Contents

1 Introduction

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.

2 Installation

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)

3 Data description

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* AIG92471.1

4 Algorithm description

TF 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

5 Identifying and classifying TFs

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 PF00072

Now 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)
#> 
#>        bHLH        bZIP        C2H2         C3H     CO-like         CPP 
#>           2           8           4          11           2           3 
#>      E2F/DP     G2-like        GATA    HB-other         HSF         LSD 
#>           8           2           8           2           6           3 
#>      M-type         MYB MYB-related       NF-X1       NF-YA       NF-YB 
#>           1          16          25           1           1           4 
#>       NF-YC    Nin-like 
#>           5          10

6 Counting TFs per family in multiple species at once

If 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* gsu43660.1

Great, 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  Brazil

You 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      Brazil

Cool, 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).

Session information

This document was created under the following conditions:

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References

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.

Osuna-Cruz, Cristina Maria, Gust Bilcke, Emmelien Vancaester, Sam De Decker, Atle M Bones, Per Winge, Nicole Poulsen, et al. 2020. “The Seminavis Robusta Genome Provides Insights into the Evolutionary Adaptations of Benthic Diatoms.” Nature Communications 11 (1): 1–13.