Mutual enrichment in ranked lists and the statistical assessment of position weight matrix motifs.

Background: Statistics in ranked lists is useful in analysing molecular biology measurement data, such as differential expression, resulting in ranked lists of genes, or ChIP-Seq, which yields ranked lists of genomic sequences. State of the art methods study fixed motifs in ranked lists of sequences. More flexible models such as position weight matrix (PWM) motifs are more challenging in this context, partially because it is not clear how to avoid the use of arbitrary thresholds.

DRIMust: a web server for discovering rank imbalanced motifs using suffix trees.

Cellular regulation mechanisms that involve proteins and other active molecules interacting with specific targets often involve the recognition of sequence patterns. Short sequence elements on DNA, RNA and proteins play a central role in mediating such molecular recognition events. Studies that focus on measuring and investigating sequence-based recognition processes make use of statistical and computational tools that support the identification and understanding of sequence motifs.

Efficient motif search in ranked lists and applications to variable gap motifs.

Sequence elements, at all levels-DNA, RNA and protein, play a central role in mediating molecular recognition and thereby molecular regulation and signaling. Studies that focus on -measuring and investigating sequence-based recognition make use of statistical and computational tools, including approaches to searching sequence motifs. State-of-the-art motif searching tools are limited in their coverage and ability to address large motif spaces.

Divergent RNA binding specificity of yeast Puf2p.

PUF proteins bind mRNAs and regulate their translation, stability, and localization. Each PUF protein binds a selective group of mRNAs, enabling their coordinate control. We focus here on the specificity of Puf2p and Puf1p of Saccharomyces cerevisiae, which copurify with overlapping groups of mRNAs.

A structural-based statistical approach suggests a cooperative activity of PUM1 and miR-410 in human 3'-untranslated regions.

Background: Micro (mi)RNAs comprise a large family of small non-coding RNAs that are thought to regulate a large fraction of protein-coding genes. Generally, miRNAs downregulate messenger (m)RNA expression by binding to the 3' untranslated regions (UTRs) of the RNA molecules. An important factor for binding specificity is the matching in the seed region.