Transcription factors (TFs) are proteins that bind to cis-regulatory elements
in promoter regions of genes and regulate their expression.
Identifying them in a genome is useful for a variety of reasons, such as
exploring their evolutionary history across clades and inferring
gene regulatory networks. planttfhunter
allows users to identify plant TFs from whole-genome protein sequences and
classify them into families and subfamilies (when applicable) using the
classification scheme implemented in PlantTFDB.
As planttfhunter interoperates with
core Bioconductor packages (i.e., AAStringSet objects as input,
SummarizedExperiment objects as output), it can be easily incorporated in
pipelines for TF identification and classification in large-scale genomic
data sets.
You can install planttfhunter with the following code:
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("planttfhunter")Loading package after installation:
library(planttfhunter)In this vignette, we will use protein sequences of TFs from the algae
species Galdieria sulphuraria as an example, as its proteome is
very small. The proteome file was
downloaded from the PLAZA Diatoms database (Osuna-Cruz et al. 2020), and
it was filtered to keep only TFs for demonstration purposes.
The object gsu stores the protein sequences in an AAStringSet object.
data(gsu)
gsu
#> AAStringSet object of length 290:
#> width seq names
#> [1] 383 MSVLLQTSRGDIVVDLFTDLAP...EKALFQEHRRSSYSKTSKRYK* gsu00340.1
#> [2] 301 MESMSQTKRWSCVIYQVDSILP...PNISLQQLLGDEDEISWSGLP* gsu04730.1
#> [3] 232 MEKGPCSHKWKQLHVGDYQHDN...PNISLQQLLGDEDEISWSGLP* gsu05000.1
#> [4] 340 MDVPSYRFECTPLVEVKKEARD...EVTQSSTRNCNDKEWNPNANE* gsu06140.1
#> [5] 120 MAPKERSKPKSRSSKAGLQFPV...SGGVLPNVHPNLLPKKKAKEE* gsu06160.1
#> ... ... ...
#> [286] 394 MSKLEAASDSGSLKTSSCSFQE...NETNSHDKPGEDRMNIQENTS* gsu98790.1
#> [287] 365 MITEVDNPMSFVHSEHFMNYSS...RRHSRLPVFQTLEEKSDIHSK* gsu99250.1
#> [288] 365 MITEVDNPMSFVHSEHFMNYSS...RRHSRLPVFQTLEEKSDIHSK* gsu99270.1
#> [289] 284 MTSFYIKKGITFSSIVYNHNYK...LFQRIIEINKNYNPLIQLQRI* AIG92462.1
#> [290] 219 MKYKLLVIDDELSIRQSLKKYL...TFTRSRTELVRYAIKNNLIIE* AIG92471.1TF identification and classification is based on the presence of signature
protein domains, which are identified using profile hidden Markov models (HMMs).
The family classification scheme is the same as the one used by
PlantTFDB (Jin et al. 2016), and it is summarized below:1 Tip: You can access this classification scheme in your R session by
loading the data frame data(classification_scheme).
| Family | Subfamily | DBD | Auxiliary | Forbidden | |
|---|---|---|---|---|---|
| 2 | AP2/ERF | AP2 | AP2 (>=2) (PF00847) | NA | NA |
| 3 | AP2/ERF | ERF | AP2 (1) (PF00847) | NA | NA |
| 4 | AP2/ERF | RAV | AP2 (PF00847) and B3 (PF02362) | NA | NA |
| 5 | B3 superfamily | ARF | B3 (PF02362) | Auxin_resp (PF06507) | NA |
| 6 | B3 superfamily | B3 | B3 (PF02362) | NA | NA |
| 7 | BBR-BPC | BBR-BPC | GAGA_bind (PF06217) | NA | NA |
| 8 | BES1 | BES1 | DUF822 (PF05687) | NA | NA |
| 9 | bHLH | bHLH | HLH (PF00010) | NA | NA |
| 10 | bZIP | bZIP | bZIP_1 (PF00170) | NA | NA |
| 11 | C2C2 | CO-like | zf-B_box (PF00643) | CCT (PF06203) | NA |
| 12 | C2C2 | Dof | Zf-Dof (PF02701) | NA | NA |
