scholarly journals Frontiers in RNA biology: advances from a small fly Drosophila melanogaster

2016 ◽  
Vol 38 (2) ◽  
pp. 21-25
Author(s):  
James B. Brown ◽  
Susan E. Celniker
Keyword(s):  
Rna Processing ◽  
Small Sample ◽  
Rna Seq ◽  
Ribonucleoprotein Complexes ◽  
Rna Biology ◽  
Cell Type Specific ◽  
Exciting Time ◽  
Small Sample Sizes ◽  
Analytical Tools ◽  
Processing Mechanisms

In this article, we discuss emerging frontiers in RNA biology from a historical perspective. The field is currently undergoing yet another transformative expansion. RNA-seq has revealed that splicing, and, more generally, RNA processing is far more complex than expected, and the mechanisms of regulation are correspondingly sophisticated. Our understanding of the molecular machines involved in RNA metabolism is incomplete and derives from small sample sizes. Even if we manage to complete a catalogue of molecular species, RNA isoforms and the ribonucleoprotein complexes that drive their genesis, the horizons of molecular dynamics and cell-type-specific processing mechanisms await. This is an exciting time to enter into the study of RNA biology; analytical tools, wet and dry, are advancing rapidly, and each new measurement modality brings into view another new function or activity of versatile RNA. Since the dawn of sequence-based RNA biology, we have come a long way.

What if we ignore the random effects when analyzing RNA-seq data in a multifactor experiment

2016 ◽  
Vol 15 (2) ◽  
Author(s):  
Shiqi Cui ◽  
Tieming Ji ◽  
Jilong Li ◽  
Jianlin Cheng ◽  
Jing Qiu
Keyword(s):  
Data Analysis ◽  
Random Effects ◽  
Early Stage ◽  
Real Data ◽  
Small Sample ◽  
Repeated Measure ◽  
Group Comparison ◽  
Rna Seq ◽  
Multifactor Experiment ◽  
Small Sample Sizes

AbstractIdentifying differentially expressed (DE) genes between different conditions is one of the main goals of RNA-seq data analysis. Although a large amount of RNA-seq data were produced for two-group comparison with small sample sizes at early stage, more and more RNA-seq data are being produced in the setting of complex experimental designs such as split-plot designs and repeated measure designs. Data arising from such experiments are traditionally analyzed by mixed-effects models. Therefore an appropriate statistical approach for analyzing RNA-seq data from such designs should be generalized linear mixed models (GLMM) or similar approaches that allow for random effects. However, common practices for analyzing such data in literature either treat random effects as fixed or completely ignore the experimental design and focus on two-group comparison using partial data. In this paper, we examine the effect of ignoring the random effects when analyzing RNA-seq data. We accomplish this goal by comparing the standard GLMM model to the methods that ignore the random effects through simulation studies and real data analysis. Our studies show that, ignoring random effects in a multi-factor experiment can lead to the increase of the false positives among the top selected genes or lower power when the nominal FDR level is controlled.

scholarly journals Molecular approaches for a better understanding of the epidemiology and population genetics ofLeishmania

Parasitology ◽  
2010 ◽  
Vol 138 (4) ◽  
pp. 405-425 ◽  
Author(s):  
G. SCHÖNIAN ◽  
K. KUHLS ◽  
I. L. MAURICIO
Keyword(s):  
Gold Standard ◽  
Population Genetic ◽  
Field Studies ◽  
Epidemiological Studies ◽  
Small Sample ◽  
Clinical Samples ◽  
Molecular Approaches ◽  
Sexual Recombination ◽  
Small Sample Sizes ◽  
Analytical Tools

SUMMARYMolecular approaches are being used increasingly for epidemiological studies of visceral and cutaneous leishmaniases. Several molecular markers resolving genetic differences betweenLeishmaniaparasites at species and strain levels have been developed to address key epidemiological and population genetic questions. The current gold standard, multilocus enzyme typing (MLEE), needs cultured parasites and lacks discriminatory power. PCR assays identifying species directly with clinical samples have proven useful in numerous field studies. Multilocus sequence typing (MLST) is potentially the most powerful phylogenetic approach and will, most probably, replace MLEE in the future. Multilocus microsatellite typing (MLMT) is able to discriminate below the zymodeme level and seems to be the best candidate for becoming the gold standard for distinction of strains. Population genetic studies by MLMT revealed geographical and hierarchic population structure inL. tropica, L. majorand theL. donovanicomplex. The existence of hybrids and gene flow betweenLeishmaniapopulations suggests that sexual recombination is more frequent than previously thought. However, typing and analytical tools need to be further improved. Accessible databases should be created and sustained for integrating data obtained by different researchers. This would allow for global analyses and help to avoid biases in analyses due to small sample sizes.

