Modeling Genetic Complexity in the Classroom

2018 ◽  
Vol 80 (2) ◽  
pp. 140-142
Author(s):  
Julie J. Lesnik
Keyword(s):  
Gene Expression ◽  
Environmental Effects ◽  
Independent Assortment ◽  
Genetic Complexity ◽  
Classroom Exercise ◽  
Variable Gene ◽  
Random Individual

This classroom exercise aims to help students understand the three Ps of genetic complexity: polymorphic, polygenic, and pleiotropic. Using coin flips and dice rolls, students are able to generate the genotype and phenotype of a random individual. From there, students find a mate for this individual and determine the phenotype of their offspring. The randomness generated by the coin and dice mechanics illustrates the principles of independent assortment and segregation, variable gene expression, and environmental effects.

scholarly journals Evaluation of tools for highly variable gene discovery from single-cell RNA-seq data

2018 ◽  
Vol 20 (4) ◽  
pp. 1583-1589 ◽  
Author(s):  
Shun H Yip ◽  
Pak Chung Sham ◽  
Junwen Wang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Differentially Expressed Gene ◽  
Embryonic Stem ◽  
Rna Seq ◽  
Software Packages ◽  
Homogeneous Cell ◽  
Variable Gene ◽  
Cell Variation ◽  
Larger Sample

Abstract Traditional RNA sequencing (RNA-seq) allows the detection of gene expression variations between two or more cell populations through differentially expressed gene (DEG) analysis. However, genes that contribute to cell-to-cell differences are not discoverable with RNA-seq because RNA-seq samples are obtained from a mixture of cells. Single-cell RNA-seq (scRNA-seq) allows the detection of gene expression in each cell. With scRNA-seq, highly variable gene (HVG) discovery allows the detection of genes that contribute strongly to cell-to-cell variation within a homogeneous cell population, such as a population of embryonic stem cells. This analysis is implemented in many software packages. In this study, we compare seven HVG methods from six software packages, including BASiCS, Brennecke, scLVM, scran, scVEGs and Seurat. Our results demonstrate that reproducibility in HVG analysis requires a larger sample size than DEG analysis. Discrepancies between methods and potential issues in these tools are discussed and recommendations are made.

scholarly journals CD5 and immunoglobulin V gene expression in B-cell lymphomas and chronic lymphocytic leukemia

Blood ◽  
1990 ◽  
Vol 75 (7) ◽  
pp. 1518-1524
Author(s):  
R Mayer ◽  
T Logtenberg ◽  
J Strauchen ◽  
A Dimitriu-Bona ◽  
L Mayer ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cell ◽  
Gene Families ◽  
Lymphocytic Leukemia ◽  
B Cell Lymphomas ◽  
Barr Virus ◽  
Gene Usage ◽  
Variable Gene ◽  
Epstein Barr ◽  
T Cell Malignancies

We studied the expression of CD5 and immunoglobulin variable gene families in a panel of monoclonal Epstein-Barr virus (EBV) transformed lines, chronic lymphocytic leukemias (CLLs) and CD5+ and CD5- B-cell lymphomas. The CD5 gene expression was in all cases identical to that of T-cell malignancies. The utilization of the various VH and VK gene families was roughly proportional to the estimated gene family size in EBV lines obtained from adult healthy subjects. In contrast we found a statistically significant biased usage of VH6 in CLL and VH5 in CD5+ lymphomas as compared with EBV lines, and of VKIII in both CLL and CD5+ lymphomas as compared with EBV lines. Some differences in the variable gene usage were also noted when comparing CD5+ and CD5- lymphomas. These findings are analyzed in the context of possible mechanisms involved in the malignant transformation of CD5+ B cells.

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