Performance of Some Estimators of Relative Variability

Ospina, Raydonal; Marmolejo‐Ramos, Fernando

doi:10.3389/fams.2019.00043

Cited by 61 publications

(45 citation statements)

References 112 publications

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“…Based on this criterion, all seven genes were suitable reference genes according to NormFinder. This result is supported by the distribution of C q values, which indicated that all seven candidate genes were stably expressed with CV values < 1, which is considered to indicate low variance 28 . In BestKeeper analysis, all seven genes were also determined to be appropriate reference genes for analysis of honey bee gene expression in summer, autumn and among the three tissue types as indicated by SD values < 1 22,23 .…”

Section: Discussionmentioning

confidence: 63%

Reference gene selection for qRT-PCR analysis of season- and tissue-specific gene expression profiles in the honey bee Apis mellifera

Jeon

Moon

Kim

et al. 2020

Sci Rep

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Honey bees are both important pollinators and model insects due to their highly developed sociality and colony management. To better understand the molecular mechanisms underlying honey bee colony management, it is important to investigate the expression of genes putatively involved in colony physiology. Although quantitative real-time PCR (qRT-PCR) can be used to quantify the relative expression of target genes, internal reference genes (which are stably expressed across different conditions) must first be identified to ensure accurate normalisation of target genes. To identify reliable reference genes in honey bee (Apis mellifera) colonies, therefore, we evaluated seven candidate genes (ACT , EIF, EF1, RPN2, RPS5, RPS18 and GAPDH) in samples collected from three honey bee tissue types (head, thorax and abdomen) across all four seasons using three analysis programmes (NormFinder, BestKeeper and geNorm). Subsequently, we validated various normalisation methods using each of the seven reference genes and a combination of multiple genes by calculating the expression of catalase (CAT). Although the genes ranked as the most stable gene were slightly different on conditions and analysis methods, our results suggest that RPS5, RPS18 and GAPDH represent optimal honey bee reference genes for target gene normalisation in qRT-PCR analysis of various honey bee tissue samples collected across seasons. The Western honey bee, Apis mellifera L., plays an important role as a pollinator 1. In addition, the honey bee is considered to be a key model insect due to its relatively complex behaviours, including sociality, labour division and colony management 2. Previous studies have demonstrated that endocrine system status and gene expression are important factors for flexible honey bee colony management, which involves colonies seasonally regulating their labour division and population dynamics 3-5. In order to extend our understanding of the molecular mechanisms that underlie the regulation of honey bee colony physiology, information on the physiological functions of the genes putatively associated with colony management can be determined by analysing their expression profiles among different seasons and honey bee tissues 6,7. In quantitative real-time PCR (qRT-PCR), gene-specific mRNA (or cDNA) is quantified; this method has been used extensively because of its relative speed, sensitivity, replicability and accuracy 8,9. Therefore, qRT-PCR would be an ideal method for analysing the expression patterns of honey bee genes putatively involved in the plasticity of colony molecular physiology in samples collected across different seasons and tissues. However, because qRT-PCR results are highly sensitive to the initial amount of RNA content in the amplification reaction, the interpretation of target gene expression levels among various conditions would result in appreciable errors without the use of a reliable internal standard 7-10. Therefore, prior to analysing target gene expression levels among conditions, reference genes are requir...

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Section: Discussionmentioning

confidence: 63%

Reference gene selection for qRT-PCR analysis of season- and tissue-specific gene expression profiles in the honey bee Apis mellifera

Jeon

Moon

Kim

et al. 2020

Sci Rep

View full text Add to dashboard Cite

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“…The coefficient of variation (CV) is a standardized, dimensionless measure of dispersion relative to a data set's average. It enables the comparison of several datasets on genotypes with different units of measurement (Ospina and Marmolejo-Ramos 2019). It is also used as a measure to compare the robustness of different biological traits (Félix and Barkoulas 2015).…”

Section: Discussionmentioning

confidence: 99%

Assessment of diversity of Indian aromatic rice germplasm collections for morphological, agronomical, quality traits and molecular characters to identify a core set for crop improvement