| 13 | C2C2 | GATA | GATA-zf (PF00320) | NA | NA |
| 14 | C2C2 | LSD | Zf-LSD1 (PF06943) | NA | Peptidase_C14 (PF00656) |
| 15 | C2C2 | YABBY | YABBY (PF04690) | NA | NA |
| 16 | C2H2 | C2H2 | zf-C2H2 (PF00096) | NA | RNase_T (PF00929) |
| 17 | C3H | C3H | Zf-CCCH (PF00642) | NA | RRM_1 (PF00076) or Helicase_C (PF00271) |
| 18 | CAMTA | CAMTA | CG1 (PF03859) | NA | NA |
| 19 | CPP | CPP | TCR (PF03638) | NA | NA |
| 20 | DBB | DBB | zf-B_box (>=2) (PF00643) | NA | NA |
| 21 | E2F/DP | E2F/DP | E2F_TDP (PF02319) | NA | NA |
| 22 | EIL | EIL | EIN3 (PF04873) | NA | NA |
| 23 | FAR1 | FAR1 | FAR1 (PF03101) | NA | NA |
| 24 | GARP | ARR-B | G2-like | Response_reg (PF00072) | NA |
| 25 | GARP | G2-like | G2-like | NA | NA |
| 26 | GeBP | GeBP | DUF573 (PF04504) | NA | NA |
| 27 | GRAS | GRAS | GRAS (PF03514) | NA | NA |
| 28 | GRF | GRF | WRC (PF08879) | QLQ (PF08880) | NA |
| 29 | HB | HD-ZIP | Homeobox (PF00046) | HD-ZIP_I/II or SMART (PF01852) | NA |
| 30 | HB | TALE | Homeobox (PF00046) | BELL or ELK (PF03789) | NA |
| 31 | HB | WOX | homeobox (PF00046) | Wus type homeobox | NA |
| 32 | HB | HB-PHD | homeobox (PF00046) | PHD (PF00628) | NA |
| 33 | HB | HB-other | homeobox (PF00046) | NA | NA |
| 34 | HRT-like | HRT-like | HRT-like | NA | NA |
| 35 | HSF | HSF | HSF_dna_bind (PF00447) | NA | NA |
| 36 | LBD (AS2/LOB) | LBD (AS2/LOB) | DUF260 (PF03195) | NA | NA |
| 37 | LFY | LFY | FLO_LFY (PF01698) | NA | NA |
| 38 | MADS | M_type | SRF-TF (PF00319) | NA | NA |
| 39 | MADS | MIKC | SRF-TF (PF00319) | K-box (PF01486) | NA |
| 40 | MYB superfamily | MYB | Myb_dna_bind (>=2) (PF00249) | NA | SWIRM (PF04433) |
| 41 | MYB superfamily | MYB_related | Myb_dna_bind (1) (PF00249) | NA | SWIRM (PF04433) |
| 42 | NAC | NAC | NAM (PF02365) | NA | NA |
| 43 | NF-X1 | NF-X1 | Zf-NF-X1 (PF01422) | NA | NA |
| 44 | NF-Y | NF-YA | CBFB_NFYA (PF02045) | NA | NA |
| 45 | NF-Y | NF-YB | NF-YB | NA | NA |
| 46 | NF-Y | NF-YC | NF-YC | NA | NA |
| 47 | Nin-like | Nin-like | RWP-RK (PF02042) | NA | NA |
| 48 | NZZ/SPL | NZZ/SPL | NOZZLE (PF08744) | NA | NA |
| 49 | S1Fa-like | S1Fa-like | S1FA (PF04689) | NA | NA |
| 50 | SAP | SAP | SAP | NA | NA |
| 51 | SBP | SBP | SBP (PF03110) | NA | NA |
| 52 | SRS | SRS | DUF702 (PF05142) | NA | NA |
| 53 | STAT | STAT | STAT | NA | NA |
| 54 | TCP | TCP | TCP (PF03634) | NA | NA |
| 55 | Trihelix | Trihelix | Trihelix | NA | NA |
| 56 | VOZ | VOZ | VOZ | NA | NA |
| 57 | Whirly | Whirly | Whirly (PF08536) | NA | NA |
| 58 | WRKY | WRKY | WRKY (PF03106) | NA | NA |
| 59 | ZF-HD | ZF-HD | ZF-HD_dimer (PF04770) | NA | NA |
To identify TFs from protein sequence data, you will use the
function annotate_pfam(). This function takes as input an AAStringSet
object2 Tip: If you have protein sequences in a FASTA file, you can read
them into an AAStringSet object with the function readAAStringSet() from
the Biostrings package. and returns a data frame of protein domains associated with
each sequence. The HMMER program (Finn, Clements, and Eddy 2011) is used to scan protein
sequences for the presence of DNA-binding protein domains, as well as
auxiliary and forbidden domains. Pre-built HMM profiles can be found in the
extdata/ directory of this package.