Current challenges in metabolomics for diabetes research: a vital functional genomic tool or just a ploy for gaining funding?

2008 ◽  
Vol 34 (1) ◽  
pp. 1-5 ◽  
Author(s):  
Julian L. Griffin ◽  
Antonio Vidal-Puig
Keyword(s):  
Animal Studies ◽  
Gene Knockout ◽  
Tca Cycle ◽  
Small Sample ◽  
Human Populations ◽  
Diabetes Research ◽  
Functional Genomic ◽  
High Concentration ◽  
Small Sample Sizes ◽  
Analytical Tools

Metabolomics aims to profile all the small molecule metabolites found within a cell, tissue, organ, or organism and use this information to understand a biological manipulation such as a drug intervention or a gene knockout. While neither mass spectrometry or NMR spectroscopy, the two most commonly used analytical tools in metabolomics, can provide a complete coverage of the metabolome, compared with other functional genomic tools for profiling biological moieties the approach is cheap and high throughput. In diabetes and obesity research this has provided the opportunity to assess large human populations or investigate a range of different tissues in animal studies both rapidly and cheaply. However, the approach has a number of major challenges, particularly with the interpretation of the data obtained. For example, some key pathways are better represented by high concentration metabolites inside the cell, and thus, the coverage of the metabolome may become biased towards these pathways (e.g., the TCA cycle, amino acid metabolism). There is also the challenge of statistically modeling datasets with large numbers of variables but relatively small sample sizes. This perspective discusses our own experience of some of the benefits and pitfalls with using metabolomics to understand diseases associated with type 2 diabetes.

Integrating Visual and Bayesian Statistical Analyses in Single Case Experimental Research to Evaluate the Effectiveness and Magnitude of a Comprehensive Behavioral Intervention

2018 ◽  
Author(s):  
Prathiba Natesan ◽  
Smita Mehta
Keyword(s):  
Behavioral Intervention ◽  
Effect Size ◽  
Rate Ratio ◽  
Visual Analysis ◽  
Single Case ◽  
Small Sample ◽  
Statistical Analyses ◽  
Data Types ◽  
Ratio Effect ◽  
Small Sample Sizes

Single case experimental designs (SCEDs) have become an indispensable methodology where randomized control trials may be impossible or even inappropriate. However, the nature of SCED data presents challenges for both visual and statistical analyses. Small sample sizes, autocorrelations, data types, and design types render many parametric statistical analyses and maximum likelihood approaches ineffective. The presence of autocorrelation decreases interrater reliability in visual analysis. The purpose of the present study is to demonstrate a newly developed model called the Bayesian unknown change-point (BUCP) model which overcomes all the above-mentioned data analytic challenges. This is the first study to formulate and demonstrate rate ratio effect size for autocorrelated data, which has remained an open question in SCED research until now. This expository study also compares and contrasts the results from BUCP model with visual analysis, and rate ratio effect size with nonoverlap of all pairs (NAP) effect size. Data from a comprehensive behavioral intervention are used for the demonstration.

No Evidence that Experiencing Physical Warmth Promotes Interpersonal Warmth: Two Failures to Replicate Williams and Bargh (2008)

2018 ◽  
Author(s):  
Christopher Chabris ◽  
Patrick Ryan Heck ◽  
Jaclyn Mandart ◽  
Daniel Jacob Benjamin ◽  
Daniel J. Simons
Keyword(s):  
Null Hypothesis ◽  
Small Sample ◽  
Sample Sizes ◽  
Double Blind ◽  
Bayesian Analyses ◽  
Physical Warmth ◽  
Small Sample Sizes ◽  
Interpersonal Warmth

Williams and Bargh (2008) reported that holding a hot cup of coffee caused participants to judge a person’s personality as warmer, and that holding a therapeutic heat pad caused participants to choose rewards for other people rather than for themselves. These experiments featured large effects (r = .28 and .31), small sample sizes (41 and 53 participants), and barely statistically significant results. We attempted to replicate both experiments in field settings with more than triple the sample sizes (128 and 177) and double-blind procedures, but found near-zero effects (r = –.03 and .02). In both cases, Bayesian analyses suggest there is substantially more evidence for the null hypothesis of no effect than for the original physical warmth priming hypothesis.

