The evolution of gene expression levels in mammalian organs

By D. Brawand, M. Soumillon, A. Necsulea, P. Julien, Gábor Csárdi, Patrick Harrigan, M. Weier, A. Liechti, Ayinuer Aximu-Petri, Martin Kircher and 8 more. Nature, 2011

1,209 citations recorded by the source corpus.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/324430be830b6646908193be5c6a4cb4d2c11ba5

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Citations in the corpus

Widespread intron retention in mammals functionally tunes transcriptomes (2014, methodology): “This study used poly(A)+ RNA-seq data generated as part of the Illumina Human BodyMap 2.0 Project and as described in Brawand et al. (2011) and Barbosa-Morais et al. (2012), as well as multiple other data sets described in the literature (Supplemental Table S1).”
RNA-seq differential expression studies: more sequence or more replication? (2014, background): “Yet frequently large-scale RNA-seq studies with extensive differential expression analyses have used limited biological replication , instead favoring a strategy of low level biological replication with deep sequencing (e.g. Brawand et al., 2011; Graveley et al., 2011; Hah et al., 2011).”
Deep learning of the tissue-regulated splicing code (2014, methodology): “Random forests is an example, as is the Bayesian model averaging method previously used to model the regulation of splicing.”
Intrinsic inference difficulties for trait evolution with Ornstein‐Uhlenbeck models (2014, positive): “For example, Brawand et al. (2011) detected evolutionary shifts in gene expression in testes 10 50 100 500 1000 4507 Small shift, β1 = 0·5 10 50 100 500 1000 4507 Medium shift, β1 = 1 10 50 100 500 1000 4507 Large shift, β1 = 2 −1·0 0·0 0·5 1·0 2·0 4·0 Number of taxa E st im at ed β 1 Fig.”
Evidence for Weak Selective Constraint on Human Gene Expression (2018, background): “Many polygenic trait models assume that all variants, regardless of their allele frequency, contribute equally to genetic variance (2pqb2; e.g., Yang et al. 2011; Bulik-Sullivan et al. 2015).”
Enhancer Evolution across 20 Mammalian Species (2015, background): “These studies and others (Barbosa-Morais et al., 2012; Brawand et al., 2011) suggest that regulation in other somatic tissues evolves similarly,”
A Comparative Encyclopedia of DNA Elements in the Mouse Genome (2014, background): “…it is not clear how extensive the changes in expression patterns are between more distantly related species, such as mouse and human, with some studies emphasizing similarities in transcriptome patterns of orthologous tissues 39–41 and others emphasizing substantial interspecies differences 42 .”
Detecting differential usage of exons from RNA-seq data (2012, methodology): “, 2010b), which introduced the use of the negative binomial distribution to RNA-Seq analysis. Robinson et al. (2010a) extended edgeR with generalized linear models (GLMs) and the Cox-Reid dispersion estimator, discussed later. The basic approach of using exon–condition interactions in linear or gene”
NONCODE 2016: an informative and valuable data source of long non-coding RNAs (2015, background): “NONCODE has added six new species, mainly from multi-species RNA-Seq data (37,38).”
A benchmark of gene expression tissue-specificity metrics (2015, methodology): “…project (The ENCODE Project Consortium 2011; Lin et al. 2014) as used in Kryuchkova-Mostacci & Robinson-Rechavi (Kryuchkova-Mostacci & Robinson-Rechavi 2015), and 8 human tissues and 6 mouse tissues from Brawand et al. (Brawand et al. 2011), as processed in the Bgee database (Bastian et al. 2008).”
Genomic positional conservation identifies topological anchor point (tap)RNAs linked to developmental loci (2016, background): “…(Harrow et al., 2012); 2) human and mouse RNA-sequencing (RNA-Seq) from six matched tissues (brain, cerebellum, heart, kidney, liver and testis) (Brawand et al., 2011); and 3) RNA-Seq data from four similar human and mouse cell lines (embryonic stem (ES), leukemia, lymphoblast and muscle cells)…”
The human splicing code reveals new insights into the genetic determinants of disease (2014, methodology): “The human splicing code (a) For a given cell type, the computational model extracts the regulatory code from a test DNA sequence and predicts the percent of transcripts with the exon spliced in, Ψ. (b) Predictions were made for 10,689 test exons profiled in 16 tissues, exons and tissues were binned ”