### Environment-like Access for Bimap Objects

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

Bimap objects support environment-like access methods including ls(), exists(), get(), and mget() for listing keys, checking existence, and retrieving values. Double-bracket and dollar sign access are also supported.

```r
library(org.Hs.eg.db)

# List keys (like ls on environment)
all_keys <- ls(org.Hs.egSYMBOL)
head(all_keys)

# Pattern matching
brca_keys <- ls(org.Hs.egSYMBOL, pattern = "^67")

# Check if key exists
exists("1", org.Hs.egSYMBOL)    # TRUE
exists("invalid", org.Hs.egSYMBOL)  # FALSE

# Get single value
symbol <- get("1", org.Hs.egSYMBOL)  # "A1BG"

# Get multiple values
symbols <- mget(c("1", "2", "10"), org.Hs.egSYMBOL)
# Returns list: list("1" = "A1BG", "2" = "A2M", "10" = "NAT2")

# Handle missing keys
symbols <- mget(c("1", "invalid"), org.Hs.egSYMBOL, ifnotfound = NA)

# Double-bracket access
org.Hs.egSYMBOL[["1"]]  # "A1BG"

# Dollar sign access
org.Hs.egSYMBOL$`1`     # "A1BG"
```

--------------------------------

### select() for ChipDb: Probe ID to Gene Info

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

Example using `select()` with a chip database (`hgu95av2.db`) to map probe IDs to gene information such as symbol, Entrez ID, and gene name. Requires loading the specific chip package.

```r
library(hgu95av2.db)
probes <- head(keys(hgu95av2.db, keytype = "PROBEID"))
probe_info <- select(hgu95av2.db,
                     keys = probes,
                     columns = c("SYMBOL", "ENTREZID", "GENENAME"),
                     keytype = "PROBEID")
```

--------------------------------

### mapIds() with Custom Function for Multi-Value Selection

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

Apply a custom function to handle multiple values returned by `mapIds()`. This example defines a function `last_match` to select the last value from a list of matches.

```r
library(org.Hs.eg.db)

# Custom function to select last match
last_match <- function(x) x[[length(x)]]
result <- mapIds(org.Hs.eg.db,
                 keys = genes,
                 column = "ALIAS",
                 keytype = "ENTREZID",
                 multiVals = last_match)
```

--------------------------------

### Convert Bimap to Data Frame with toTable()

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

The toTable() method converts a Bimap object to a data frame, displaying all links between keys and values. You can also get dimensions and column names, or a subset table.

```r
library(org.Hs.eg.db)

# Convert bimap to table
go_table <- toTable(org.Hs.egGO)
head(go_table)
#   gene_id      go_id Evidence Ontology
# 1       1 GO:0002576      TAS       BP
# 2       1 GO:0003674       ND       MF
# 3       1 GO:0005576      IDA       CC

# Get dimensions
nrow(org.Hs.egGO)    # Number of mappings
col(org.Hs.egGO)    # Number of columns

# Get column names
colnames(org.Hs.egGO)
# "gene_id" "go_id" "Evidence" "Ontology"

# Get subset table
subset_table <- toTable(org.Hs.egGO["1"])
```

--------------------------------

### List Columns for GO Database

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

Demonstrates using `columns()` with the `GO.db` package to list the available data fields specific to Gene Ontology annotations.

```r
library(GO.db)

# List columns for GO database
columns(GO.db)
# Returns: "DEFINITION" "GOID" "ONTOLOGY" "TERM"
```

--------------------------------

### List Columns for Chip Database

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

Shows how to use `columns()` with a chip database (e.g., `hgu95av2.db`) to list all available annotation columns, including chip-specific ones like 'PROBEID'.

```r
library(hgu95av2.db)
columns(hgu95av2.db)
# Returns all above plus "PROBEID"
```

--------------------------------

### Apply functions to Bimap entries

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

Demonstrates applying functions to all entries and sampling from a Bimap.

```r
# Apply function to all entries
lengths <- eapply(org.Hs.egGO, length)

# Random sample
sample_mappings <- sample(org.Hs.egSYMBOL, 5)
```

--------------------------------

### Access Database Metadata with metadata()

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

The metadata() method returns information about the annotation database, such as version, organism, and creation date. Use species() and taxonomyId() for specific details.

```r
library(org.Hs.eg.db)

# Get metadata as data frame
meta <- metadata(org.Hs.eg.db)
head(meta)
#          name                    value
# 1   DBSCHEMAVERSION                2.1
# 2       ORGANISM            Homo sapiens
# 3       SPECIES                   Human
# 4   DBSCHEMA               HUMAN_DB
# 5   CENTRALID                     EG
# ...

# Get species information
species(org.Hs.eg.db)  # "Homo sapiens"

# Get taxonomy ID
taxonomyId(org.Hs.eg.db)  # 9606
```

