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opencc-by-4.0Apr 2020View details →

Number of genes per function within mobile genetic elements in Martinez Arbas, Narayanasamy et. al. (2020)

This repository contains a set of tables separated by COG functional categories and the type of mobile genetic element, i.e. phage or plasmid. Each table contains predicted gene functions for each COG category and information on protospacer-containing contigs (PSCCs) and non-PSCCs This repository is related to the work published in Martinez Arbas, Narayanasamy et. al. (2020).

opencc-zeroJul 2020View details →

Chemical-genetic interrogation of RNA polymerase mutants reveals structure-function relationships and physiological tradeoffs

The multi-subunit bacterial RNA polymerase (RNAP) and its associated regulators carry out transcription and integrate myriad regulatory signals. Numerous studies have interrogated the inner workings of RNAP, and mutations in genes encoding RNAP drive adaptation of Escherichia coli to many health- and industry-relevant environments, yet a paucity of systematic analyses has hampered our understanding of the fitness benefits and trade-offs from altering RNAP function. Here, we conduct a chemical-genetic analysis of a library of RNAP mutants. We discover phenotypes for non-essential insertions, show that clustering mutant phenotypes increases their predictive power for drawing functional inferences, and demonstrate that some RNA polymerase mutants both decrease average cell length and confer insensitivity to killing by cell-wall targeting antibiotics. Our findings demonstrate that RNAP chemical-genetic interactions provide a general platform for interrogating structure-function relationships in vivo and for identifying physiological trade-offs of mutations, including those relevant for disease and biotechnology. This strategy should have broad utility for illuminating the role of other important protein complexes.

opencc-by-4.0Mar 2015View details →

Fig. 2. Canonical discriminant functional analyses showing 3 in Life history traits of three cryptic species Asia I, Asia II-1 and Asia II-7 of Bemisia tabaci (Hemiptera: Aleyrodidae) reconfirm their genetic identities

Fig. 2. Canonical discriminant functional analyses showing 3 genetic groups of Bemisia tabaci species complex.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 15. Drawing of autocorrelation function and partial correlation of the residues for males and females primary stage students

After diagnosing and evaluating the models, the accommodating and the sufficiency of the models must be checked for males and females of primary stage students, through applying the compute (Ljung-Box Q) to check the model accommodation on the Function level 0.05 so the Q value occurs of males and females of primary stage students: Ljung-Box Q' = 1.10306, With p-value = P(Chi-square(1) > 1.10306) = 0.2936 Note that the Tabulated value   equals 3.841 while the Q value is less than   Tabulated value, so it takes the Null Hypothesis which manifests that the emptiness of the evaluated model out of the contrast in accordance trouble. It's possible to notice that the two parameters functions (Autocorrelation and Partial correlation Functions)  of the residues for male females primary stage, in which the residues  value is located within confidence interval limits, which means the residues series is random and the evaluated model is good and convenient as it is presented.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 13. Drawing of autocorrelation Function and partial correlation of the residues for primary stage males students

It's possible to notice the two parameters functions (Autocorrelation and Partial correlation Functions) of the residues for males primary stage, in which the residues  value is located within the confidence interval limits which means the residues series is random and the Evaluated Model is good and convenient as it is presented.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm- Figure 12. Drawing of autocorrelation function and partial correlation for females primary stage students

We use the Unit Radix Dickey-Fuller Test to ensure the series’ stability. The results are: Dickey-Fuller Test Estimated Value = 0.369693, Statistic Test =1.01829, P-Value=0.9194 We notice from the values above P-Value = 0.9194 on the abstract level of 0.05 which leads to accepting the Null Hypothesis and refusing the Alternative Hypothesis (Existence of a Radix Unit) implies that the time series is instable. By taking the first difference, we notice that the stability of the time series has been achieved. See Figure 11.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 10. Drawing of autocorrelation function and partial correlation for Males and Females primary stage students

The instability of the time series is recognized, and to be more accurate, we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to assure the stability according to the figure (10).

