Organism.dplyr
1 November 2023
Contents
1 Introduction
The Organism.dplyr creates an on disk sqlite database to hold data of an organism combined from an ‘org’ package (e.g., org.Hs.eg.db) and a genome coordinate functionality of the ‘TxDb’ package (e.g., TxDb.Hsapiens.UCSC.hg38.knownGene). It aims to provide an integrated presentation of identifiers and genomic coordinates. And a src_organism object is created to point to the database.
The src_organism object is created as an extension of src_sql and
src_sqlite from dplyr, which inherited all dplyr methods. It also
implements the select() interface from AnnotationDbi and
genomic coordinates extractors from GenomicFeatures.
2 Constructing a src_organism
2.1 Make sqlite datebase from ‘TxDb’ package
The src_organism() constructor creates an on disk sqlite database file with
data from a given ‘TxDb’ package and corresponding ‘org’ package. When dbpath
is given, file is created at the given path, otherwise temporary file is
created.
library(Organism.dplyr)Running src_organism() without a given path will save the sqlite file to a
tempdir():
src <- src_organism("TxDb.Hsapiens.UCSC.hg38.knownGene")Alternatively you can provide explicit path to where the sqlite file should be saved, and re-use the data base at a later date.
path <- "path/to/my.sqlite"
src <- src_organism("TxDb.Hsapiens.UCSC.hg38.knownGene", path)supportedOrganisms() provides a list of organisms with corresponding ‘org’
and ‘TxDb’ packages being supported.
supportedOrganisms()## # A tibble: 21 × 3
## organism OrgDb TxDb
## <chr> <chr> <chr>
## 1 Bos taurus org.Bt.eg.db TxDb.Btaurus.UCSC.bosTau8.refGene
## 2 Caenorhabditis elegans org.Ce.eg.db TxDb.Celegans.UCSC.ce11.refGene
## 3 Caenorhabditis elegans org.Ce.eg.db TxDb.Celegans.UCSC.ce6.ensGene
## 4 Canis familiaris org.Cf.eg.db TxDb.Cfamiliaris.UCSC.canFam3.refGene
## 5 Drosophila melanogaster org.Dm.eg.db TxDb.Dmelanogaster.UCSC.dm3.ensGene
## 6 Drosophila melanogaster org.Dm.eg.db TxDb.Dmelanogaster.UCSC.dm6.ensGene
## 7 Danio rerio org.Dr.eg.db TxDb.Drerio.UCSC.danRer10.refGene
## 8 Gallus gallus org.Gg.eg.db TxDb.Ggallus.UCSC.galGal4.refGene
## 9 Homo sapiens org.Hs.eg.db TxDb.Hsapiens.UCSC.hg18.knownGene
## 10 Homo sapiens org.Hs.eg.db TxDb.Hsapiens.UCSC.hg19.knownGene
## # ℹ 11 more rows2.2 Make sqlite datebase from organism name
Organism name, genome and id could be specified to create sqlite database. Organism name (either Organism or common name) must be provided to create the database, if genome and/or id are not provided, most recent ‘TxDb’ package is used.
src <- src_ucsc("human", path)2.3 Access existing sqlite file
An existing on-disk sqlite file can be accessed without recreating the database. A version of the database created with TxDb.Hsapiens.UCSC.hg38.knownGene, with just 50 Entrez gene identifiers, is distributed with the Organism.dplyr package
src <- src_organism(dbpath = hg38light())
src## src: sqlite 3.42.0 [/tmp/Rtmp6bo2Uu/Rinst33202ce404580/Organism.dplyr/extdata/light.hg38.knownGene.sqlite]
## tbls: id, id_accession, id_go, id_go_all, id_omim_pm, id_protein,
## id_transcript, ranges_cds, ranges_exon, ranges_gene, ranges_tx3 The “dplyr” interface
All methods from package dplyr can be used for a src_organism object.
