The biobtreeR package provides an interface to biobtree tool which allows mapping the bioinformatics datasets via identifiers and special keywors with simple or advance chain query capability.
library(biobtreeR)
# Create an folder and set as an output directory
# It is used for database and configuration files
# temporary directory is used for demonstration purpose
bbUseOutDir(tempdir())## [1] 0For mapping queries, biobtreeR use a database which stored in local storage. Database can be built 2 ways, first way is to retrieve pre built database. These database consist of commonly studied datasets and model organism and updated regularly following the major uniprot and ensembl data releases.
# Called once and saves the built in database to local disk
# Included datasets hgnc,hmdb,taxonomy,go,efo,eco,chebi,interpro
# Included uniprot proteins and ensembl genomes belongs to following organisms:
# homo_sapiens, danio_rerio(zebrafish), gallus_gallus(chicken), mus_musculus, Rattus norvegicus, saccharomyces_cerevisiae,
# arabidopsis_thaliana, drosophila_melanogaster, caenorhabditis_elegans, Escherichia coli, Escherichia coli str. K-12 substr. MG1655, Escherichia coli K-12
# Requires ~ 6 GB free storage
bbBuiltInDB()For the genomes which are not included in the pre built database can be built in local computer. All the ensembl and ensembl genomes organisms are supported. List of these genomes and their taxonomy identifiers can be seen from ensembl websites 1,2,3,4,5,6,
Once database is retrieved or built to local disk queries are performed via lightweight local server. Local server provide web interface for data expoloration in addition to the R functions for performing queries for R pipelines. Local server runs as a background process so both web interface and R functions can be used at the same time once it is started. While web server running web interface can be accessed via address http://localhost:8888/ui
## [1] "/tmp/Rtmpqs7eN6"
## [1] "Starting biobtree..."## [1] "biobtreeR started"Searching dataset identfiers and keywords such as gene name or accessions is performed with bbSearch function by passing comma seperated terms.
## input identifier dataset mapping_count
## 1 TPI1 ENSG00000111669 ensembl 64
## 2 TPI1 ENSGALG00010023392 ensembl 33
## 3 TPI1 ENSMUSG00000023456 ensembl 49
## 4 TPI1 ENSRNOG00000015290 ensembl 59
## 5 TPI1 HGNC:12009 hgnc 8
## 6 VAV_HUMAN P15498 uniprot 281
## 7 ENST00000297261 ENST00000297261 transcript 301If source parameter is passed search performed within the dataset.
## input identifier dataset mapping_count
## 1 TPI1 ENSG00000111669 ensembl 64
## 2 TPI1 ENSGALG00010023392 ensembl 33
## 3 TPI1 ENSMUSG00000023456 ensembl 49
## 4 TPI1 ENSRNOG00000015290 ensembl 59
## 5 ENSG00000164690 ENSG00000164690 ensembl 286Search results url is retrieved with
## input identifier dataset mapping_count
## 1 TPI1 ENSG00000111669 ensembl 64
## 2 TPI1 ENSGALG00010023392 ensembl 33
## 3 TPI1 ENSMUSG00000023456 ensembl 49
## 4 TPI1 ENSRNOG00000015290 ensembl 59
## 5 TPI1 HGNC:12009 hgnc 8
## 6 VAV_HUMAN P15498 uniprot 281
## 7 ENST00000297261 ENST00000297261 transcript 301
## url
## 1 https://www.ensembl.org/homo_sapiens/Gene/Summary?db=core;g=ENSG00000111669
## 2 https://www.ensembl.org/gallus_gallus/Gene/Summary?db=core;g=ENSGALG00010023392
## 3 https://www.ensembl.org/mus_musculus/Gene/Summary?db=core;g=ENSMUSG00000023456
## 4 https://www.ensembl.org/rattus_norvegicus/Gene/Summary?db=core;g=ENSRNOG00000015290
## 5 https://www.genenames.org/data/gene-symbol-report/#!/hgnc_id/HGNC:12009
## 6 //www.uniprot.org/uniprot/P15498
## 7 #Mapping and filtering queries are performed via bibobtree’s mapping
query syntax which allowed chain mapping and filtering capability from
identifiers to target identifiers or attributes. Mapping query syntax
consist of single or multiple mapping queries in the format
map(dataset_id).filter(Boolean query expression).map(...).filter(...)...
and allow performing chain mapping among datasets. For mapping queries
bbMapping function is used, for instance in following example, maps
protein to its go terms and in the second query mapping has been done
with filter.