--------------------------------

### loadDb() - Load Annotation Database

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

Loads a SQLite annotation database from a file path and returns an appropriate AnnotationDb object.

```APIDOC
## loadDb(file)

### Description
Loads a standalone SQLite database file and returns an AnnotationDb object (OrgDb, ChipDb, TxDb, or GODb).

### Parameters
- **file** (character) - Required - The path to the SQLite database file.

### Request Example
db <- loadDb("/path/to/annotation.sqlite")
```

--------------------------------

### Create custom Bimaps

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

Template for creating a simple bidirectional map from a database table.

```r
library(AnnotationDbi)

# Create a simple bimap from a database table
# (Requires an existing database connection)
# simple_map <- createSimpleBimap(
#   tablename = "gene2symbol",
#   Lcolname = "gene_id",
#   Rcolname = "symbol",
#   datacache = datacache_object,
#   objName = "SYMBOL",
#   objTarget = "org.Hs.eg.db"
# )
```

--------------------------------

### GO Ontology (full label)

Source: https://github.com/bioconductor/annotationdbi/blob/devel/inst/DBschemas/schemas_2.1/DataTypes.txt

Describes the full label for GO ontology terms.

```APIDOC
## GO Ontology (full label)

### Description
Provides the full textual label for Gene Ontology (GO) terms.

### SQL Type
VARCHAR(18)

### Possible Values
- 'universal'
- 'biological_process'
- 'cellular_component'
- 'molecular_function'
```

--------------------------------

### Analyze mapping structure and coverage

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

Use links() and nhit() to inspect mapping edges and hit counts.

```r
library(org.Hs.eg.db)

# Get links (mapping edges) without attributes
link_table <- links(org.Hs.egGO)
head(link_table)
#   gene_id      go_id
# 1       1 GO:0002576
# 2       1 GO:0003674

# Count total links
count.links(org.Hs.egGO)

# Get hit counts per key
hits <- nhit(org.Hs.egGO)
head(hits)
#    1    2   10  100
#   42   37   12   28

# Find keys with most hits
names(hits)[hits == max(hits)]

# Analyze coverage
sum(hits == 0)  # Keys with no mappings
table(hits)     # Distribution of hit counts
```

--------------------------------

### Create custom GO and KEGG annotation frames

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

Construct GOFrame and KEGGFrame objects for gene set analysis.

```r
library(AnnotationDbi)
library(GO.db)

# Create GOFrame from custom data
go_data <- data.frame(
  go_id = c("GO:0008150", "GO:0008150", "GO:0003674"),
  evidence = c("IEA", "TAS", "IDA"),
  gene_id = c("1", "2", "1"),
  stringsAsFactors = FALSE
)
go_frame <- GOFrame(go_data, organism = "Homo sapiens")

# Access frame data
getGOFrameData(go_frame)

# Create GOAllFrame (includes ancestor terms)
go_all_frame <- GOAllFrame(go_frame)
getGOFrameData(go_all_frame)

# Create KEGGFrame
kegg_data <- data.frame(
  path_id = c("hsa04110", "hsa04110", "hsa04115"),
  gene_id = c("1", "2", "1"),
  stringsAsFactors = FALSE
)
kegg_frame <- KEGGFrame(kegg_data, organism = "Homo sapiens")
getKEGGFrameData(kegg_frame)

# Get available KEGG organisms
organisms <- organismKEGGFrame()
head(organisms)
```

--------------------------------

### Work with GOTerms objects

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

Create and access Gene Ontology term objects and query GO.db directly.

```r
library(GO.db)

# Create a GOTerms object
go_term <- new("GOTerms",
               GOID = "GO:0008150",
               Term = "biological_process",
               Ontology = "BP",
               Definition = "A biological process is...")

# Access slots
GOID(go_term)       # "GO:0008150"
Term(go_term)       # "biological_process"
Ontology(go_term)   # "BP"
Definition(go_term) # The definition text

# Work with GO.db directly
go_ids <- c("GO:0008150", "GO:0003674", "GO:0005575")

# Get terms for GO IDs
Term(go_ids)
# GO:0008150       GO:0003674       GO:0005575
# "biological_process" "molecular_function" "cellular_component"

# Get ontology categories
Ontology(go_ids)  # "BP" "MF" "CC"

# Get definitions
Definition(go_ids)

# Access GO term bimap
goterms <- as.list(GOTERM[go_ids])
```

--------------------------------

### select() with Different Keytype (SYMBOL)

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

Demonstrates using `select()` with `org.Hs.eg.db` to query annotation data using gene symbols as keys. Note that the 'GO' column can result in a one-to-many mapping, expanding the output rows.

```r
library(org.Hs.eg.db)

# Select with different keytype: lookup by gene symbol
symbols <- c("BRCA1", "BRCA2", "TP53")
result <- select(org.Hs.eg.db,
                 keys = symbols,
                 columns = c("ENTREZID", "GENENAME", "GO", "ENSEMBL"),
                 keytype = "SYMBOL")
# Note: GO column creates 1:many mapping, expanding rows
```

