find_tss {primirTSS} | R Documentation

## Predict TSSs of miRNA

### Description

Search for putative TSSs of miRNA, together with integrating available data such as H3K4me3 data, Pol II data, miRNA expression data, and protein-coding gene data, as well as provide the transcriptional regulation relationship between TF and miRNA.

### Usage

    find_tss(
      bed_merged,
      expressed_mir = "all",
      flanking_num = 1000,
      threshold = 0.7,
      ignore_DHS_check = TRUE,
      DHS,
      allmirdhs_byforce = TRUE,
      expressed_gene = "all",
      allmirgene_byforce = TRUE,
      seek_tf = FALSE,
      tf_n = 1000,
      min.score = 0.8
    )

### Arguments

`bed_merged` | Peaks from ChIP-seq data to be provided for analysis can be H3K4me3 peaks, Pol II peaks or both. Notice that peaks are supposed to be merged(see also `peak_merge`) before `find_TSS` if using only one kind of peak data, while peaks should be firstly merged and then join together(see also `peak_join`) if both H3K4me3 data and Pol II are input.
`expressed_mir` | This parameter allows users to specify certain miRNAs, the TSSs of which they want to search for by providing a list of miRNAs(e.g., expressed miRNAs in a certain cell-line). If `expressed_mir` is not specified, the default value of the parameter is "`all`" and the function will acquiescently employ all the miRNAs currently listed on "`miRbase`" database.
`flanking_num` | A parameter in Eponine model to detect TSSs. It is concluded that a peak signal with flanking regions of C-G enrichment are important to mark TSSs. The default value is 1000.
`threshold` | The threshold for candidate TSSs scored with Eponine method. The default value is 0.7.
`ignore_DHS_check` | The process of DHS_check further assists to filter putative TSSs. When there is a DHS peak that locates within 1 kb upstream of a putative TSS, this predicted TSS will be retained for its character is consistent with that of an authentic TSS. Or the TSSs with no DHSs locating within 1 kb upstream of them would be discarded.
`DHS` | ChIP-seq data of DNase I hypersensitive sites(DHSs).
`allmirdhs_byforce` | When we use DHS data to check the validity of TSSs, there is a possibility where no DHSs locates within 1 kb upstream of all putative TSSs and all these putative TSSs might be filtered out by our method resulting no outputs. While "`allmirdhs_byforce` = TRUE", it ensures to output at least 1 most possible TSS even if the nearest DHS signal locates more than 1 kb upstream of this TSS.
`expressed_gene` | Users can specify genes expressed in certain cell-lines that are analyzed. Or the default value is "`all`", which means all the expressed genes annotated on Ensemble will be employed.
`allmirgene_byforce` | While integrating expressed_gene data to improve prediction, there might be a circumstance where all the putative TSS are discarded. To prevent this condition, users are allowed to use "`allmirgene_byforce = TRUE`" to ensure at least 1 putative TSS for each miRNA will be output.
`seek_tf` | With the result of predicted TSSs, seek_tf provides users with an option to predict related TFs for miRNA. The data of transcription factors refer to `JASPAR2018` database.
`tf_n` | TFBS locates on the upstream of the TSS of a certain TF, which is considered as the promoter region. `tf_n` set the length of promoter region for predicting transcription regulation between miRNAs and TFs.
`min.score` | The threshold for scoring transcription factor binding sites. A single absolute value between 0 and 1.

### Value

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`:

`mir_name`: Name of miRNA.

`chrom`: Chromosome.

`stem_loop_p1`: The start site of a stem-loop.

`stem_loop_p2`: The end site of a stem-loop.

`strand`: Polynucleotide strands. (`+/-`)

`mir_context`: The relative positon relationship between stem-loop and protein-coding gene. (`intra/inter`)

`tss_type`: Four types of predicted TSSs. See the section below `TSS types` for details. (`host_TSS/intra_TSS/overlap_inter_TSS/inter_TSS`)

`gene`: Ensembl gene ID

`predicted_tss`: Predicted transcription start sites(TSSs).

`pri_tss_distance`: The distance between a predicted TSS and the start site of the stem-loop.

### TSS types

TSSs are catalogued 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` is "`intra`". (See above: `Value-mir_context`)

`intra_TSS` The TSSs of miRNA that are NOT close to the TSS of the protein-coding gene, on the condition where `mir_context` is "`intra`".

`overlap_inter_TSS` The TSSs of miRNA are catalogued as "`overlap_inter_TSS`" when the pri-miRNA gene overlaps with Ensembl gene, on the condition where "`mir_context`" is "`inter`".

`inter_inter_TSS` The TSSs of miRNA are catalogued as "`inter_inter_TSS`" when the miRNA gene does NOT overlap with Ensembl gene, on the condition where "`mir_context`" is "`inter`".

(See Xu HUA et al 2016 for more details)

### Log

The second part of the result returns logs 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`.

### Reference

Xu Hua, Luxiao Chen, Jin Wang*, Jie Li* and Edgar Wingender*, Identifying cell-specific microRNA transcriptional start sites. Bioinformatics 2016, 32(16), 2403-10.

### Examples

    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")

    ## Not run:
    ownmiRNA <- find_tss(bed_merged, expressed_mir = "hsa-mir-5697",
                         ignore_DHS_check = TRUE,
                         expressed_gene = "all",
                         allmirgene_byforce = TRUE)

    ## End(Not run)

[Package primirTSS version 1.10.0 Index]