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The gene transcription of bacteria starts with a promoter sequence being recognized by a transcription factor found in the RNAP enzyme, this process is assisted through the conservation of nucleotides as well as other factors governing these intergenic regions. Faced with this, the coding of genetic information into physical aspects of the DNA such as enthalpy, stability, and base-pair stacking could suggest promoter activity as well as protrude differentiation of promoter and non-promoter data. In this work, a total of 3131 promoter sequences associated to six different sigma factors in the bacterium E. coli were converted into numeric attributes, a strong set of control sequences referring to a shuffled version of the original sequences as well as coding regions is provided. Then, the parameterized genetic information was normalized, exhaustively analyzed through statistical tests. The results suggest that strong signals in the promoter sequences match the binding site of transcription factor proteins, indicating that promoter activity is well represented by its conversion into physical attributes. Moreover, the features tested in this report conveyed significant variances between promoter and control data, enabling these features to be employed in bacterial promoter classification. The results produced here may aid in bacterial promoter recognition by providing a robust set of biological inferences.

Penicillium echinulatum 2HH and Penicillium oxalicum 114-2 are well-known cellulase fungal producers. However, few studies addressing global mechanisms for gene regulation of these two important organisms are available so far. A recent finding that the 2HH wild-type is closely related to P. oxalicum leads to a combined study of these two species. Firstly, we provide a global gene regulatory network for P. echinulatum 2HH and P. oxalicum 114-2, based on TF-TG orthology relationships, considering three related species with well-known regulatory interactions combined with TFBSs prediction. The network was then analyzed in terms of topology, identifying TFs as hubs, and modules. Based on this approach, we explore numerous identified modules, such as the expression of cellulolytic and xylanolytic systems, where XlnR plays a key role in positive regulation of the xylanolytic system. It also regulates positively the cellulolytic system by acting indirectly through the cellodextrin induction system. This remarkable finding suggests that the XlnR-dependent cellulolytic and xylanolytic regulatory systems are probably conserved in both P. echinulatum and P. oxalicum. Finally, we explore the functional congruency on the genes clustered in terms of communities, where the genes related to cellular nitrogen, compound metabolic process and macromolecule metabolic process were the most abundant. Therefore, our approach allows us to confer a degree of accuracy regarding the existence of each inferred interaction.

This paper presents a prediction of Bacillus subtilis promoters using a Support Vector Machine system. In the literature, there is a lack of information on Gram-positive bacterial promoter sequences compared to Gram-negative bacteria. Promoter sequence identification is essential for studying gene expression. Initially, we collected the B. subtilis genome sequence from the NCBI database, and promoters were identified by their sigma factors in the DBTBS database. We then grouped the promoters according to 15 factors in 2 domains, corresponding to sigma 54 and sigma 70 of Gram-negative bacteria. Based on these data we developed a script in Python to search for promoters in the B. subtilis genome. After processing the data, we obtained 767 promoter sequences for B. subtilis, most of which were recognized by sigma SigA. To validate the data we found, we developed a software package called BacSVM+, which receives promoters as input and returns the best combination of parameters in a LibSVM library to predict promoter regions in the bacteria used in the simulation. All data gathered as well as the BacSVM+ software is available for download at http://bacpp.bioinfoucs.com/rafael/Sigmas.zip. Keywords: Promoter sequences, Bacillus subtilis, SVM

Introduction A dataset with patient information allows a comparison between different clinical treatments in many fields of medicine as well as the effective use of medical resources. Patient-reported outcome measures (PROMs) collect data directly from patients for use in clinical practice by helping decision making and tailoring treatments according to the patients’ needs. The authors present a novel database to overcome data collection related barriers, calculating automatically the questionnaire results, displayed in graphics on the patients’ dashboard in real time, and focused on the active follow of the patients. Objective To present electronic health record database for monitoring clinical or surgical interventions and assess the usability. Methodology Process modeling and specification of system requirements were performed using the Iconix methodology along with the Post-Study System Usability Questionnaire (PSSUQ) to validate the usability and usefulness of the proposed system. The system and the questionnaires were performed in three languages: Brazilian Portuguese, Spanish, and English. Results The database enables researchers to use the questionnaires defining the time of data collection according to the needs of each clinical study. The system facilitates the patient answers without any personal interference from smartphones, tablets or computers. The questionnaire scores were calculated automatically in real time and displayed in graphics on the patients’ dashboard. The evaluation of the usability and usefulness of the developed database used 8 people divided into two equal groups (4 physicians and 4 medical students). Conclusion The proposed electronic health record database allows a friendly and flexible use of PROMs according to the population, needs in practice and clinical settings. The platform promotes active and direct data collection from patients and physicians in English, Portuguese and Spanish.