--------------------------------

### List Valid Key Types for Annotation Query

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

Use the `keytypes()` function with an organism database (e.g., `org.Hs.eg.db`) to identify all valid identifier types that can be used as keys in `select()` and `keys()` queries.

```r
library(org.Hs.eg.db)

# List valid keytypes
keytypes(org.Hs.eg.db)
# Returns: "ACCNUM" "ALIAS" "ENSEMBL" "ENSEMBLPROT" "ENSEMBLTRANS"
#          "ENTREZID" "ENZYME" "EVIDENCE" "EVIDENCEALL" "GENENAME"
```

--------------------------------

### List Columns for Organism Database

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

Use the `columns()` function with an organism database (e.g., `org.Hs.eg.db`) to retrieve a list of all available data columns that can be queried.

```r
library(org.Hs.eg.db)

# List columns for organism database
columns(org.Hs.eg.db)
# Returns: "ACCNUM" "ALIAS" "ENSEMBL" "ENSEMBLPROT" "ENSEMBLTRANS"
#          "ENTREZID" "ENZYME" "EVIDENCE" "EVIDENCEALL" "GENENAME"
#          "GENETYPE" "GO" "GOALL" "MAP" "OMIM" "ONTOLOGY" "ONTOLOGYALL"
#          "PATH" "PFAM" "PMID" "PROSITE" "REFSEQ" "SYMBOL" "UCSCKG" "UNIPROT"
```

--------------------------------

### Load Annotation Database with loadDb()

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

The loadDb() function loads a SQLite annotation database from a file path. The returned object type depends on the database metadata (OrgDb, ChipDb, TxDb, GODb).

```r
library(AnnotationDbi)

# Load a standalone SQLite database
db <- loadDb("/path/to/annotation.sqlite")

# Access connection and file info
dbconn(db)    # Get the database connection
dbfile(db)    # Get the path to the SQLite file
```

--------------------------------

### Metadata Discovery Methods

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

Methods to list available data columns and valid key types for a given annotation database.

```APIDOC
## columns(x)

### Description
Returns all available data types that can be retrieved from an annotation database.

## keytypes(x)

### Description
Returns the types of identifiers that can be used as keys in select() and keys() queries.

### Parameters
- **x** (Object) - Required - The annotation database object.
```

--------------------------------

### keys() - Retrieve Available Keys

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

Retrieves all valid keys of a specified keytype from an annotation database, with support for pattern filtering and column-based constraints.

```APIDOC
## keys(x, keytype, pattern, column, fuzzy)

### Description
Returns all valid keys of a specified keytype, with optional filtering by pattern or column.

### Parameters
- **x** (AnnotationDb) - Required - The annotation database object.
- **keytype** (character) - Optional - The type of key to retrieve (e.g., "SYMBOL", "ENTREZID").
- **pattern** (character) - Optional - A regular expression pattern to filter keys.
- **column** (character) - Optional - A column to filter by existence of values.
- **fuzzy** (logical) - Optional - Whether to use fuzzy matching for the pattern.

### Request Example
keys(org.Hs.eg.db, keytype = "SYMBOL", pattern = "BRCA")
```

--------------------------------

### GO Term Textual Label

Source: https://github.com/bioconductor/annotationdbi/blob/devel/inst/DBschemas/schemas_2.1/DataTypes.txt

Information about the textual representation of GO terms.

```APIDOC
## Textual label for the GO term

### Description
Provides the human-readable textual label for a Gene Ontology (GO) term.

### SQL Type
VARCHAR(255)

### Examples
- 'larval fat body development'
- 'age-dependent response to reactive oxygen species during chronological cell aging'

### Note
The maximum observed length for this field is 193 characters.
```

--------------------------------

### Manage Bimap keys and mapping direction

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

Methods for accessing source/target keys, filtering mapped entries, and reversing mapping direction.

```r
library(org.Hs.eg.db)

# Get left keys (source identifiers)
lkeys <- Lkeys(org.Hs.egSYMBOL)
head(lkeys)  # Entrez Gene IDs

# Get right keys (target identifiers)
rkeys <- Rkeys(org.Hs.egSYMBOL)
head(rkeys)  # Gene Symbols

# Get mapped keys only
mapped <- mappedkeys(org.Hs.egGO)

# Count mapped keys
count.mappedkeys(org.Hs.egGO)

# Get mapping direction
direction(org.Hs.egSYMBOL)  # 1 (L --> R)

# Reverse the mapping
rev_map <- revmap(org.Hs.egSYMBOL)
direction(rev_map)  # -1 (L <-- R)

# Now left keys are symbols
Lkeys(rev_map)[1:5]  # Gene symbols

# Subset bimap
subset_map <- org.Hs.egSYMBOL[c("1", "2", "10")]
```

--------------------------------

### GO Term Textual Definition

Source: https://github.com/bioconductor/annotationdbi/blob/devel/inst/DBschemas/schemas_2.1/DataTypes.txt

Details on the textual definition of GO terms.

```APIDOC
## Textual definition for the GO term

### Description
Provides the detailed textual definition for a Gene Ontology (GO) term.

### SQL Type
TEXT

### Note
This field can contain text of any length.
```

