Here is a paper that might be of interest:

A practical comparison of methods for detecting transcription factor binding sites in ChIP-seq experiments

We use our self developed method called GeneTrack (also discussed in the comparison above).

I use MACS, it seems pretty good to me.

QuEST is a good tool to use for Chip-Seq data analysis, I found it very easy to use and it can handle at the same time TF Chip-Seq and Histone Mark Chip-seq It is however a good practice to use more than one tool and then overlap the results, the paper that Istvan pointed to is a good one because it present an overview of the different approaches used for peak estimation, not all the tools use the same algorithm so we always have to expect difference even not so big but it depends on how stringeant are you in calculating your peaks. QuEST also allow the cration of wig and bed files that you can postprocess or visualize at UCSC Genome Browser.

There is also an interesting paper by Pepke et al called "Computation of Chip-Seq and RNA-seq studies" that could be useful for you

Hope that helps

The encode project has looked at multiple chip-seq peak callers and found that the ones that are most reproducible are MACS and PeakSeq

see figure2 here: http://www.stat.washington.edu/qli/IDR101.pdf

I typically use SISSRs or MACS for transcription factor ChIP-seq, and CCAT for histone mark ChIP-seq.

I collected some callers here https://github.com/crazyhottommy/ChIP-seq-analysis#peak-calling

A second vote for MACS 1.4.2. Aside from being open-source and well-documented, an interesting benefit is that the author tries to force you into the MACS user group in order to download the software. This seems annoying, but it's actually a huge help because Tao Liu quickly responds to user group queries and the discussions are very informative to follow.

Here some peak callers are discussed

Bailey T, Krajewski P, Ladunga I, Lefebvre C, Li Q, et al. (2013) Practical Guidelines for the Comprehensive Analysis of ChIP-seq Data. PLoS Comput Biol 9(11): e1003326. doi:10.1371/journal.pcbi.1003326

http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003326

I've used BayesPeak running in R. It is much easier to install than MACS (failed for me), which require some (strange to me) files.

But I'm not sure how it handle paired-end data and how to include biological replicates, so I can't recommend it. I'm still searching...

This is an interesting tool https://github.com/gersteinlab/MUSIC