Look at all available tables.
src_tbls(src)## [1] "id_accession" "id_transcript" "id" "id_omim_pm"
## [5] "id_protein" "id_go" "id_go_all" "ranges_gene"
## [9] "ranges_tx" "ranges_exon" "ranges_cds"Look at data from one specific table.
tbl(src, "id")## # Source: table<id> [?? x 6]
## # Database: sqlite 3.42.0 []
## entrez map ensembl symbol genename alias
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 1 19q13.43 ENSG00000121410 A1BG alpha-1-B glycoprotein A1B
## 2 1 19q13.43 ENSG00000121410 A1BG alpha-1-B glycoprotein ABG
## 3 1 19q13.43 ENSG00000121410 A1BG alpha-1-B glycoprotein GAB
## 4 1 19q13.43 ENSG00000121410 A1BG alpha-1-B glycoprotein HYST2477
## 5 1 19q13.43 ENSG00000121410 A1BG alpha-1-B glycoprotein A1BG
## 6 10 8p22 ENSG00000156006 NAT2 N-acetyltransferase 2 AAC2
## 7 10 8p22 ENSG00000156006 NAT2 N-acetyltransferase 2 NAT-2
## 8 10 8p22 ENSG00000156006 NAT2 N-acetyltransferase 2 PNAT
## 9 10 8p22 ENSG00000156006 NAT2 N-acetyltransferase 2 NAT2
## 10 100 20q13.12 ENSG00000196839 ADA adenosine deaminase ADA1
## # ℹ more rowsLook at fields of one table.
colnames(tbl(src, "id"))## [1] "entrez" "map" "ensembl" "symbol" "genename" "alias"Below are some examples of querying tables using dplyr.
- Gene symbol starting with “SNORD” (the notation
SNORD%is from SQL, with%representing a wild-card match to any string)
tbl(src, "id") %>%
filter(symbol %like% "SNORD%") %>%
dplyr::select(entrez, map, ensembl, symbol) %>%
distinct() %>% arrange(symbol) %>% collect()## # A tibble: 8 × 4
## entrez map ensembl symbol
## <chr> <chr> <chr> <chr>
## 1 100033413 15q11.2 ENSG00000207063 SNORD116-1
## 2 100033414 15q11.2 ENSG00000207001 SNORD116-2
## 3 100033415 15q11.2 ENSG00000207014 SNORD116-3
## 4 100033416 15q11.2 ENSG00000275529 SNORD116-4
## 5 100033417 15q11.2 ENSG00000207191 SNORD116-5
## 6 100033418 15q11.2 ENSG00000207442 SNORD116-6
## 7 100033419 15q11.2 ENSG00000207133 SNORD116-7
## 8 100033420 15q11.2 ENSG00000207093 SNORD116-8- Gene ontology (GO) info for gene symbol “ADA”
inner_join(tbl(src, "id"), tbl(src, "id_go")) %>%
filter(symbol == "ADA") %>%
dplyr::select(entrez, ensembl, symbol, go, evidence, ontology)## Joining with `by = join_by(entrez)`## # Source: SQL [?? x 6]
## # Database: sqlite 3.42.0 []
## entrez ensembl symbol go evidence ontology
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 100 ENSG00000196839 ADA GO:0000255 IEA BP
## 2 100 ENSG00000196839 ADA GO:0001666 IDA BP
## 3 100 ENSG00000196839 ADA GO:0001829 IEA BP
## 4 100 ENSG00000196839 ADA GO:0001889 IEA BP
## 5 100 ENSG00000196839 ADA GO:0001890 IEA BP
## 6 100 ENSG00000196839 ADA GO:0002314 IEA BP
## 7 100 ENSG00000196839 ADA GO:0002467 IEA BP
## 8 100 ENSG00000196839 ADA GO:0002636 IEA BP
## 9 100 ENSG00000196839 ADA GO:0002686 IEA BP
## 10 100 ENSG00000196839 ADA GO:0002901 IEA BP
## # ℹ more rows- Gene transcript counts per gene symbol
txcount <- inner_join(tbl(src, "id"), tbl(src, "ranges_tx")) %>%
dplyr::select(symbol, tx_id) %>%
group_by(symbol) %>%
summarize(count = n()) %>%