scholarly journals Non-Invasive Monitoring of Adrenocortical Activity in Three Sympatric Desert Gerbil Species

Animals ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 75
Author(s):  
Álvaro Navarro-Castilla ◽  
Mario Garrido ◽  
Hadas Hawlena ◽  
Isabel Barja
Keyword(s):  
Small Sample ◽  
Post Injection ◽  
Control Groups ◽  
Treatment Groups ◽  
Adrenocortical Activity ◽  
Non Invasive ◽  
Endocrine Status ◽  
Corticosterone Metabolites ◽  
Small Sample Sizes ◽  
Fecal Corticosterone Metabolites

The study of the endocrine status can be useful to understand wildlife responses to the changing environment. Here, we validated an enzyme immunoassay (EIA) to non-invasively monitor adrenocortical activity by measuring fecal corticosterone metabolites (FCM) in three sympatric gerbil species (Gerbillus andersoni, G. gerbillus and G. pyramidum) from the Northwestern Negev Desert’s sands (Israel). Animals included into treatment groups were injected with adrenocorticotropic hormone (ACTH) to stimulate adrenocortical activity, while control groups received a saline solution. Feces were collected at different intervals and FCM were quantified by an EIA. Basal FCM levels were similar in the three species. The ACTH effect was evidenced, but the time of FCM peak concentrations appearance differed between the species (6–24 h post-injection). Furthermore, FCM peak values were observed sooner in G. andersoni females than in males (6 h and 18 h post-injection, respectively). G. andersoni and G. gerbillus males in control groups also increased FCM levels (18 h and 48 h post-injection, respectively). Despite the small sample sizes, our results confirmed the EIA suitability for analyzing FCM in these species as a reliable indicator of the adrenocortical activity. This study also revealed that close species, and individuals within a species, can respond differently to the same stressor.

scholarly journals Use of Antiplatelet Agents Decreases the Positive Predictive Value of Fecal Immunochemical Tests for Colorectal Cancer but Does Not Affect Their Sensitivity

2021 ◽  
Vol 11 (6) ◽  
pp. 497
Author(s):  
Yoonsuk Jung ◽  
Eui Im ◽  
Jinhee Lee ◽  
Hyeah Lee ◽  
Changmo Moon
Keyword(s):  
Colorectal Cancer ◽  
Large Scale ◽  
Screening Program ◽  
Antiplatelet Agents ◽  
Population Based ◽  
Small Sample ◽  
Antithrombotic Agents ◽  
Predictive Values ◽  
Prescription Rates ◽  
Small Sample Sizes

Previous studies have evaluated the effects of antithrombotic agents on the performance of fecal immunochemical tests (FITs) for the detection of colorectal cancer (CRC), but the results were inconsistent and based on small sample sizes. We studied this topic using a large-scale population-based database. Using the Korean National Cancer Screening Program Database, we compared the performance of FITs for CRC detection between users and non-users of antiplatelet agents and warfarin. Non-users were matched according to age and sex. Among 5,426,469 eligible participants, 768,733 used antiplatelet agents (mono/dual/triple therapy, n = 701,683/63,211/3839), and 19,569 used warfarin, while 4,638,167 were non-users. Among antiplatelet agents, aspirin, clopidogrel, and cilostazol ranked first, second, and third, respectively, in terms of prescription rates. Users of antiplatelet agents (3.62% vs. 4.45%; relative risk (RR): 0.83; 95% confidence interval (CI): 0.78–0.88), aspirin (3.66% vs. 4.13%; RR: 0.90; 95% CI: 0.83–0.97), and clopidogrel (3.48% vs. 4.88%; RR: 0.72; 95% CI: 0.61–0.86) had lower positive predictive values (PPVs) for CRC detection than non-users. However, there were no significant differences in PPV between cilostazol vs. non-users and warfarin users vs. non-users. For PPV, the RR (users vs. non-users) for antiplatelet monotherapy was 0.86, while the RRs for dual and triple antiplatelet therapies (excluding cilostazol) were 0.67 and 0.22, respectively. For all antithrombotic agents, the sensitivity for CRC detection was not different between users and non-users. Use of antiplatelet agents, except cilostazol, may increase the false positives without improving the sensitivity of FITs for CRC detection.

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