (mRNA)
  - NP_000537.3 (protein)
  - ENSG00000141510 (gene)

Format:
  - Prefix
  - Number
  - Version
""")

print("\n=== 9. Format ===")
print("""
GenBank format:
  LOCUS       NC_000001   248956422 bp    DNA
  DEFINITION  Homo sapiens chromosome 1
  ACCESSION   NC_000001
  VERSION     NC_000001.11
  KEYWORDS    RefSeq
  SOURCE      Homo sapiens
  FEATURES    ...
  ORIGIN      ...
  //

FASTA format:
  >NC_000001.11 Homo sapiens chromosome 1
  NNNNNNNNNNNNNNNNNNNN...
""")

print("\n=== 10. Use Cases ===")
print("""
Use cases:
  - Sequence retrieval
  - BLAST search
  - Primer design
  - Phylogenetics
  - Variant analysis
  - Gene annotation
  - Comparative genomics
""")

🧪 Protein Databases

🎯 Protein Sequence and Function

Protein databases store protein sequences, structures, families, and functional annotations. They are essential for proteomics, structural biology, and functional genomics.

Protein Databases

UniProt: Sequences. Pfam: Families. InterPro: Domains. PDB: Structures. STRING: Interactions. PROSITE: Motifs.

🧪 UniProt🧪 Pfam🧪 InterPro🧪 PDB

# Protein Databases
print("=== 1. UniProt ===")
print("""
UniProt:
  - Universal Protein Resource
  - Most comprehensive
  - Protein sequences
  - Functional annotation

Components:
  - Swiss-Prot (curated)
  - TrEMBL (unreviewed)
  - UniParc (archive)

Access:
  - Web: uniprot.org
  - REST API
  - FTP

Example:
  P04637
  → Human TP53
""")

print("\n=== 2. Pfam ===")
print("""
Pfam:
  - Protein families
  - HMM profiles
  - Domains
  - Curated

Access:
  - Web: pfam.xfam.org
  - API
  - FTP

Example:
  PF00001
  → 7 transmembrane receptor
""")

print("\n=== 3. InterPro ===")
print("""
InterPro:
  - Integrative
  - Protein families
  - Domains
  - Functional sites

Access:
  - Web: ebi.ac.uk/interpro
  - API

Members:
  - Pfam
  - PROSITE
  - PRINTS
  - SMART
  - PANTHER

Example:
  IPR001841
  → Zinc finger
""")

print("\n=== 4. PDB ===")
print("""
PDB:
  - Protein Data Bank
  - 3D structures
  - Experimental
  - X-ray, NMR, Cryo-EM

Access:
  - Web: rcsb.org
  - API
  - FTP

Example:
  1TUP
  → p53 DNA-binding domain
""")

print("\n=== 5. STRING ===")
print("""
STRING:
  - Protein-protein interactions
  - Functional associations
  - Confidence scores

Access:
  - Web: string-db.org
  - API

Example:
  TP53
  → Interaction network
""")

print("\n=== 6. PROSITE ===")
print("""
PROSITE:
  - Protein motifs
  - Patterns
  - Profiles
  - Functional sites

Access:
  - Web: prosite.expasy.org
  - API

Example:
  PS00001
  → N-glycosylation site
""")

print("\n=== 7. Other Protein Databases ===")
print("""
1. PRIDE
   - Proteomics
   - Mass spectrometry

2. PeptideAtlas
   - Peptides
   - Proteomics

3. Human Protein Atlas
   - Expression
   - Localization

4. neXtProt
   - Human proteins
   - Annotation

5. MEROPS
   - Proteases
   - Peptidases
""")

print("\n=== 8. UniProt Format ===")
print("""
UniProt format:
  ID   TP53_HUMAN              Reviewed;         393 AA.
  AC   P04637; A0A024R1X2; ...
  DT   01-AUG-1988, integrated into UniProtKB/Swiss-Prot.
  DE   Cellular tumor antigen p53
  GN   Name=TP53; Synonyms=P53;
  OS   Homo sapiens (Human).
  OX   NCBI_TaxID=9606;
  ...
  SQ   SEQUENCE   393 AA;
       MEEPQSDPSV EPPLSQETFS DLWKLLPENN VLSPLPSQAM DDLMLSPDDI ...
  //
""")

print("\n=== 9. Use Cases ===")
print("""
Use cases:
  - Protein sequence retrieval
  - BLAST search
  - Domain analysis
  - Structure prediction
  - Interaction networks
  - Functional annotation
  - Phylogenetics
  - Drug discovery
""")

print("\n=== 10. Cross-References ===")
print("""
Cross-references:
  - UniProt → PDB
  - UniProt → Ensembl
  - UniProt → KEGG
  - UniProt → Pfam
  - UniProt → InterPro
  - UniProt → STRING

Example:
  UniProt P04637
  → PDB: 1TUP, 2OCJ, ...
  → Ensembl: ENSG00000141510
  → KEGG: hsa:7157
""")

🌐 Genome Databases

🎯 Whole Genome Data

Genome databases store complete genome sequences, annotations, and comparative data. They are essential for genomics, evolutionary biology, and precision medicine.

