Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing

Abstract

We developed a method, ChIP-sequencing (ChIP-seq), combining chromatin immunoprecipitation (ChIP) and massively parallel sequencing to identify mammalian DNA sequences bound by transcription factors in vivo. We used ChIP-seq to map STAT1 targets in interferon-gamma (IFN-gamma)-stimulated and unstimulated human HeLa S3 cells, and compared the method's performance to ChIP-PCR and to ChIP-chip for four chromosomes. By ChIP-seq, using 15.1 and 12.9 million uniquely mapped sequence reads, and an estimated false discovery rate of less than 0.001, we identified 41,582 and 11,004 putative STAT1-binding regions in stimulated and unstimulated cells, respectively. Of the 34 loci known to contain STAT1 interferon-responsive binding sites, ChIP-seq found 24 (71%). ChIP-seq targets were enriched in sequences similar to known STAT1 binding motifs. Comparisons with two ChIP-PCR data sets suggested that ChIP-seq sensitivity was between 70% and 92% and specificity was at least 95%.

Genome-wide identification and annotation of HIF-1α binding sites in two cell lines using massively parallel sequencing

“…1), we searched for HIF-1a binding sites by quantifying the local enrichment of tags (Robertson et al 2007) (see ''Materials and methods''). Next, we classified the putatively identified binding sites as ''hypoxia-responsive'' or ''non-hypoxia-responsive.''…”

“…In the HIF-1a ChIP-Seq analysis, if the identified regions demonstrated an overlap between 1% O 2 (hypoxia) and 21% O 2 (normoxia), regions with more than a twofold (hypoxia/ normoxia) change in peak strength were identified as ''bindings enriched in hypoxia.'' The statistical significance determined for this selection procedure compared to the background rate was evaluated using Poisson probabilities as previously described (Robertson et al 2007):Pðx; kÞ ¼ 1 À P x t¼0 e Àk k t t! ;where P(x,k) is the probability of enrichment, k is the expected tag number in the 121-bp window calculated for the WCE sample, and x is the observed tag number in the 121-bp window.…”

“…For example, the Illumina GA sequencer can sequence 200-250 million sequences per run. By utilizing Illumina GA and ChIP technologies (ChIP-Seq), the binding sites of several transcription factors have been reported (Valouev et al 2008;Robertson et al 2007;Kwon et al 2009;Welboren et al 2009;Park 2009). On the other hand, we also developed a method for sequencing the 5 0 -ends of mRNAs by combining our oligo-capping technique with the Illumina GA sequencer (Tsuchihara et al 2009).…”

Disease-associated loci are significantly over-represented among genes bound by transcription factor 7-like 2 (TCF7L2) in vivo

“…However, ChIP-sequencing, which combines ChIP and massively parallel sequencing, can be used to directly identify the DNA sequences bound by transcription factors, including strength of binding, in vivo [17]. Here, we performed global DNA sequence analysis of triplicate TCF7L2 ChIP samples in the human colorectal cell line, HCT116, where TCF7L2 is abundantly produced.…”

Genome-Wide Mapping of RNA Pol-II Promoter Usage in Mouse Tissues by ChIP-Seq

“…The knowledge of alternative promoter usage in different mammalian tissues is very limited and cannot be addressed without high-resolution genome-wide mapping of the promoter regions. However, the high-throughput approaches, such as CAGE (14), deepCAGE (15), ChIP-chip (16,17) or ChIP-seq (7), to annotate promoters at genome level need to be applied with caution because of the inherent problems with each method. For example, cytoplasmic enzyme complexes can add caps to 5 0 -monophosphate RNA molecules generated by ribonuclease cleavage (18), and hence CAGE tags could represent 5 0 ends of RNAs generated by cleavage and subsequent re-capping (19).…”