## mapping_id type
## 1 GO:0000276 cellular_component
## 2 GO:0005741 cellular_component
## 3 GO:0005743 cellular_component
## 4 GO:0005753 cellular_component
## 5 GO:0008289 molecular_function
## 6 GO:0015078 molecular_function
## 7 GO:0015986 biological_process
## 8 GO:0016021 cellular_component
## 9 GO:0045263 cellular_component## mapping_id type
## 1 GO:0015986 biological_processIn the example for the first parameter single protein accession has
been used but similar with bbSearch functions multiple identifers or
keywords can be used. In the last query type attribute was used to
filter mapping only with biological process go terms. Dataset attributes
are used in the filters starts with their dataset name as in the above
example it starts with go.
In order use in filter expressions, each datasets attributes lists
with bbListAttrsfunction via sample identifier. For
instance following query shows gene ontology attributes.
## [1] "type" "name" "[]synonyms"## [1] "[]accessions" "[]genes" "[]names"
## [4] "[]alternative_names" "[]submitted_names" "sequence.seq"
## [7] "sequence.mass" "reviewed"In this section biobtreeR functionalities will be discussed in detail via gene and protein centric example use cases. For live demo of web interface including these use cases with additional chemistry centric use cases can be accessed via https://www.ebi.ac.uk/~tgur/biobtree/
Ensembl, Ensembl Genomes and HGNC datasets are used for gene related
data. One of the most common gene related dataset identfiers are
ensembl,hgnc,transcript,exon.
Let’s start with listing their attiributes,
## [1] "[]names" "[]symbols" "locus_group" "[]aliases"
## [5] "locus_type" "[]prev_names" "[]prev_symbols" "status"
## [9] "[]gene_groups"## [1] "name" "description" "start" "end" "biotype"
## [6] "genome" "strand" "seq_region" "branch"## [1] "name" "start" "end" "biotype" "strand"
## [6] "seq_region" "utr5Start" "utr5End" "utr3Start" "utr3End"
## [11] "source"## [1] "start" "end" "strand" "seq_region"## [1] "start" "end" "strand" "seq_region" "frame"Note that there are several other gene related datasets without
attributes and can be used in mapping queries such as probesets,
genebank and entrez etc. Full dataset list can be discovered with
bbListDatasets. Now lets build example mapping queries,
Map gene names to Ensembl transcript identifiers
## input input_dataset mapping_id
## 1 ATP5MC3-ENSG00000154518 ensembl ENST00000284727
## 2 - - ENST00000392541
## 3 - - ENST00000409194
## 4 - - ENST00000472782
## 5 - - ENST00000497075
## 6 ATP5MC3-ENSRNOG00000001596 ensembl ENSRNOT00000058234Map gene names to exon identifiers and retrieve the region
## input input_dataset mapping_id seq_region
## 1 ATP5MC3-ENSG00000154518 ensembl ENSE00001016410 2
## 2 - - ENSE00001016412 2
## 3 - - ENSE00001588911 2
## 4 - - ENSE00003574305 2
## 5 - - ENSE00003602002 2
## 6 - - ENSE00001512331 2Map human gene to its ortholog identifiers
## mapping_id
## 1 ENSDARG00000038867
## 2 ENSDARG00000068567
## 3 ENSGALG00010025259
## 4 ENSMUSG00000002633
## 5 ENSRNOG00000006120
## 6 WBGENE00001690Map gene to its paralogs
## input input_dataset mapping_id
## 1 FRY-ENSG00000073910 ensembl ENSG00000075539
## 2 FRY-ENSGALG00010006835 ensembl ENSGALG00010016597
## 3 FRY-ENSMUSG00000056602 ensembl ENSMUSG00000070733
## 4 FRY-ENSRNOG00000000894 ensembl ENSRNOG00000002248
## 5 MOG-ENSG00000204655 ensembl ENSG00000026950
## 6 - - ENSG00000103855
## 7 - - ENSG00000111801
## 8 - - ENSG00000112763
## 9 - - ENSG00000113303
## 10 - - ENSG00000114455
## 11 - - ENSG00000124508
## 12 - - ENSG00000124557
## 13 - - ENSG00000134258
## 14 - - ENSG00000160223
## 15 - - ENSG00000164010
## 16 - - ENSG00000165810
## 17 - - ENSG00000168903
## 18 - - ENSG00000186470
## 19 - - ENSG00000204290
## 20 