--------------------------------

### Retrieve Available Keys with keys()

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

Use the keys() method to retrieve valid keys from an AnnotationDb object. You can specify the keytype and filter by pattern or column values. Fuzzy matching is also supported.

```r
library(org.Hs.eg.db)

# Get all Entrez Gene IDs (default keytype)
all_genes <- keys(org.Hs.eg.db)
length(all_genes)  # Thousands of gene IDs

# Get keys of a specific type
symbols <- keys(org.Hs.eg.db, keytype = "SYMBOL")
head(symbols)  # Gene symbols

# Filter keys by pattern
brca_genes <- keys(org.Hs.eg.db, keytype = "SYMBOL", pattern = "BRCA")
# Returns: "BRCA1" "BRCA2" "BRCA1P1" etc.

# Filter by pattern with fuzzy matching

fuzzy_match <- keys(org.Hs.eg.db,
                    keytype = "SYMBOL",
                    pattern = "BRCA",
                    fuzzy = TRUE)

# Get keys where a specific column has values
genes_with_pathway <- keys(org.Hs.eg.db,
                           keytype = "ENTREZID",
                           column = "PATH")

# Combine pattern and column filtering
tp53_genes <- keys(org.Hs.eg.db,
                   keytype = "ENTREZID",
                   pattern = "TP53",
                   column = "SYMBOL")
```

--------------------------------

### Basic select() for Gene Symbols and Names

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

Use `select()` with `org.Hs.eg.db` to retrieve gene symbols and full names for a list of Entrez IDs. This demonstrates a basic query returning a data frame.

```r
library(org.Hs.eg.db)

# Basic select: get gene symbols and names for Entrez IDs
keys <- c("1", "2", "3", "10")
result <- select(org.Hs.eg.db,
                 keys = keys,
                 columns = c("SYMBOL", "GENENAME"),
                 keytype = "ENTREZID")
# Returns:
#   ENTREZID SYMBOL                                GENENAME
# 1        1   A1BG              alpha-1-B glycoprotein
# 2        2    A2M               alpha-2-macroglobulin
# 3        3  A2MP1 alpha-2-macroglobulin pseudogene 1
# 4       10   NAT2     N-acetyltransferase 2
```

--------------------------------

### mapIds() Handling Multiple Values as List

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

Demonstrates using `mapIds()` with `multiVals = "list"` to retrieve all matches for a given key when multiple values exist in the specified column.

```r
library(org.Hs.eg.db)

# Handle multiple values: return as list
alias <- mapIds(org.Hs.eg.db,
                  keys = genes,
                  column = "ALIAS",
                  keytype = "ENTREZID",
                  multiVals = "list")
```

--------------------------------

### GO Child-Parent Relationship Type

Source: https://github.com/bioconductor/annotationdbi/blob/devel/inst/DBschemas/schemas_2.1/DataTypes.txt

Describes the types of relationships between GO terms.

```APIDOC
## Type of GO child-parent relationship

### Description
Specifies the type of relationship between a child GO term and its parent GO term.

### SQL Type
VARCHAR(7)

### Possible Values
- 'isa'
- 'part_of'
```

--------------------------------

### select() - Extract Annotation Data

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

Retrieves annotation data as a data frame based on specified keys, columns, and keytype.

```APIDOC
## select(x, keys, columns, keytype)

### Description
Retrieves annotation data based on specified keys, columns, and keytype. Returns a data frame with one row per match, expanding rows for one-to-many relationships.

### Parameters
- **x** (Object) - Required - The annotation database object (e.g., OrgDb, ChipDb).
- **keys** (Character vector) - Required - The identifiers to query.
- **columns** (Character vector) - Required - The columns to return.
- **keytype** (String) - Required - The type of identifier provided in keys.

### Response
- **data.frame** - A data frame containing the requested columns for the provided keys.
```

--------------------------------

### toTable() - Convert Bimap to Data Frame

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

Converts a legacy Bimap object to a data frame representation showing all links between keys and values.

```APIDOC
## toTable(x)

### Description
Converts a Bimap object to a data frame showing all mappings.

### Parameters
- **x** (Bimap) - Required - The Bimap object to convert.

### Request Example
go_table <- toTable(org.Hs.egGO)
```

--------------------------------

### metadata() - Access Database Metadata

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

Retrieves metadata information about the annotation database, such as version, organism, and creation date.

```APIDOC
## metadata(x)

### Description
Returns a data frame containing database metadata including version, source, and creation date.

### Parameters
- **x** (AnnotationDb) - Required - The annotation database object.

### Request Example
meta <- metadata(org.Hs.eg.db)
```

--------------------------------

### GO Evidence Code

Source: https://github.com/bioconductor/annotationdbi/blob/devel/inst/DBschemas/schemas_2.1/DataTypes.txt

