In shwetagopaul92/TFutils: TFutils

Introduction

suppressPackageStartupMessages({
library(BiocStyle)
library(TFutils)
library(org.Hs.eg.db)
library(GenomicFiles)
library(GO.db)
library(data.table)
library(knitr)
library(ggplot2)
library(data.table)
library(SummarizedExperiment)
library(BiocParallel)
data(fimoMap)
})

A central concern of genome biology is improving understanding of gene transcription. In simple terms, transcription factors (TFs) are proteins that bind to DNA, typically near gene promoter regions. The role of TFs in gene expression variation is of great interest. Progress in deciphering genetic and epigenetic processes that affect TF abundance and function will be essential in clarifying and interpreting gene expression variation patterns and their effects on phenotype. Difficulties of identifying TFs, and opportunities for doing so in systems biology contexts, are reviewed in @Weirauch2014.

This paper describes an R/Bioconductor package called TFutils, which assembles various resources intended to clarify and unify approaches to working with TF concepts in bioinformatic analysis. Computations described in this paper can be carried out with Bioconductor version 3.6. The package can be installed with

library(BiocInstaller) 
# use source("http://www.bioconductor.org/biocLite.R") if not available
biocLite("TFutils")

In the next section we describe the basic concepts of enumerating and classifying TFs, enumerating their targets, and representing genome-wide quantification of TF binding affinity. This is followed by a review of the key data structures and functions provided in the package, and an example in cancer infomatics.

Basic concepts of transcription factor bioinformatics

Enumerating transcription factors

Given the importance of the topic, it is not surprising that a number of bioinformatic research groups have published catalogs of transcription factors along with metadata about their features. Standard nomenclature for TFs has yet to be established. Gene symbols, motif sequences, and position-weight matrix catalog entries have all been used as TF identifiers.

In TFutils we have gathered information from four widely used resources: Gene Ontology (GO, @Ashburner2000, in which GO:0003700 is the tag for the molecular function concept "DNA binding transcription factor activity"), CISBP (@Weirauch2014), HOCOMOCO (@Kulakovskiy2018), and MSigDb (@Subramanian15545). Figure \@ref(fig:lkupset) depicts the sizes of these catalogs, measured using counts of unique HGNC gene symbols. The enumeration for GO uses Bioconductor's r Biocpkg("org.Hs.eg.db") package to find direct associations from GO:0003700 to HGNC symbols. The enumeration for MSigDb is heuristic and involves parsing the gene set identifiers used in MSigDb for exact or close matches to HGNC symbols. For CISBP and HOCOMOCO, the associated web servers provide easily parsed tabular catalogs.

library(TFutils)
library(AnnotationDbi)
suppressMessages({
tfdf = select(org.Hs.eg.db::org.Hs.eg.db, 
    keys="GO:0003700", keytype="GO", 
    columns=c("ENTREZID", "SYMBOL"))
})
tfdf = tfdf[, c("ENTREZID", "SYMBOL")]
TFs_GO = TFCatalog(name="GO.0003700", nativeIds=tfdf$ENTREZID,
 HGNCmap=tfdf)

data(tftColl)
data(tftCollMap)
TFs_MSIG = TFCatalog(name="MsigDb.TFT", nativeIds=names(tftColl),
 HGNCmap=data.frame(tftCollMap,stringsAsFactors=FALSE))

data(cisbpTFcat)
TFs_CISBP = TFCatalog(name="CISBP.info", nativeIds=cisbpTFcat[,1],
 HGNCmap = cisbpTFcat)

data(hocomoco.mono)
TFs_HOCO = TFCatalog(name="hocomoco11", nativeIds=hocomoco.mono[,1],
 HGNCmap=hocomoco.mono)

suppressPackageStartupMessages({library(UpSetR)})
allhg = keys(org.Hs.eg.db::org.Hs.eg.db, keytype="SYMBOL")
#activesym = unique(unlist(list(TFs_GO@HGNCmap[,2], TFs_HOCO@HGNCmap[,2], TFs_MSIG@HGNCmap[,2], TFs_CISBP@HGNCmap[,2])))
activesym = unique(unlist(list(HGNCmap(TFs_GO)[,2], HGNCmap(TFs_HOCO)[,2], HGNCmap(TFs_MSIG)[,2], HGNCmap(TFs_CISBP)[,2])))
use = intersect(allhg, activesym)
mymat = matrix(0, nr=length(use), nc=4)
rownames(mymat) = use
iu = function(x) intersect(x,use)
mymat[na.omit(iu(HGNCmap(TFs_GO)[,2])),1] = 1
mymat[na.omit(iu(HGNCmap(TFs_MSIG)[,2])),2] = 1
mymat[na.omit(iu(HGNCmap(TFs_HOCO)[,2])),3] = 1
mymat[na.omit(iu(HGNCmap(TFs_CISBP)[,2])),4] = 1
colnames(mymat) = c("GO", "MSigDb", "HOCO", "CISBP")
upset(data.frame(mymat),nsets=4,sets=c("MSigDb", "HOCO", "GO", "CISBP"), keep.order=TRUE, order.by="degree"
)

Classification of transcription factors

As noted by @Weirauch2014, interpretation of the "function and evolution of DNA sequences" is dependent on the analysis of sequence-specific DNA binding domains. These domains are dynamic and cell-type specific (@Gertz2013). Classifying TFs according to features of the binding domain is an ongoing process of increasing intricacy. Figure \@ref(fig:TFclass) shows excerpts of hierarchies of terms related to TF type derived from GO (on the left) and TFclass (@Wingender2018). There is a disagreement between our enumeration of TFs based on GO in Figure \@ref(fig:lkupset) and the 1919 shown in AmiGO, as the latter includes a broader collection of receptor activities.

knitr::include_graphics('AMIGOplus.png')

Table \@ref(tab:classtab) provides examples of frequently encountered TF classifications in the CISBP and HOCOMOCO catalogs. The numerical components of the HOCOMOCO classes correspond to TFClass subfamilies (@Wingender2018).

