A Quantitative Approach to Investigating the Hypothesis of Prokaryotic Intron Loss

Using a novel method, we show that ordered triplets of motifs usually associated with spliceosomal intron recognition are underrepresented in the protein coding sequence of complete Thermotogae, archaeal and bacterial genomes. The underrepresentation observed does not extend to the noncoding strand, suggesting that the cause of the asymmetry is related to mRNA rather than DNA. Our data do not suggest that the underrepresentation is due to gene transfer from eukaryotes. We speculate that one possible explanation for these observations is that the protein coding sequence of Thermotogae, Archaea and Bacteria was at some time in the past subjected to selection against certain motifs appearing in an order which might initiate splicing in environments harboring a functional spliceosome. This is consistent with, but certainly does not prove, a hypothetical scenario in which at least some prokaryote lineages once possessed a functional spliceosome. Thus, we present a new quantitative method, observations obtained using the method, and a speculative discussion of a possible explanation of the observations

Transcription factor target prediction using multiple short expression time series from Arabidopsis thaliana

BACKGROUND: The central role of transcription factors (TFs) in higher eukaryotes has led to much interest in deciphering transcriptional regulatory interactions. Even in the best case, experimental identification of TF target genes is error prone, and has been shown to be improved by considering additional forms of evidence such as expression data. Previous expression based methods have not explicitly tried to associate TFs with their targets and therefore largely ignored the treatment specific and time dependent nature of transcription regulation. RESULTS: In this study we introduce CERMT, Covariance based Extraction of Regulatory targets using Multiple Time series. Using simulated and real data we show that using multiple expression time series, selecting treatments in which the TF responds, allowing time shifts between TFs and their targets and using covariance to identify highly responding genes appear to be a good strategy. We applied our method to published TF - target gene relationships determined using expression profiling on TF mutants and show that in most cases we obtain significant target gene enrichment and in half of the cases this is sufficient to deliver a usable list of high-confidence target genes. CONCLUSION: CERMT could be immediately useful in refining possible target genes of candidate TFs using publicly available data, particularly for organisms lacking comprehensive TF binding data. In the future, we believe its incorporation with other forms of evidence may improve integrative genome-wide predictions of transcriptional networks

Culture Outsmarts Nature in the Evolution of Cooperation

A one dimensional cellular automata model describes the evolutionary dynamics of cooperation when grouping by cooperators provides protection against predation. It is used to compare the dynamics of evolution of cooperation in three settings. G: only vertical transmission of information is allowed, as an analogy of genetic evolution with heredity; H: only horizontal information transfer is simulated, through diffusion of the majority\'s opinion, as an analogy of opinion dynamics or social learning; and C: analogy of cultural evolution, where information is transmitted both horizontally (H) and vertically (V) so that learned behavior can be transmitted to offspring. The results show that the prevalence of cooperative behavior depends on the costs and benefits of cooperation so that: a- cooperation becomes the dominant behavior, even in the presence of free-riders (i.e., non-cooperative obtaining benefits from the cooperation of others), under all scenarios, if the benefits of cooperation compensate for its cost; b- G is more susceptible to selection pressure than H achieving a closer adaptation to the fitness landscape; c- evolution of cooperative behavior in H is less sensitive to the cost of cooperation than in G; d- C achieves higher levels of cooperation than the other alternatives at low costs, whereas H does it at high costs. The results suggest that a synergy between H and V is elicited that makes the evolution of cooperation much more likely under cultural evolution than under the hereditary kind where only V is present.Social Simulation, Interactions, Group Size, Selfish Heard, Cultural Evolution, Biological Evolution

The Genomic Context and Corecruitment of SP1 Affect ERRα Coactivation by PGC-1α in Muscle Cells

The peroxisome proliferator-activated receptor-γ coactivator 1α (PGC-1α) coordinates the transcriptional network response to promote an improved endurance capacity in skeletal muscle, eg, by coactivating the estrogen-related receptor-α (ERRα) in the regulation of oxidative substrate metabolism. Despite a close functional relationship, the interaction between these 2 proteins has not been studied on a genomic level. We now mapped the genome-wide binding of ERRα to DNA in a skeletal muscle cell line with elevated PGC-1α and linked the DNA recruitment to global PGC-1α target gene regulation. We found that, surprisingly, ERRα coactivation by PGC-1α is only observed in the minority of all PGC-1α recruitment sites. Nevertheless, a majority of PGC-1α target gene expression is dependent on ERRα. Intriguingly, the interaction between these 2 proteins is controlled by the genomic context of response elements, in particular the relative GC and CpG content, monomeric and dimeric repeat-binding site configuration for ERRα, and adjacent recruitment of the transcription factor specificity protein 1. These findings thus not only reveal a novel insight into the regulatory network underlying muscle cell plasticity but also strongly link the genomic context of DNA-response elements to control transcription factor-coregulator interactions

