TERribly Difficult: Searching for Telomerase RNAs in Saccharomycetes.

The telomerase RNA in yeasts is large, usually >1000 nt, and contains functional elements that have been extensively studied experimentally in several disparate species. Nevertheless, they are very difficult to detect by homology-based methods and so far have escaped annotation in the majority of the genomes of Saccharomycotina. This is a consequence of sequences that evolve rapidly at nucleotide level, are subject to large variations in size, and are highly plastic with respect to their secondary structures. Here, we report on a survey that was aimed at closing this gap in RNA annotation. Despite considerable efforts and the combination of a variety of different methods, it was only partially successful. While 27 new telomerase RNAs were identified, we had to restrict our efforts to the subgroup Saccharomycetacea because even this narrow subgroup was diverse enough to require different search models for different phylogenetic subgroups. More distant branches of the Saccharomycotina remain without annotated telomerase RNA.

Exploring the molecular complexity of Triatoma dimidiata sialome.

Triatoma dimidiata, a Chagas disease vector widely distributed along Central America, has great capability for domestic adaptation as the majority of specimens caught inside human dwellings or in peridomestic areas fed human blood. Exploring the salivary compounds that overcome host haemostatic and immune responses is of great scientific interest. Here, we provide a deeper insight into its salivary gland molecules. We used high-throughput RNA sequencing to examine in depth the T. dimidiata salivary gland transcriptome. From >51 million reads assembled, 92.21% are related to putative secreted proteins. Lipocalin is the most abundant gene family, confirming it is an expanded family in Triatoma genus salivary repertoire. Other putatively secreted members include phosphatases, odorant binding protein, hemolysin, proteases, protease inhibitors, antigen-5 and antimicrobial peptides. This work expands the previous set of functionally annotated sequences from T. dimidiata salivary glands available in NCBI from 388 to 3815. Additionally, we complemented the salivary analysis through proteomics (available data via ProteomeXchange with identifier PXD008510), disclosing the set complexity of 119 secreted proteins and validating the transcriptomic results. Our large-scale approach enriches the pharmacologically active molecules database and improves our knowledge about the complexity of salivary compounds from haematophagous vectors and their biological interactions. SIGNIFICANCE: Several haematophagous triatomine species can transmit Trypanosoma cruzi, the etiological agent of Chagas disease. Due to the reemergence of this disease, new drugs for its prevention and treatment are considered priorities. For this reason, the knowledge of vector saliva emerges as relevant biological finding, contributing to the design of different strategies for vector control and disease transmission. Here we report the transcriptomic and proteomic compositions of the salivary glands (sialome) of the reduviid bug Triatoma dimidiata, a relevant Chagas disease vector in Central America. Our results are robust and disclosed unprecedented insights into the notable diversity of its salivary glands content, revealing relevant anti-haemostatic salivary gene families. Our work expands almost ten times the previous set of functionally annotated sequences from T. dimidiata salivary glands available in NCBI. Moreover, using an integrated transcriptomic and proteomic approach, we showed a correlation pattern of transcription and translation processes for the main gene families found, an important contribution to the research of triatomine sialomes. Furthermore, data generated here reinforces the secreted proteins encountered can greatly contribute for haematophagic habit, Trypanosoma cruzi transmission and development of therapeutic agent studies.

SnoReport 2.0: new features and a refined Support Vector Machine to improve snoRNA identification.

BACKGROUND: snoReport uses RNA secondary structure prediction combined with machine learning as the basis to identify the two main classes of small nucleolar RNAs, the box H/ACA snoRNAs and the box C/D snoRNAs. Here, we present snoReport 2.0, which substantially improves and extends in the original method by: extracting new features for both box C/D and H/ACA box snoRNAs; developing a more sophisticated technique in the SVM training phase with recent data from vertebrate organisms and a careful choice of the SVM parameters C and γ; and using updated versions of tools and databases used for the construction of the original version of snoReport. To validate the new version and to demonstrate its improved performance, we tested snoReport 2.0 in different organisms. RESULTS: Results of the training and test phases of boxes H/ACA and C/D snoRNAs, in both versions of snoReport, are discussed. Validation on real data was performed to evaluate the predictions of snoReport 2.0. Our program was applied to a set of previously annotated sequences, some of them experimentally confirmed, of humans, nematodes, drosophilids, platypus, chickens and leishmania. We significantly improved the predictions for vertebrates, since the training phase used information of these organisms, but H/ACA box snoRNAs identification was improved for the other ones. CONCLUSION: We presented snoReport 2.0, to predict H/ACA box and C/D box snoRNAs, an efficient method to find true positives and avoid false positives in vertebrate organisms. H/ACA box snoRNA classifier showed an F-score of 93 % (an improvement of 10 % regarding the previous version), while C/D box snoRNA classifier, an F-Score of 94 % (improvement of 14 %). Besides, both classifiers exhibited performance measures above 90 %. These results show that snoReport 2.0 avoid false positives and false negatives, allowing to predict snoRNAs with high quality. In the validation phase, snoReport 2.0 predicted 67.43 % of vertebrate organisms for both classes. For Nematodes and Drosophilids, 69 % and 76.67 %, for H/ACA box snoRNAs were predicted, respectively, showing that snoReport 2.0 is good to identify snoRNAs in vertebrates and also H/ACA box snoRNAs in invertebrates organisms.