primirTSS

Installation

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install("primirTSS")

BiocManager::install("ipumin/primirTSS")

library(primirTSS)
#> Warning: replacing previous import 'BiocGenerics::setequal' by
#> 'dplyr::setequal' when loading 'primirTSS'
#> Warning: replacing previous import 'utils::findMatches' by
#> 'S4Vectors::findMatches' when loading 'phastCons100way.UCSC.hg38'

Step 1: Process of H3K4me3 and Pol II data

• peak_merge(): Merge one kind of peaks (H3K4me3 or Pol II)

H3K4me3 and Pol II data are key points for accurate prediction with our method. If one of these two peak data is input, before executing the main function find_TSS, the function peak_merge should be used to merge adjacent peaks whose distance between each other is less than n base pairs and return the merged peaks as an output.

library(primirTSS)
peak_df <- data.frame(chrom = c("chr1", "chr2", "chr1"),
                       chromStart = c(450, 460, 680),
                       chromEnd = c(470, 480, 710),
                       stringsAsFactors = FALSE)
peak <-  as(peak_df, "GRanges")
peak_merge(peak, n =250)
#> GRanges object with 2 ranges and 0 metadata columns:
#>       seqnames    ranges strand
#>          <Rle> <IRanges>  <Rle>
#>   [1]     chr1   450-710      *
#>   [2]     chr2   460-480      *
#>   -------
#>   seqinfo: 2 sequences from an unspecified genome; no seqlengths

• peak_join(): Join two kinds of peaks (H3K4me3 and Pol II)

If both of H3K4me3 and Pol II data, after separately merging these two kinds of evidence first, peak_join should be employed to integrate H3K4me3 and Pol II peaks and return the result as bed_merged parameter for the main function find_tss.

peak_df1 <- data.frame(chrom = c("chr1", "chr1", "chr1", "chr2"),
                       start = c(100, 460, 600, 70),
                       end = c(200, 500, 630, 100),
                       stringsAsFactors = FALSE)
peak1 <-  as(peak_df1, "GRanges")

peak_df2 <- data.frame(chrom = c("chr1", "chr1", "chr1", "chr2"),
                       start = c(160, 470, 640, 71),
                       end = c(210, 480, 700, 90),
                       stringsAsFactors = FALSE)
peak2 <-  as(peak_df2, "GRanges")

peak_join(peak1, peak2)
#> GRanges object with 3 ranges and 0 metadata columns:
#>       seqnames    ranges strand
#>          <Rle> <IRanges>  <Rle>
#>   [1]     chr1   160-200      *
#>   [2]     chr1   470-480      *
#>   [3]     chr2     71-90      *
#>   -------
#>   seqinfo: 2 sequences from an unspecified genome; no seqlengths

Step 2: Predict most possible TSS for miRNA

• find_tss is the primary function in the package. The program will first score the candidate TSSs of miRNA and pick up the best candidate in the first step of prediction, (where users can set flanking_num and threshold).
• After the first step, H3K4me3 and Pol II data, miRNA expression profiles and DHS check, protein-coding genes expression profiles, if provided, will be integrated to decrease the rate of false positive.

There will be different circumstances where not all miRNA expression profiles, DHS data, protein-coding gene(‘gene’) expression profiles are available:

Circumstance 1: no miRNA expression data; then suggest DHS check and protein-coding gene check.

• ignore_DHS_check: If users do not have their own miRNA expression profile, the function will employ all the miRNAs already annotated in human, but we suggest using DHS data of the cell line from ENCODE to check whether this miRNA is expressed in the cell line or not as well as and all human gene expression profiles from Ensemble to check the relative position of TSSs and protein-coding genes to improve the accuracy of prediction.

peakfile <- system.file("testdata", "HMEC_h3.csv", package = "primirTSS")
DHSfile <- system.file("testdata", "HMEC_DHS.csv", package = "primirTSS")
peak_h3 <- read.csv(peakfile, stringsAsFactors = FALSE)
DHS <- read.csv(DHSfile, stringsAsFactors = FALSE)
DHS <- as(DHS, "GRanges")
peak_h3 <-  as(peak_h3, "GRanges")
peak <- peak_merge(peak_h3)

no_ownmiRNA <- find_tss(peak, ignore_DHS_check = FALSE,
                        DHS = DHS, allmirdhs_byforce = FALSE,
                        expressed_gene = "all",
                        allmirgene_byforce = FALSE,
                        seek_tf = FALSE)

Circumstance 2: miRNA expression data provided; then no need for DHS check but protein-coding gene check.