Genome Databases

Ensembl: Vertebrates. UCSC: Browser. NCBI: Genomes. JGI: Plants/microbes. TAIR: Arabidopsis. FlyBase: Drosophila.

🌐 Ensembl🌐 UCSC🌐 NCBI🌐 JGI

# Genome Databases
print("=== 1. Ensembl ===")
print("""
Ensembl:
  - EBI + Sanger
  - Vertebrate genomes
  - Annotation
  - Comparative genomics

Access:
  - Web: ensembl.org
  - REST API
  - FTP

Features:
  - Genomes
  - Genes
  - Transcripts
  - Variants
  - Regulation
  - Comparative

Example:
  ENSG00000141510
  → TP53
""")

print("\n=== 2. UCSC Genome Browser ===")
print("""
UCSC:
  - University of California
  - Genome browser
  - Many genomes
  - Tracks

Access:
  - Web: genome.ucsc.edu
  - API
  - FTP

Features:
  - Genomes
  - Tracks
  - Annotation
  - Comparative
  - Table Browser

Example:
  hg38
  → Human genome
""")

print("\n=== 3. NCBI Genome ===")
print("""
NCBI Genome:
  - NCBI
  - All genomes
  - Assembly
  - Annotation

Access:
  - Web: ncbi.nlm.nih.gov/genome
  - Entrez
  - FTP

Features:
  - Assemblies
  - Annotation
  - BLAST
  - Downloads

Example:
  GCF_000001405.40
  → Human GRCh38
""")

print("\n=== 4. JGI ===")
print("""
JGI (Joint Genome Institute):
  - DOE
  - Plants
  - Microbes
  - Fungi

Access:
  - Web: jgi.doe.gov
  - Phytozome
  - MycoCosm

Features:
  - Genomes
  - Annotation
  - Comparative
  - Tools

Example:
  Phytozome
  → Plant genomes
""")

print("\n=== 5. TAIR ===")
print("""
TAIR:
  - Arabidopsis
  - Model plant
  - Annotation
  - Tools

Access:
  - Web: arabidopsis.org

Features:
  - Genome
  - Genes
  - Markers
  - Expression
  - Literature
""")

print("\n=== 6. FlyBase ===")
print("""
FlyBase:
  - Drosophila
  - Model organism
  - Annotation
  - Tools

Access:
  - Web: flybase.org

Features:
  - Genome
  - Genes
  - Mutants
  - Expression
  - Literature
""")

print("\n=== 7. SGD ===")
print("""
SGD (Saccharomyces Genome Database):
  - Yeast
  - Model organism
  - Annotation
  - Tools

Access:
  - Web: yeastgenome.org

Features:
  - Genome
  - Genes
  - Proteins
  - Interactions
  - Expression
""")

print("\n=== 8. WormBase ===")
print("""
WormBase:
  - C. elegans
  - Model organism
  - Annotation
  - Tools

Access:
  - Web: wormbase.org

Features:
  - Genome
  - Genes
  - Expression
  - Phenotypes
""")

print("\n=== 9. Other Genome Databases ===")
print("""
1. ZFIN
   - Zebrafish

2. MGI
   - Mouse

3. RGD
   - Rat

4. Gramene
   - Grasses

5. Phytozome
   - Plants

6. Ensembl Genomes
   - Non-vertebrates

7. NCBI Assembly
   - All assemblies
""")

print("\n=== 10. Use Cases ===")
print("""
Use cases:
  - Genome browsing
  - Gene retrieval
  - Variant analysis
  - Comparative genomics
  - Phylogenetics
  - Annotation
  - Primer design
  - CRISPR design
""")

🏗️ Structure Databases

🎯 3D Structural Data

Structure databases store 3D structures of biological macromolecules. They are essential for structural biology, drug discovery, and understanding molecular function.

Structure Databases

PDB: Experimental. AlphaFold: Predicted. EMDB: EM maps. CATH: Classification. SCOP: Classification. BMRB: NMR.

🏗️ PDB🏗️ AlphaFold🏗️ EMDB🏗️ CATH

# Structure Databases
print("=== 1. PDB ===")
print("""
PDB (Protein Data Bank):
  - Experimental structures
  - X-ray, NMR, Cryo-EM
  - > 200,000 structures
  - Worldwide

Access:
  - Web: rcsb.org
  - API
  - FTP

Formats:
  - PDB
  - mmCIF
  - PDBML

Example:
  1TUP
  → p53 DNA-binding domain
""")

print("\n=== 2. AlphaFold DB ===")
print("""
AlphaFold DB:
  - Predicted structures
  - DeepMind + EBI
  - > 200 million
  - Proteomes

Access:
  - Web: alphafold.ebi.ac.uk
  - API
  - FTP

Coverage:
  - Human
  - Model organisms
  - UniProt

Example:
  AF-P04637-F1
  → TP53 predicted structure
""")

print("\n=== 3. EMDB ===")
print("""
EMDB (Electron Microscopy Data Bank):
  - Cryo-EM maps
  - 3D reconstructions
  - EBI

