scholarly journals RNA‐Seq Analysis of Genetic and Transcriptome Network Effects of Dual‐Trait Selection for Ethanol Preference and Withdrawal Using SOT and NOT Genetic Models

2020 ◽  
Vol 44 (4) ◽  
pp. 820-830
Author(s):  
Laura B. Kozell ◽  
Denesa Lockwood ◽  
Priscila Darakjian ◽  
Stephanie Edmunds ◽  
Karen Shepherdson ◽  
...  
Keyword(s):  
Network Effects ◽  
Genetic Models ◽  
Rna Seq ◽  
Ethanol Preference ◽  
Selection For

EVOLUTION OF FERTILITY IN ADVANCED GENERATIONS OF AN ALFALFA SINGLE CROSS HYBRID

1971 ◽  
Vol 13 (4) ◽  
pp. 834-841 ◽  
Author(s):  
L. Dessureaux ◽  
A. Gallais
Keyword(s):  
Genetic Models ◽  
Mother Plant ◽  
Seed Setting ◽  
Three Generations ◽  
Single Cross ◽  
Selection For

Cross-fertility of a single cross hybrid was found to decrease gradually in advanced generations. When the same parents were selfed without selection for two or three generations, seed setting increased from Syn1 to Syn2, then decreased in Syn3 and Syn4. Interpretation of these data according to various genetic models, assuming no epistasis, indicates that fertility is influenced more by the mother plant than by the zygote, especially in the F1. Even with the best model, deviations from regression remain significant, except when the Syn1 generation is removed from the analysis.

scholarly journals Evolutionary Genetic Models of the Ovarian Time Bomb Hypothesis for the Evolution of Genomic Imprinting

Genetics ◽  
2002 ◽  
Vol 162 (1) ◽  
pp. 425-439 ◽  
Author(s):  
Anton E Weisstein ◽  
Marcus W Feldman ◽  
Hamish G Spencer
Keyword(s):  
Growth Factor ◽  
Genetic System ◽  
Early Embryogenesis ◽  
Genetic Models ◽  
Insulin Like Growth Factor ◽  
Deleterious Mutations ◽  
Trophoblastic Disease ◽  
Evolutionary Genetic ◽  
The Face ◽  
Selection For

Abstract At a small number of loci in eutherian mammals, only one of the two copies of a gene is expressed; the other is silenced. Such loci are said to be “imprinted,” with some having the maternally inherited allele inactivated and others showing paternal inactivation. Several hypotheses have been proposed to explain how such a genetic system could evolve in the face of the selective advantages of diploidy. In this study, we examine the “ovarian time bomb” hypothesis, which proposes that imprinting arose through selection for reduced risk of ovarian trophoblastic disease in females. We present three evolutionary genetic models that incorporate both this selection pressure and the effect of deleterious mutations to elucidate the conditions under which imprinting could evolve. Our findings suggest that the ovarian time bomb hypothesis can explain why some growth-enhancing genes active in early embryogenesis [e.g., mouse insulin-like growth factor 2 (Igf2)] have evolved to be maternally rather than paternally inactive and why the opposite imprinting status has evolved at some growth-inhibiting loci [e.g., mouse insulin-like growth factor 2 receptor (Igf2r)].

scholarly journals sc-REnF: An Entropy Guided Robust Feature Selection for Single-Cell RNA-seq Data

2021 ◽  
Author(s):  
Snehalika Lall ◽  
Abhik Ghosh ◽  
Sumanta Ray ◽  
Sanghamitra Bandyopadhyay
Keyword(s):  
Single Cell ◽  
Gene Selection ◽  
Small Sample ◽  
Rna Seq ◽  
Homogeneous Grouping ◽  
Cell Clustering ◽  
Selection For ◽  
Downstream Analysis ◽  
Cell Data

Abstract Annotation of cells in single-cell clustering requires a homogeneous grouping of cell populations. Since single cell data is susceptible to technical noise, the quality of genes selected prior to clustering is of crucial importance in the preliminary steps of downstream analysis. Therefore, interest in robust gene selection has gained considerable attention in recent years. We introduce sc-REnF, (robust entropy based feature (gene) selection method), aiming to leverage the advantages of Rényi and Tsallis> entropies in gene selection for single cell clustering. Experiments demonstrate that with tuned parameter (q), Rényi and Tsallis entropies select genes that improved the clustering results significantly, over the other competing methods. sc-REnF can capture relevancy and redundancy among the features of noisy data extremely well due to its robust objective function. Moreover, the selected features/genes can able to clusters the unknown cells with a high accuracy. Finally, sc-REnF yields good clustering performance in small sample, large feature scRNA-seq data.

scholarly journals Splicing QTL analysis focusing on coding sequences reveals pathogenicity of disease susceptibility loci.

2022 ◽  
Author(s):  
Kensuke Yamaguchi ◽  
Kazuyoshi Ishigaki ◽  
Akari Suzuki ◽  
Yumi Tsuchida ◽  
Haruka Tsuchiya ◽  
...  
Keyword(s):  
Protein Structure ◽  
Disease Susceptibility ◽  
Susceptibility Loci ◽  
Rna Seq ◽  
Coding Sequences ◽  
Altered Protein ◽  
Long Read ◽  
Novel Approaches ◽  
Selection For