dplyr::select(symbol, count) %>%
arrange(desc(count)) %>%
collect(n=Inf)## Joining with `by = join_by(entrez)`txcount## # A tibble: 23 × 2
## symbol count
## <chr> <int>
## 1 AKT3 540
## 2 NAALAD2 160
## 3 ADA 122
## 4 CDH2 88
## 5 A1BG 40
## 6 MED6 39
## 7 NR2E3 18
## 8 GAGE12F 16
## 9 NAT2 12
## 10 LINC02584 8
## # ℹ 13 more rowslibrary(ggplot2)
ggplot(txcount, aes(x = symbol, y = count)) +
geom_bar(stat="identity") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
ggtitle("Transcript count") +
labs(x = "Symbol") +
labs(y = "Count")
- Gene coordinates of symbol “ADA” and “NAT2” as GRanges
inner_join(tbl(src, "id"), tbl(src, "ranges_gene")) %>%
filter(symbol %in% c("ADA", "NAT2")) %>%
dplyr::select(gene_chrom, gene_start, gene_end, gene_strand,
symbol, map) %>%
collect() %>% GenomicRanges::GRanges()## Joining with `by = join_by(entrez)`## GRanges object with 6 ranges and 2 metadata columns:
## seqnames ranges strand | symbol map
## <Rle> <IRanges> <Rle> | <character> <character>
## [1] chr8 18386311-18401218 + | NAT2 8p22
## [2] chr8 18386311-18401218 + | NAT2 8p22
## [3] chr8 18386311-18401218 + | NAT2 8p22
## [4] chr8 18386311-18401218 + | NAT2 8p22
## [5] chr20 44584896-44652252 - | ADA 20q13.12
## [6] chr20 44584896-44652252 - | ADA 20q13.12
## -------
## seqinfo: 2 sequences from an unspecified genome; no seqlengths4 The “select” interface
Methods select(), keytypes(), keys(), columns() and mapIds from
AnnotationDbi are implemented for src_organism objects.
Use keytypes() to discover which keytypes can be passed to keytype argument
of methods select() or keys().
keytypes(src)## [1] "accnum" "alias" "cds_chrom" "cds_end" "cds_id"
## [6] "cds_name" "cds_start" "cds_strand" "ensembl" "ensemblprot"
## [11] "ensembltrans" "entrez" "enzyme" "evidence" "evidenceall"
## [16] "exon_chrom" "exon_end" "exon_id" "exon_name" "exon_rank"
## [21] "exon_start" "exon_strand" "gene_chrom" "gene_end" "gene_start"
## [26] "gene_strand" "genename" "go" "goall" "ipi"
## [31] "map" "omim" "ontology" "ontologyall" "pfam"
## [36] "pmid" "prosite" "refseq" "symbol" "tx_chrom"
## [41] "tx_end" "tx_id" "tx_name" "tx_start" "tx_strand"
## [46] "tx_type" "uniprot"Use columns() to discover which kinds of data can be returned for the
src_organism object.
columns(src)## [1] "accnum" "alias" "cds_chrom" "cds_end" "cds_id"
## [6] "cds_name" "cds_start" "cds_strand" "ensembl" "ensemblprot"
## [11] "ensembltrans" "entrez" "enzyme" "evidence" "evidenceall"
## [16] "exon_chrom" "exon_end" "exon_id" "exon_name" "exon_rank"
## [21] "exon_start" "exon_strand" "gene_chrom" "gene_end" "gene_start"
## [26] "gene_strand" "genename" "go" "goall" "ipi"
## [31] "map" "omim" "ontology" "ontologyall" "pfam"
## [36] "pmid" "prosite" "refseq" "symbol" "tx_chrom"
## [41] "tx_end" "tx_id" "tx_name" "tx_start" "tx_strand"
## [46] "tx_type" "uniprot"keys() returns keys for the src_organism object. By default it returns the
primary keys for the database, and returns the keys from that keytype when the
keytype argument is used.
