RNA-seq: technical variability and sampling - BMC Genomics
Research article Open access Published: 06 June 2011 RNA-seq: technical variability and sampling Lauren M McIntyre1, Kenneth K Lopiano2, Alison M Morse1, Victor Amin1, Ann L Oberg3, Linda J Young2 & …Sergey V Nuzhdin4 Show authors BMC Genomics volume 12, Article number: 293 (2011) Cite this article BackgroundRNA-seq is revolutionizing the way we study transcriptomes. mRNA can be surveyed without p
1
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
Transcription-factor occupancy at HOT regions quantitatively predicts RNA polymerase recruitment in five human cell lines - BMC Genomics
MMDiff: quantitative testing for shape changes in ChIP-Seq data sets - BMC Genomics
QChIPat: a quantitative method to identify distinct binding patterns for two biological ChIP-seq samples in different experimental conditions - BMC Genomics
NEXT-peak: a normal-exponential two-peak model for peak-calling in ChIP-seq data - BMC Genomics
Limitations and possibilities of low cell number ChIP-seq - BMC Genomics
Transcription factor co-localization patterns affect human cell type-specific gene expression - BMC Genomics
Optimizing illumina next-generation sequencing library preparation for extremely at-biased genomes - BMC Genomics
Quantitative Interactor Screening with next-generation Sequencing (QIS-Seq) identifies Arabidopsis thaliana MLO2 as a target of the Pseudomonas syringae type III effector HopZ2 - BMC Genomics
BioVenn – a web application for the comparison and visualization of biological lists using area-proportional Venn diagrams - BMC Genomics
Modelling and measuring single cell RNA expression levels find considerable transcriptional differences among phenotypically identical cells - BMC Genomics
The distribution of SNPs in human gene regulatory regions - BMC Genomics