Access:
  - Web: emdb-empiar.org
  - API

Example:
  EMD-1234
  → Cryo-EM map
""")

print("\n=== 4. CATH ===")
print("""
CATH:
  - Protein structure classification
  - Class, Architecture, Topology, Homology
  - Hierarchical

Access:
  - Web: cathdb.info
  - API

Example:
  1.10.8.10
  → Classification
""")

print("\n=== 5. SCOP ===")
print("""
SCOP:
  - Structural Classification of Proteins
  - Hierarchical
  - Manual curation

Access:
  - Web: scop.mrc-lmb.cam.ac.uk
  - API

Example:
  a.1.1.1
  → Classification
""")

print("\n=== 6. BMRB ===")
print("""
BMRB (Biological Magnetic Resonance Data Bank):
  - NMR data
  - Chemical shifts
  - Metabolomics

Access:
  - Web: bmrb.io
  - API

Example:
  15000
  → NMR entry
""")

print("\n=== 7. Other Structure Databases ===")
print("""
1. PDBe
   - PDB Europe

2. RCSB PDB
   - PDB US

3. PDBj
   - PDB Japan

4. ModBase
   - Comparative models

5. SWISS-MODEL
   - Homology models

6. AlphaFold
   - Predicted
""")

print("\n=== 8. PDB Format ===")
print("""
PDB format:
  HEADER    DNA-BINDING PROTEIN
  TITLE     CRYSTAL STRUCTURE OF P53
  COMPND    MOL_ID: 1;
  SOURCE    ORGANISM_SCIENTIFIC: HOMO SAPIENS;
  KEYWDS    DNA-BINDING, TUMOR SUPPRESSOR
  EXPDTA    X-RAY DIFFRACTION
  AUTHOR    Y. CHO, S. GORINA, P.D. JEFFREY, N.P. PAVLETICH
  REVDAT    ...
  ATOM      1  N   MET A   1      35.123  45.678  55.789  1.00 50.00           N
  ...
  END
""")

print("\n=== 9. mmCIF Format ===")
print("""
mmCIF:
  - Modern format
  - PDBx/mmCIF
  - Larger structures
  - More metadata

Example:
  _entry.id   1TUP
  _struct.title  'CRYSTAL STRUCTURE OF P53'
  _atom_site.group_PDB  ATOM
  _atom_site.id  1
  _atom_site.label_atom_id  N
  _atom_site.label_comp_id  MET
  _atom_site.label_asym_id  A
  _atom_site.label_seq_id  1
  _atom_site.Cartn_x  35.123
  _atom_site.Cartn_y  45.678
  _atom_site.Cartn_z  55.789
""")

print("\n=== 10. Use Cases ===")
print("""
Use cases:
  - Structure visualization
  - Docking
  - Drug design
  - Structure prediction
  - Molecular dynamics
  - Comparative modeling
  - Function prediction
""")

🔗 Pathway Databases

🎯 Pathways and Networks

Pathway databases store metabolic, signaling, and regulatory pathways. They are essential for systems biology, drug discovery, and understanding cellular processes.

Pathway Databases

KEGG: Metabolic. Reactome: Curated. WikiPathways: Community. BioCyc: Many. MetaCyc: Metabolic. PANTHER: Classification.

🔗 KEGG🔗 Reactome🔗 WikiPathways🔗 BioCyc

# Pathway Databases
print("=== 1. KEGG ===")
print("""
KEGG (Kyoto Encyclopedia of Genes and Genomes):
  - Metabolic pathways
  - Signaling pathways
  - Disease pathways
  - Drug pathways

Access:
  - Web: kegg.jp
  - REST API

Databases:
  - PATHWAY
  - GENES
  - COMPOUND
  - DRUG
  - DISEASE
  - BRITE

Example:
  hsa04115
  → p53 signaling pathway
""")

print("\n=== 2. Reactome ===")
print("""
Reactome:
  - Curated pathways
  - Human + model organisms
  - Expert-written
  - Open source

Access:
  - Web: reactome.org
  - API

Features:
  - Pathways
  - Reactions
  - Proteins
  - Compounds
  - Disease

Example:
  R-HSA-69541
  → TP53 regulation
""")

print("\n=== 3. WikiPathways ===")
print("""
WikiPathways:
  - Community-curated
  - Open
  - Many organisms
  - Wiki-style

Access:
  - Web: wikipathways.org
  - API

Features:
  - Pathways
  - Community
  - Curation
  - GPML format

Example:
  WP1742
  → p53 pathway
""")

print("\n=== 4. BioCyc ===")
print("""
BioCyc:
  - Many organisms
  - Metabolic pathways
  - Genome databases
  - Curated

Access:
  - Web: biocyc.org
  - API

Features:
  - Pathways
  - Compounds
  - Genes
  - Reactions

Example:
  HUMAN
  → HumanCyc
""")

print("\n=== 5. MetaCyc ===")
print("""
MetaCyc:
  - Metabolic pathways
  - Non-redundant
  - Curated
  - Reference

Access:
  - Web: metacyc.org
  - API

Features:
  - Pathways
  - Compounds
  - Enzymes
  - Reactions

Example:
  PWY-1234
  → Pathway
""")

print("\n=== 6. PANTHER ===")
print("""
PANTHER:
  - Protein classification
  - Pathways
  - Families
  - Curated