Keys of entrez
head(keys(src))## [1] "1" "10" "100" "1000" "10000" "100008586"Keys of symbol
head(keys(src, "symbol"))## [1] "A1BG" "NAT2" "ADA" "CDH2" "AKT3" "GAGE12F"select() retrieves the data as a tibble based on parameters for selected
keys columns and keytype arguments. If requested columns that have multiple
matches for the keys, select_tbl() will return a tibble with one row for
each possible match, and select() will return a data frame.
keytype <- "symbol"
keys <- c("ADA", "NAT2")
columns <- c("entrez", "tx_id", "tx_name","exon_id")
select_tbl(src, keys, columns, keytype)## Joining with `by = join_by(entrez)`## # Source: SQL [?? x 5]
## # Database: sqlite 3.42.0 []
## symbol entrez tx_id tx_name exon_id
## <chr> <chr> <int> <chr> <int>
## 1 NAT2 10 104459 ENST00000660285.1 276118
## 2 NAT2 10 104459 ENST00000660285.1 276119
## 3 NAT2 10 104459 ENST00000660285.1 276122
## 4 NAT2 10 104461 ENST00000286479.4 276123
## 5 NAT2 10 104461 ENST00000286479.4 276125
## 6 NAT2 10 104462 ENST00000520116.1 276124
## 7 NAT2 10 104462 ENST00000520116.1 276126
## 8 NAT2 10 104459 ENST00000660285.1 276118
## 9 NAT2 10 104459 ENST00000660285.1 276119
## 10 NAT2 10 104459 ENST00000660285.1 276122
## # ℹ more rowsmapIds() gets the mapped ids (column) for a set of keys that are of a
particular keytype. Usually returned as a named character vector.
mapIds(src, keys, column = "tx_name", keytype)## Joining with `by = join_by(entrez)`## ADA NAT2
## "ENST00000372874.9" "ENST00000286479.4"mapIds(src, keys, column = "tx_name", keytype, multiVals="CharacterList")## Joining with `by = join_by(entrez)`## CharacterList of length 2
## [["ADA"]] ENST00000372874.9 ENST00000464097.5 ... ENST00000696105.1
## [["NAT2"]] ENST00000286479.4 ENST00000520116.1 ... ENST00000660285.15 Genomic Coordinates Extractor Interfaces
Eleven genomic coordinates extractor methods are available in this
package: transcripts(), exons(), cds(), genes(),
promoters(), transcriptsBy(), exonsBy(), cdsBy(),
intronsByTranscript(), fiveUTRsByTranscript(),
threeUTRsByTranscript(). Data can be returned in two versions, for
instance tibble (transcripts_tbl()) and GRanges or GRangesList
(transcripts()).
Filters can be applied to all extractor functions. The output can be resctricted
by an AnnotationFilter, an AnnotationFilterList, or an expression that can
be tranlated into an AnnotationFilterList. Valid filters can be retrieved by
supportedFilters(src).