• expressed_mir: If users have their own miRNA expression profiles, we will use the expressed miRNAs and we suggest not using DHS data of the cell line or others to check the expression of miRNAs.But the protein-coding gene check to check the relative position of TSSs and protein-coding genes is necessary, which helps to verify the precision of prediction.

bed_merged <- data.frame(
                chrom = c("chr1", "chr1", "chr1", "chr1", "chr2"),
                start = c(9910686, 9942202, 9996940, 10032962, 9830615),
                end = c(9911113, 9944469, 9998065, 10035458, 9917994),
                stringsAsFactors = FALSE)
bed_merged <- as(bed_merged, "GRanges")

expressed_mir <- c("hsa-mir-5697")

ownmiRNA <- find_tss(bed_merged, expressed_mir = expressed_mir,
                     ignore_DHS_check = TRUE,
                     expressed_gene = "all",
                     allmirgene_byforce = TRUE,
                     seek_tf = FALSE)

• expressed_gene: Additionally, users can also specify certain genes expressed in the cell-line being analyzed:

Step 3: Searching for TFs

• seek_tf = TRUE: If user want to predict transcriptional regulation relationship between TF and miRNA, like which TFs might regulate miRNA after get TSSs, they can change seek_tf = FALSE from seek_tf = TRUE directly in the comprehensive function find_TSS().

Step4: Analysis of results

Here is a demo of predicting TSS for hsa-mir-5697, ignore DHS check.

PART1, $tss_df:

ownmiRNA$tss_df
#> # A tibble: 1 × 10
#>   mir_name     chrom stem_loop_p1 stem_loop_p2 strand mir_context tss_type gene 
#>   <chr>        <chr>        <dbl>        <dbl> <chr>  <chr>       <chr>    <chr>
#> 1 hsa-mir-5697 chr1       9967381      9967458 +      intra       host_TSS ENSG…
#> # ℹ 2 more variables: predicted_tss <dbl>, pri_tss_distance <dbl>

The first part of the result returns details of predicted TSSs, composed of seven columns: mir_name, chrom, stem_loop_p1, stem_loop_p2, strand mir_context, tss_type gene and predicted_tss:

TSSs are cataloged into 4 types as below:

• host_TSS: The TSSs of miRNA that are close to the TSS of protein-coding gene implying they may share the same TSS, on the condition where mir_context = intra. (See above: mir_context)

• intra_TSS: The TSSs of miRNA that are NOT close to the TSS of protein-coding gene, on the condition where mir_context = intra.

• overlap_inter_TSS: The TSSs of miRNA are cataloged as overlap_inter_TSS when the pri-miRNA gene overlaps with Ensembl gene, on the condition where “mir_context = inter”.

• inter_inter_TSS: The TSSs of miRNA are cataloged as inter_inter_TSS when the miRNA gene does NOT overlap with Ensembl gene, on the condition where “mir_context = inter”.

(See Xu HUA et al 2016 for more details)

PART2, $log:

The second part of the result returns 4 logs created during the process of prediction:

• find_nearest_peak_log: If no peaks locate in the upstream of a stem-loop to help determine putative TSSs of miRNA, we will fail to find the nearest peak, and this miRNA will be logged in find_nearest_peak_log.

• eponine_score_log: For a certain miRNA, if none of the candidate TSSs scored with Eponine method meet the threshold we set, we will fail to get an eponine score, and this miRNA will be logged in eponine_score_log.

• DHS_check_log: For a certain miRNA, if no DHS signals locate within 1 kb upstream of each putative TSSs, these putative TSSs will be filtered out, and this miRNA will be logged in DHS_check_log.

• gene_filter_log: For a certain miRNA, when integrating expressed_gene data to improve prediction, if no putative TSSs are confirmed after considering the relative position relationship among TSSs, stem-loops and expressed genes, this miRNA will be filtered out and logged in gene_filter_log.

TAG1: Find the best putative TSS

　Figure S2. Graphical web interface of Find pri-miRNA TSS()

As Figure S2 shows, if we want to use the shiny app, we should select the appropriate options or upload the appropriate files. Histone peaks, Pol II peaks and DHS files are comma-separated values (CSV) files, whose first line is chrom,start,end. Every line of miRNA expression profiles has only one miRNA name which start with hsa-mir, such as hsa-mir-5697. Every line of gene expression profiles has only one gene name which derived from Ensembl, such as ENSG00000261657. All of miRNA expression profiles and gene expression profiles do not have column names. If we have prepared, we can push the Start the analysis button to start finding the TSSs. The process of analysis may need to take a few minutes, and a process bar will appear in right corner.

As a result, we will view first six rows of the result. The first five columns are about miRNA information, next five columns are about TSS information. The column of gene denotes the gene whose TSS is closest to the miRNA TSS. The column of pri_tss_distance denotes the distance between miRNA TSS and stem-loop. If users choose to get TFs simultaneously, they will have an additional column, tf, which stores related TFs.

TAG2: Plot pri-miRNA TSS

　Figure S3. Graphical web interface of Plot pri-miRNA TSS()

As Figure S4 shows, if we select the appropriate options and upload the appropriate files, we can have a picture of miRNA TSSs.