Reference for GO evidence codes used to indicate the basis for a GO annotation.

```APIDOC
## GO evidence code

### Description
Indicates the type of evidence supporting a Gene Ontology (GO) annotation.

### SQL Type
CHAR(3)

### Possible Values
- 'IC'
- 'IDA'
- 'IEA'
- 'IEP'
- 'IGC'
- 'IGI'
- 'IMP'
- 'IPI'
- 'ISS'
- 'NAS'
- 'ND'
- 'RCA'
- 'TAS'
- 'NR'

### Note
Refer to http://www.geneontology.org/GO.evidence.shtml for the meaning of each code.
```

--------------------------------

### Genomic Location and Ontology Fields

Source: https://github.com/bioconductor/annotationdbi/blob/devel/inst/DBschemas/schemas_2.1/DataTypes.txt

Details for cytoband locations and Gene Ontology (GO) terms.

```APIDOC
## Genomic Location and Ontology Fields

### Cytoband location
- **SQL type**: VARCHAR(20)
- **Examples**: '1p34.2', 'Yp11.32', '19q13.11-q13.12'
- **Note**: the maximum length observed so far is 15 but using VARCHAR(20) is a safety precaution for longer cytoband locations that could appear in the future

### GO ontology (short label)
- **SQL type**: VARCHAR(9)
- **Possible values**: 'universal', 'BP', 'CC', 'MF'
```

--------------------------------

### Convert Bimap to Named List with as.list()

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

Convert a Bimap object to a named list for easy programmatic access to mappings. You can convert all mappings or a subset, and use revmap for reverse lookups.

```r
library(org.Hs.eg.db)

# Convert to list (all mappings)
symbol_list <- as.list(org.Hs.egSYMBOL)
symbol_list[["1"]]   # "A1BG"
symbol_list[["10"]]  # "NAT2"

# Convert subset to list
subset_list <- as.list(org.Hs.egSYMBOL[c("1", "2", "10")])

# Use revmap for reverse lookup
symbol2eg <- revmap(org.Hs.egSYMBOL)
eg_list <- as.list(symbol2eg)
eg_list[["BRCA1"]]   # "672"
```

--------------------------------

### KEGG Pathway Short ID

Source: https://github.com/bioconductor/annotationdbi/blob/devel/inst/DBschemas/schemas_2.1/DataTypes.txt

Information on the short identifier for KEGG pathways.

```APIDOC
## KEGG pathway short ID

### Description
Provides the short identifier for a KEGG pathway.

### SQL Type
CHAR(5)

### Examples
- '00100'
- '05223'
- '07218'

### Note
The highest observed value for this ID is 07218.
```

--------------------------------

### Enzyme and Pathway Fields

Source: https://github.com/bioconductor/annotationdbi/blob/devel/inst/DBschemas/schemas_2.1/DataTypes.txt

Details for enzyme commission (EC) numbers and pathway names.

```APIDOC
## Enzyme and Pathway Fields

### EC number (no "EC:" prefix)
- **SQL type**: VARCHAR(13)
- **Examples**: '1.1.4.1', '3.2.1.14', '1.14.99.36'
- **Note**: the maximum length observed so far is 10 but using VARCHAR(13) is a safety precaution for longer EC numbers that could appear in the future

### EC number (with "EC:" prefix)
- **SQL type**: VARCHAR(16)
- **Examples**: 'EC:1.1.4.1', 'EC:3.2.1.14', 'EC:1.14.99.36'

### EC name
- **SQL type**: VARCHAR(255)
- **Examples**: 'lipase', 'photosystem I', '6-phosphofructokinase'
- **Note**: the maximum length observed so far is 99

### AraCyc pathway name
- **SQL type**: VARCHAR(255)
- **Examples**: 'SAM cycle', 'trans,trans-farnesyl diphosphate biosynthesis'
- **Note**: the maximum length observed so far is 77 but using VARCHAR(255) is a safety precaution for longer AraCyc pathways that could appear in the future
```

--------------------------------

### KEGG Pathway Name

Source: https://github.com/bioconductor/annotationdbi/blob/devel/inst/DBschemas/schemas_2.1/DataTypes.txt

Information about the common name for KEGG pathways.

```APIDOC
## KEGG pathway name

### Description
Provides the common name for a KEGG pathway.

### SQL Type
VARCHAR(80)

### Examples
- 'Butanoate metabolism'
- '3-Chloroacrylic acid degradation'

### Note
The maximum observed length for this field is 63 characters.
```

--------------------------------

### Save Annotation Database with saveDb()

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

The saveDb() method saves an AnnotationDb object to a new SQLite file. This is useful for creating portable annotation snapshots.

```r
library(org.Hs.eg.db)

# Save database to a new file
saveDb(org.Hs.eg.db, file = "my_annotation_copy.sqlite")
```

--------------------------------

### Supported Biological Identifiers

Source: https://github.com/bioconductor/annotationdbi/blob/devel/inst/DBschemas/schemas_2.1/DataTypes.txt