supportedFilters(src)## filter field
## 1 AccnumFilter accnum
## 2 AliasFilter alias
## 3 CdsChromFilter cds_chrom
## 44 CdsEndFilter cds_end
## 42 CdsIdFilter cds_id
## 4 CdsNameFilter cds_name
## 43 CdsStartFilter cds_start
## 5 CdsStrandFilter cds_strand
## 6 EnsemblFilter ensembl
## 7 EnsemblprotFilter ensemblprot
## 8 EnsembltransFilter ensembltrans
## 9 EntrezFilter entrez
## 10 EnzymeFilter enzyme
## 11 EvidenceFilter evidence
## 12 EvidenceallFilter evidenceall
## 13 ExonChromFilter exon_chrom
## 47 ExonEndFilter exon_end
## 45 ExonIdFilter exon_id
## 14 ExonNameFilter exon_name
## 48 ExonRankFilter exon_rank
## 46 ExonStartFilter exon_start
## 15 ExonStrandFilter exon_strand
## 17 FlybaseCgFilter flybase_cg
## 16 FlybaseFilter flybase
## 18 FlybaseProtFilter flybase_prot
## 54 GRangesFilter granges
## 19 GeneChromFilter gene_chrom
## 50 GeneEndFilter gene_end
## 49 GeneStartFilter gene_start
## 20 GeneStrandFilter gene_strand
## 21 GenenameFilter genename
## 22 GoFilter go
## 23 GoallFilter goall
## 24 IpiFilter ipi
## 25 MapFilter map
## 26 MgiFilter mgi
## 27 OmimFilter omim
## 28 OntologyFilter ontology
## 29 OntologyallFilter ontologyall
## 30 PfamFilter pfam
## 31 PmidFilter pmid
## 32 PrositeFilter prosite
## 33 RefseqFilter refseq
## 34 SymbolFilter symbol
## 35 TxChromFilter tx_chrom
## 53 TxEndFilter tx_end
## 51 TxIdFilter tx_id
## 36 TxNameFilter tx_name
## 52 TxStartFilter tx_start
## 37 TxStrandFilter tx_strand
## 38 TxTypeFilter tx_type
## 39 UniprotFilter uniprot
## 40 WormbaseFilter wormbase
## 41 ZfinFilter zfinAll filters take two parameters: value and condition, condition could be one of “==”, “!=”, “startsWith”, “endsWith”, “>”, “<”, “>=” and “<=”, default condition is “==”.
EnsemblFilter("ENSG00000196839")## class: EnsemblFilter
## condition: ==
## value: ENSG00000196839SymbolFilter("SNORD", "startsWith")## class: SymbolFilter
## condition: startsWith
## value: SNORDThe following illustrates several ways of inputting filters to an extractor function.
smbl <- SymbolFilter("SNORD", "startsWith")
transcripts_tbl(src, filter=smbl)## # A tibble: 8 × 7
## tx_chrom tx_start tx_end tx_strand tx_id tx_name symbol
## <chr> <int> <int> <chr> <int> <chr> <chr>
## 1 chr15 25051477 25051571 + 174763 ENST00000384335.1 SNORD116-1
## 2 chr15 25054210 25054304 + 174767 ENST00000384274.1 SNORD116-2
## 3 chr15 25056860 25056954 + 174768 ENST00000384287.1 SNORD116-3
## 4 chr15 25059538 25059633 + 174769 ENST00000384733.1 SNORD116-4
## 5 chr15 25062333 25062427 + 174771 ENST00000384462.1 SNORD116-5
## 6 chr15 25065026 25065121 + 174774 ENST00000384711.1 SNORD116-6
## 7 chr15 25067788 25067882 + 174782 ENST00000384404.1 SNORD116-7
## 8 chr15 25070432 25070526 + 174784 ENST00000384365.1 SNORD116-8filter <- AnnotationFilterList(smbl)
transcripts_tbl(src, filter=filter)## # A tibble: 8 × 7