Access:
  - Web: pantherdb.org
  - API

Features:
  - Pathways
  - Families
  - Classification
  - Ontology

Example:
  P00001
  → Pathway
""")

print("\n=== 7. Other Pathway Databases ===")
print("""
1. SMPDB
   - Small molecule pathways
   - Disease

2. PharmGKB
   - Pharmacogenomics
   - Drug pathways

3. INOH
   - Signaling
   - Networks

4. NetPath
   - Signaling
   - Curated

5. Pathway Commons
   - Integration
   - Many sources
""")

print("\n=== 8. KEGG Format ===")
print("""
KEGG format:
  ENTRY       hsa04115                    Pathway
  NAME        p53 signaling pathway
  DESCRIPTION ...
  CLASS       Cellular Processes; Cell growth and death
  PATHWAY_MAP hsa04115
  GENE        7157   TP53
  ...
  COMPOUND    C00002  ATP
  ...
  REFERENCE   ...
  //

KGML (KEGG Markup Language):
  
    
      
    
    ...
  
""")

print("\n=== 9. Use Cases ===")
print("""
Use cases:
  - Pathway analysis
  - Enrichment analysis
  - Systems biology
  - Drug discovery
  - Disease mechanisms
  - Metabolic engineering
  - Network analysis
""")

print("\n=== 10. Cross-References ===")
print("""
Cross-references:
  - KEGG → UniProt
  - KEGG → Ensembl
  - KEGG → PDB
  - Reactome → UniProt
  - Reactome → Ensembl
  - WikiPathways → Ensembl

Example:
  KEGG hsa04115
  → UniProt P04637
  → Ensembl ENSG00000141510
""")

📈 Expression Databases

🎯 Gene Expression Data

Expression databases store gene expression data from microarrays, RNA-seq, and proteomics. They are essential for transcriptomics, functional genomics, and disease research.

Expression Databases

GEO: NCBI. ArrayExpress: EBI. GTEx: Human tissues. TCGA: Cancer. HPA: Protein. CCLE: Cell lines.

📈 GEO📈 ArrayExpress📈 GTEx📈 TCGA

# Expression Databases
print("=== 1. GEO ===")
print("""
GEO (Gene Expression Omnibus):
  - NCBI
  - Microarray + RNA-seq
  - > 5 million samples
  - Free

Access:
  - Web: ncbi.nlm.nih.gov/geo
  - Entrez
  - FTP

Features:
  - Datasets
  - Samples
  - Platforms
  - Series

Example:
  GSE12345
  → Expression dataset
""")

print("\n=== 2. ArrayExpress ===")
print("""
ArrayExpress:
  - EBI
  - Microarray + RNA-seq
  - Curated
  - Free

Access:
  - Web: ebi.ac.uk/arrayexpress
  - API

Features:
  - Experiments
  - Samples
  - Protocols
  - Data

Example:
  E-MTAB-1234
  → Experiment
""")

print("\n=== 3. GTEx ===")
print("""
GTEx (Genotype-Tissue Expression):
  - Human tissues
  - Expression + genotype
  - Multiple tissues
  - Free

Access:
  - Web: gtexportal.org
  - API

Features:
  - Expression
  - eQTLs
  - Tissues
  - Genotypes

Example:
  GTEX-1234
  → Sample
""")

print("\n=== 4. TCGA ===")
print("""
TCGA (The Cancer Genome Atlas):
  - Cancer
  - Multi-omics
  - Many cancer types
  - Free

Access:
  - Web: cancer.gov/tcga
  - GDC

Features:
  - Expression
  - Mutations
  - Copy number
  - Methylation

Example:
  TCGA-01-1234
  → Sample
""")

print("\n=== 5. Human Protein Atlas ===")
print("""
Human Protein Atlas:
  - Protein expression
  - Localization
  - Tissue
  - Cell

Access:
  - Web: proteinatlas.org
  - API

Features:
  - Expression
  - Localization
  - Tissue
  - Cancer

Example:
  TP53
  → Protein expression
""")

print("\n=== 6. CCLE ===")
print("""
CCLE (Cancer Cell Line Encyclopedia):
  - Cancer cell lines
  - Expression
  - Mutations
  - Drug response

Access:
  - Web: broadinstitute.org/ccle
  - API

Features:
  - Expression
  - Mutations
  - Copy number
  - Drug

Example:
  A549
  → Cell line
""")

print("\n=== 7. Other Expression Databases ===")
print("""
1. ENCODE
   - Functional genomics
   - Epigenomics

2. Roadmap Epigenomics
   - Epigenomes
   - Human

3. FANTOM
   - Transcriptomics
   - Mouse/human

4. Single Cell Portal
   - Single-cell
   - Broad

5. Human Cell Atlas
   - Single-cell
   - Human
""")

print("\n=== 8. GEO Format ===")
print("""
GEO:
  Series: GSE12345
  Samples: GSM123456, GSM123457, ...
  Platform: GPL12345