A list of biological identifiers and their associated database information.

```APIDOC
## Supported Biological Identifiers

This section details the various biological identifiers supported, including their SQL data types, example values, and any relevant notes.

### AC ID

*   **SQL type**: VARCHAR(12)
*   **Examples**: 'PF00389.21', 'PF02826.10', 'P63103.1'
*   **Note**: Central ID for PFAM.

### CAZY ID

*   **SQL type**: VARCHAR(6)
*   **Examples**: 'GT_57', 'GH_29', 'CBM_19'

### HOMSTRAD ID

*   **SQL type**: VARCHAR(20)
*   **Examples**: 'Dala_Dala_ligas_N', 'A2M_B', 'Haloperoxidase'

### INTERPRO ID

*   **SQL type**: VARCHAR(9)
*   **Examples**: 'IPR003391', 'IPR002612', 'IPR005830'

### LOAD ID

*   **SQL type**: VARCHAR(15)
*   **Examples**: 'Adrenomedullin', 'Adeno_PIX', 'BCMA-Tall_bind'

### MEROPS ID

*   **SQL type**: VARCHAR(3)
*   **Examples**: 'S10', 'M55', 'T1'

### MIM ID

*   **SQL type**: VARCHAR(6)
*   **Examples**: '104311', '128230', '222600'

### PDB ID

*   **SQL type**: VARCHAR(6)
*   **Examples**: '1u69 B', '1odn A', '2kfn A'

### start of alignment

*   **SQL type**: INTEGER
*   **Examples**: '3', '180', '256'

### end of alignment

*   **SQL type**: INTEGER
*   **Examples**: '288', '224', '334'

### PFAMB ID

*   **SQL type**: VARCHAR(8)
*   **Examples**: 'PB012689', 'PB177858', 'PB176422'
*   **Note**: Machine called PFAM IDs.

### PRINTS ID

*   **SQL type**: VARCHAR(7)
*   **Examples**: 'PR00178', 'PR01233', 'PR00543'

### PROSITE ID

*   **SQL type**: VARCHAR(9)
*   **Examples**: 'PDOC00403', 'PDOC00174', 'PDOC00578'

### PROSITE_PROFILE ID

*   **SQL type**: VARCHAR(7)
*   **Examples**: 'PS50032', 'PS50203', 'PS50119'

### RM ID

*   **SQL type**: VARCHAR(8)
*   **Examples**: '11011151', '2254264', '15531590'

### SCOP ID

*   **SQL type**: VARCHAR(4)
*   **Examples**: '1rla', '1by6', '3gcc'

### SCOP placement

*   **SQL type**: VARCHAR(2)
*   **Examples**: 'fa', 'fa', 'sf'
*   **Note**: Only 'fa' and 'sf' are observed.

### SMART ID

*   **SQL type**: VARCHAR(9)
*   **Examples**: 'ZnF_UBR1', 'RasGEFN', 'PLDc'

### TC ID

*   **SQL type**: VARCHAR(6)
*   **Examples**: '3.A.15', '9.B.33', '2.A.27'

### PFAM ID ID

*   **SQL type**: VARCHAR(15)
*   **Examples**: 'ADP_ribosyl_GH', 'Adeno_E1B_55K_N', 'AfaD'

### DE ID

*   **SQL type**: VARCHAR(80)
*   **Examples**: 'Adenovirus GP19K', 'ADP-specific Phosphofructokinase/Glucokinase conserved region'
*   **Note**: Description field, can be large.

### TP ID

*   **SQL type**: VARCHAR(6)
*   **Examples**: 'Repeat', 'Family', 'Domain', 'Motif'
*   **Note**: Limited to these four values.

### URL ID

*   **SQL type**: VARCHAR(80)
*   **Examples**: 'http://bioinformatics.weizmann.ac.il/hotmolecbase/entries/ps1.htm'

### Inparanoid ID

*   **SQL type**: VARCHAR(30)
*   **Examples**: 'ENSP00000351750', 'FBpp0073215', 'MGI:1274784'
*   **Note**: Corresponds to a gene or protein ID.

### Inparanoid Cluster ID

*   **SQL type**: INTEGER
*   **Examples**: '1', '2', '34'
*   **Note**: Groupings assigned by Inparanoid algorithm.

### Inparanoid Species ID

*   **SQL type**: VARCHAR(15)
*   **Examples**: 'ensHOMSA.fa', 'modMUSMU.fa', 'modDROME.fa'
*   **Note**: Indicates species, ID type, and table source.

### Inparanoid Score

*   **SQL type**: VARCHAR(20)
*   **Examples**: '1.0', '0.1513', '0.3578'
*   **Note**: Degree of belonging to the Inparanoid grouping.

### Inparanoid Seed Status

*   **SQL type**: VARCHAR(4)
*   **Examples**: '100%', '', '99%'
*   **Note**: 0-100%, '100%' indicates a seed paralog.

### Entrez Gene ID

*   **SQL type**: VARCHAR(10)
*   **Examples**: '10251', '283297', '100113407'

### Ensembl Gene ID

*   **SQL type**: VARCHAR(20)
*   **Examples**: 'ENSRNOG00000016924', 'ENSMUSG00000028125', 'ENSG00000127837'

### Ensembl Protein ID

*   **SQL type**: VARCHAR(20)
*   **Examples**: 'ENSP00000370606', 'ENSRNOP00000027'
*   **Note**: One peptide per ID.

### manufacturer ID

*   **SQL type**: VARCHAR(80)
*   **Examples**: '1000_at', '1002_f_at', 'AFFX-HUMISGF3A/M97935_MA_at'

### GenBank accession number

*   **SQL type**: VARCHAR(20)