MOG-ENSMUSG00000076439 ensembl ENSMUSG00000000706
## 21 - - ENSMUSG00000000732
## 22 - - ENSMUSG00000020490
## 23 - - ENSMUSG00000024340
## 24 - - ENSMUSG00000028644
## 25 - - ENSMUSG00000034359
## 26 - - ENSMUSG00000035914
## 27 - - ENSMUSG00000040283
## 28 - - ENSMUSG00000049214
## 29 - - ENSMUSG00000051076
## 30 - - ENSMUSG00000053216
## 31 - - ENSMUSG00000055960
## 32 - - ENSMUSG00000058435
## 33 - - ENSMUSG00000062638
## 34 - - ENSMUSG00000070868
## 35 - - ENSMUSG00000078598
## 36 - - ENSMUSG00000078599
## 37 - - ENSMUSG00000087194
## 38 - - ENSMUSG00000089773
## 39 - - ENSMUSG00000092618
## 40 - - ENSMUSG00000108763
## 41 MOG-ENSRNOG00000000775 ensembl ENSRNOG00000000335
## 42 - - ENSRNOG00000000446
## 43 - - ENSRNOG00000015279
## 44 - - ENSRNOG00000017514
## 45 - - ENSRNOG00000023109
## 46 - - ENSRNOG00000028541
## 47 - - ENSRNOG00000033608
## 48 - - ENSRNOG00000037680
## 49 - - ENSRNOG00000038877
## 50 - - ENSRNOG00000039396
## 51 - - ENSRNOG00000042047
## 52 - - ENSRNOG00000043019
## 53 - - ENSRNOG00000043194
## 54 - - ENSRNOG00000046377
## 55 - - ENSRNOG00000047304
## 56 - - ENSRNOG00000052710
## 57 - - ENSRNOG00000063827
## 58 - - ENSRNOG00000064738
## 59 - - ENSRNOG00000067562
## 60 - - ENSRNOG00000068088
## 61 - - ENSRNOG00000068592
## 62 - - ENSRNOG00000068993Map ensembl identifier or gene name to the entrez identifier
## input input_dataset mapping_id
## 1 ENSG00000073910 ensembl 10129
## 2 SHH-ENSG00000164690 ensembl 6469
## 3 SHH-ENSGALG00010025259 ensembl 395615
## 4 SHH-ENSMUSG00000002633 ensembl 20423
## 5 SHH-ENSRNOG00000006120 ensembl 29499Map refseq identifiers to hgnc identifiers
## input input_dataset mapping_id symbols
## 1 NM_005359 refseq HGNC:6770 SMAD4
## 2 NM_000546 refseq HGNC:11998 TP53Get all Ensembl human identifiers and gene names on chromosome Y with lncRNA type
res<-bbMapping("homo_sapiens",'map(ensembl).filter(ensembl.seq_region=="Y" && ensembl.biotype=="lncRNA")',attrs = 'name')
head(res)## mapping_id name
## 1 ENSG00000129816 TTTY1B
## 2 ENSG00000129845 TTTY1
## 3 ENSG00000131007 TTTY9B
## 4 ENSG00000131538 TTTY6
## 5 ENSG00000131548 TTTY6B
## 6 ENSG00000147753 TTTY7Get CDS from genes
## input input_dataset mapping_id
## 1 TPI1-ENSG00000111669 ensembl ENSP00000229270
## 2 - - ENSP00000379933
## 3 - - ENSP00000475184
## 4 - - ENSP00000475620
## 5 - - ENSP00000475364
## 6 - - ENSP00000475829
## 7 - - ENSP00000443599
## 8 - - ENSP00000484435
## 9 TPI1-ENSGALG00010023392 ensembl ENSGALP00010034676
## 10 TPI1-ENSMUSG00000023456 ensembl ENSMUSP00000125292
## 11 - - ENSMUSP00000130858
## 12 - - ENSMUSP00000159368
## 13 TPI1-ENSRNOG00000015290 ensembl ENSRNOP00000020647
## 14 - - ENSRNOP00000067409
## 15 - - ENSRNOP00000088185
## 16 SHH-ENSG00000164690 ensembl ENSP00000297261
## 17 - - ENSP00000396621
## 18 - - ENSP00000413871
## 19 - - ENSP00000410546
## 20 SHH-ENSGALG00010025259 ensembl ENSGALP00010038072
## 21 - - ENSGALP00010038078
## 22 SHH-ENSMUSG00000002633 ensembl ENSMUSP00000002708
## 23 SHH-ENSRNOG00000006120 ensembl ENSRNOP00000008497Get all Ensembl human identifiers and gene names within or overlapping range
bbMapping("9606",'map(ensembl).filter((114129278>ensembl.start && 114129278<ensembl.end) || (114129328>ensembl.start && 114129328<ensembl.end))',attrs = "name")## mapping_id name
## 1 ENSG00000080709 KCNN2
## 2 ENSG00000109906 ZBTB16
## 3 ENSG00000128573 FOXP2
## 4 ENSG00000134207 SYT6
## 5 ENSG00000151577 DRD3
## 6 ENSG00000165813 CCDC186
## 7 ENSG00000185989 RASA3
## 8 ENSG00000228624 HDAC2-AS2
## 9 ENSG00000249853 HS3ST5In the above example as a first parameter taxonomy identifier is used instead of specifying as homo sapiens like in the previous example. Both of these usage are equivalent and produce same output as homo sapiens refer to taxonomy identifer 9606.
