Orthology analysis is an important part of data analysis in many areas of bioinformatics such as comparative genomics and molecular phylogenetics. The ever-increasing flood of sequence data, and hence the rapidly increasing number of genomes that can be compared simultaneously, calls for efficient software tools as brute-force approaches with quadratic memory requirements become infeasible in practise. The rapid pace at which new data become available, furthermore, makes it desirable to compute genome-wide orthology relations for a given dataset rather than relying on relations listed in databases.

With the availability of genome-wide transcription data and massive comparative sequencing, the discrimination of coding from noncoding RNAs and the assessment of coding potential in evolutionarily conserved regions arose as a core analysis task. Here we present RNAcode, a program to detect coding regions in multiple sequence alignments that is optimized for emerging applications not covered by current protein gene-finding software. Our algorithm combines information from nucleotide substitution and gap patterns in a unified framework and also deals with real-life issues such as alignment and sequencing errors. It uses an explicit statistical model with no machine learning component and can therefore be applied “out of the box,” without any training, to data from all domains of life. We describe the RNAcode method and apply it in combination with mass spectrometry experiments to predict and confirm seven novel short peptides in Escherichia coli and to analyze the coding potential of RNAs previously annotated as “noncoding.” RNAcode is open source software and available for all major platforms at http://wash.github.com/rnacode.

Riboswitches are regulatory RNA elements typically located in the 5′-untranslated region of certain mRNAs and control gene expression at the level of transcription or translation. These elements consist of a sensor and an adjacent actuator domain. The sensor usually is an aptamer that specifically interacts with a ligand. The actuator contains an intrinsic terminator or a ribosomal binding site for transcriptional or translational regulation, respectively. Ligand binding leads to structural rearrangements of the riboswitch and to presentation or masking of these regulatory elements. Based on this modular organization, riboswitches are an ideal target for constructing synthetic regulatory systems for gene expression. Although riboswitches for translational control have been designed successfully, attempts to construct synthetic elements regulating transcription have failed so far. Here, we present an in silico pipeline for the rational design of synthetic riboswitches that regulate gene expression at the transcriptional level. Using the well-characterized theophylline aptamer as sensor, we designed the actuator part as RNA sequences that can fold into functional intrinsic terminator structures. In the biochemical characterization, several of the designed constructs show ligand-dependent control of gene expression in Escherichia coli , demonstrating that it is possible to engineer riboswitches not only for translational but also for transcriptional regulation.

The Gram-negative plant-pathogenic bacterium Xanthomonas campestris pv. vesicatoria (Xcv) is an important model to elucidate the mechanisms involved in the interaction with the host. To gain insight into the transcriptome of the Xcv strain 85–10, we took a differential RNA sequencing (dRNA-seq) approach. Using a novel method to automatically generate comprehensive transcription start site (TSS) maps we report 1421 putative TSSs in the Xcv genome. Genes in Xcv exhibit a poorly conserved −10 promoter element and no consensus Shine-Dalgarno sequence. Moreover, 14% of all mRNAs are leaderless and 13% of them have unusually long 5′-UTRs. Northern blot analyses confirmed 16 intergenic small RNAs and seven cis-encoded antisense RNAs in Xcv. Expression of eight intergenic transcripts was controlled by HrpG and HrpX, key regulators of the Xcv type III secretion system. More detailed characterization identified sX12 as a small RNA that controls virulence of Xcv by affecting the interaction of the pathogen and its host plants. The transcriptional landscape of Xcv is unexpectedly complex, featuring abundant antisense transcripts, alternative TSSs and clade-specific small RNAs.