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http://tasn.org.tw/misnc2020

Paper Submission: July 16, 2020
Acceptance Notification:  Aug. 6, 2020
Final Camera Ready Paper: Aug. 20, 2020
Author Registration: Aug. 20, 2020

https://www.caa.gov.tw/Article.aspx?a=2193&lang=1#twelve

https://catalog.digitalarchives.tw/item/00/14/d0/cd.html

安裝適用於 Linux 的 Windows 子系統
https://docs.microsoft.com/zh-tw/windows/wsl/install-win10

Docker Desktop
https://hub.docker.com/editions/community/docker-ce-desktop-windows

Anaconda-Solutions for Data Science Practitioners and Enterprise Machine Learning
https://www.anaconda.com/distribution/

pandas - Python Data Analysis Library
https://pandas.pydata.org/

The R Project for Statistical Computing
https://www.r-project.org/

RStudio Desktop
https://rstudio.com/products/rstudio/download/

Bioconductor - Home
https://www.bioconductor.org/

Install MATLAB Engine API for Python
https://www.mathworks.com/help/matlab/matlab_external/install-the-matlab-engine-for-python.html

A Matlab kernel for Jupyter
https://github.com/Calysto/matlab_kernel

Matlab tutorial in Jupyter Notebook
https://github.com/alamgirh/matlab-tutorial

https://www.pnas.org/content/116/43/21350

Transcriptomic analysis of human endogenous retroviruses in systemic lupus erythematosus

Endogenous retroviruses (ERVs) are integrated retroviral elements within the human genome. Tokuyama et al. (1) recently published a computational tool, “ERVmap,” to analyze genome-wide, locus-specific expression of human ERVs. The authors found increased expression of 124 ERV loci in patients with systemic lupus erythematosus (SLE), compared to controls, and 0 down-regulated loci. In contrast, our reanalysis of their data using a Bayesian reassignment algorithm, Telescope (2), detected only 23 ERV locations with significant differential expression (DE), including 4 loci with significantly lower expression. We found that the differences between the results could be due to methodological aspects of their analysis, including alignment ambiguity, ERV annotation, and failure to account for sequencing platform as a source of variance (3).

Accurate Classification of Differential Expression Patterns in a Bayesian Framework With Robust Normalization for Multi-Group RNA-Seq Count Data

https://journals.sagepub.com/doi/full/10.1177/1177932219860817

Empirical Bayes is a choice framework for differential expression (DE) analysis for multi-group RNA-seq count data. Its characteristic ability to compute posterior probabilities for predefined expression patterns allows users to assign the pattern with the highest value to the gene under consideration. However, current Bayesian methods such as baySeq and EBSeq can be improved, especially with respect to normalization. Two R packages (baySeq and EBSeq) with their default normalization settings and with other normalization methods (MRN and TCC) were compared using three-group simulation data and real count data. Our findings were as follows: (1) the Bayesian methods coupled with TCC normalization performed comparably or better than those with the default normalization settings under various simulation scenarios, (2) default DE pipelines provided in TCC that implements a generalized linear model framework was still superior to the Bayesian methods with TCC normalization when overall degree of DE was evaluated, and (3) baySeq with TCC was robust against different choices of possible expression patterns. In practice, we recommend using the default DE pipeline provided in TCC for obtaining overall gene ranking and then using the baySeq with TCC normalization for assigning the most plausible expression patterns to individual genes.