```

--------------------------------

### Gene and Sequence Information Fields

Source: https://github.com/bioconductor/annotationdbi/blob/devel/inst/DBschemas/schemas_2.1/DataTypes.txt

Details for fields related to gene symbols, aliases, sequence names, and chromosome information.

```APIDOC
## Gene and Sequence Information Fields

### Gene symbol or alias
- **SQL type**: VARCHAR(80)
- **Examples**: 'NPEPPS', 'myr4', 'DKFZp434G0625PRO34003'
- **Note**: the maximum length observed so far is 21 but using VARCHAR(80) is a safety precaution for longer aliases that could appear in the future

### Sequence name
- **SQL type**: VARCHAR(20)
- **Examples**: '1', '22', 'X', 'Y', '6_cox_hap1', '22_random'

### Chromosome name
- **SQL type**: VARCHAR(2)
- **Examples**: '1', '22', 'X', 'Y', 'M', 'MT', 'Un'
- **Note**: a chromosome name is a particular sequence name

### Gene name
- **SQL type**: VARCHAR(255)
- **Examples**: 'deoxyribonuclease I-like 1', 'vitrin'
- **Note**: the maximum length observed so far is 251

### Yeast gene name
- **SQL type**: VARCHAR(14)
- **Examples**: 'ADK2', 'TMA23', '21S_RRNA_4', 'MF(ALPHA)1'
- **Note**: the maximum length observed so far is 10 but using VARCHAR(14) is a safety precaution for longer Yeast gene names that could appear in the future

### Yeast gene alias
- **SQL type**: VARCHAR(13)
- **Examples**: 'CDH1', 'DNA33', 'EF-1 alpha', 'ATPEPSILON'
- **Note**: the maximum length observed so far is 10 but using VARCHAR(13) is a safety precaution for longer Yeast gene aliases that could appear in the future

### Arabidopsis chromosome
- **SQL type**: CHAR(1)
- **Possible values**: '1', '2', '3', '4', '5', 'C', 'M'
```

--------------------------------

### mapIds() Handling Multiple Values as CharacterList

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

Use `mapIds()` with `multiVals = "CharacterList"` to retrieve all matches for a given key, returning the results as a `CharacterList` object.

```r
library(org.Hs.eg.db)

# Handle multiple values: return CharacterList
go_terms <- mapIds(org.Hs.eg.db,
                   keys = genes,
                   column = "GO",
                   keytype = "ENTREZID",
                   multiVals = "CharacterList")
```

--------------------------------

### Basic mapIds() for Gene Symbols

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

Use `mapIds()` with `org.Hs.eg.db` to retrieve a single column of annotation data (e.g., 'SYMBOL') as a named character vector. By default, it returns the first match if multiple exist.

```r
library(org.Hs.eg.db)

# Basic mapIds: get first match (default)
genes <- c("1", "2", "10", "100")
symbols <- mapIds(org.Hs.eg.db,
                  keys = genes,
                  column = "SYMBOL",
                  keytype = "ENTREZID")
# Returns named character vector:
# c("1" = "A1BG", "2" = "A2M", "10" = "NAT2", "100" = "ADA")
```

--------------------------------

### mapIds() Handling Multiple Values as NA

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

Configure `mapIds()` with `multiVals = "asNA"` to return `NA` for any key that has multiple matches in the specified column. This is useful for strict, single-value lookups.

```r
library(org.Hs.eg.db)

# Handle multiple values: return NA for multi-match keys
symbols_strict <- mapIds(org.Hs.eg.db,
                         keys = genes,
                         column = "ALIAS",
                         keytype = "ENTREZID",
                         multiVals = "asNA")
```

--------------------------------

### GO ID

Source: https://github.com/bioconductor/annotationdbi/blob/devel/inst/DBschemas/schemas_2.1/DataTypes.txt

Details on the format and possible values for Gene Ontology (GO) identifiers.

```APIDOC
## GO ID

### Description
Represents the Gene Ontology (GO) identifier.

### SQL Type
CHAR(10)

### Examples
- 'all'
- 'GO:0000001'
- 'GO:0016491'

### Note
Except for 'all', identifiers are typically in the format 'GO:1234567'.
```

--------------------------------

### KEGG Pathway Long ID

Source: https://github.com/bioconductor/annotationdbi/blob/devel/inst/DBschemas/schemas_2.1/DataTypes.txt

