XSAnno: a framework for building ortholog models in cross-species transcriptome comparisons
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The accurate characterization of RNA transcripts and expression levels across species is critical for understanding transcriptome evolution. As available RNA-seq data accumulate rapidly, there is a great demand for tools that build gene annotations for cross-species RNA-seq analysis. However, prevailing methods of ortholog annotation for RNA-seq analysis between closely-related species do not take inter-species variation in mappability into consideration. Here we present XSAnno, a computational framework that integrates previous approaches with multiple filters to improve the accuracy of inter-species transcriptome comparisons. The implementation of this approach in comparing RNA-seq data of human, chimpanzee, and rhesus macaque brain transcriptomes has reduced the false discovery of differentially expressed genes, while maintaining a low false negative rate. The present study demonstrates the utility of the XSAnno pipeline in building ortholog annotations and improving the accuracy of cross-species transcriptome comparisons.Machine learning tool TEEBoT predicts an individual’s tissue-specific gene expression from his/her blood transcriptome.
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Abstract Age is well-known to be a significant factor in both disease pathology and response to treatment, yet the molecular changes that occur with age in humans remain ill-defined. Here, using transcriptome profiling of healthy human male skin, we demonstrate that there is a period of significantly elevated, transcriptome-wide expression changes occurring predominantly in middle age. Both pre and post this period, the transcriptome appears to undergo much smaller, linear changes with increasing age. Functional analysis of the transient changes in middle age suggest a period of heightened metabolic activity and cellular damage associated with NF-kappa-B and TNF signaling pathways. Through meta-analysis we also show the presence of global, tissue independent linear transcriptome changes with age which appear to be regulated by NF-kappa-B. These results suggest that aging in human skin is associated with a critical mid-life period with widespread transcriptome changes, both preceded and proceeded by a relatively steady rate of linear change in the transcriptome. The data provides insight into molecular changes associated with normal aging and will help to better understand the increasingly important pathological changes associated with aging.
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Transcriptome analysis is a powerful tool to characterize changes in leukocyte gene expression patterns and reveal very early molecular abnormalities in tissue. Herein, we report on characterization of the very earliest abnormalities in the transcriptome of leukocytes from young "prepathologic" NOD and NON female mice.
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Abstract The human genome is thought to contain 100 000 genes of which a subset of approximately 15 000 to 20 000 genes is expressed in an individual cell. The set of genes expressed and the stoichiometry of the resulting messenger RNAs, together called a transcriptome, determine the phenotype of a cell, tissue, and whole organism. It is generally accepted that a transcriptome is largely determined by an interplay of hereditary and environmental factors. For example, in the CNS, a challenge from the environment, e.g. a learning or a traumatic experience may lead to an alteration of the transcriptome of target neurons. Thus, transcriptome analysis and subsequent transcriptome comparisons may reveal novel insights in the molecular mechanisms underlying complex processes such learning and memory formation.
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Our ability to study thousands of genes simultaneously over the last few years owing not only to the development of DNA microarray technology, but also to large-scale yeast two-hybrid and other methods, has brought about a paradigmatic shift in biology. Armed with the complete sequences of several genomes including humans, more and more biomedical investigators are now interested in understanding the functions of gene products, or proteins, on a global scale. In this article, the promises and pitfalls of two methods used for proteomics studies - protein microarrays and mass spectrometry - towards achieving this goal are discussed. Keywords: paradigmatic, protein microarrays, spectrometry, genomes, Proteomics
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Abstract Since age related perturbations in gene expression profiles have been described and transcriptomic changes in specific biological pathways have been implicated in the aging process, we performed whole transcriptome sequencing on 4000 HRS participants using RNA obtained from Paxgene tubes collected during the 2016 interview. We will describe design and implementation of innovative quality control procedures to minimize technical variability in transcriptomic measurements and monitor analytical variation in large population studies such as HRS. We will also report the distribution of transcriptomic profiles according to various demographic characteristics (age, sex, racial/ethnic and socioeconomic differences) and describe the prevalence of previously reported aging related transcriptomic signatures in HRS. We will describe the associations between transcriptomic profiles and other measures of biological aging in HRS and report how changes in cell composition can affect transcriptomic profiles observed in population studies such as HRS.
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Brassica napus is one of the most important oilseed crops in the world. However, there is currently no enough stem transcriptome information and comparative transcriptome analysis of different tissues, which impedes further functional genomics research on B. napus. In this study, the stem transcriptome of B. napus was characterized by RNA-seq technology. Approximately 13.4 Gb high-quality clean reads with an average length of 100 bp were generated and used for comparative transcriptome analysis with the existing transcriptome sequencing data of roots, leaves, flower buds and immature embryos of B. napus. All the transcripts were annotated against GO and KEGG databases. The common genes in five tissues, differentially expressed genes (DEGs) of the common genes between stems and other tissues, and tissue-specific genes were detected, and the main biochemical activities and pathways implying the common genes, DEGs and tissue-specific genes were investigated. Accordingly, the common transcription factors (TFs) in the five tissues and tissue-specific TFs were identified, and a TFs-based regulation network between TFs and the target genes involved in "Phenylpropanoid biosynthesis" pathway were constructed to show several important TFs and key nodes in the regulation process. Collectively, this study not only provided an available stem transcriptome resource in B. napus, but also revealed a valuable comparative transcriptome information of five tissues of B. napus for future investigation on specific processes, functions and pathways.
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The transcriptome represents the entire set of RNA transcripts expressed in a cell, reflecting both the underlying genetic and epigenetic landscape and environmental influences, providing a comprehensive view of functional cellular states at any given time. Recent technological advances now enable the study of the transcriptome at the resolution of individual cells, providing exciting opportunities to characterise cellular and molecular events that underpin immune-medicated diseases. Here, we draw on recent examples from the literature to highlight the application of advanced bioinformatics tools to extract mechanistic insight and disease biology from bulk and single-cell transcriptomic profiles. Key considerations for the use of available analysis techniques are presented throughout.
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