Modeling sequencing errors by combining Hidden Markov models.

Among the largest resources for biological sequence data is the large amount of expressed sequence tags (ESTs) available in public and proprietary databases. ESTs provide information on transcripts but for technical reasons they often contain sequencing errors. Therefore, when analyzing EST sequences computationally, such errors must be taken into account. Earlier attempts to model error prone coding regions have shown good performance in detecting and predicting these while correcting sequencing errors using codon usage frequencies. In the research presented here, we improve the detection of translation start and stop sites by integrating a more complex mRNA model with codon usage bias based error correction into one hidden Markov model (HMM), thus generalizing this error correction approach to more complex HMMs. We show that our method maintains the performance in detecting coding sequences.

Uncovering hidden duplicated content in public transcriptomics data.

As part of the development of the database Bgee (a dataBase for Gene Expression Evolution), we annotate and analyse expression data from different types and different sources, notably Affymetrix data from GEO and ArrayExpress, and RNA-Seq data from SRA. During our quality control procedure, we have identified duplicated content in GEO and ArrayExpress, affecting ∼14% of our data: fully or partially duplicated experiments from independent data submissions, Affymetrix chips reused in several experiments, or reused within an experiment. We present here the procedure that we have established to filter such duplicates from Affymetrix data, and our procedure to identify future potential duplicates in RNA-Seq data. Database URL: http://bgee.unil.ch/

MAMOT: hidden Markov modeling tool.

Hidden Markov models (HMMs) are probabilistic models that are well adapted to many tasks in bioinformatics, for example, for predicting the occurrence of specific motifs in biological sequences. MAMOT is a command-line program for Unix-like operating systems, including MacOS X, that we developed to allow scientists to apply HMMs more easily in their research. One can define the architecture and initial parameters of the model in a text file and then use MAMOT for parameter optimization on example data, decoding (like predicting motif occurrence in sequences) and the production of stochastic sequences generated according to the probabilistic model. Two examples for which models are provided are coiled-coil domains in protein sequences and protein binding sites in DNA. A wealth of useful features include the use of pseudocounts, state tying and fixing of selected parameters in learning, and the inclusion of prior probabilities in decoding. AVAILABILITY: MAMOT is implemented in C++, and is distributed under the GNU General Public Licence (GPL). The software, documentation, and example model files can be found at http://bcf.isb-sib.ch/mamot

Hidden branches: developments in root system architecture.

The root system is fundamentally important for plant growth and survival because of its role in water and nutrient uptake. Therefore, plants rely on modulation of root system architecture (RSA) to respond to a changing soil environment. Although RSA is a highly plastic trait and varies both between and among species, the basic root system morphology and its plasticity are controlled by inherent genetic factors. These mediate the modification of RSA, mostly at the level of root branching, in response to a suite of biotic and abiotic factors. Recent progress in the understanding of the molecular basis of these responses suggests that they largely feed through hormone homeostasis and signaling pathways. Novel factors implicated in the regulation of RSA in response to the myriad endogenous and exogenous signals are also increasingly isolated through alternative approaches such as quantitative trait locus analysis.

Que cache l'agenda caché [What is behind the hidden agenda?]

The physicians often forget to ask their patients if they would like to discuss other complaints or topics. It is sometimes quite difficult to explore the patient's complaints; while the physicians tend to focus on the immediate problem, the patients may have not only one, but several hidden agendas during a visit. In a caring relation there is a clear advantage to clarify the implicit. The search for the hidden agenda is to improve the care of i) biomedical problems ii) the social quest presented to the physicians. The sentence "Oh, by the way, doctor..." should not be only understood as an information but also as a relational expression and a reaction to the imminent separation from the physician.

Backseat drivers: the hidden influence of microbial viruses on disease.

Because viral replication depends on the vigour of its host, many viruses have evolved incentives of fitness to pay their keep. When the viral host is a human pathogen, these fitness factors can surface as virulence: creating a Russian doll of pathogenesis where pathogens within pathogens complicate the disease process. Microbial viruses can even be independently immunogenic, as we recently reported for leishmania-virus. Thus, the incidence of this 'hyperpathogenism' is becoming an important clinical consideration and by appreciating the microbial-virus as a backseat driver of human disease, we could exploit its presence as a diagnostic biomarker and molecular target for therapeutic intervention. Here we discuss the prevalence of clinically relevant hyperpathogenism as well as the environmental sanctuaries that breed it.

The Foundational Significance of Leggett's Non-local Hidden-Variable Theories

Laudisa (Found. Phys. 38:1110-1132, 2008) claims that experimental research on the class of non-local hidden-variable theories introduced by Leggett is misguided, because these theories are irrelevant for the foundations of quantum mechanics. I show that Laudisa's arguments fail to establish the pessimistic conclusion he draws from them. In particular, it is not the case that Leggett-inspired research is based on a mistaken understanding of Bell's theorem, nor that previous no-hidden-variable theorems already exclude Leggett's models. Finally, I argue that the framework of Bohmian mechanics brings out the importance of Leggett tests, rather than proving their irrelevance, as Laudisa supposes.

Hidden diversity in honey bee gut symbionts detected by single-cell genomics.

Microbial communities in animal guts are composed of diverse, specialized bacterial species, but little is known about how gut bacteria diversify to produce genetically and ecologically distinct entities. The gut microbiota of the honey bee, Apis mellifera, presents a useful model, because it consists of a small number of characteristic bacterial species, each showing signs of diversification. Here, we used single-cell genomics to study the variation within two species of the bee gut microbiota: Gilliamella apicola and Snodgrassella alvi. For both species, our analyses revealed extensive variation in intraspecific divergence of protein-coding genes but uniformly high levels of 16S rRNA similarity. In both species, the divergence of 16S rRNA loci appears to have been curtailed by frequent recombination within populations, while other genomic regions have continuously diverged. Furthermore, gene repertoires differ markedly among strains in both species, implying distinct metabolic capabilities. Our results show that, despite minimal divergence at 16S rRNA genes, in situ diversification occurs within gut communities and generates bacterial lineages with distinct ecological niches. Therefore, important dimensions of microbial diversity are not evident from analyses of 16S rRNA, and single cell genomics has potential to elucidate processes of bacterial diversification.