This is how you can run annotate_pfam():3 Note: in the code chunk below, the if statement is not required.
We just added it to make sure that the function annotate_pfam() is only
executed if HMMER is installed, to avoid problems when building this
vignette in machines that do not have HMMER installed.
data(gsu_annotation)
# Annotate TF-related domains using a local installation of HMMER
if(hmmer_is_installed()) {
gsu_annotation <- annotate_pfam(gsu)
}
# Take a look at the first few lines of the output
head(gsu_annotation)
#> Gene Domain
#> 1 gsu144370.1 PF00010
#> 2 gsu140730.1 PF00010
#> 3 gsu127100.1 PF00046
#> 4 gsu109490.1 PF00046
#> 5 AIG92462.1 PF00072
#> 6 AIG92471.1 PF00072Now that we have our TF-related domains, we can classify TFs in families
with the function classify_tfs().
# Classify TFs into families
gsu_families <- classify_tfs(gsu_annotation)
# Take a look at the output
head(gsu_families)
#> Gene Family
#> 1 gsu04730.1 Nin-like
#> 2 gsu05000.1 Nin-like
#> 3 gsu06140.1 G2-like
#> 4 gsu06140.1 MYB-related
#> 5 gsu09770.1 C3H
#> 6 gsu100410.1 Nin-like
# Count number of TFs per family
table(gsu_families$Family)
#>
#> C2H2 C3H CO-like CPP E2F/DP G2-like
#> 4 11 2 3 8 2
#> GATA HB-other HSF LSD M-type MYB
#> 8 2 6 3 1 16
#> MYB-related NF-X1 NF-YA NF-YB NF-YC Nin-like
#> 25 1 1 4 5 10
#> bHLH bZIP
#> 2 8If you want to get TF counts per family for multiple species, you can use
the function get_tf_counts(). This function takes a list of AAStringSet
objects containing proteomes as input,4 Tip: If you have whole-genome protein sequences for multiple
species as FASTA files in a given directory, you can read them all as a list
of AAStringSet objects with the function fasta2AAStringSetlist() from
the Bioconductor package syntenet. and it returns a SummarizedExperiment
object containing TF counts per family in each species, as well as species
metadata (optional). If you are not familiar with the SummarizedExperiment
class, you should consider checking the vignettes of the
SummarizedExperiment Bioconductor package.
To demonstrate how get_tf_counts() works, we will simulate a
list of 4 AAStringSet objects by sampling 50 random genes from the example
data set gsu 4 times.
set.seed(123) # for reproducibility
# Simulate 4 different species by sampling 100 random genes from `gsu`
proteomes <- list(
Gsu1 = gsu[sample(names(gsu), 50, replace = FALSE)],
Gsu2 = gsu[sample(names(gsu), 50, replace = FALSE)],
Gsu3 = gsu[sample(names(gsu), 50, replace = FALSE)],
Gsu4 = gsu[sample(names(gsu), 50, replace = FALSE)]
)
proteomes
#> $Gsu1
#> AAStringSet object of length 50:
#> width seq names
#> [1] 405 MSQDTYTFEKLGVCKILCEECKN...SSSYNCPTDERMESEEEKLET* gsu49120.1
#> [2] 292 MGRSTTVFVGNIAYNTSEEQLQE...TLGAQMGQPGLGSSAFSSNNN* gsu102250.1
#> [3] 530 MTFTRIENNLKKYFGYENFRPLQ...KMSKTQNINQKTLLRWFSETM* gsu56420.1
#> [4] 672 MNRSLFTPTLRWSQWFAKTCTTL...EKRKLSVVHNKTQQIVSFNRH* gsu22760.1
#> [5] 606 MSQERLTPFERHLKKVEEKNQRE...EEDLDGVPLEEDEEAIEIEIK* gsu68790.1
#> ... ... ...