Sample:
  GSM123456
  Title: Control sample
  Organism: Homo sapiens
  Tissue: Liver
  Treatment: None

Data:
  Raw: .CEL, .fastq
  Processed: .txt, .csv
""")

print("\n=== 9. Use Cases ===")
print("""
Use cases:
  - Differential expression
  - Co-expression
  - Biomarker discovery
  - Drug response
  - Disease mechanisms
  - Tissue specificity
  - Single-cell
""")

print("\n=== 10. Analysis ===")
print("""
Analysis:
  - Differential expression
  - Clustering
  - PCA
  - Pathway enrichment
  - Network analysis
  - Machine learning

Tools:
  - GEO2R
  - R (limma, DESeq2)
  - Python (scikit-learn)
""")

🛠️ Tools (Entrez, UniProt API)

🎯 Accessing Databases

Most biological databases offer APIs and tools for programmatic access. Here are the most important ones.

Access Tools

Entrez: NCBI. UniProt API: REST. Ensembl API: REST. PDB API: REST. KEGG API: REST. Biopython: Python.

🛠️ Entrez🛠️ UniProt API🛠️ Ensembl API🛠️ Biopython

# Tools
print("=== 1. NCBI Entrez (E-utilities) ===")
print("""
Entrez:
  - NCBI API
  - Access to all NCBI databases
  - E-utilities

Databases:
  - nucleotide
  - protein
  - pubmed
  - gene
  - genome
  - structure

E-utilities:
  - esearch: search
  - efetch: retrieve
  - esummary: summary
  - elink: cross-reference

URL:
  https://eutils.ncbi.nlm.nih.gov/entrez/eutils/

Example:
  esearch.fcgi?db=protein&term=TP53&retmax=10
""")

print("\n=== 2. UniProt REST API ===")
print("""
UniProt API:
  - RESTful
  - Protein data
  - Search, retrieve, map

Endpoints:
  - /uniprotkb/search
  - /uniprotkb/{accession}
  - /uniprotkb/stream
  - /idmapping/run

URL:
  https://rest.uniprot.org/

Example:
  https://rest.uniprot.org/uniprotkb/P04637
""")

print("\n=== 3. Ensembl REST API ===")
print("""
Ensembl API:
  - RESTful
  - Genome data
  - Genes, variants, sequences

Endpoints:
  - /lookup/id/{id}
  - /sequence/id/{id}
  - /vep/{species}/hgvs/{hgvs}
  - /overlap/region/{species}/{region}

URL:
  https://rest.ensembl.org/

Example:
  https://rest.ensembl.org/lookup/id/ENSG00000141510
""")

print("\n=== 4. PDB API ===")
print("""
PDB API:
  - RESTful
  - Structure data
  - Search, retrieve

Endpoints:
  - /rest/v1/entry/{id}
  - /rest/v1/search
  - /rest/v1/core/{id}

URL:
  https://data.rcsb.org/

Example:
  https://data.rcsb.org/rest/v1/core/entry/1TUP
""")

print("\n=== 5. KEGG API ===")
print("""
KEGG API:
  - RESTful
  - Pathway data
  - Genes, compounds

Endpoints:
  - /get/{db}/{entry}
  - /list/{db}
  - /find/{db}/{query}

URL:
  https://rest.kegg.jp/

Example:
  https://rest.kegg.jp/get/hsa04115
""")

print("\n=== 6. Biopython ===")
print("""
Biopython:
  - Python library
  - Entrez
  - Parsing
  - Analysis

Modules:
  - Bio.Entrez
  - Bio.SeqIO
  - Bio.PDB
  - Bio.Blast

Example:
  from Bio import Entrez
  Entrez.email = "your@email.com"
  handle = Entrez.esearch(db="protein", term="TP53")
  record = Entrez.read(handle)
""")

print("\n=== 7. Other Tools ===")
print("""
1. Requests
   - Python HTTP
   - REST APIs

2. curl
   - Command line
   - HTTP

3. wget
   - Download
   - FTP

4. Entrez Direct
   - Command line
   - NCBI

5. Dataset
   - NCBI
   - Command line
""")

print("\n=== 8. Rate Limits ===")
print("""
Rate limits:
  - NCBI: 3 requests/second (without API key)
  - NCBI: 10 requests/second (with API key)
  - UniProt: reasonable
  - Ensembl: 15 requests/second
  - PDB: reasonable
  - KEGG: reasonable

Best practices:
  - Use API key
  - Cache results
  - Respect limits
  - Batch requests
""")

print("\n=== 9. Example Queries ===")
print("""
NCBI Entrez:
  esearch.fcgi?db=protein&term=TP53[gene]+AND+human[orgn]
  efetch.fcgi?db=protein&id=NP_000537&rettype=fasta

UniProt:
  https://rest.uniprot.org/uniprotkb/search?query=TP53+AND+organism_id:9606

Ensembl:
  https://rest.ensembl.org/lookup/id/ENSG00000141510?expand=1

PDB:
  https://data.rcsb.org/rest/v1/core/entry/1TUP

KEGG:
  https://rest.kegg.jp/get/hsa04115
""")

print("\n=== 10. Best Practices ===")
print("""
1. Use API key
2. Cache results
3. Respect rate limits
4. Handle errors
5. Use HTTPS
6. Read documentation
7. Cite databases
8. Version control
""")

🐍 Python Integration

🎯 Accessing Databases from Python

Python is the language of choice for bioinformatics. Here's how to access biological databases from Python.