## tx_chrom tx_start tx_end tx_strand tx_id tx_name symbol
## <chr> <int> <int> <chr> <int> <chr> <chr>
## 1 chr15 25051477 25051571 + 174763 ENST00000384335.1 SNORD116-1
## 2 chr15 25054210 25054304 + 174767 ENST00000384274.1 SNORD116-2
## 3 chr15 25056860 25056954 + 174768 ENST00000384287.1 SNORD116-3
## 4 chr15 25059538 25059633 + 174769 ENST00000384733.1 SNORD116-4
## 5 chr15 25062333 25062427 + 174771 ENST00000384462.1 SNORD116-5
## 6 chr15 25065026 25065121 + 174774 ENST00000384711.1 SNORD116-6
## 7 chr15 25067788 25067882 + 174782 ENST00000384404.1 SNORD116-7
## 8 chr15 25070432 25070526 + 174784 ENST00000384365.1 SNORD116-8transcripts_tbl(src, filter=~smbl) ## # A tibble: 8 × 7
## tx_chrom tx_start tx_end tx_strand tx_id tx_name symbol
## <chr> <int> <int> <chr> <int> <chr> <chr>
## 1 chr15 25051477 25051571 + 174763 ENST00000384335.1 SNORD116-1
## 2 chr15 25054210 25054304 + 174767 ENST00000384274.1 SNORD116-2
## 3 chr15 25056860 25056954 + 174768 ENST00000384287.1 SNORD116-3
## 4 chr15 25059538 25059633 + 174769 ENST00000384733.1 SNORD116-4
## 5 chr15 25062333 25062427 + 174771 ENST00000384462.1 SNORD116-5
## 6 chr15 25065026 25065121 + 174774 ENST00000384711.1 SNORD116-6
## 7 chr15 25067788 25067882 + 174782 ENST00000384404.1 SNORD116-7
## 8 chr15 25070432 25070526 + 174784 ENST00000384365.1 SNORD116-8A GRangesFilter() can also be used as a filter for the methods with
result displaying as GRanges or GRangesList.
gr <- GRangesFilter(GenomicRanges::GRanges("chr15:25062333-25065121"))
transcripts(src, filter=~smbl & gr)## GRanges object with 2 ranges and 3 metadata columns:
## seqnames ranges strand | tx_id tx_name
## <Rle> <IRanges> <Rle> | <integer> <character>
## [1] chr15 25062333-25062427 + | 174771 ENST00000384462.1
## [2] chr15 25065026-25065121 + | 174774 ENST00000384711.1
## symbol
## <character>
## [1] SNORD116-5
## [2] SNORD116-6
## -------
## seqinfo: 711 sequences (1 circular) from hg38 genomeFilters in extractor functions support &, |, !(negation), and
()(grouping). Transcript coordinates of gene symbol equal to “ADA” and
transcript start position less than 44619810.
transcripts_tbl(src, filter = AnnotationFilterList(
SymbolFilter("ADA"),
TxStartFilter(44619810,"<"),
logicOp="&")
)## # A tibble: 53 × 7
## tx_chrom tx_start tx_end tx_strand tx_id tx_name symbol
## <chr> <int> <int> <chr> <int> <chr> <chr>
## 1 chr20 44584896 44651702 - 234115 ENST00000696034.1 ADA
## 2 chr20 44618605 44651745 - 234116 ENST00000537820.2 ADA
## 3 chr20 44618618 44651699 - 234117 ENST00000696003.1 ADA
## 4 chr20 44618625 44651699 - 234118 ENST00000696004.1 ADA
## 5 chr20 44619521 44651678 - 234119 ENST00000695991.1 ADA
## 6 chr20 44619522 44621147 - 234120 ENST00000695956.1 ADA
## 7 chr20 44619522 44621347 - 234121 ENST00000696072.1 ADA
## 8 chr20 44619522 44623919 - 234122 ENST00000696073.1 ADA
## 9 chr20 44619522 44624329 - 234123 ENST00000696005.1 ADA
## 10 chr20 44619522 44624423 - 234124 ENST00000696074.1 ADA
## # ℹ 43 more rows## Equivalent to
transcripts_tbl(src, filter = ~symbol == "ADA" & tx_start < 44619810)## # A tibble: 53 × 7
