I have downloaded a histone mark data from roadmap epigenomics:
CD4+_CD25-_CD45RO+_memory_primary_cells/H3K4me3/GSM772862_BI.CD4+_CD25-_CD45RO+_Memory_Primary_Cells.H3K4me3.Donor_62.bed

but I am not quite sure I understand the content. It is chip-seq analysis, with peak calling, so I expected to see coordinates of the peaks and their height.

However I see such information in a file: chromosome, start, end, id, something, strand

```
chr1    9984    10183    B09JPABXX110526:5:1101:19182:47634    0    -
chr1    9990    10189    B09JPABXX110526:5:2207:9781:41112    0    -
```

I was intended to use this data to generate plots with GVIZ to get such plots but I am not sure if start and end positions will be enough somehow.

<img alt="< image not found >" src="http://freeimagehosting.nl/site2/image.php?di=5V63" style="height:279px; width:384px" />
Ah, Roadmap data. It's a bit of a head-ache to understand it sometimes, but bear with me:

What you are looking at is raw (unconsolidated) input that was output by Pash mapper. It is a misformatted [bed file][1]. All values in start column should have one subtracted from their coordinate (not respecting the strand), i.e. it should be:

```
chr1    9983    10183    B09JPABXX110526:5:1101:19182:47634    0    -
chr1    9989    10189    B09JPABXX110526:5:2207:9781:41112    0    -
```

I am not really sure about what name parameter encodes or why the score is zero...

Once you fix this (using [bedtools slop][2], `bedtools slop -l 1 -r 0 -g hg19.genome`), just pass the fixed bed-file to some sort of pileup tool (for instance, MACS).

The way ROADMAP does it:

1. They shorten all the reads back to 36 (so its consistent across all experiments)
2. They filter duplicate reads, and [reads that could not be mapped][3] to the genome at all had they been 36bp long
3. They estimate fragment length using [SPP][4] and run [macs2][5] with appropriate fragment lengths (`--nomodel --ext-size=fragment_length`) to generate both peak lists and the signal/foldchange track.

I really suggest you skip doing the preprocessing yourself and just get the data from their [download page][6]

Namely you want to look at c (peak calling) or section d (signal tracks). Either go with consolidated reads for your cell line (consolidated = all technical/biological replicates lumped together, recommended), or with unconsolidated (which is what you are looking at at the moment).

 [1]: https://genome.ucsc.edu/FAQ/FAQformat.html#format1
 [2]: https://bedtools.readthedocs.org/en/latest/content/tools/slop.html
 [3]: https://sites.google.com/site/anshulkundaje/projects/mappability
 [4]: https://code.google.com/p/phantompeakqualtools/
 [5]: https://github.com/taoliu/MACS/
 [6]: http://egg2.wustl.edu/roadmap/web_portal/processed_data.html