#> [46] 465 MNMFSYSTIETPKYVRSNSSDDR...STQMSHLLSVALQDWVEWNQE* gsu47220.1
#> [47] 2225 MSFPPKFRNYLLAQKGQPVSVEQ...MWRWVDNKSQFLSSYQVAWNE* gsu96990.1
#> [48] 292 MRGKSKSVVFPASRIKRIMRINE...LDKVESPSRVFISIEELVHSV* gsu124540.1
#> [49] 477 MDSSKKSTNPKLSESGTKDNRGN...PSDTTAPQVAVNVHAGNGSSK* gsu67550.1
#> [50] 740 MERLQPPYRYLIILDLEATAVDL...CTQDKSVSLGSVSLEGKGDSV* gsu102410.1
#>
#> $Gsu2
#> AAStringSet object of length 50:
#> width seq names
#> [1] 764 MSQSVPVKNDTEDSCGVQKLSNA...NSDDTSRIRRRTLNVHDLLSE* gsu21860.1
#> [2] 406 MQPHVSDHRYPTTVEQREYHSGS...QTTDVTGGAVFLRKETEHKDI* gsu140730.1
#> [3] 714 MEDERNSRLLIQGLPKYIAEKRL...LVFDYLVNWMALIVALVLSSF* gsu84990.1
#> [4] 236 MDTSEQGSEQGEESISNNSQQLC...YTNTASHRFRKLLKASVGDTS* gsu72770.1
#> [5] 524 MKKNAKEIGAQYSALVFIHVALF...PVSTVPYFVMDNNSGGSYSFV* gsu136350.1
#> ... ... ...
#> [46] 284 MTSFYIKKGITFSSIVYNHNYKF...LFQRIIEINKNYNPLIQLQRI* AIG92462.1
#> [47] 546 MAPRLNKTTQTKLKKQSSFREQP...NACGLFWAKHKQLRPKEKWVR* gsu134260.1
#> [48] 310 MSGGSGIYQPSGMSLYVGNLDPR...EIAGCVVQCEWGREGLKSRYF* gsu10640.1
#> [49] 445 MEPISKDDVYGGFSTVENCDSSM...NCKCVDCKNQSSLSLLKNTAM* gsu21090.1
#> [50] 318 MFICGHMIQNLVSVCAHSRIFCI...DCSYIFATDASDYPPPWQYFP* gsu64800.1
#>
#> $Gsu3
#> AAStringSet object of length 50:
#> width seq names
#> [1] 196 MAYLYEDRPVTLYRDRRFQGTQE...RDETDEVFQTEKNPRFREEED* gsu18660.1
#> [2] 171 MEDRMQVVVSETSKGEERGRGRG...SWAFSRGPLGTRLSTRRRSEK* gsu34810.1
#> [3] 461 MTERIDKSRRKKYVLTKKREYWT...HKESFSKRTYPDSVQAVIVGQ* gsu38570.1
#> [4] 1207 MELVSGPLLDQFPFVAGHSRQTL...KYGREHHWQYSLEHPFVSPIL* gsu143710.1
#> [5] 928 MFILVVEVKVEEYFSFQLDDFQQ...LRRVLDILRQIPRLPAKQWLS* gsu79190.1
#> ... ... ...
#> [46] 150 MAAVEDNRVFVGGLPWSVGEDDL...GHGHGGRGGRSGGFRRREDFE* gsu58080.1
#> [47] 122 MAPKERSKPKSRSSKAGLQFPVG...GVLPNVHPNLLPKKKAKEDMQ* gsu59350.1
#> [48] 680 MWSTVDYSLNCDEEFRELSNAAA...QQSTNVVYPSNTNNSETCENS* gsu44800.1
#> [49] 148 MVANGEPGVIYIGHLPHGFYENE...ERRNKELEVKLKKLGVSFSLS* gsu97960.1
#> [50] 226 MAYRKLETRVPSYLDEVLGKVSS...SAPEGVWRCPDCRSGGANRAR* gsu111770.1
#>
#> $Gsu4
#> AAStringSet object of length 50:
#> width seq names
#> [1] 740 MERLQPPYRYLIILDLEATAVDL...CTQDKSVSLGSVSLEGKGDSV* gsu102410.1
#> [2] 701 MFKSSLLTFPALKTVVGAQDQYT...NLTKSFDLKKSQLSKRKKKWK* gsu110850.1
#> [3] 484 MSEVSWEAGRSPVQPGKDHKSSS...DRSSTENRNSGRRRSVEGRAR* gsu114840.1
#> [4] 483 MEEERKQKKGTGTSVSKTRAVQE...AKSADDESSRPVRQYDVENVA* gsu100590.1
#> [5] 122 MAPKERSKPKSRSSKAGLQFPVG...GVLPNVHPNLLPKKKAKEDMQ* gsu59350.1
#> ... ... ...