et al. 2020

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Background Besides the Basmati, the aromatic rice germplasm (ARG) accessions are treasured for quality, medicinal value and aroma. The demand for aromatic rice is ever increasing. Genetic diversity is the source of variability to identify superior alleles controlling morphological, agronomic and quality traits, and molecular attributes. This study reports on the characterization of traits in ARG to identify a core set for breeding high-yielding varieties. Methods The genetic diversity was measured on the distinctness, uniformity and stability (DUS) of 46 traits in 208 Indian ARG in field, greenhouse and laboratory tests. We performed individual and combined analysis of DUS traits and molecular data generated using 55 SSR markers. The genetic distances between genotypes were estimated using Mahalanobis D2 analysis and clustering by standardized Euclidean2 distances, Ward Minimum variance, Gowers’ similarity index and PowerMarker. The aim was to derive a core set of non-Basmati ARG using PowerCore to deploy in crop improvement. Results Eighty-two alleles were detected. Alleles per marker ranged from 2 (RM505) to 5 (RM276) with an average of 3.04 alleles. The markers are informative in analyzing the diversity as the PIC values estimated varied from 0.17 (RM577 on chromosome 1) to 0.72 (RM276 on chromosome 6) with an average of 0.54 per locus. RM276 with repeat motif of (AG)8A3(GA) 33 on chromosome 6 was the most informative (amplified 5 alleles). The combined analysis had shown genotypes in a few clusters to be more diverse than others. SSR markers RM289, RM505, RM577 and RM22866 were identified as genotype specific markers. With PowerCore, 46 genotypes (22%) were identified as a core set of ARG that represent all the alleles detected in the entire set investigated. 2-Acetyl-1-pyrroline is considered to impart aroma; it was not detected by GC–MS tests in many ARG. Conclusions Forty-six genotypes in the core set have different maturity periods, plant statures, grain types and grain quality traits. A parent can be selected from the core set to improve aromatic rice depending on the breeding objective. The olfactory sensing of strong aroma emitted by cooked kernels of all ARG was found more decisive than the costly GC–MS tests.

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“…In the present work, CV is adopted to quantify the variability of muscle rhythmic activation during walking in three different populations, namely hemiplegic children, healthy school children and young adult. Motivation for choosing the CV is threefold: (i) We aim for applying and testing this index in the evaluation of sEMG variability during walking in hemiplegic children for the first time at our best knowledge; (ii) we aim for checking the suitability of such an easy-to-compute index in reflecting different characteristics between pathological and control children and then between children and young adults, in order to promote the adoption of sEMG in clinical practice: Despite its simplicity, the index is able to satisfactorily discriminate the muscular recruitment during walking exhibited by different populations [14,28]; (iii) CV is a unit-free measure, suitable to compare normally distributed data by directly quantifying the degree of variability relative to the mean of the distributions [28]. The CV index, indeed, is not directly computed on sEMG samples, but it is derived from the standard deviation of the signal, which is by definition a direct measurement of the signal variability.…”

Section: Indices For Semg Variability Analysismentioning

confidence: 99%

“…These characteristics seem to make this index more suitable to the aim of the present study, respect to CQV and VR indices. CQV index, indeed, depends on mean and quartiles, which in turn can be influenced by how they are estimated [28]. VR index, requiring a more articulated computation algorithm, is more indicated for intra-individual variability, being insensitive to mean sEMG amplitude and data smoothing applied to different waveforms [29].…”

Section: Indices For Semg Variability Analysismentioning

confidence: 99%

Variability of Muscular Recruitment in Hemiplegic Walking Assessed by EMG Analysis

et al. 2020

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Adaptive variability during walking is typical of child motor development. It has been reported that neurological disorders could affect this physiological phenomenon. The present work is designed to assess the adaptive variability of muscular recruitment during hemiplegic walking and to detect possible changes compared to control populations. In the attempt of limiting the complexity of computational procedure, the easy-to-measure coefficient of variation (CV) index is adopted to assess surface electromyography (sEMG) variability. The target population includes 34 Winters’ type I and II hemiplegic children (H-group). Two further healthy populations, 34 age-matched children (C-group) and 34 young adults (A-group), are involved as controls. Results show a significant decrease (p < 0.05) of mean CV for gastrocnemius lateralis (GL) in H-group compared to both C-group (15% reduction) and A-group (35% reduction). Reductions of mean CV are detected also for tibialis anterior (TA) in H-group compared to C-group (7% reduction, p > 0.05) and A-group (15% reduction, p < 0.05). Lower CVs indicate a decreased intra-subject variability of ankle-muscle activity compared to controls. Novel contribution of the study is twofold: (1) To propose a CV-based approach for an easy-to-compute assessment of sEMG variability in hemiplegic children, useful in different experimental environments and different clinical purposes; (2) to provide a quantitative assessment of the reduction of intra-subject variability of ankle-muscle activity in mild-hemiplegic children compared to controls (children and adults), suggesting that hemiplegic children present a limited capability of adapting their muscle recruitment to the different stimuli met during walking task. This finding could be very useful in deepening the knowledge of this neurological disorder.

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Performance of Some Estimators of Relative Variability

Cited by 61 publications

References 112 publications

Reference gene selection for qRT-PCR analysis of season- and tissue-specific gene expression profiles in the honey bee Apis mellifera

Reference gene selection for qRT-PCR analysis of season- and tissue-specific gene expression profiles in the honey bee Apis mellifera

Assessment of diversity of Indian aromatic rice germplasm collections for morphological, agronomical, quality traits and molecular characters to identify a core set for crop improvement

Variability of Muscular Recruitment in Hemiplegic Walking Assessed by EMG Analysis

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