Splicing QTL (sQTL) are one of the major causal mechanisms in GWAS loci, but their role in disease pathogenesis is poorly understood. One reason is the huge complexity of alternative splicing events producing many unknown isoforms. Here, we proposed two novel approaches, namely integration and selection, for this complexity by focusing on protein-structure of isoforms. First, we integrated isoforms with the same coding sequence (CDS) and identified 369-601 integrated-isoform ratio QTLs (i2-rQTLs), which altered protein-structure, in six immune subsets. Second, we selected CDS incomplete isoforms annotated in GENCODE and identified 175-337 isoform-ratio QTL (i-rQTL). By comprehensive long-read capture RNA-seq among these incomplete isoforms, we revealed 29 full-length isoforms with novel CDSs associated with GWAS traits. Furthermore, we have shown that disease-causal sQTL genes can be identified by evaluating their trans-eQTL effects. Our approaches highlight the understudied role of protein-altering sQTLs and are broadly applicable to other tissues and diseases.

scholarly journals Reference genes selection for transcript normalization in kenaf (Hibiscus cannabinusL.) under salinity and drought stress

PeerJ ◽  
2015 ◽  
Vol 3 ◽  
pp. e1347 ◽  
Author(s):  
Xiaoping Niu ◽  
Jianmin Qi ◽  
Meixia Chen ◽  
Gaoyang Zhang ◽  
Aifen Tao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Reference Genes ◽  
18S Rrna ◽  
Hibiscus Cannabinus ◽  
Rna Seq ◽  
Qrt Pcr ◽  
Reverse Transcription Pcr ◽  
Yield And Quality ◽  
Selection For ◽  
Transcriptomic Level

Kenaf (Hibiscus cannabinus) is an economic and ecological fiber crop but suffers severe losses in fiber yield and quality under the stressful conditions of excess salinity and drought. To explore the mechanisms by which kenaf responds to excess salinity and drought, gene expression was performed at the transcriptomic level using RNA-seq. Thus, it is crucial to have a suitable set of reference genes to normalize target gene expression in kenaf under different conditions using real-time quantitative reverse transcription-PCR (qRT-PCR). In this study, we selected 10 candidate reference genes from the kenaf transcriptome and assessed their expression stabilities by qRT-PCR in 14 NaCl- and PEG-treated samples using geNorm, NormFinder, and BestKeeper. The results indicated thatTUBαand 18S rRNA were the optimum reference genes under conditions of excess salinity and drought in kenaf. Moreover,TUBαand 18S rRNA were used singly or in combination as reference genes to validate the expression levels of WRKY28 and WRKY32 in NaCl- and PEG-treated samples by qRT-PCR. The results further proved the reliability of the two selected reference genes. This work will benefit future studies on gene expression and lead to a better understanding of responses to excess salinity and drought in kenaf.

scholarly journals Effects of mapping algorithms on gene selection for RNA-Seq analysis: pulmonary response to acute neonatal hyperoxia

2015 ◽  
Author(s):  
Chin-Yi Chu ◽  
Soumyaroop Bhattacharya ◽  
Zhongyang Zhou ◽  
Min Yee ◽  
Ashley Lopez ◽  
...  
Keyword(s):  
Data Analysis ◽  
Gene Selection ◽  
Quantitative Polymerase Chain Reaction ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Mapping Algorithms ◽  
Expression Levels ◽  
Selection For ◽  
Neonatal Hyperoxia ◽  
The Impact

Background: A major goal of RNA-Seq data analysis is to reconstruct the full set of gene transcripts expressed in a biological sample in order to quantify their expression levels. The process typically involves multiple steps including mapping short sequence reads to a reference genome, and estimating expression levels based on these mappings. Multiple algorithms and approaches for each processing step exist, and the impact of different methods on estimation of gene expression is not entirely clear. Methods: We evaluated the impact of three common mapping algorithms on differential expression analysis in an RNA-Seq dataset describing the lung response to acute neonatal hyperoxia. RNA-Seq data generated using the Illumina platform were mapped and aligned using CASAVA, TopHat, and SHRiMP against the mouse genome. Significance Analysis of Microarrays and Cuffdiff were used to identify differentially expressed genes between hyperoxia-challenged and age matched control mice. Results: 1403 genes were detected as differentially expressed by least one mapping and gene selection method. A majority of genes (>65%) were identified by all three mapping methods, regardless of the gene selection approach. Expression patterns for 52 genes were examined by quantitative polymerase chain reaction (qPCR). Importantly, we found different validation rates for genes selected by each method; 72% for CASAVA, 69% for TopHat and 63% for SHRiMP. Surprisingly, the validation rate for genes selected by all three mapping methods was no greater than the best single method. Conclusion: The choice of mapping strategy impacts the reliability of gene selection for RNA-Seq data analysis.

scholarly journals Variability in scRNA-Seq analysis is mitigated by regularized or supervised approaches

2021 ◽  
Author(s):  
Arda Durmaz ◽  
Jacob G. Scott
Keyword(s):  
Single Cell ◽  
Rna Seq ◽  
Trajectory Estimation ◽  
Future Studies ◽  
Transcriptional Dynamics ◽  
Analysis Tools ◽  
Evolution Of Resistance ◽  
Selection For ◽  
Combinatorial Space ◽  
The Impact

ABSTRACTTranscriptional dynamics of evolutionary processes through time are highly complex and require single-cell resolution datasets. This is especially important in cancer during the evolution of resistance, where stochasticity can lead to selection for divergent transcriptional mechanisms. Statistical methods developed to address various questions in single-cell datasets are prone to variability and require careful adjustments of multiple parameter space. To assess the impact of this variation, we utilized commonly used single-cell RNA-Seq analysis tools in a combinatorial fashion to evaluate how repeatable the results are when different methods are combined. In the context of clustering and trajectory estimation, we benchmark the combinatorial space and highlight ares and methods that are sensitive to parameter changes. We have observed that utilizing temporal information in a supervised framework or regularization in latent modeling reduces variability leading to improved overlap when different parameters/methods are used. We hope that future studies can benefit from the results presented here as use of scRNA-Seq analysis tools as out of the box is becoming a standard approach in cancer research.

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