Python

Biopython: Entrez. Requests: REST. Pandas: Data. Matplotlib: Plot. Jupyter: Notebook. Automate: Pipeline.

🐍 Biopython🐍 Requests🐍 Pandas🐍 Automate

# Python Integration
print("=== 1. NCBI Entrez with Biopython ===")
print("""
from Bio import Entrez

# Set email (required)
Entrez.email = "your.email@example.com"

# Search
handle = Entrez.esearch(db="protein", term="TP53 AND human", retmax=10)
record = Entrez.read(handle)
handle.close()

print(record["IdList"])

# Fetch
handle = Entrez.efetch(db="protein", id=record["IdList"], rettype="fasta")
sequences = handle.read()
handle.close()
""")

print("\n=== 2. UniProt REST API ===")
print("""
import requests

def get_uniprot(accession):
    url = f"https://rest.uniprot.org/uniprotkb/{accession}.json"
    response = requests.get(url)
    if response.status_code == 200:
        return response.json()
    return None

# Example
data = get_uniprot("P04637")
if data:
    print(data["primaryAccession"])
    print(data["proteinDescription"]["recommendedName"]["fullName"]["value"])
""")

print("\n=== 3. Ensembl REST API ===")
print("""
import requests

def get_ensembl_gene(gene_id):
    url = f"https://rest.ensembl.org/lookup/id/{gene_id}?expand=1"
    headers = {"Content-Type": "application/json"}
    response = requests.get(url, headers=headers)
    if response.status_code == 200:
        return response.json()
    return None

# Example
gene = get_ensembl_gene("ENSG00000141510")
if gene:
    print(gene["display_name"])
    print(gene["description"])
""")

print("\n=== 4. PDB API ===")
print("""
import requests

def get_pdb_entry(pdb_id):
    url = f"https://data.rcsb.org/rest/v1/core/entry/{pdb_id}"
    response = requests.get(url)
    if response.status_code == 200:
        return response.json()
    return None

# Example
entry = get_pdb_entry("1TUP")
if entry:
    print(entry["struct"]["title"])
""")

print("\n=== 5. KEGG API ===")
print("""
import requests

def get_kegg_pathway(pathway_id):
    url = f"https://rest.kegg.jp/get/{pathway_id}"
    response = requests.get(url)
    if response.status_code == 200:
        return response.text
    return None

# Example
pathway = get_kegg_pathway("hsa04115")
if pathway:
    print(pathway[:500])
""")

print("\n=== 6. Parse FASTA ===")
print("""
from Bio import SeqIO

def parse_fasta(filepath):
    sequences = []
    for record in SeqIO.parse(filepath, "fasta"):
        sequences.append({
            "id": record.id,
            "description": record.description,
            "sequence": str(record.seq)
        })
    return sequences

# Example
# sequences = parse_fasta("sequences.fasta")
""")

print("\n=== 7. Parse GenBank ===")
print("""
from Bio import SeqIO

def parse_genbank(filepath):
    records = []
    for record in SeqIO.parse(filepath, "genbank"):
        records.append({
            "id": record.id,
            "name": record.name,
            "description": record.description,
            "sequence": str(record.seq),
            "features": [(f.type, f.location) for f in record.features]
        })
    return records

# Example
# records = parse_genbank("sequence.gb")
""")

print("\n=== 8. Parse PDB ===")
print("""
from Bio.PDB import PDBParser

def parse_pdb(filepath):
    parser = PDBParser()
    structure = parser.get_structure("protein", filepath)
    
    for model in structure:
        for chain in model:
            for residue in chain:
                print(residue)
    return structure

# Example
# structure = parse_pdb("1tup.pdb")
""")

print("\n=== 9. Complete Pipeline ===")
print("""
from Bio import Entrez, SeqIO
import requests
import pandas as pd

def database_pipeline(gene):
    \"\"\"Complete database pipeline.\"\"\"
    # Search NCBI
    Entrez.email = "your.email@example.com"
    handle = Entrez.esearch(db="protein", term=f"{gene} AND human", retmax=5)
    record = Entrez.read(handle)
    handle.close()
    
    # Fetch
    handle = Entrez.efetch(db="protein", id=record["IdList"], rettype="fasta")
    sequences = list(SeqIO.parse(handle, "fasta"))
    handle.close()
    
    # Get UniProt
    uniprot = get_uniprot("P04637")
    
    # Get Ensembl
    ensembl = get_ensembl_gene("ENSG00000141510")
    
    # Combine
    data = {
        "gene": gene,
        "ncbi_count": len(sequences),
        "uniprot": uniprot["primaryAccession"] if uniprot else None,
        "ensembl": ensembl["display_name"] if ensembl else None
    }
    
    return data

# Use
# data = database_pipeline("TP53")
# print(data)
""")

print("\n=== 10. Visualization ===")
print("""
import matplotlib.pyplot as plt
import pandas as pd

def plot_expression(data):
    \"\"\"Plot expression data.\"\"\"
    df = pd.DataFrame(data)
    
    plt.figure(figsize=(10, 6))
    plt.bar(df["tissue"], df["expression"], color='purple')
    plt.xlabel("Tissue")
    plt.ylabel("Expression")
    plt.title("Gene Expression")
    plt.xticks(rotation=45)
    plt.tight_layout()
    plt.savefig("expression.png")
    plt.show()

# Use
# plot_expression(data)
""")

⚖️ Database Comparison

🎯 Which Database?

