ChIP-seq data:
Filename | Description | Feature | GEO-ID | |
1 | input_liver_cfa3.sga | liver|input||cfa3 | input | GSM548893 |
2 | CEBPA_liver_cfa3.sga | liver|CEBPA||cfa3 | CEBPA | GSM548917, GSM548918, GSM548919 |
3 | HNF4A_liver_cfa3.sga | liver|HNF4A||cfa3 | HNF4A | GSM548912, GSM548913, GSM548914 |
4 | input_liver_cfa4.sga | liver|input||cfa4 | input | GSM548865, GSM548866 |
5 | CEBPA_liver_cfa4.sga | liver|CEBPA||cfa4 | CEBPA | GSM548904, GSM548905, GSM548906 |
6 | HNF4A_liver_cfa4.sga | liver|HNF4A||cfa4 | HNF4A | GSM548915, GSM548916 |
cfa3 and cfa4 referes to the two individuals from which the data were generated.
Data were downloaded from ArrayExpress. For input, CEBPA and HNF4A ChIP-seq data corresponding to the same individual (cfa3 and cfa4), were merged, so one sample corresponds to several GEO entries.
FASTQ files were mapped to the genome using Bowtie v0.12.8. SAM files were then converted into bam using samtools v0.1.14 and to bed using bamToBed v2.12.0 (bedtools). SGA conversion was carried out using bed2sga.pl (ChIP-Seq v. 1.5.3). SGA files containing data for the same factor, for the same individual were merged.