Utilizing Tumor Exome Variation to Predict Cancer Treatment Outcomes

Cancer genomics, in the context of informing clinical decisions with tumor genotype, is a field characterized by high-dimensional data. Computational approaches for evaluating sets of features to be utilized in machine learning methods are essential for yielding accurate predictive and prognostic models. Additionally, the publicly-available results of the Broad Institute’s Firehose cancer genomics analysis pipeline presents a wealth of information that may be useful for cancer genotyping. Power analysis and classifier comparison are performed with the goal of evaluating a gene-based mutation significance feature set (MutSig) from Firehose. They reveal that while the MutSig features likely contain some prognostic information, the methods with which they are currently integrated do not provide enough predictive power to result in clinically-useful decision support. Results also suggest that Random Forest or other bagged classifiers are potential good candidates for feature selection and model building in this context

RNAstrand: reading direction of structured RNAs in multiple sequence alignments

<p>Abstract</p> <p>Motivation</p> <p>Genome-wide screens for structured ncRNA genes in mammals, urochordates, and nematodes have predicted thousands of putative ncRNA genes and other structured RNA motifs. A prerequisite for their functional annotation is to determine the reading direction with high precision.</p> <p>Results</p> <p>While folding energies of an RNA and its reverse complement are similar, the differences are sufficient at least in conjunction with substitution patterns to discriminate between structured RNAs and their complements. We present here a support vector machine that reliably classifies the reading direction of a structured RNA from a multiple sequence alignment and provides a considerable improvement in classification accuracy over previous approaches.</p> <p>Software</p> <p>RNAstrand is freely available as a stand-alone tool from <url>http://www.bioinf.uni-leipzig.de/Software/RNAstrand</url> and is also included in the latest release of RNAz, a part of the Vienna RNA Package.</p

Incorporating linkage learning into the GeLog framework

This article introduces modifications that have been applied to GeLog, a genetic logic programming framework, in order to improve its performance. The main emphasis of this work is the structure processing of genetic algorithms. As studies have shown, the linkage of genes plays an important role in the performance of genetic algorithms. Thus, different approaches that take linkage learning into account have been reviewed and the most promising has been implemented and tested with GeLog. It is demonstrated that the modified program solves problems that proved hard for the original system

De novo prediction of PTBP1 binding and splicing targets reveals unexpected features of its RNA recognition and function.

The splicing regulator Polypyrimidine Tract Binding Protein (PTBP1) has four RNA binding domains that each binds a short pyrimidine element, allowing recognition of diverse pyrimidine-rich sequences. This variation makes it difficult to evaluate PTBP1 binding to particular sites based on sequence alone and thus to identify target RNAs. Conversely, transcriptome-wide binding assays such as CLIP identify many in vivo targets, but do not provide a quantitative assessment of binding and are informative only for the cells where the analysis is performed. A general method of predicting PTBP1 binding and possible targets in any cell type is needed. We developed computational models that predict the binding and splicing targets of PTBP1. A Hidden Markov Model (HMM), trained on CLIP-seq data, was used to score probable PTBP1 binding sites. Scores from this model are highly correlated (ρ = -0.9) with experimentally determined dissociation constants. Notably, we find that the protein is not strictly pyrimidine specific, as interspersed Guanosine residues are well tolerated within PTBP1 binding sites. This model identifies many previously unrecognized PTBP1 binding sites, and can score PTBP1 binding across the transcriptome in the absence of CLIP data. Using this model to examine the placement of PTBP1 binding sites in controlling splicing, we trained a multinomial logistic model on sets of PTBP1 regulated and unregulated exons. Applying this model to rank exons across the mouse transcriptome identifies known PTBP1 targets and many new exons that were confirmed as PTBP1-repressed by RT-PCR and RNA-seq after PTBP1 depletion. We find that PTBP1 dependent exons are diverse in structure and do not all fit previous descriptions of the placement of PTBP1 binding sites. Our study uncovers new features of RNA recognition and splicing regulation by PTBP1. This approach can be applied to other multi-RRM domain proteins to assess binding site degeneracy and multifactorial splicing regulation