Choosing the right database depends on your data type and analysis goals.

Comparison

NCBI: Comprehensive. UniProt: Proteins. Ensembl: Genomes. PDB: Structures. KEGG: Pathways. GEO: Expression.

⚖️ NCBI⚖️ UniProt⚖️ Ensembl⚖️ PDB

Database	Type	Data	Access	Best For
NCBI	Composite	Sequences	Web, API	General
UniProt	Secondary	Proteins	Web, API	Protein sequences
Ensembl	Composite	Genomes	Web, API	Genomes
PDB	Primary	Structures	Web, API	3D structures
KEGG	Secondary	Pathways	Web, API	Pathways
Reactome	Secondary	Pathways	Web, API	Curated pathways
GEO	Primary	Expression	Web, API	Expression
Pfam	Secondary	Families	Web, API	Protein families
InterPro	Composite	Domains	Web, API	Domains
AlphaFold	Secondary	Structures	Web, API	Predicted

# Decision Guide
print("=== Choose Database by Data Type ===")
print("""
DNA sequences:
  → GenBank
  → RefSeq
  → Ensembl

Protein sequences:
  → UniProt
  → RefSeq

Protein structures:
  → PDB
  → AlphaFold DB

Protein families:
  → Pfam
  → InterPro

Genomes:
  → Ensembl
  → UCSC
  → NCBI Genome

Pathways:
  → KEGG
  → Reactome
  → WikiPathways

Expression:
  → GEO
  → ArrayExpress
  → GTEx

Interactions:
  → STRING
  → BioGRID

Diseases:
  → OMIM
  → ClinVar
""")

print("\n=== Speed Comparison ===")
print(f"{'Database':<20} {'Size':<20} {'Access':<15} {'API':<10}")
print("-" * 65)
print(f"{'NCBI':<20} {'3B+ sequences':<20} {'Web, API':<15} {'Yes':<10}")
print(f"{'UniProt':<20} {'200M+ proteins':<20} {'Web, API':<15} {'Yes':<10}")
print(f"{'Ensembl':<20} {'100+ genomes':<20} {'Web, API':<15} {'Yes':<10}")
print(f"{'PDB':<20} {'200K+ structures':<20} {'Web, API':<15} {'Yes':<10}")
print(f"{'KEGG':<20} {'500+ pathways':<20} {'Web, API':<15} {'Yes':<10}")

print("\n=== When to Use Which ===")
print("""
USE NCBI:
  ✓ General
  ✓ Sequences
  ✓ Literature
  ✓ BLAST

USE UniProt:
  ✓ Proteins
  ✓ Functional annotation
  ✓ Cross-references

USE Ensembl:
  ✓ Genomes
  ✓ Comparative
  ✓ Variants

USE PDB:
  ✓ Structures
  ✓ Drug design
  ✓ Visualization

USE KEGG:
  ✓ Pathways
  ✓ Metabolic
  ✓ Disease

USE GEO:
  ✓ Expression
  ✓ Differential
  ✓ Biomarkers
""")

❓ FAQ

🎯 Frequently Asked Questions

Answers to common questions about biological databases.

What is a biological database?

What is the difference between primary and secondary databases?

Primary databases store raw experimental data (e.g., GenBank, PDB, GEO). Secondary databases store derived, curated, or integrated data (e.g., UniProt, Pfam, KEGG, Reactome). Primary data is submitted by researchers; secondary data is curated and annotated.

Which biological database is best for protein sequences?

UniProt is the most comprehensive protein sequence database, with Swiss-Prot (curated) and TrEMBL (unreviewed). For protein structures, use PDB. For protein families and domains, use Pfam and InterPro.

How do I access biological databases programmatically?

Most databases offer APIs: NCBI Entrez (E-utilities), UniProt REST API, Ensembl REST API, PDB API, and KEGG REST API. You can access them from Python using Biopython (Entrez), requests, or dedicated libraries.

What is the difference between GenBank and RefSeq?

GenBank is a primary database containing all submitted sequences (may include redundancy and errors). RefSeq is a secondary, curated database of reference sequences for genomes, transcripts, and proteins, with non-redundant, well-annotated entries.

What is an accession number?

An accession number is a unique identifier assigned to a sequence, structure, or other biological record in a database. It is stable and versioned. Examples: NC_000001.11, NM_000546.6, P04637.

What is the difference between KEGG and Reactome?

KEGG is a comprehensive database of metabolic and signaling pathways, with a focus on metabolism and disease. Reactome is a curated, expert-written database of human and model organism pathways, with detailed molecular reactions.

