ArticleProceedings of the National Academy of Sciences of the United States of America2014
Comparison of the transcriptional landscapes between human and mouse tissues.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 252 papers.
What it found
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Who cites it
252 citing papers in PubMed, 451 citations in OpenAlex.
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192 more citing papers are in PubMed but not listed here.
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Authors and funding
13 authors at 6 institutions in 2 countries.
Funding
Abstract
Although the similarities between humans and mice are typically highlighted, morphologically and genetically, there are many differences. To better understand these two species on a molecular level, we performed a comparison of the expression profiles of 15 tissues by deep RNA sequencing and examined the similarities and differences in the transcriptome for both protein-coding and -noncoding transcripts. Although commonalities are evident in the expression of tissue-specific genes between the two species, the expression for many sets of genes was found to be more similar in different tissues within the same species than between species. These findings were further corroborated by associated epigenetic histone mark analyses. We also find that many noncoding transcripts are expressed at a low level and are not detectable at appreciable levels across individuals. Moreover, the majority lack obvious sequence homologs between species, even when we restrict our attention to those which are most highly reproducible across biological replicates. Overall, our results indicate that there is considerable RNA expression diversity between humans and mice, well beyond what was described previously, likely reflecting the fundamental physiological differences between these two organisms.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.