Dataset Information

Discovering Numerical Differences between Animal and Plant microRNAs.

ABSTRACT: Previous studies have confirmed that there are many differences between animal and plant microRNAs (miRNAs), and that numerical features based on sequence and structure can be used to predict the function of individual miRNAs. However, there is little research regarding numerical differences between animal and plant miRNAs, and whether a single numerical feature or combination of features could be used to distinguish animal and plant miRNAs or not. Therefore, in current study we aimed to discover numerical features that could be used to accomplish this. We performed a large-scale analysis of 132 miRNA numerical features, and identified 17 highly significant distinguishing features. However, none of the features independently could clearly differentiate animal and plant miRNAs. By further analysis, we found a four-feature subset that included helix number, stack number, length of pre-miRNA, and minimum free energy, and developed a logistic classifier that could distinguish animal and plant miRNAs effectively. The precision of the classifier was greater than 80%. Using this tool, we confirmed that there were universal differences between animal and plant miRNAs, and that a single feature was unable to adequately distinguish the difference. This feature set and classifier represent a valuable tool for identifying differences between animal and plant miRNAs at a molecular level.

SUBMITTER: Zhu R

PROVIDER: S-EPMC5074594 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Discovering Numerical Differences between Animal and Plant microRNAs.

Zhu Rongsheng R Zhang Zhanguo Z Li Yang Y Hu Zhenbang Z Xin Dawei D Qi Zhaoming Z Chen Qingshan Q

PloS one 20161021 10

Previous studies have confirmed that there are many differences between animal and plant microRNAs (miRNAs), and that numerical features based on sequence and structure can be used to predict the function of individual miRNAs. However, there is little research regarding numerical differences between animal and plant miRNAs, and whether a single numerical feature or combination of features could be used to distinguish animal and plant miRNAs or not. Therefore, in current study we aimed to discove ...[more]

PMID: 27768749

Similar Datasets

Project description:BACKGROUND:In recent years, the differences between left-sided colon cancer (LCC) and right-sided colon cancer (RCC) have received increasing attention due to the clinicopathological variation between them. However, some of these differences have remained unclear and conflicting results have been reported. METHODS:From The Cancer Genome Atlas (TCGA), we obtained RNA sequencing data and gene mutation data on 323 and 283 colon cancer patients, respectively. Differential analysis was firstly done on gene expression data and mutation data between LCC and RCC, separately. Machine learning (ML) methods were then used to select key genes or mutations as features to construct models to classify LCC and RCC patients. Finally, we conducted correlation analysis to identify the correlations between differentially expressed genes (DEGs) and mutations using logistic regression (LR) models. RESULTS:We found distinct gene mutation and expression patterns between LCC and RCC patients and further selected the 30 most important mutations and 17 most important gene expression features using ML methods. The classification models created using these features classified LCC and RCC patients with high accuracy (areas under the curve (AUC) of 0.8 and 0.96 for mutation and gene expression data, respectively). The expression of PRAC1 and BRAF V600E mutation (rs113488022) were the most important feature for each model. Correlations of mutations and gene expression data were also identified using LR models. Among them, rs113488022 was found to have significance relevance to the expression of four genes, and thus should be focused on in further study. CONCLUSIONS:On the basis of ML methods, we found some key molecular differences between LCC and RCC, which could differentiate these two groups of patients with high accuracy. These differences might be key factors behind the variation in clinical features between LCC and RCC and thus help to improve treatment, such as determining the appropriate therapy for patients.

