Bayesian Markov models improve the prediction of binding motifs beyond first order.

Transcription factors (TFs) regulate gene expression by binding to specific DNA motifs. Accurate models for predicting binding affinities are crucial for quantitatively understanding of transcriptional regulation. Motifs are commonly described by position weight matrices, which assume that each position contributes independently to the binding energy.

The BaMM web server for de-novo motif discovery and regulatory sequence analysis.

The BaMM web server offers four tools: (i) de-novo discovery of enriched motifs in a set of nucleotide sequences, (ii) scanning a set of nucleotide sequences with motifs to find motif occurrences, (iii) searching with an input motif for similar motifs in our BaMM database with motifs for >1000 transcription factors, trained from the GTRD ChIP-seq database and (iv) browsing and keyword searching the motif database. In contrast to most other servers, we represent sequence motifs not by position weight matrices (PWMs) but by Bayesian Markov Models (BaMMs) of order 4, which we showed previously to perform substantially better in ROC analyses than PWMs or first order models. To address the inadequacy of P- and E-values as measures of motif quality, we introduce the AvRec score, the average recall over the TP-to-FP ratio between 1 and 100.

First Insights into the Genome of the Amino Acid-Metabolizing Bacterium Clostridium litorale DSM 5388.

Clostridium litorale is a Gram-positive, rod-shaped, and spore-forming bacterium, which is able to use amino acids such as glycine, sarcosine, proline, and betaine as single carbon and energy sources via Stickland reactions. The genome consists of a circular chromosome (3.41 Mb) and a circular plasmid (27 kb).