#> [46] 798 MVLYQSYSSDTNSDVKPSEVSNS...EDENNKILCLCGAPTCRKFLN* gsu40400.1
#> [47] 383 MSVLLQTSRGDIVVDLFTDLAPL...EKALFQEHRRSSYSKTSKRYK* gsu139150.1
#> [48] 518 MRSGTTLSSLHNSHTEDATSLRA...EIGDIASLLEGEEVNYERLER* gsu32730.1
#> [49] 287 MKASQVLASQLCELCQSANSSIY...RKQLAERRCRFKGRFIKNTAS* gsu55840.1
#> [50] 1886 MDDTEYVPVKKRRQRILQQAKEL...TEVTRKQLIYYLSDVLANNKE* gsu43660.1Great, we have a list of 4 AAStringSet objects. Now, let’s also create a
simulated species metadata data frame for each “species” (simulated).
# Create simulated species metadata
species_metadata <- data.frame(
row.names = names(proteomes),
Division = "Rhodophyta",
Origin = c("US", "Belgium", "China", "Brazil")
)
species_metadata
#> Division Origin
#> Gsu1 Rhodophyta US
#> Gsu2 Rhodophyta Belgium
#> Gsu3 Rhodophyta China
#> Gsu4 Rhodophyta BrazilYou can add as many columns as you want to the species metadata data frame,
but make sure that species names are in row names, and
that names(proteomes) match rownames(species),
otherwise get_tf_counts() will return an error.
Now that we have a list of AAStringSet objects and species metadata, we
can execute get_tf_counts(). This function uses annotate_pfam() under
the hood, so you also need to have HMMER installed and in your PATH to run it.
Here is how you can run it:
data(tf_counts)
# Get TF counts per family in each species as a SummarizedExperiment object
if(hmmer_is_installed()) {
tf_counts <- get_tf_counts(proteomes, species_metadata)
}
# Take a look at the SummarizedExperiment object
tf_counts
#> class: SummarizedExperiment
#> dim: 19 4
#> metadata(0):
#> assays(1): counts
#> rownames(19): C2H2 C3H ... NF-YA NF-YB
#> rowData names(0):
#> colnames(4): Gsu1 Gsu2 Gsu3 Gsu4
#> colData names(2): Division Origin
# Look at the matrix of counts: assay() function from SummarizedExperiment
SummarizedExperiment::assay(tf_counts)
#> Gsu1 Gsu2 Gsu3 Gsu4
#> C2H2 3 1 1 2
#> C3H 3 2 0 3
#> CPP 1 1 1 0
#> E2F/DP 1 2 2 2
#> GATA 1 2 1 0
#> HSF 2 0 0 3
#> MYB 3 3 0 2
#> MYB-related 4 5 3 9
#> NF-X1 1 0 0 0
#> NF-YC 3 0 0 1
#> Nin-like 2 0 1 1
#> bHLH 0 1 1 0
#> bZIP 0 2 4 0
#> G2-like 0 1 0 0
#> HB-other 0 1 0 0
#> LSD 0 1 0 0
#> M-type 0 1 1 0
#> NF-YA 0 1 0 0
#> NF-YB 0 2 0 1
# Look at the species metadata: colData() function from SummarizedExperiment
SummarizedExperiment::colData(tf_counts)
#> DataFrame with 4 rows and 2 columns
#> Division Origin
#> <character> <character>
#> Gsu1 Rhodophyta US
#> Gsu2 Rhodophyta Belgium
#> Gsu3 Rhodophyta China
#> Gsu4 Rhodophyta BrazilCool, huh? In real-world analyses, once you have TF counts per family in
multiple species obtained with get_tf_counts(),
you can try to find associations between TF counts and
eco-evolutionary aspects or traits of each species (e.g., higher frequencies of
a stress-related TF family in a species that inhabits a stressful environment).