Project description:Stockmanship is a term used to describe the management of animals with a good stockperson someone who does this in a in a safe, effective, and low-stress manner for both the stock-keeper and animals involved. Although impacts of unfamiliar zoo visitors on animal behaviour have been extensively studied, the impact of stockmanship i.e familiar zoo keepers is a new area of research; which could reveal significant ramifications for zoo animal behaviour and welfare. It is likely that different relationships are formed dependant on the unique keeper-animal dyad (human-animal interaction, HAI). The aims of this study were to (1) investigate if unique keeper-animal dyads were formed in zoos, (2) determine whether keepers differed in their interactions towards animals regarding their attitude, animal knowledge and experience and (3) explore what factors affect keeper-animal dyads and ultimately influence animal behaviour and welfare. Eight black rhinoceros (Diceros bicornis), eleven Chapman's zebra (Equus burchellii), and twelve Sulawesi crested black macaques (Macaca nigra) were studied in 6 zoos across the UK and USA. Subtle cues and commands directed by keepers towards animals were identified. The animals latency to respond and the respective behavioural response (cue-response) was recorded per keeper-animal dyad (n = 93). A questionnaire was constructed following a five-point Likert Scale design to record keeper demographic information and assess the job satisfaction of keepers, their attitude towards the animals and their perceived relationship with them. There was a significant difference in the animals' latency to appropriately respond after cues and commands from different keepers, indicating unique keeper-animal dyads were formed. Stockmanship style was also different between keepers; two main components contributed equally towards this: "attitude towards the animals" and "knowledge and experience of the animals". In this novel study, data demonstrated unique dyads were formed between keepers and zoo animals, which influenced animal behaviour.

Project description:ImportanceAlthough emphasis has recently been placed on the importance of high-protein diets to overall health, a comprehensive analysis of long-term cause-specific mortality in association with the intake of plant protein and animal protein has not been reported.ObjectiveTo examine the associations between overall mortality and cause-specific mortality and plant protein intake.Design, setting, and participantsThis prospective cohort study analyzed data from 416 104 men and women in the US National Institutes of Health-AARP Diet and Health Study from 1995 to 2011. Data were analyzed from October 2018 through April 2020.ExposuresValidated baseline food frequency questionnaire dietary information, including intake of plant protein and animal protein.Main outcomes and measuresHazard ratios and 16-year absolute risk differences for overall mortality and cause-specific mortality.ResultsThe final analytic cohort included 237 036 men (57%) and 179 068 women. Their overall median (SD) ages were 62.2 (5.4) years for men and 62.0 (5.4) years for women. Based on 6 009 748 person-years of observation, 77 614 deaths (18.7%; 49 297 men and 28 317 women) were analyzed. Adjusting for several important clinical and other risk factors, greater dietary plant protein intake was associated with reduced overall mortality in both sexes (hazard ratio per 1 SD was 0.95 [95% CI, 0.94-0.97] for men and 0.95 [95% CI, 0.93-0.96] for women; adjusted absolute risk difference per 1 SD was -0.36% [95% CI, -0.48% to -0.25%] for men and -0.33% [95% CI, -0.48% to -0.21%] for women; hazard ratio per 10 g/1000 kcal was 0.88 [95% CI, 0.84-0.91] for men and 0.86 [95% CI, 0.82-0.90] for women; adjusted absolute risk difference per 10 g/1000 kcal was -0.95% [95% CI, -1.3% to -0.68%] for men and -0.86% [95% CI, -1.3% to -0.55%] for women; all P < .001). The association between plant protein intake and overall mortality was similar across the subgroups of smoking status, diabetes, fruit consumption, vitamin supplement use, and self-reported health status. Replacement of 3% energy from animal protein with plant protein was inversely associated with overall mortality (risk decreased 10% in both men and women) and cardiovascular disease mortality (11% lower risk in men and 12% lower risk in women). In particular, the lower overall mortality was attributable primarily to substitution of plant protein for egg protein (24% lower risk in men and 21% lower risk in women) and red meat protein (13% lower risk in men and 15% lower risk in women).Conclusions and relevanceIn this large prospective cohort, higher plant protein intake was associated with small reductions in risk of overall and cardiovascular disease mortality. Our findings provide evidence that dietary modification in choice of protein sources may influence health and longevity.

Dataset Information

Discovering Numerical Differences between Animal and Plant microRNAs.

Publications

Discovering Numerical Differences between Animal and Plant microRNAs.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets