| Title |
Evolutionary hallmarks of the human proteome: chasing the age and coregulation of protein-coding genes
|
|---|---|
| Published in |
BMC Genomics, October 2016
|
| DOI | 10.1186/s12864-016-3062-y |
| Pubmed ID | |
| Authors |
Katia de Paiva Lopes, Francisco José Campos-Laborie, Ricardo Assunção Vialle, José Miguel Ortega, Javier De Las Rivas |
| Abstract |
The development of large-scale technologies for quantitative transcriptomics has enabled comprehensive analysis of the gene expression profiles in complete genomes. RNA-Seq allows the measurement of gene expression levels in a manner far more precise and global than previous methods. Studies using this technology are altering our view about the extent and complexity of the eukaryotic transcriptomes. In this respect, multiple efforts have been done to determine and analyse the gene expression patterns of human cell types in different conditions, either in normal or pathological states. However, until recently, little has been reported about the evolutionary marks present in human protein-coding genes, particularly from the combined perspective of gene expression and protein evolution. We present a combined analysis of human protein-coding gene expression profiling and time-scale ancestry mapping, that places the genes in taxonomy clades and reveals eight evolutionary major steps ("hallmarks"), that include clusters of functionally coherent proteins. The human expressed genes are analysed using a RNA-Seq dataset of 116 samples from 32 tissues. The evolutionary analysis of the human proteins is performed combining the information from: (i) a database of orthologous proteins (OMA), (ii) the taxonomy mapping of genes to lineage clades (from NCBI Taxonomy) and (iii) the evolution time-scale mapping provided by TimeTree (Timescale of Life). The human protein-coding genes are also placed in a relational context based in the construction of a robust gene coexpression network, that reveals tighter links between age-related protein-coding genes and finds functionally coherent gene modules. Understanding the relational landscape of the human protein-coding genes is essential for interpreting the functional elements and modules of our active genome. Moreover, decoding the evolutionary history of the human genes can provide very valuable information to reveal or uncover their origin and function. |
X Demographics
The data shown below were collected from the profiles of 10 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 40% |
| Germany | 1 | 10% |
| Montenegro | 1 | 10% |
| United Kingdom | 1 | 10% |
| Brazil | 1 | 10% |
| Denmark | 1 | 10% |
| Spain | 1 | 10% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 9 | 90% |
| Members of the public | 1 | 10% |
Mendeley readers
The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 4% |
| Unknown | 27 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 21% |
| Student > Ph. D. Student | 4 | 14% |
| Student > Master | 4 | 14% |
| Professor > Associate Professor | 2 | 7% |
| Student > Doctoral Student | 1 | 4% |
| Other | 5 | 18% |
| Unknown | 6 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 8 | 29% |
| Biochemistry, Genetics and Molecular Biology | 7 | 25% |
| Computer Science | 2 | 7% |
| Nursing and Health Professions | 1 | 4% |
| Unspecified | 1 | 4% |
| Other | 2 | 7% |
| Unknown | 7 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 August 2018.
All research outputs
#4,721,979
of 22,896,955 outputs
Outputs from BMC Genomics
#2,011
of 10,673 outputs
Outputs of similar age
#79,232
of 313,870 outputs
Outputs of similar age from BMC Genomics
#40
of 223 outputs
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So far Altmetric has tracked 10,673 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 79% of its peers.
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