Table: (#tab:classtab) Most frequently represented TF classes in CISBP and HOCOMOCO. Entries in columns Nc (Nh) are numbers of distinct TFs annotated to classes in columns CISBP (HOCOMOCO) respectively.

library(knitr)
cismap = HGNCmap(TFs_CISBP)
scis = split(cismap, cismap$HGNC)
uf = vapply(scis, function(x) x$Family_Name[1],"character")
CISTOP = sort(table(uf),decreasing=TRUE)[1:10]
hoc = HGNCmap(TFs_HOCO)
shoc = split(hoc, hoc$HGNC)
sfam = vapply(shoc, function(x)x$`TF family`[1], "character")
HOTOP = sort(table(sfam),decreasing=TRUE)[1:10]
kable(data.frame(CISBP=names(CISTOP), Nc=as.numeric(CISTOP), 
   HOCOMOCO=names(HOTOP), Nh=as.numeric(HOTOP)), format="markdown")

Enumerating TF targets

The Broad Institute MSigDb (@Subramanian15545) includes a gene set collection devoted to cataloging TF targets. We have used Bioconductor's r Biocpkg("GSEABase") package to import and serialize the gmt representation of this collection.

TFutils::tftColl

Names of TFs for which target sets are assembled are encoded in a systematic way, with underscores separating substrings describing motifs, genes, and versions. Some peculiarity in nomenclature in the MSigDb labels can be observed:

grep("NFK", names(TFutils::tftColl), value=TRUE)

Manual curation will be needed to improve the precision with which MSigDb TF target sets can used.

Cataloging TF targets

The MSigDb collection is provided primarily for the purpose of defining gene sets in terms of TF targets. We use the r Biocpkg("GSEABase") package GeneSetCollection class to manage these sets.

Quantitative predictions of TF binding affinities

The FIMO algorithm of the MEME suite (@Grant2011) was used to score the human reference genome for TF binding affinity for r nrow(fimoMap) motif matrices to which genes are associated. Sixteen (16) tabix-indexed BED files are lodged in an AWS S3 bucket for illustration purposes.

library(GenomicFiles)
data(fimo16)
fimo16
head(colData(fimo16))

We harvest scores in a genomic interval of interest (bound to fimo16 in the rowRanges assignment below) using reduceByFile. This yields a list with one element per file. Each such element holds a list of scanTabix results, one per query range.

library(BiocParallel)
register(SerialParam()) # important for macosx?
rowRanges(fimo16) = GRanges("chr17", IRanges(38.077e6, 38.084e6))
rr = GenomicFiles::reduceByFile(fimo16, MAP=function(r,f)
  scanTabix(f, param=r))

scanTabix produces a list of vectors of text strings, which we parse with data.table::fread. The resulting tables are then reduced to a genomic location and -log10 of the p-value derived from the binding affinity statistic of FIMO in the vicinity of that location.

asdf = function(x) data.table::fread(paste0(x, collapse="\n"), header=FALSE)
gg = lapply(rr, function(x) {
       tmp = asdf(x[[1]][[1]]) 
       data.frame(loc=tmp$V2, score=-log10(tmp$V7))
     })
for (i in 1:length(gg))  gg[[i]]$tf = colData(fimo16)[i,2]

It turns out there are too many distinct TFs to display individually, so let's also label the scores with the TF families as defined in CISBP.

matchcis = match(colData(fimo16)[,2], cisbpTFcat[,2])
famn = cisbpTFcat[matchcis,]$Family_Name
for (i in 1:length(gg))  gg[[i]]$tffam = famn[i]
nn = do.call(rbind, gg)

A simple display of predicted TF binding affinity in a genomic region is then

library(ggplot2)
ggplot(nn, aes(x=loc,y=score,group=tffam, colour=tffam)) + geom_point()

Summary

We have compared enumerations of human transcription factors by different projects, provided access to two forms of binding domain classification, and illustrated the use of cloud-resident genome-wide binding predictions. In the next section we review selected details of data structures and methods of the r Biocpkg("TFutils") package.

Methods

Implementation

The TFCatalog class

A number of relatively small reference

data(tftColl)
data(tftCollMap)
data(cisbpTFcat)

TFs_MSIG = TFCatalog(name="MsigDb.TFT", nativeIds=names(tftColl),
 HGNCmap=data.frame(tftCollMap,stringsAsFactors=FALSE))

TFs_CISBP = TFCatalog(name="CISBP.info", nativeIds=cisbpTFcat[,1],
 HGNCmap = cisbpTFcat)

TFs_MSIG

TFs_CISBP

Operation

shwetagopaul92/TFutils documentation built on May 26, 2019, 4:32 a.m.