Built in function for genomic range queries
To simplfy previous use case query 3 builtin range query functions
are provided. These functions are overlaps,
within and covers. These functions can be used
for ensembl, transcript, exon and
cds entries which have start and end genome coordinates.
For instance previous query can be written following way with
overlaps function which list all the overlapping genes in
human with given range.
## mapping_id name
## 1 ENSG00000080709 KCNN2
## 2 ENSG00000109906 ZBTB16
## 3 ENSG00000128573 FOXP2
## 4 ENSG00000134207 SYT6
## 5 ENSG00000151577 DRD3
## 6 ENSG00000165813 CCDC186
## 7 ENSG00000185989 RASA3
## 8 ENSG00000228624 HDAC2-AS2
## 9 ENSG00000249853 HS3ST5Map Affymetrix identifiers to Ensembl identifiers and gene names
bbMapping("202763_at,213596_at,209310_s_at",source ="affy_hg_u133_plus_2" ,'map(transcript).map(ensembl)',attrs = "name")## input input_dataset mapping_id name
## 1 202763_AT affy_hg_u133_plus_2 ENSG00000164305 CASP3
## 2 213596_AT affy_hg_u133_plus_2 ENSG00000196954 CASP4
## 3 209310_S_AT affy_hg_u133_plus_2 ENSG00000196954 CASP4Note that all mappings can be done with opposite way, for instance from gene name to Affymetrix identifiers mapping is performed following way
## input input_dataset mapping_id
## 1 CASP3-ENSG00000164305 ensembl 202763_AT
## 2 - - 236729_AT
## 3 CASP4-ENSG00000196954 ensembl 209310_S_AT
## 4 - - 213596_ATRetrieve all the human gene names which contains TTY
res<-bbMapping("homo sapiens",'map(ensembl).filter(ensembl.name.contains("TTY"))',attrs = "name")
head(res)## mapping_id name
## 1 ENSG00000129816 TTTY1B
## 2 ENSG00000129845 TTTY1
## 3 ENSG00000131007 TTTY9B
## 4 ENSG00000131538 TTTY6
## 5 ENSG00000131548 TTTY6B
## 6 ENSG00000136295 TTYH3Uniprot is used for protein related dataset such as protein identifiers, accession, sequence, features, variants, and mapping information to other datasets. Let’s list some protein related datasets attributes and then execute example queries similary with gene centric examples,
## [1] "[]accessions" "[]genes" "[]names"
## [4] "[]alternative_names" "[]submitted_names" "sequence.seq"
## [7] "sequence.mass" "reviewed"## [1] "type" "description" "id"
## [4] "original" "variation" "location.begin"
## [7] "location.end" "[]evidences.type" "[]evidences.source"
## [10] "[]evidences.id"## [1] "method" "chains" "resolution"## [1] "[]names" "short_name" "type" "protein_count"Map gene names to reviewed uniprot identifiers
## input input_dataset mapping_id
## 1 MSH6-HGNC:7329 hgnc P52701
## 2 STK11-HGNC:11389 hgnc Q15831
## 3 BMPR1A-HGNC:1076 hgnc P36894
## 4 SMAD4-HGNC:6770 hgnc Q13485
## 5 BRCA2-HGNC:1101 hgnc P51587Filter proteins by sequence mass and retrieve protein sequences
bbMapping("clock_human,shh_human,aicda_human,at5g3_human,p53_human","filter(uniprot.sequence.mass > 45000)" ,attrs = "sequence$mass,sequence$seq")## input input_dataset mapping_id sequence$mass
