Methods for analyzing next-generation sequencing data 20. Properties and statistical models of RNA-seq count data.-Reference-Cited by-同舟云学术

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Methods for analyzing next-generation sequencing data 20. Properties and statistical models of RNA-seq count data.

Published:2023-03-24 Issue:1 Volume:34 Page:21-29
ISSN:1343-327X
Container-title:Japanese Journal of Lactic Acid Bacteria
language:en
Short-container-title:Jpn. J. Lactic Acid Bact.

Author:

Makino Manon¹,Sakamoto Mitsuo²,Shimizu Kentaro¹³⁴,Kadota Koji¹³⁴

Affiliation:

1. Graduate School of Agricultural and Life Sciences, The University of Tokyo.

2. BioResource Research Center, RIKEN.

3. Interfaculty Initiative in Information Studies, The University of Tokyo.

4. Collaborative Research Institute for Innovative Microbiology, The University of Tokyo.

Publisher

Japan Society for Lactic Acid Bacteria

Link

https://www.jstage.jst.go.jp/article/jslab/34/1/34_21/_pdf

Reference12 articles.

1. 1) 牧野磨音，清水謙多郎，門田幸二（2022）次世代シーケンサーデータの解析手法：第 19 回 R Markdown，日本乳酸菌学会誌 33: 195-205．

2. 2) 牧野磨音，清水謙多郎，門田幸二 (2022) 次世代シーケンサーデータの解析手法：第 18 回遺伝子発現データのクラスタリング，日本乳酸菌学会誌 33: 87-94．

3. 3) Bang M, Yong CC, Ko HJ, Choi IG, Oh S (2018) Transcriptional Response and Enhanced Intestinal Adhesion Ability of Lactobacillus rhamnosus GG after Acid Stress. J Microbiol Biotechnol 28: 1604-13.

4. 4) Zakharkin SO, Kim K, Mehta T, Chen L, Barnes S, Scheirer KE, et al. (2005) Sources of variation in Affymetrix microarray experiments. BMC Bioinformatics 6: 214.

5. 5) Archer KJ, Dumur CI, Ramakrishnan V (2004) Graphical technique for identifying a monotonic variance stabilizing transformation for absolute gene intensity signals. BMC Bioinformatics 5: 60.

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