This document was created under the following conditions:
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.3.1 (2023-06-16)
#> os Ubuntu 22.04.3 LTS
#> system x86_64, linux-gnu
#> ui X11
#> language (EN)
#> collate C
#> ctype en_US.UTF-8
#> tz America/New_York
#> date 2023-10-24
#> pandoc 2.7.3 @ /usr/bin/ (via rmarkdown)
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────
#> package * version date (UTC) lib source
#> abind 1.4-5 2016-07-21 [2] CRAN (R 4.3.1)
#> Biobase 2.62.0 2023-10-24 [2] Bioconductor
#> BiocGenerics 0.48.0 2023-10-24 [2] Bioconductor
#> BiocManager 1.30.22 2023-08-08 [2] CRAN (R 4.3.1)
#> BiocStyle * 2.30.0 2023-10-24 [2] Bioconductor
#> Biostrings 2.70.0 2023-10-24 [2] Bioconductor
#> bitops 1.0-7 2021-04-24 [2] CRAN (R 4.3.1)
#> bookdown 0.36 2023-10-16 [2] CRAN (R 4.3.1)
#> bslib 0.5.1 2023-08-11 [2] CRAN (R 4.3.1)
#> cachem 1.0.8 2023-05-01 [2] CRAN (R 4.3.1)
#> cli 3.6.1 2023-03-23 [2] CRAN (R 4.3.1)
#> crayon 1.5.2 2022-09-29 [2] CRAN (R 4.3.1)
#> DelayedArray 0.28.0 2023-10-24 [2] Bioconductor
#> digest 0.6.33 2023-07-07 [2] CRAN (R 4.3.1)
#> evaluate 0.22 2023-09-29 [2] CRAN (R 4.3.1)
#> fastmap 1.1.1 2023-02-24 [2] CRAN (R 4.3.1)
#> GenomeInfoDb 1.38.0 2023-10-24 [2] Bioconductor
#> GenomeInfoDbData 1.2.11 2023-10-17 [2] Bioconductor
#> GenomicRanges 1.54.0 2023-10-24 [2] Bioconductor
#> htmltools 0.5.6.1 2023-10-06 [2] CRAN (R 4.3.1)
#> IRanges 2.36.0 2023-10-24 [2] Bioconductor
#> jquerylib 0.1.4 2021-04-26 [2] CRAN (R 4.3.1)
#> jsonlite 1.8.7 2023-06-29 [2] CRAN (R 4.3.1)
#> knitr 1.44 2023-09-11 [2] CRAN (R 4.3.1)
#> lattice 0.22-5 2023-10-24 [3] CRAN (R 4.3.1)
#> Matrix 1.6-1.1 2023-09-18 [3] CRAN (R 4.3.1)
#> MatrixGenerics 1.14.0 2023-10-24 [2] Bioconductor
#> matrixStats 1.0.0 2023-06-02 [2] CRAN (R 4.3.1)
#> planttfhunter * 1.2.0 2023-10-24 [1] Bioconductor
#> R6 2.5.1 2021-08-19 [2] CRAN (R 4.3.1)
#> RCurl 1.98-1.12 2023-03-27 [2] CRAN (R 4.3.1)
#> rlang 1.1.1 2023-04-28 [2] CRAN (R 4.3.1)
#> rmarkdown 2.25 2023-09-18 [2] CRAN (R 4.3.1)
#> S4Arrays 1.2.0 2023-10-24 [2] Bioconductor
#> S4Vectors * 0.40.0 2023-10-24 [2] Bioconductor
#> sass 0.4.7 2023-07-15 [2] CRAN (R 4.3.1)
#> sessioninfo 1.2.2 2021-12-06 [2] CRAN (R 4.3.1)
#> SparseArray 1.2.0 2023-10-24 [2] Bioconductor
#> SummarizedExperiment 1.32.0 2023-10-24 [2] Bioconductor
#> xfun 0.40 2023-08-09 [2] CRAN (R 4.3.1)
#> XVector 0.42.0 2023-10-24 [2] Bioconductor
#> yaml 2.3.7 2023-01-23 [2] CRAN (R 4.3.1)
#> zlibbioc 1.48.0 2023-10-24 [2] Bioconductor
#>
#> [1] /tmp/Rtmp1fcerg/Rinst30f4071b7ace71
#> [2] /home/biocbuild/bbs-3.18-bioc/R/site-library
#> [3] /home/biocbuild/bbs-3.18-bioc/R/library
#>
#> ──────────────────────────────────────────────────────────────────────────────Finn, Robert D, Jody Clements, and Sean R Eddy. 2011. “HMMER Web Server: Interactive Sequence Similarity Searching.” Nucleic Acids Research 39 (suppl_2): W29–W37.
Jin, Jinpu, Feng Tian, De-Chang Yang, Yu-Qi Meng, Lei Kong, Jingchu Luo, and Ge Gao. 2016. “PlantTFDB 4.0: Toward a Central Hub for Transcription Factors and Regulatory Interactions in Plants.” Nucleic Acids Research, gkw982.