## 1 CLOCK_HUMAN-O15516 uniprot O15516 95304
## 2 SHH_HUMAN-Q15465 uniprot Q15465 49607
## sequence$seq
## 1 MLFTVSCSKMSSIVDRDDSSIFDGLVEEDDKDKAKRVSRNKSEKKRRDQFNVLIKELGSMLPGNARKMDKSTVLQKSIDFLRKHKEITAQSDASEIRQDWKPTFLSNEEFTQLMLEALDGFFLAIMTDGSIIYVSESVTSLLEHLPSDLVDQSIFNFIPEGEHSEVYKILSTHLLESDSLTPEYLKSKNQLEFCCHMLRGTIDPKEPSTYEYVKFIGNFKSLNSVSSSAHNGFEGTIQRTHRPSYEDRVCFVATVRLATPQFIKEMCTVEEPNEEFTSRHSLEWKFLFLDHRAPPIIGYLPFEVLGTSGYDYYHVDDLENLAKCHEHLMQYGKGKSCYYRFLTKGQQWIWLQTHYYITYHQWNSRPEFIVCTHTVVSYAEVRAERRRELGIEESLPETAADKSQDSGSDNRINTVSLKEALERFDHSPTPSASSRSSRKSSHTAVSDPSSTPTKIPTDTSTPPRQHLPAHEKMVQRRSSFSSQSINSQSVGSSLTQPVMSQATNLPIPQGMSQFQFSAQLGAMQHLKDQLEQRTRMIEANIHRQQEELRKIQEQLQMVHGQGLQMFLQQSNPGLNFGSVQLSSGNSSNIQQLAPINMQGQVVPTNQIQSGMNTGHIGTTQHMIQQQTLQSTSTQSQQNVLSGHSQQTSLPSQTQSTLTAPLYNTMVISQPAAGSMVQIPSSMPQNSTQSAAVTTFTQDRQIRFSQGQQLVTKLVTAPVACGAVMVPSTMLMGQVVTAYPTFATQQQQSQTLSVTQQQQQQSSQEQQLTSVQQPSQAQLTQPPQQFLQTSRLLHGNPSTQLILSAAFPLQQSTFPQSHHQQHQSQQQQQLSRHRTDSLPDPSKVQPQ
## 2 MLLLARCLLLVLVSSLLVCSGLACGPGRGFGKRRHPKKLTPLAYKQFIPNVAEKTLGASGRYEGKISRNSERFKELTPNYNPDIIFKDEENTGADRLMTQRCKDKLNALAISVMNQWPGVKLRVTEGWDEDGHHSEESLHYEGRAVDITTSDRDRSKYGMLARLAVEAGFDWVYYESKAHIHCSVKAENSVAAKSGGCFPGSATVHLEQGGTKLVKDLSPGDRVLAADDQGRLLYSDFLTFLDRDDGAKKVFYVIETREPRERLLLTAAHLLFVAPHNDSATGEPEASSGSGPPSGGALGPRALFASRVRPGQRVYVVAERDGDRRLLPAAVHSVTLSEEAAGAYAPLTAQGTILINRVLASCYAVIEEHSWAHRAFAPFRLAHALLAALAPARTDRGGDSGGGDRGGGGGRVALTAPGAADAPGAGATAGIHWYSQLLYQIGTWLLDSEALHPLGMAVKSSHelix type feature locations of a protein
bbMapping("shh_human",'map(ufeature).filter(ufeature.type=="helix")' ,attrs = "location$begin,location$end")## mapping_id location$begin location$end
## 1 Q15465_F123 71 74
## 2 Q15465_F126 94 96
## 3 Q15465_F127 100 116
## 4 Q15465_F129 139 141
## 5 Q15465_F131 155 157
## 6 Q15465_F132 158 167
## 7 Q15465_F135 188 190Get all variation identifiers from a gene with given condition
bbMapping("tp53",'map(uniprot).map(ufeature).filter(ufeature.original=="I" && ufeature.variation=="S").map(variantid)',source = "hgnc")## mapping_id
## 1 RS730882027
## 2 RS1330865474
## 3 RS876659675
## 4 RS587780069
## 5 RS760043106When working with biobtreeR completed, the biobtreeR web server should stop.
## Warning in system2("killall", args = "biobtree"): error in running command## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 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: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] biobtreeR_1.19.0 BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] httr_1.4.7 cli_3.6.3 knitr_1.49
## [4] rlang_1.1.4 xfun_0.49 stringi_1.8.4
## [7] promises_1.3.2 jsonlite_1.8.9 buildtools_1.0.0
## [10] htmltools_0.5.8.1 maketools_1.3.1 httpuv_1.6.15
## [13] sys_3.4.3 sass_0.4.9 rmarkdown_2.29
## [16] evaluate_1.0.1 jquerylib_0.1.4 fastmap_1.2.0
## [19] yaml_2.3.10 lifecycle_1.0.4 BiocManager_1.30.25
## [22] compiler_4.4.2 Rcpp_1.0.13-1 later_1.4.1
## [25] digest_0.6.37 R6_2.5.1 curl_6.0.1
## [28] magrittr_2.0.3 bslib_0.8.0 tools_4.4.2
## [31] cachem_1.1.0