Details on the long identifier for KEGG pathways.

```APIDOC
## KEGG pathway long ID

### Description
Provides the longer, more descriptive identifier for a KEGG pathway.

### SQL Type
CHAR(8)

### Examples
- 'hsa00680'
- 'rno05220'
- 'ath00120'
- 'dme00910'
```

--------------------------------

### Accession Number Fields

Source: https://github.com/bioconductor/annotationdbi/blob/devel/inst/DBschemas/schemas_2.1/DataTypes.txt

Details for various accession number formats used in biological databases.

```APIDOC
## Accession Number Fields

### RefSeq accession number
- **SQL type**: VARCHAR(20)
- **Examples**: 'NM_018009', 'NP_001035700'
- **Note**: RefSeq accession numbers seem to be valid GenBank accession numbers

### IPI accession number
- **SQL type**: CHAR(11)
- **Examples**: 'IPI00328276', 'IPI00789644'

### Pfam ID
- **SQL type**: CHAR(7)
- **Examples**: 'PF00069', 'PF08266'

### PROSITE ID
- **SQL type**: CHAR(7)
- **Examples**: 'PS00107', 'PS00657'

### PubMed ID
- **SQL type**: VARCHAR(10)
- **Examples**: '2437', '2583089', '17652175'
- **Note**: highest value observed so far is 17652175

### UniGene ID
- **SQL type**: VARCHAR(10)
- **Examples**: 'Hs.2', 'Hs.511848', 'Hs.695912'
- **Note**: highest value observed so far for Human is Hs.695912

### FlyBase ID
- **SQL type**: CHAR(11)
- **Examples**: 'FBgn0001942', 'FBgn0030936', 'FBgn0051992'

### FlyBase CG ID
- **SQL type**: CHAR(10)
- **Examples**: 'CG3038', 'CG13377', 'CG2945'

### Flybase Protein ID
- **SQL type**: VARCHAR(20)
- **Examples**: 'FBpp0073215', 'FBpp0110310', 'FBpp0071474'
- **Note**: Flybase protein IDs, one peptide per ID.

### Yeast ORF ID
- **SQL type**: VARCHAR(14)
- **Examples**: 'YPL141C', 'YGRWsigma7', 'YPRWdelta14'
- **Note**: the maximum length observed so far is 11 but using VARCHAR(14) is a safety precaution for longer Yeast ORF IDs that could appear in the future

### SGD ID
- **SQL type**: CHAR(10)
- **Examples**: 'S000004794', 'S000037040', 'S000123281'
- **Note**: highest value observed so far is S000123281

### InterPro ID
- **SQL type**: CHAR(9)
- **Examples**: 'IPR001440', 'IPR008688', 'IPR015809'
- **Note**: highest value observed so far is IPR015809

### SMART ID
- **SQL type**: CHAR(7)
- **Examples**: 'SM00055', 'SM00220', 'SM00717'

### AGI locus ID
- **SQL type**: CHAR(9)
- **Examples**: 'AT1G01010', 'ATCG00830', 'ATMG01410'
```

--------------------------------

### Other Identifier Fields

Source: https://github.com/bioconductor/annotationdbi/blob/devel/inst/DBschemas/schemas_2.1/DataTypes.txt

Details for other specific biological identifiers.

```APIDOC
## Other Identifier Fields

### OMIM ID
- **SQL type**: CHAR(6)
- **Examples**: '231550', '601421', '611258'
- **Note**: highest value observed so far is 611258

### Yeast feature description
- **SQL type**: TEXT
- **Note**: this can be a text of any length
```

--------------------------------

### mapIds() - Extract Single Column

Source: https://context7.com/bioconductor/annotationdbi/llms.txt

Extracts a single column of data as a named character vector, with configurable handling for multiple matches.

```APIDOC
## mapIds(x, keys, column, keytype, multiVals)

### Description
Extracts a single column of data as a named character vector. Provides options for handling multiple matches per key via the multiVals argument.

### Parameters
- **x** (Object) - Required - The annotation database object.
- **keys** (Character vector) - Required - The identifiers to query.
- **column** (String) - Required - The column to extract.
- **keytype** (String) - Required - The type of identifier provided in keys.
- **multiVals** (String/Function) - Optional - Strategy for handling multiple matches: 'first' (default), 'list', 'CharacterList', 'asNA', or a custom function.

### Response
- **vector/list** - A named vector or list containing the mapped values.
```

--------------------------------

### Entrez Gene or ORF ID

Source: https://github.com/bioconductor/annotationdbi/blob/devel/inst/DBschemas/schemas_2.1/DataTypes.txt

Details on identifiers for Entrez Genes or Open Reading Frames (ORFs).

```APIDOC
## Entrez Gene or ORF ID

### Description
Represents an identifier for an Entrez Gene or an Open Reading Frame (ORF).

### SQL Type
VARCHAR(20)

### Examples
- 'YDR156W'
- 'AT4G15280'
- 'Dmel_CG10045'
- [Entrez Gene ID]

### Note
The maximum observed length for this identifier is 12 characters.
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.