## tx_chrom tx_start tx_end tx_strand tx_id tx_name symbol
## <chr> <int> <int> <chr> <int> <chr> <chr>
## 1 chr20 44584896 44651702 - 234115 ENST00000696034.1 ADA
## 2 chr20 44618605 44651745 - 234116 ENST00000537820.2 ADA
## 3 chr20 44618618 44651699 - 234117 ENST00000696003.1 ADA
## 4 chr20 44618625 44651699 - 234118 ENST00000696004.1 ADA
## 5 chr20 44619521 44651678 - 234119 ENST00000695991.1 ADA
## 6 chr20 44619522 44621147 - 234120 ENST00000695956.1 ADA
## 7 chr20 44619522 44621347 - 234121 ENST00000696072.1 ADA
## 8 chr20 44619522 44623919 - 234122 ENST00000696073.1 ADA
## 9 chr20 44619522 44624329 - 234123 ENST00000696005.1 ADA
## 10 chr20 44619522 44624423 - 234124 ENST00000696074.1 ADA
## # ℹ 43 more rowsTranscripts coordinates of gene symbol equal to “ADA” or transcript end position equal to 243843236.
txend <- TxEndFilter(243843236, '==')
transcripts_tbl(src, filter = ~symbol == "ADA" | txend)## # A tibble: 62 × 7
## tx_chrom tx_start tx_end tx_strand tx_id tx_name symbol
## <chr> <int> <int> <chr> <int> <chr> <chr>
## 1 chr1 243488233 243843236 - 22225 ENST00000336199.9 AKT3
## 2 chr20 44584896 44651702 - 234115 ENST00000696034.1 ADA
## 3 chr20 44618605 44651745 - 234116 ENST00000537820.2 ADA
## 4 chr20 44618618 44651699 - 234117 ENST00000696003.1 ADA
## 5 chr20 44618625 44651699 - 234118 ENST00000696004.1 ADA
## 6 chr20 44619521 44651678 - 234119 ENST00000695991.1 ADA
## 7 chr20 44619522 44621147 - 234120 ENST00000695956.1 ADA
## 8 chr20 44619522 44621347 - 234121 ENST00000696072.1 ADA
## 9 chr20 44619522 44623919 - 234122 ENST00000696073.1 ADA
## 10 chr20 44619522 44624329 - 234123 ENST00000696005.1 ADA
## # ℹ 52 more rowsUsing negation to find transcript coordinates of gene symbol equal to “ADA” and transcript start positions NOT less than 44619810.
transcripts_tbl(src, filter = ~symbol == "ADA" & !tx_start < 44618910)## # A tibble: 57 × 7
