Background: The CO₂-concentrating mechanism associated to Crassulacean acid metabolism (CAM) alters the catalytic context for Rubisco by increasing CO₂ availability and provides an advantage in particular ecological conditions. We hypothesized about the existence of molecular changes linked to these particular adaptations in CAM Rubisco. We investigated molecular evolution of the Rubisco large (L-) subunit in 78 orchids and 144 bromeliads with C₃ and CAM photosynthetic pathways. The sequence analyses were complemented with measurements of Rubisco kinetics in some species with contrasting photosynthetic mechanism and differing in the L-subunit sequence. Results: We identified potential positively selected sites and residues with signatures of co-adaptation. The implementation of a decision tree model related Rubisco specific variable sites to the leaf carbon isotopic composition of the species. Differences in the Rubisco catalytic traits found among C₃ orchids and between strong CAM and C₃ bromeliads suggested Rubisco had evolved in response to differing CO₂ concentration. Conclusions: The results revealed that the variability in the Rubisco L-subunit sequence in orchids and bromeliads is composed of coevolving sites under potential positive adaptive signal. The sequence variability was related to δ¹³C in orchids and bromeliads, however it could not be linked to the variability found in the kinetic properties of the studied species.

Motivation: Throughout evolution, homologous proteins have common regions that stay semi-rigid relative to each other and other parts that vary in a more noticeable way. In order to compare the increasing number of structures in the PDB, flexible geometrical alignments are needed, that are reliable and easy to use. Results: We present a protein structure alignment method whose main feature is the ability to consider different rigid transformations at different sites, allowing for deformations beyond a global rigid transformation. The performance of the method is comparable with that of the best ones from 10 aligners tested, regarding both the quality of the alignments with respect to hand curated ones, and the classification ability. An analysis of some structure pairs from the literature that need to be matched in a flexible fashion are shown. The use of a series of local transformations can be exported to other classifiers, and a future golden protein similarity measure could benefit from it. Availability: A public server for the program is available at http://dmi.uib.es/ProtDeform/. Contact: jairo@uib.es Supplementary information: All data used, results and examples are available at http://dmi.uib.es/people/jairo/bio/ProtDeform.Supplementary data are available at Bioinformatics online.

Cancer arises from the complex interplay of various factors. Traditionally, the identification of driver genes focuses primarily on the analysis of somatic mutations. We describe a new method for the detection of driver gene pairs based on an epistasis analysis that considers both germline and somatic variations. Specifically, the identification of significantly mutated gene pairs entails the calculation of a contingency table, wherein one of the co-mutated genes can exhibit a germline variant. By adopting this approach, it is possible to select gene pairs in which the individual genes do not exhibit significant associations with cancer. Finally, a survival analysis is used to select clinically relevant gene pairs. To test the efficacy of the new algorithm, we analyzed the colon adenocarcinoma (COAD) and lung adenocarcinoma (LUAD) samples available at The Cancer Genome Atlas (TCGA). In the analysis of the COAD and LUAD samples, we identify epistatic gene pairs significantly mutated in tumor tissue with respect to normal tissue. We believe that further analysis of the gene pairs detected by our method will unveil new biological insights, enhancing a better description of the cancer mechanism.

Interpretation of gaps and touching characters continues to challenge current OCR designs. We approach this and other difficult problems in character recognition by deferring decisions to a stage where a character-specific knowledge base can be applied to the problem. We show how to extract and interpret saddle ridge features, at locations where there is either a narrow gap or a thin stroke. Since the color of the ideal image at these points cannot be locally deduced reliably from the features, special treatment is needed. Mathematically a saddle ridge is one where, roughly, there are strong eigenvalues of the Hessian of the gray scale surface that are of opposite sign. The recognition module is based on the matching of subgraphs homomorphic to previously defined prototypes. It generates candidate matchings of groups of input features with each part of the prototype. In this context, each saddle ridge is decided to be a piece of a stroke or a separation between strokes. The quality of each grouping is measured by the cost of transformations carrying the candidate features into the prototype.