We present GraphProt, a computational framework for learning sequence- and structure-binding preferences of RNA-binding proteins (RBPs) from high-throughput experimental data. We benchmark GraphProt, demonstrating that the modeled binding preferences conform to the literature, and showcase the biological relevance and two applications of GraphProt models. First, estimated binding affinities correlate with experimental measurements. Second, predicted Ago2 targets display higher levels of expression upon Ago2 knockdown, whereas control targets do not. Computational binding models, such as those provided by GraphProt, are essential for predicting RBP binding sites and affinities in all tissues. GraphProt is freely available at http://www.bioinf.uni-freiburg.de/Software/GraphProt.

Motivation : The CRISPR-Cas system is an adaptive immune system in many archaea and bacteria, which provides resistance against invading genetic elements. The first phase of CRISPR-Cas immunity is called adaptation, in which small DNA fragments are excised from genetic elements and are inserted into a CRISPR array generally adjacent to its so called leader sequence at one end of the array. It has been shown that transcription initiation and adaptation signals of the CRISPR array are located within the leader. However, apart from promoters, there is very little knowledge of sequence or structural motifs or their possible functions. Leader properties have mainly been characterized through transcriptional initiation data from single organisms but large-scale characterization of leaders has remained challenging due to their low level of sequence conservation. Results : We developed a method to successfully detect leader sequences by focusing on the consensus repeat of the adjacent CRISPR array and weak upstream conservation signals. We applied our tool to the analysis of a comprehensive genomic database and identified several characteristic properties of leader sequences specific to archaea and bacteria, ranging from distinctive sizes to preferential indel localization. CRISPRleader provides a full annotation of the CRISPR array, its strand orientation as well as conserved core leader boundaries that can be uploaded to any genome browser. In addition, it outputs reader-friendly HTML pages for conserved leader clusters from our database. Availability and Implementation:CRISPRleader and multiple sequence alignments for all 195 leader clusters are available at http://www.bioinf.uni-freiburg.de/Software/CRISPRleader/ . Contact:costa@informatik.uni-freiburg.de or backofen@informatik.uni-freiburg.de Supplementary information:Supplementary data are available at Bioinformatics online.

Tissue-specific alternative splicing is critical for the emergence of tissue identity during development, yet the role of this process in malignant transformation is undefined. Tissue-specific splicing involves evolutionarily conserved, alternative exons that represent only a minority of the total alternative exons identified. Many of these conserved exons have functional features that influence signaling pathways to profound biological effect. Here, we determined that lineage-specific splicing of a brain-enriched cassette exon in the membrane-binding tumor suppressor annexin A7 (ANXA7) diminishes endosomal targeting of the EGFR oncoprotein, consequently enhancing EGFR signaling during brain tumor progression. ANXA7 exon splicing was mediated by the ribonucleoprotein PTBP1, which is normally repressed during neuronal development. PTBP1 was highly expressed in glioblastomas due to loss of a brain-enriched microRNA (miR-124) and to PTBP1 amplification. The alternative ANXA7 splicing trait was present in precursor cells, suggesting that glioblastoma cells inherit the trait from a potential tumor-initiating ancestor and that these cells exploit this trait through accumulation of mutations that enhance EGFR signaling. Our data illustrate that lineage-specific splicing of a tissue-regulated alternative exon in a constituent of an oncogenic pathway eliminates tumor suppressor functions and promotes glioblastoma progression. This paradigm may offer a general model as to how tissue-specific regulatory mechanisms can reprogram normal developmental processes into oncogenic ones.

Aptamers, short RNA or DNA molecules that bind distinct targets with high affinity and specificity, can be identified using high-throughput systematic evolution of ligands by exponential enrichment (HT-SELEX), but scalable analytic tools for understanding sequence-function relationships from diverse HT-SELEX data are not available. Here we present AptaTRACE, a computational approach that leverages the experimental design of the HT-SELEX protocol, RNA secondary structure, and the potential presence of many secondary motifs to identify sequence-structure motifs that show a signature of selection. We apply AptaTRACE to identify nine motifs in C-C chemokine receptor type 7 targeted by aptamers in an in vitro cell-SELEX experiment. We experimentally validate two aptamers whose binding required both sequence and structural features. AptaTRACE can identify low-abundance motifs, and we show through simulations that, because of this, it could lower HT-SELEX cost and time by reducing the number of selection cycles required.

Motivation: Clustering according to sequence–structure similarity has now become a generally accepted scheme for ncRNA annotation. Its application to complete genomic sequences as well as whole transcriptomes is therefore desirable but hindered by extremely high computational costs. Results: We present a novel linear-time, alignment-free method for comparing and clustering RNAs according to sequence and structure. The approach scales to datasets of hundreds of thousands of sequences. The quality of the retrieved clusters has been benchmarked against known ncRNA datasets and is comparable to state-of-the-art sequence–structure methods although achieving speedups of several orders of magnitude. A selection of applications aiming at the detection of novel structural ncRNAs are presented. Exemplarily, we predicted local structural elements specific to lincRNAs likely functionally associating involved transcripts to vital processes of the human nervous system. In total, we predicted 349 local structural RNA elements. Availability: The GraphClust pipeline is available on request. Contact: backofen@informatik.uni-freiburg.de Supplementary information: Supplementary data are available at Bioinformatics online.