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 14. Drawing of autocorrelation function and partial correlation of the residues for primary stage males students

After diagnosing and evaluating the models, the accommodating and the sufficiency of the models must be checked for primary stage female students, through applying the compute (Ljung- Box Q) to check the model accommodation on the function level 0.05 so the Q value occurs of primary stage female students: Ljung-Box Q' = 0.966626, With p-value = P(Chi-square(1) > 0.966626) = 0.3255 However, the Tabulated value   equals 3.841 whilst the Q value is less than Tabulated value, so it accepts the Null Hypothesis which indicates the emptiness of the evaluated model out of the contrast accordance trouble. It's possible to notice the two parameters functions (Autocorrelation and Partial Correlation Functions)  of the residues for  females primary stage students, in which the residues  value is located within the confidence interval limits, which means the residues series is random and the evaluated model is good and convenient as it is shown.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 6. Drawing of auto correlation function and partial correlation for females primary stage students

The instability of the time series is noticed and to be more precise we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to affirm the stability according to the figure 6.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 4. Drawing of autocorrelation function and partial correlation for males primary stage students

We get to notice the stability of the time series, and to be more accurate we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to ensure the stability according to the figure (4).

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 8. Drawing of autocorrelation function and partial correlation for Females primary stage students

The stability of the time series is observed and to be more accurate we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to assure the stability according to the figure (8).

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 2. Drawing of autocorrelation function and partial correlation for males primary stage

We get to notice the instability of the time series, and to be more precise we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to ensure the stability according to the figure 2.

opencc-zeroMar 2020View details →

Data from: Genetic and functional variation across regional and local scales is associated with climate in a foundational prairie grass

<ul> <li>Global change forecasts in ecosystems require knowledge of within species diversity, particularly of dominant species within communities. We assessed site-level diversity and capacity for adaptation of the dominant species of the shortgrass steppe biome of the Central US, Bouteloua gracilis.</li> <li>We quantified genetic diversity from 17 sites across regional scales, north-south from New Mexico to South Dakota, and local scales in Northern Colorado. We also quantified phenotype and plasticity within and among sites and determined the extent to which phenotypic diversity in B. gracilis was related to climate.</li> <li>Genome sequencing indicated pronounced population structure at the regional scale, and local differences indicated gene flow and/or dispersal may also be limited. Within a common environment, we found evidence for genetic divergence in biomass-related phenotypes, plasticity, and phenotypic variance, indicating functional divergence and different adaptive potential. Phenotypes differentiated according to climate, chiefly median Palmer Hydrological Drought Index and other aridity metrics.</li> <li>Our results indicate conclusive differences in genetic variation, phenotype, and plasticity in this species and suggest a mechanism explaining variation in shortgrass steppe community responses to global change. This analysis of B. gracilis intraspecific diversity across spatial scales will improve conservation and management of the shortgrass steppe ecosystem moving forward.</li> </ul>

opencc-zeroMar 2023View details →

Genetic variation in mouse islet Ca2+ oscillations reveals novel regulators of islet function

Insufficient insulin secretion to meet metabolic demand results in diabetes. The intracellular flux of Ca2+ into β-cells triggers insulin release. Since genetics strongly influences variation in islet secretory responses, we surveyed islet Ca2+ dynamics in eight genetically diverse mouse strains. We found high strain variation in response to four conditions: 1) 8 mM glucose; 2) 8 mM glucose plus amino acids; 3) 8 mM glucose, amino acids, plus 10nM GIP; and 4) 2 mM glucose. These stimuli interrogate β-cell function, α-cell to β-cell signaling, and incretin responses. We then correlated components of the Ca2+ waveforms to islet protein abundances in the same strains used for the Ca2+ measurements. To focus on proteins relevant to human islet function, we identified human orthologues of correlated mouse proteins that are proximal to glycemic-associated SNPs in human GWAS. Several orthologues have previously been shown to regulate insulin secretion (e.g. ABCC8, PCSK1, and GCK), supporting our mouse-to-human integration as a discovery platform. By integrating these data, we nominated novel regulators of islet Ca2+ oscillations and insulin secretion with potential relevance for human islet function. We also provide a resource for identifying appropriate mouse strains in which to study these regulators.

publicDec 2018View details →

Data from: Functional connectivity and home range inferred at a microgeographic landscape genetics scale in a desert-dwelling rodent

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publicSep 2023View details →

Genetic variation in mouse islet Ca2+ oscillations reveals novel regulators of islet function

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publicMar 2020View details →

Genetic and functional variation across regional and local scales is associated with climate in a foundational prairie grass

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