How do I download data from biological databases?

Most databases offer FTP downloads, API access, and web interfaces. Use FTP for bulk downloads, APIs for programmatic access, and web for browsing. Examples: NCBI FTP, UniProt FTP, Ensembl FTP.

What is the best database for gene expression data?

GEO (Gene Expression Omnibus) is the largest public repository for gene expression data (microarray, RNA-seq). For human tissue-specific expression, use GTEx. For cancer, use TCGA. For protein expression, use Human Protein Atlas.

How do I cite a biological database?

Always cite the database and its primary publication. For example: "UniProt Consortium (2023) UniProt: the Universal Protein Knowledgebase. Nucleic Acids Research." Check each database's website for citation guidelines.

# Quick Reference
print("=== Quick Answers ===")
print("""
Q: What is a biological database?
A: Organized collection of biological data.

Q: Primary vs secondary?
A: Primary = raw; secondary = curated.

Q: Best for protein?
A: UniProt.

Q: Best for structures?
A: PDB, AlphaFold.

Q: Best for genomes?
A: Ensembl, UCSC.

Q: Best for pathways?
A: KEGG, Reactome.

Q: Best for expression?
A: GEO, GTEx.

Q: How to access?
A: Web, API, FTP, Biopython.

Q: GenBank vs RefSeq?
A: GenBank = primary; RefSeq = curated.

Q: Accession number?
A: Unique identifier.

Q: How to cite?
A: Cite database and publication.

Q: Free?
A: Yes — all major databases are free.
""")

🚀 Future

🎯 Where Databases Are Going

The future is AI-integrated, knowledge graphs, cloud-native, and multi-omics.

Future Directions

AI: Integration. Graphs: Knowledge. Cloud: Native. Multi-omics: Integrated. Real-time: Updates. FAIR: Data principles.

🚀 AI🚀 Knowledge Graphs🚀 Cloud🚀 Multi-omics

# Future of Biological Databases
print("=== 1. AI Integration ===")
print("""
Trend: AI + databases

Ideas:
  - AI-powered search
  - Intelligent annotation
  - Predictive models
  - Knowledge discovery

Examples:
  - AlphaFold DB
  - GPT for biology
  - AI curation

Future:
  - Smart databases
  - Automated annotation
  - Predictive
""")

print("\n=== 2. Knowledge Graphs ===")
print("""
Trend: Knowledge graphs

Current:
  - Relational databases
  - Flat files

Future:
  - Knowledge graphs
  - Linked data
  - Semantic web

Examples:
  - Bio2RDF
  - Wikidata
  - SPOKE
  - Hetionet

Future:
  - Integrated knowledge
  - Reasoning
  - Discovery
""")

print("\n=== 3. Cloud-Native ===")
print("""
Trend: Cloud databases

Current:
  - Local servers
  - FTP downloads

Future:
  - Cloud-native
  - Serverless
  - Auto-scaling
  - Pay per use

Examples:
  - AWS Open Data
  - Google Cloud
  - Azure
  - Terra

Future:
  - Petabyte-scale
  - Real-time
  - Global
""")

print("\n=== 4. Multi-Omics ===")
print("""
Trend: Multi-omics integration

Current:
  - Separate databases
  - Genomics, proteomics, etc.

Future:
  - Integrated
  - Multi-omics
  - Systems biology

Examples:
  - TCGA
  - GTEx
  - Human Cell Atlas
  - UK Biobank

Future:
  - Unified
  - Integrated
  - Systems-level
""")

print("\n=== 5. Real-Time Data ===")
print("""
Trend: Real-time updates

Current:
  - Periodic releases
  - Static

Future:
  - Real-time
  - Streaming
  - Live

Examples:
  - COVID-19 databases
  - GISAID
  - Nextstrain

Future:
  - Real-time
  - Continuous
  - Dynamic
""")

print("\n=== 6. FAIR Principles ===")
print("""
Trend: FAIR data

FAIR:
  - Findable
  - Accessible
  - Interoperable
  - Reusable

Future:
  - All databases FAIR
  - Standardized
  - Machine-readable

Examples:
  - DataCite
  - DOIs
  - Metadata standards
""")

# TIMELINE
print("\n=== Timeline ===")
print("""
Past:
  1970s: PDB, GenBank
  1980s: EMBL, Swiss-Prot
  1990s: NCBI, Entrez, KEGG
  2000s: Ensembl, UniProt
  2010s: Big data, APIs
  2020s: AI, knowledge graphs

Present:
  2025: Cloud-native
  2026: AI-integrated

Future:
  2030: Knowledge graphs
  2035: Multi-omics
  2040: Real-time?
""")

print("\n=== Final Thoughts ===")
print("""
Biological databases are:
  - Essential for research
  - Growing exponentially
  - Becoming more integrated
  - AI-powered
  - Cloud-native

The future is:
  - AI-integrated
  - Knowledge graphs
  - Cloud-native
  - Multi-omics
  - Real-time
  - FAIR

"Data is the new oil — but
 biological data is the new gold."

Keep exploring. Keep building.
""")