Abstract
BackgroundThe 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.ResultsWe 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.ConclusionsUnderstanding 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3062-y) contains supplementary material, which is available to authorized users.
Highlights
The development of large-scale technologies for quantitative transcriptomics has enabled comprehensive analysis of the gene expression profiles in complete genomes
The Human Protein Atlas applies RNA-Seq to 32 human tissues to find the correlation between gene expression and protein presence in the characterization of several parts of the human proteome: membrane proteome, druggable proteome, cancer proteome, secretome and proteome involved in metabolic processes [3]
The heatmap shows a clear relationship between the samples from the same tissue and presents the proximity between the tissues that have strong biological and physiological links, such as: spleen, lymph nodes and tonsils; or stomach, duodenum, small intestine, colon and rectum
Summary
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. RNA-Seq allows the measurement of the gene expression levels in a manner far more precise than previous methods Studies using this approach have already altered our view of the extent and complexity of the eukaryotic transcriptomes [1]. Other studies are more focused on specific cells or tissue types, like the article entitled “A comprehensive analysis of the human placenta transcriptome” that characterizes the transcriptome of placenta from 20 healthy women with uncomplicated pregnancies using RNA-Seq [7]
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