## tx_chrom tx_start tx_end tx_strand tx_id tx_name symbol
## <chr> <int> <int> <chr> <int> <chr> <chr>
## 1 chr20 44619521 44651678 - 234119 ENST00000695991.1 ADA
## 2 chr20 44619522 44621147 - 234120 ENST00000695956.1 ADA
## 3 chr20 44619522 44621347 - 234121 ENST00000696072.1 ADA
## 4 chr20 44619522 44623919 - 234122 ENST00000696073.1 ADA
## 5 chr20 44619522 44624329 - 234123 ENST00000696005.1 ADA
## 6 chr20 44619522 44624423 - 234124 ENST00000696074.1 ADA
## 7 chr20 44619522 44626491 - 234125 ENST00000464097.5 ADA
## 8 chr20 44619522 44626927 - 234126 ENST00000696035.1 ADA
## 9 chr20 44619522 44627507 - 234127 ENST00000696036.1 ADA
## 10 chr20 44619522 44628494 - 234128 ENST00000696037.1 ADA
## # ℹ 47 more rowsUsing grouping to find transcript coordinates of a long filter statement.
transcripts_tbl(src,
filter = ~(symbol == 'ADA' & !(tx_start >= 44619810 | tx_end < 44651742)) |
(smbl & !tx_end > 25056954)
)## # A tibble: 10 × 7
## tx_chrom tx_start tx_end tx_strand tx_id tx_name symbol
## <chr> <int> <int> <chr> <int> <chr> <chr>
## 1 chr15 25051477 25051571 + 174763 ENST00000384335.1 SNORD116-1
## 2 chr15 25054210 25054304 + 174767 ENST00000384274.1 SNORD116-2
## 3 chr15 25056860 25056954 + 174768 ENST00000384287.1 SNORD116-3
## 4 chr20 44618605 44651745 - 234116 ENST00000537820.2 ADA
## 5 chr20 44619522 44651743 - 234146 ENST00000696078.1 ADA
## 6 chr20 44619522 44652215 - 234147 ENST00000696062.1 ADA
## 7 chr20 44619522 44652220 - 234148 ENST00000536076.2 ADA
## 8 chr20 44619596 44652252 - 234165 ENST00000696064.1 ADA
## 9 chr20 44619606 44652222 - 234166 ENST00000696039.1 ADA
## 10 chr20 44619656 44651915 - 234167 ENST00000696065.1 ADAsessionInfo()## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.18-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] ggplot2_3.4.4 GenomicRanges_1.54.1 GenomeInfoDb_1.38.0
## [4] IRanges_2.36.0 S4Vectors_0.40.1 BiocGenerics_0.48.1
## [7] Organism.dplyr_1.30.1 AnnotationFilter_1.26.0 dplyr_1.1.3
## [10] BiocStyle_2.30.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.0 farver_2.1.1
## [3] blob_1.2.4 filelock_1.0.2
## [5] Biostrings_2.70.1 bitops_1.0-7
## [7] fastmap_1.1.1 lazyeval_0.2.2
## [9] RCurl_1.98-1.12 BiocFileCache_2.10.1
## [11] GenomicAlignments_1.38.0 XML_3.99-0.14
## [13] digest_0.6.33 lifecycle_1.0.3
## [15] KEGGREST_1.42.0 RSQLite_2.3.2
## [17] magrittr_2.0.3 compiler_4.3.1
## [19] rlang_1.1.1 sass_0.4.7
## [21] progress_1.2.2 tools_4.3.1
## [23] utf8_1.2.4 yaml_2.3.7
## [25] rtracklayer_1.62.0 knitr_1.45
## [27] labeling_0.4.3 prettyunits_1.2.0
## [29] S4Arrays_1.2.0 bit_4.0.5
## [31] curl_5.1.0 DelayedArray_0.28.0
## [33] xml2_1.3.5 abind_1.4-5
## [35] BiocParallel_1.36.0 purrr_1.0.2
## [37] withr_2.5.2 grid_4.3.1
## [39] fansi_1.0.5 colorspace_2.1-0
## [41] scales_1.2.1 biomaRt_2.58.0
## [43] SummarizedExperiment_1.32.0 cli_3.6.1
## [45] rmarkdown_2.25 crayon_1.5.2
## [47] generics_0.1.3 httr_1.4.7
## [49] rjson_0.2.21 DBI_1.1.3
## [51] cachem_1.0.8 stringr_1.5.0
## [53] zlibbioc_1.48.0 parallel_4.3.1
## [55] AnnotationDbi_1.64.0 BiocManager_1.30.22
## [57] XVector_0.42.0 restfulr_0.0.15
## [59] matrixStats_1.0.0 vctrs_0.6.4
## [61] Matrix_1.6-1.1 jsonlite_1.8.7
## [63] bookdown_0.36 hms_1.1.3
## [65] bit64_4.0.5 magick_2.8.1
## [67] GenomicFeatures_1.54.1 jquerylib_0.1.4
## [69] glue_1.6.2 codetools_0.2-19
## [71] gtable_0.3.4 stringi_1.7.12
## [73] BiocIO_1.12.0 munsell_0.5.0
## [75] tibble_3.2.1 pillar_1.9.0
## [77] rappdirs_0.3.3 htmltools_0.5.6.1
## [79] GenomeInfoDbData_1.2.11 R6_2.5.1
## [81] dbplyr_2.4.0 lattice_0.22-5
## [83] evaluate_0.23 Biobase_2.62.0
## [85] highr_0.10 png_0.1-8
## [87] Rsamtools_2.18.0 memoise_2.0.1
## [89] bslib_0.5.1 Rcpp_1.0.11
## [91] SparseArray_1.2.0 xfun_0.41
## [93] MatrixGenerics_1.14.0 pkgconfig_2.0.3