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Old 01-23-2009, 11:16 PM   #1
forevermark4
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Default Updated How to convert .txt file to .bed .GFF or .BAR file format,

Hi Everyone,

Myself Yogesh kumar and I am new in illumina solexa work for chip seq analysis (cisgenome software).. I need only help to conert this dataset in format which supported by UCSC browser or cisgenome software..Other analysis part I can do.. I need your help I attached my datasets links

http://www.well.ox.ac.uk/htseq/qXZxnawHREhej1c2lFut

http://www.well.ox.ac.uk/htseq/qXZxn...s_1_export.txt

http://www.well.ox.ac.uk/htseq/qXZxn...s_3_export.txt

About these datasets some information

The “s_#” suffix stands for the lane number used in the flow cell.
The “export” format is defined by multiple, tab separated, columns.
For the definition of each column see below.


EXPORT file definitions

Export files are generated by the GERALD step* of the Illumina pipeline. The program called ELAND aligns each read from each lane to the reference genome (in our case the Mus Musculus genome, UCSC release MM8).
When a match is found, then the relative position on the genome is reported on the “s_#_export.txt” file (only chromosome, start position and strand F or R).
If no match is found a “NM” (no match) is reported.
There is a line for each read, whether it aligns or not, and multiple lines for the same read if it aligns in multiple positions.
You can either parse yourself these files (R-Bioconductor, Perl script …) or use public available software (eg CisGenome).
The file contains information on the physical position of the reads in the flow cell, the nucleotide sequence of the read itself, a string of the quality call for each nucleotide in the read (a code developed by Illumina), various flags and the genomic position (if found).
For many purposes not all these information are needed.

*the illumina pipeline consists in several steps, starting from cluster recognition, passing through basecalling and ending with the alignment of the bases to the reference genome.
Not all fields are relevant to a single-read analysis.
1. Machine (Parsed from Run Folder name)
2. Run Number (Parsed from Run Folder name)
3. Lane
4. Tile
5. X Coordinate of cluster
6. Y Coordinate of cluster
7. Index string (Bland for a non-indexed run)
8. Read number (1 or 2 for paired-read analysis, blank for a single-read analysis)
9. Read
10. Quality string—In symbolic ASCII format (ASCII character code = quality value + 64) by default (Set QUALITY_FORMAT --numeric in theGERALD config file for numeric values)
11. Match chromosome—Name of chromosome match OR code indicating why no
match resulted
12. Match Contig—Gives the contig name if there is a match and the match
chromosome is split into contigs (Blank if no match found)
13. Match Position—Always with respect to forward strand, numbering starts at 1 (Blank if no match found)
14. Match Strand—“F” for forward, “R” for reverse (Blank if no match found)
15. Match Descriptor—Concise description of alignment (Blank if no match found)
• A numeral denotes a run of matching bases
• A letter denotes substitution of a nucleotide:
For a 35 base read, “35” denotes an exact match and “32C2” denotes substitution
of a “C” at the 33rd position
16. Single-Read Alignment Score—Alignment score of a single-read match, or for a paired read, alignment score of a read if it were treated as a single read (Blank if no match found)
17. Paired-Read Alignment Score—Alignment score of a paired read and its partner, taken as a pair (Blank for single-read analysis)
18. Partner Chromosome—Name of the chromosome if the read is paired and its partner aligns to another chromosome (Blank for single-read analysis)
19. Partner Contig—Not blank if read is paired and its partner aligns to another
chromosome and that partner is split into contigs (Blank for single-read analysis)
20. Partner Offset—If a partner of a paired read aligns to the same chromosome and contig, this number, added to the Match Position, gives the alignment position of the partner (Blank for single-read analysis)
21. Partner Strand—To which strand did the partner of the paired read align? “F” for forward, “R” for reverse (Blank if no match found, blank for single-read analysis)
22. Filtering—Did the read pass quality filtering? “Y” for yes, “N” for no

Are you have any idea How can we convert these files to WIG, BED and GFF for the UCSC. Any one format is sufficient. for me . otherwise how can we convert .txt file to .BED file.. I am planning to use cisgenome (two sample analysis) software

to look at data mapped on their genomic original contest.

It 'll be great favour for me

Thanks
Yogesh Kumar
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Old 02-02-2009, 11:28 AM   #2
inesdesantiago
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ShortReads package (R) is a good one to help dealing with _export files.
ines

Last edited by inesdesantiago; 02-02-2009 at 03:31 PM.
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Old 06-30-2014, 05:02 AM   #3
inesdesantiago
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You could also use GenomeIntervals2BED.py script available within the SeqGI framework.
Have a look: http://seqgi.sourceforge.net/Genomeintervals2bed.html
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