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TSS assembly pipeline for Hs_EPDnew_006

Introduction

This document provides a technical description of the transcription start site assembly pipeline that was used to generate EPDnew version 006 for H. sapiens.

Source Data

Promoter collection:

Name Genome Assembly Promoters Genes PMID Access data
Gencode Dec 2013 GRCh38/hg38 35320 17056 27250503 SOURCE DOC DATA

Experimental data:

Name Type Samples Tags PMID Access data
FANTOM5 CAGE 941 18,244,201,540 24670764 SOURCE DOC DATA
ENCODE CAGE 145 7,134,200,060 22955620 SOURCE DOC DATA
ENCODE RAMPAGE 225 13,540,041,874 22936248 SOURCE DOC DATA

Assembly pipeline overview

Description of procedures and intermediate data files

1. Download of annotated promoters

Data for the latest reference Human GENCODE release (v28) was downloaded from EBI FTP website. Transcrips were kept if they belong to a protein coding genes (flag 'gene_type' = 'protein_coding') and the transcript support level equal to 1 (all splice junctions of the transcript are supported by at least one non-suspect mRNA, this is the most stringent level).
Gene names were taken from the field "gene_name". Since the EPD format doesn't allow gene names longer than 18 characters, we checked whether the names repsected this limitation.
Transcripts with the same TSS position were merged under a common transcript ID. As a consequence, the total number of TSS in the list was 35320 covering 17056 protein coding genes.

2. Gencode TSS collection

The Gencode TSS collection is stored as a tab-deliminated text file conforming to the SGA format. The six fields in the file contain the following kinds of information:

  1. NCBI/RefSeq chromosome id
  2. "ENSEMBL"
  3. position
  4. strand ("+" or "-")
  5. "1"
  6. TranscriptID..GeneName.
Note that the second and forth fields are invariant.

3 Import Single-end sequencing data: CAGE

CAGE Tag Data were downloaded from UCSC ftp-site and FANTOM5 http-site (see links above). The source files are in bam format mapped on hg19 genome assembly. Samples were lifted-over to hg38 genome assembly using the liftOver tool. The complete list of files can be found here for ENCODE and here for FANTOM5. Bam files were converted into bed files with bamToBed program. Files were kept and analysed individually.

4. CAGE mapping data

The compressed versions of these files are available from the MGA archive (see links above).

5. CAGE peak calling

Peak calling for each individual CAGE and RAMPAGE data file has been carried out using ChIP-Peak on-line tool with the following parameters:
  • Window width = 1
  • Vicinity range = 200
  • Peak refine = N
  • Count cutoff = 9999999
  • Threshold = 5

6. RAMPAGE data analysis

RAMPAGE is a TSS mapping technique that uses paired-end sequencing to uniquely assign a 5'-end tag (first read in the pair) to an annotated gene using the 3'-end tag (the second read in the pair). This gives high confidence when assigning TSS to genes when they map outside gene boudaries (potentially very far from them). The new nature of the data required a specific analysis and a modification in the general EPDnew validation pipeline. As for CAGE data, each RAMPAGE sample was analysed separately and merged together only at the end to generate a RAMPAGE-specific promoter collection. The new analysis is chararacterised for the following steps: (1) Data import and annotation; (2) Peak calling (3) Peak selection and (4) Quality control. Each step will be brefely described.

Data import and annotation
Data was downloaded from the ENCODE web-site as BAM files mapped on GRCh38/hg38 genome assembly and converted into SGA format using the following procedure:

  1. Keep reads that have a good mapping score (5th field in the bed file = 255)
  2. Split the reads in a pair in to two files and keep the read ID
  3. Annotate the second read file if they map inside a gene exon and have the same orientation
  4. Annotate the first read file using the read ID
  5. Discard reads if not annotated

Peak calling
Peak calling was carried out as described for CAGE data. Since RAMPAGE tags were already annotated to a specific gene, RAMPAGE peaks retained the gene annotation given to the tags that composed them.

Peak selection
Annotated RAMPAGE peaks were selected if they map outside the gene boudaries they belong to.

Quality Control
Each sample-specific peak collection (225 TSS collections) was quality checked using the usual EPD QC parameters: density distribution of known Core Preomoter Elements (CPEs) around putative TSS. For this analysis we used two well known CPEs, the TATA-box and Initiator (Inr) that are found at position -29 and 0 relative to the TSS and mesured their frequencies in each of the 225 RAMPAGE-derived TSS collections. Sample specific TATA-box and Initiator frequencies are summarised in the following figure:

Overall RAMPAGE-derived TSS collections are of poor quality compared to CAGE-derived TSS collections (compare this figure with the QC controls done on CAGE data in the figure under 'Quality controls of sample-specific promoter collections'). Both TATA-box and Initiator frequencies are on average half of expected values. For this reason, RAMPAGE data was not used to directly generate a new EPDnew TSS collection but only to identify novel start sites that need to be validated by CAGE. To do so, samples specific RAMPAGE TSS were merged togheter and added to Gencode TSS collection. Gencode+RAMPAGE TSS validation was then carried out using CAGE data.

7. Gencode+RAMPAGE TSS validation dy CAGE data

Each sample in the collection (CAGE peaks and Gencode+RAMPAGE TSS) was then processed in a pipeline aiming at validating transcription start sites with CAGE peaks. A Gencode+RAMPAGE TSS was experimentally confirmed if a CAGE peak lied in a window of 200 bp around it or if mapped in the 5'UTR region of an annotated gene and if it had a maximum high of at least 5 tags (50 tags for peaks in the 5'UTR). The validated TSS was then shifted to the nearest base with the higher tag density. Secondary promoters for genes with multiple TSSs were discarded if their expression level was below 10% of the strongher gene-specific initiation site or below 10 tags.

8. Promoter collection for each sample

Each sample in the dataset was used to generate a separate promoter collection. Potentially, the same transcript could be validated by multiple samples and it could have different start sites in different samples. To avoid redundancy, the individual collections were used as input for an additional step in the analysis (Assembly pipeline part B).

9. Quality controls of sample-specific promoter collections

The quality of promoter collections derived from each sample was tested to exclude low quality samples from the final collection. To achive this, each promoter collection was scored according to the distribution of the TATA-box and Inr motif in the expected position (-29bp from the TSS and at the TSS respectively). Samples with very low motif frequencies (Inr frequency < 10% and TATA-box < 5%) were discarded (3 samples in total) from further analyses. The figure below shows the distribution of TATA-box and Inr in all sample-specific promoter collections:

10. Merging collections and further TSS selection

The good-quality promoter collections were merged into a unique file and further analysed. The promoter of a transcript was mantained in the list only if validated by at least 3 samples. We chosed this Cut-off value since it ensured a good increase in Inr and TATA-box friquency without affecting too much the total number of validated genes:

Transcript validated by multiple samples could potentially have the TSS set on a broader region and not to single position. To avoid such inconsistency, for each transcript we selected the position that was validated by the larger number of samples as the true TSS.

11. Filtering

Transcription Start Sites that mapped closed to other TSS that belonged to the same gene (100 bp window) were merged into a unique promoter following the same rule: the promoter that was validated by the higher number of samples was kept.

12. Final EPDnew collection

The experimentally validated promoters were stored in the EPDnew database that can be downloaded from our ftp site. Scientist are wellcome to use our other tools ChIP-Seq (for correlation analysis) and SSA (for motifs analysis around promoters) to analyse EPDnew database.

Last update July 2018