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さまざまながんで異常発現するRNAを多数発見、バイオマーカー候補に - 理研
理化学研究所(理研)は12月24日、多様な臓器のがんで異常な発現を示すRNAを多数発見したと発表した。これらのRNA群はがん診断の新たなバイオマーカーとなる可能性があるという。 同成果は、理化学研究所 ライフサイエンス技術基盤研究センター ゲノム情報解析チーム ピエロ・カルニンチ チームリーダー、ボグミル・カチコフスキー 国際特別研究員と、オーストラリア・ハリー・パーキンス医療研究所 アリスター・フォレスト 教授らの研究チームによるもので、11月9日付けの米科学誌「Cancer Research」オンライン版に掲載された。 同研究チームは、理研が主導する国際研究コンソーシアム「FANTOM5プロジェクト」の一環として、さまざまな臓器・組織のがんに由来する225種の細胞株と、それらに対応する339種の正常細胞を対象に、がん細胞で発現が変化するRNAを解析。この結果、多くのがん細胞株で発現が上
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農学生命情報科学特論 I ＠東京大学アグリバイオインフォマティクス教育研究ユニット (2022-06) プログラミング言語未経験者を対象とした Python 入門講義。農学や分子生物学などの分野で利用される Python の最新事例を紹介しながら、Python の基礎文法の講義を行う。 AI in Japan (Artificial Intelligence Develop for Thai Industry Camp) @ Mahidol University (2022-04) Introduction to AI in Japanese agriculture. Most slides were deleted due to copyright.
edgeR
Bioconductor version: Release (3.19) Differential expression analysis of RNA-seq expression profiles with biological replication. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi-likelihood tests. As well as RNA-seq, it be applied to differential signal analysis of othe
Bowtie 2: Manual
Langmead B, Salzberg S. Fast gapped-read alignment with Bowtie 2. Nature Methods. 2012, 9:357-359. Langmead B, Wilks C, Antonescu V, Charles R. Scaling read aligners to hundreds of threads on general-purpose processors. Bioinformatics Vol 35, Iss 3, 2019, pp 421–432. Introduction Bowtie 2 is an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. It is par
QoRTs: a comprehensive toolset for quality control and data processing of RNA-Seq experiments - BMC Bioinformatics
Software Open access Published: 19 July 2015 QoRTs: a comprehensive toolset for quality control and data processing of RNA-Seq experiments Stephen W. Hartley1 & James C. Mullikin1 BMC Bioinformatics volume 16, Article number: 224 (2015) Cite this article BackgroundHigh-throughput next-generation RNA sequencing has matured into a viable and powerful method for detecting variations in transcript exp
How to use DESeq to analyse RNAseq data | Dave the Data Dero
Several packages have been written to measure gene expression differences using RNAseq data. In this post I’m going to do a run through of the popular R based DESeq package. Basically, I’m just following along with the packages Vignette which is really well written and informative! For the test data I’m using 2 treatments, with 3 biological reps each containing 750K reads (yes its small). A. Count
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GitHub - alexdobin/STAR: RNA-seq aligner
GitHub - jdblischak/singleCellSeq: Batch effects and the effective design of single-cell gene expression studies
Single-cell RNA-seq discovers a wealth of new RNA markers that define discrete groups of neurons | RNA-Seq Blog
GitHub - PMBio/scLVM: scLVM is a modelling framework for single-cell RNA-seq data that can be used to dissect the observed heterogeneity into different sources, thereby allowing for the correction of confounding sources of variation.
A novel approach for resolving differences in single-cell gene expression patterns from zygote to blastocyst - PubMed
A comparative study of techniques for differential expression analysis on RNA-Seq data | RNA-Seq Blog
http://www.bioconductor.org/packages/release/bioc/vignettes/sRAP/inst/doc/sRAP.pdf
gage
rpkmforgenes.py manual
Cufflinks
TopHat
The hidden treasure in RNA-seq
Gitools - A framework for analysis and visualization of genomic data