Background: Binding of transcription factors to transcription factor binding sites (TFBSs) is key to the mediation of transcriptional regulation. Information on experimentally validated functional TFBSs is limited and consequently there is a need for accurate prediction of TFBSs for gene annotation and in applications such as evaluating the effects of single nucleotide variations in causing disease. TFBSs are generally recognized by scanning a position weight matrix (PWM) against DNA using one of a number of available computer programs. Thus we set out to evaluate the best tools that can be used locally (and are therefore suitable for large-scale analyses) for creating PWMs from high-throughput ChIP-Seq data and for scanning them against DNA. Results: We evaluated a set of de novo motif discovery tools that could be downloaded and installed locally using ENCODE-ChIP-Seq data and showed that rGADEM was the best-performing tool. TFBS prediction tools used to scan PWMs against DNA fall into two classes -those that predict individual TFBSs and those that identify clusters. Our evaluation showed that FIMO and MCAST performed best respectively. Conclusions: Selection of the best-performing tools for generating PWMs from ChIP-Seq data and for scanning PWMs against DNA has the potential to improve prediction of precise transcription factor binding sites within regions identified by ChIP-Seq experiments for gene finding, understanding regulation and in evaluating the effects of single nucleotide variations in causing disease.

Antibodies are key molecules of the adaptive immune response and are now a major class of biopharmaceuticals. Pairing of heavy and light chains is one of the ways of generating antibody diversity and, while little is known about mechanisms governing V(H)/V(L) pairing, previous studies have suggested that the germline source from which chains are paired is random. By selecting paired antibody protein sequences from human and mouse antibodies from the KabatMan database and mapping them onto their corresponding germline sequences, we find that pairing preferences do exist in the germline, but only for a small proportion of germline gene segments; others are much more promiscuous showing no preferences. The closest equivalent human and mouse gene families were identified and pairing preferences compared. This work may impact on the ability to generate more stable antibodies for use as biopharmaceuticals.