Multipattern Consensus Regions in Multiple Aligned Protein Sequences and Their Segmentation

<p/> <p>Decomposing a biological sequence into its functional regions is an important prerequisite to understand the molecule. Using the multiple alignments of the sequences, we evaluate a segmentation based on the type of statistical variation pattern from each of the aligned sites. To describe such a more general pattern, we introduce multipattern consensus regions as segmented regions based on conserved as well as interdependent patterns. Thus the proposed consensus region considers patterns that are statistically significant and extends a local neighborhood. To show its relevance in protein sequence analysis, a cancer suppressor gene called p53 is examined. The results show significant associations between the detected regions and tendency of mutations, location on the 3D structure, and cancer hereditable factors that can be inferred from human twin studies.</p

Analysis of Free Energy Signals Arising from Nucleotide Hybridization Between rRNA and mRNA Sequences during Translation in Eubacteria

A decoding algorithm is tested that mechanistically models the progressive alignments that arise as the mRNA moves past the rRNA tail during translation elongation. Each of these alignments provides an opportunity for hybridization between the single-stranded, 3′-terminal nucleotides of the 16S rRNA and the spatially accessible window of mRNA sequence, from which a free energy value can be calculated. Using this algorithm we show that a periodic, energetic pattern of frequency 1/3 is revealed. This periodic signal exists in the majority of coding regions of eubacterial genes, but not in the non-coding regions encoding the 16S and 23S rRNAs. Signal analysis reveals that the population of coding regions of each bacterial species has a mean phase that is correlated in a statistically significant way with species (G + C) content. These results suggest that the periodic signal could function as a synchronization signal for the maintenance of reading frame and that codon usage provides a mechanism for manipulation of signal phase

Identification of protein-coding sequences using the hybridization of 18S rRNA and mRNA during translation

We introduce a new approach in this article to distinguish protein-coding sequences from non-coding sequences utilizing a period-3, free energy signal that arises from the interactions of the 3′-terminal nucleotides of the 18S rRNA with mRNA. We extracted the special features of the amplitude and the phase of the period-3 signal in protein-coding regions, which is not found in non-coding regions, and used them to distinguish protein-coding sequences from non-coding sequences. We tested on all the experimental genes from Saccharomyces cerevisiae and Schizosaccharomyces pombe. The identification was consistent with the corresponding information from GenBank, and produced better performance compared to existing methods that use a period-3 signal. The primary tests on some fly, mouse and human genes suggests that our method is applicable to higher eukaryotic genes. The tests on pseudogenes indicated that most pseudogenes have no period-3 signal. Some exploration of the 3′-tail of 18S rRNA and pattern analysis of protein-coding sequences supported further our assumption that the 3′-tail of 18S rRNA has a role of synchronization throughout translation elongation process. This, in turn, can be utilized for the identification of protein-coding sequences

Identification of protein-coding sequences using

the hybridization of 18S rRNA and mRNA during translatio

PIGLOW application for animal welfare self-assessment by farmers

The PIGLOW smartphone app was designed for organic and free-range farmers to monitor the welfare of their own pigs. The app contains animal-based questions that relate to one of the 4 Welfare Quality principles: good housing, good feeding, good health and appropriate behaviour. The results include scores/average percentages for each welfare indicator, automated advice with risk factors for possible welfare problems, and an anonymous comparison with the scores of other app users (benchmarking). Once a farmer has completed multiple welfare scans, a graph will show how their scores have changed over time, providing a historical record of welfare on the farm. Using the PIGLOW app could help increase awareness of potential welfare problems and its risk factors, which could make it easier to prevent problems from occurring. Farmers are advised to discuss the results with their veterinarian or other advisors to, if relevant, come up with the best approach to improve animal welfare on their farm

Opinion of organic and free-range pig farmers on animal welfare and the PIGLOW app for animal welfare self-assessments

The PIGLOW app was designed for the PPILOW project, enabling organic and free-range pig farmers to monitor the welfare of their pigs. The app is based on the 4 principles of the Welfare Quality protocol: good housing, good feeding, good health and appropriate behaviour. The tool includes automated feedback and anonymous benchmarking. A longitudinal study on 20-30 pig farms has started in order to determine the long-term effect of the use of the app on animal welfare. A survey is being conducted to assess participants’ views on animal welfare and their expectations of the app (n=10). Answers are given on a scale of 1 (disagree completely/not important at all) to 7 (agree completely/very important). When asked how they would define good animal welfare, 7/10 farmers included the possibility to express natural behaviour. The farmers scored the importance of 16 welfare aspects addressed in the PIGLOW app. The lowest score was given for thermal comfort (x̄=5.3, sd=1.1) and the highest score for the availability of drinking water (x̄=7, sd=0). Thus, even the least important of the indicators were scored above the point of neutrality (score 4). When asked how they think their own farm performs on these same 16 aspects, the scores for all except one (feed structure) were lower than those they gave for the importance of the aspect. The mean difference between these two values was largest for absence of wounds/lesions (x̄1-2=1, sd=1.3) and absence of lameness (x̄1-2=1, sd=1.7). It therefore seems likely that these are the welfare aspects for which farmers think improvement on their farm is most desirable. Farmers expect a historical record of their data (x̄=5.9, sd=1.2) and benchmarking (x̄=5.7, sd=1.5) to be the most useful aspects of the PIGLOW app. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N°816172