The Transcriptional Landscape of the Yeast Genome Defined by RNA Sequencing

By Ugrappa Nagalakshmi, Zhong Wang, Karl Waern, C. Shou, D. Raha, Mark Gerstein, Michael P. Snyder. Science, 2008

2,603 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/447bf5edc72aa9b8c96f841ff11e8f47e89d2ec4

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In Molecular Biology

Citations in the corpus

Optimization of de novo transcriptome assembly from next-generation sequencing data. (2010, methodology): “This approach has recently been used for transcriptome profiling in a method called RNA-seq that is expected to allow major breakthroughs in transcriptome analysis (Mortazavi et al. 2008; Nagalakshmi et al. 2008; Wilhelm et al 2008; Wang et al. 2009; Montgomery et al. 2010).”
ChIP-Seq: advantages and challenges of a maturing technology (2009, background): “, the sequencing of mRNA for gene expression profiling (RNA–seq”
GC-Content Normalization for RNA-Seq Data (2011, background): “Background In the last few years, high-throughput sequencing assays have been replacing microarrays as the assays of choice for measuring genome-wide transcription levels, in socalled RNA-Seq [1,2], as well as DNA copy number (DNA-Seq), protein-nucleic acid interactions (ChIP-Seq), and DNA methylati”
Deep mutational scanning: assessing protein function on a massive scale (2011, methodology): “and other assays have all been adapted to make use of counting DNA sequence reads as output [3, 14–23].”
A Comparative Study of Techniques for Differential Expression Analysis on RNA-Seq Data (2014, background): “However, when biological replicates are included, it has been noted that the Poisson distribution underestimates the variation seen in the data [20,21], a problem known as overdispersion.”
Stem cell transcriptome profiling via massive-scale mRNA sequencing (2008, methodology): “Our work on this mammalian transcriptome, using the Applied Biosystems SOLiD technology, is complementary to recent studies that have used Illumina-based sequencing to analyze the transcriptome in Arabidopsis and in yeas”
RNA-Seq gene expression estimation with read mapping uncertainty (2009, methodology): “RNA-Seq is the transcriptome analog to whole-genome shotgun sequencing (Staden, 1979), with a key difference being that RNA-Seq is primarily used for estimating the copy number of transcripts in a sample.”
Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data (2013, background): “However, in reality the variance of gene expression across multiple biological replicates is larger than its mean expression values [28-30].”
Single-cell RNA-seq: advances and future challenges (2014, positive): “The probe-independent RNA-seq technique (10,11), in which cellular RNA molecules are converted into cDNA and subsequently sequenced in parallel using next-generation sequencing technology (7), is increasingly becoming the method of choice to achieve this task.”
An RNA-Sequencing Transcriptome and Splicing Database of Glia, Neurons, and Vascular Cells of the Cerebral Cortex (2014, background): “…in lower falsenegative and false-positive discovery rates in addition to a greater linear range (Bainbridge et al., 2006; Cloonan et al., 2008; Marioni et al., 2008; Mortazavi et al., 2008; Nagalakshmi et al., 2008; Sultan et al., 2008; Wilhelm et al., 2008; Trapnell et al., 2010; Wu et al., 2010).”