Translation of mRNA into protein is a unidirectional information flow process. Analysing the input (mRNA) and output (protein) of translation, we find that local protein structure information is encoded in the mRNA nucleotide sequence. The Coding Sequence and Structure (CSandS) database developed in this work provides a detailed mapping between over 4000 solved protein structures and their mRNA. CSandS facilitates a comprehensive analysis of codon usage over many organisms. In assigning translation speed, we find that relative codon usage is less informative than tRNA concentration. For all speed measures, no evidence was found that domain boundaries are enriched with slow codons. In fact, genes seemingly avoid slow codons around structurally defined domain boundaries. Translation speed, however, does decrease at the transition into secondary structure. Codons are identified that have structural preferences significantly different from the amino acid they encode. However, each organism has its own set of ‘significant codons’. Our results support the premise that codons encode more information than merely amino acids and give insight into the role of translation in protein folding.

The importance of serum immunoglobulin (Ig)G concentration in IgG replacement therapy for primary immunodeficiency diseases is established in certain settings. Generally, IgG is infused via the intravenous (IVIG) or subcutaneous (SCIG) route. For IVIG infusion, published data demonstrate that higher IgG doses and trough levels provide patients with improved protection from infection. The same conclusions are not yet accepted for SCIG; data from two recent Phase III studies and a recent post‐hoc analysis, however, suggest the same correlation between higher SCIG dose and serum IgG concentration and decreased incidence of infection seen with IVIG. Other measures of clinical efficacy have not been considered similarly. Thus, combined analyses of these and other published SCIG studies were performed; a full comparison of the 13 studies was, however, limited by non‐standardized definitions and reporting. Despite these limitations, our analyses indicate that certain clinical outcomes improve at higher SCIG doses and associated higher serum IgG concentrations, and suggest that there might be opportunity to improve patient outcomes via SCIG dose adjustment.

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.

Several meta-analyses comparing ciclosporin with tacrolimus have been conducted since the 1994 publication of the tacrolimus registration trials, but most captured data from randomized controlled trials (RCTs) predating recent improvements in waiting list prioritization, induction protocols and concomitant medications. The present study comprised a systematic review and meta-analysis of ciclosporin and tacrolimus in liver transplant recipients using studies published since January 2000. Searches of PubMed, the Cochrane Library and EMBASE identified RCTs of tacrolimus and ciclosporin as the immunosuppressant in adult primary liver transplant recipients, published between January 2000 and August 6, 2014. A random effects meta-analysis was conducted to evaluate the relative risk of death, graft loss, acute rejection (AR), new-onset diabetes after transplantation (NODAT) and hypertension with tacrolimus relative to ciclosporin at 12 months. The literature search identified 11 RCTs comparing ciclosporin with tacrolimus. Relative to ciclosporin, tacrolimus was associated with significantly improved outcomes in terms of patient mortality (risk ratio [RR] with ciclosporin of 1.26; 95% confidence interval [95%CI] 1.01–1.58). Tacrolimus was superior to ciclosporin in terms of hypertension (RR with ciclosporin 1.26; 95%CI 1.07–1.47), but inferior in terms of NODAT (RR with ciclosporin 0.60; 95%CI 0.47–0.77). There were no significant differences between ciclosporin and tacrolimus in terms of graft loss or AR. Meta-analysis of RCTs published since 2000 showed tacrolimus to be superior to ciclosporin in terms of patient mortality and hypertension, while ciclosporin was superior in terms of NODAT. No significant differences were identified in terms of graft loss or AR. These findings provide further evidence supporting the use of tacrolimus as the cornerstone of immunosuppressive therapy in liver transplant recipients.

Motivation: The discovery of CRISPR-Cas systems almost 20 years ago rapidly changed our perception of the bacterial and archaeal immune systems. CRISPR loci consist of several repetitive DNA sequences called repeats, inter-spaced by stretches of variable length sequences called spacers. This CRISPR array is transcribed and processed into multiple mature RNA species (crRNAs). A single crRNA is integrated into an interference complex, together with CRISPR-associated (Cas) proteins, to bind and degrade invading nucleic acids. Although existing bioinformatics tools can recognize CRISPR loci by their characteristic repeat-spacer architecture, they generally output CRISPR arrays of ambiguous orientation and thus do not determine the strand from which crRNAs are processed. Knowledge of the correct orientation is crucial for many tasks, including the classification of CRISPR conservation, the detection of leader regions, the identification of target sites (protospacers) on invading genetic elements and the characterization of protospacer-adjacent motifs. Results: We present a fast and accurate tool to determine the crRNA-encoding strand at CRISPR loci by predicting the correct orientation of repeats based on an advanced machine learning approach. Both the repeat sequence and mutation information were encoded and processed by an efficient graph kernel to learn higher-order correlations. The model was trained and tested on curated data comprising >4500 CRISPRs and yielded a remarkable performance of 0.95 AUC ROC (area under the curve of the receiver operator characteristic). In addition, we show that accurate orientation information greatly improved detection of conserved repeat sequence families and structure motifs. We integrated CRISPRstrand predictions into our CRISPRmap web server of CRISPR conservation and updated the latter to version 2.0. Availability: CRISPRmap and CRISPRstrand are available at http://rna.informatik.uni-freiburg.de/CRISPRmap. Contact: backofen@informatik.uni-freiburg.de Supplementary information: Supplementary data are available at Bioinformatics online.