Detecting transcription of ribosomal protein pseudogenes in diverse human tissues from RNA-seq data

Abstract Background Small nucleolar RNAs (snoRNAs) are mid-size non-coding RNAs required for ribosomal RNA modification, implying a ubiquitous tissue distribution linked to ribosome synthesis. However, increasing numbers of studies identify extra-ribosomal roles of snoRNAs in modulating gene expression, suggesting more complex snoRNA abundance patterns. Therefore, there is a great need for mapping the snoRNome in different human tissues as the blueprint for snoRNA functions. Results We used a low structure bias RNA-Seq approach to accurately quantify snoRNAs and compare them to the entire transcriptome in seven healthy human tissues (breast, ovary, prostate, testis, skeletal muscle, liver, and brain). We identify 475 expressed snoRNAs categorized in two abundance classes that differ significantly in their function, conservation level, and correlation with their host gene: 390 snoRNAs are uniformly expressed and 85 are enriched in the brain or reproductive tissues. Most tissue-enriched snoRNAs are embedded in lncRNAs and display strong correlation of abundance with them, whereas uniformly expressed snoRNAs are mostly embedded in protein-coding host genes and are mainly non- or anticorrelated with them. Fifty-nine percent of the non-correlated or anticorrelated protein-coding host gene/snoRNA pairs feature dual-initiation promoters, compared to only 16% of the correlated non-coding host gene/snoRNA pairs. Conclusions Our results demonstrate that snoRNAs are not a single homogeneous group of housekeeping genes but include highly regulated tissue-enriched RNAs. Indeed, our work indicates that the architecture of snoRNA host genes varies to uncouple the host and snoRNA expressions in order to meet the different snoRNA abundance levels and functional needs of human tissues.

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A resource of ribosomal RNA-depleted RNA-Seq data from different normal adult and fetal human tissues

Scientific Data ◽

10.1038/sdata.2015.63 ◽

2015 ◽

Vol 2 (1) ◽

Cited By ~ 13

Author(s):

Jocelyn Y.H. Choy ◽

Priscilla L.S. Boon ◽

Nicolas Bertin ◽

Melissa J. Fullwood

Keyword(s):

Ribosomal Rna ◽

Normal Adult ◽

Human Tissues ◽

Rna Seq

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Circular RNA expression profiles and features in human tissues: a study using RNA-seq data

BMC Genomics ◽

10.1186/s12864-017-4029-3 ◽

2017 ◽

Vol 18 (S6) ◽

Cited By ~ 69

Author(s):

Tianyi Xu ◽

Jing Wu ◽

Ping Han ◽

Zhongming Zhao ◽

Xiaofeng Song

Keyword(s):

Expression Profiles ◽

Circular Rna ◽

Rna Expression ◽

Human Tissues ◽

Rna Seq

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Identifying core biological processes distinguishing human eye tissues with precise systems-level gene expression analyses and weighted correlation networks

10.1101/136960 ◽

2017 ◽

Author(s):

John M Bryan ◽

Temesgen D Fufa ◽

Kapil Bharti ◽

Brian P Brooks ◽

Robert B Hufnagel ◽

...

Keyword(s):

Gene Expression ◽

Expression Patterns ◽

Mouse Retina ◽

Human Tissues ◽

Biological Processes ◽

Rna Seq ◽

Human Eye ◽

Correlation Networks ◽

Eye Tissues ◽

Study Gene Expression

AbstractThe human eye is built from several specialized tissues which direct, capture, and pre-process information to provide vision. The gene expression of the different eye tissues has been extensively profiled with RNA-seq across numerous studies. Large consortium projects have also used RNA-seq to study gene expression patterning across many different human tissues, minus the eye. There has not been an integrated study of expression patterns from multiple eye tissues compared to other human body tissues. We have collated all publicly available healthy human eye RNA-seq datasets as well as dozens of other tissues. We use this fully integrated dataset to probe the biological processes and pan expression relationships between the cornea, retina, RPE-choroid complex, and the rest of the human tissues with differential expression, clustering, and GO term enrichment tools. We also leverage our large collection of retina and RPE-choroid tissues to build the first human weighted gene correlation networks and use them to highlight known biological pathways and eye gene disease enrichment. We also have integrated publicly available single cell RNA-seq data from mouse retina into our framework for validation and discovery. Finally, we make all these data, analyses, and visualizations available via a powerful interactive web application (https://eyeintegration.nei.nih.gov/).

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VirTect: a computational method for detecting virus species from RNA-Seq and its application in head and neck squamous cell carcinoma

10.1101/272278 ◽

2018 ◽

Author(s):

Atlas Khan ◽

Qian Liu ◽

Xuelian Chen ◽

Yunjing Zeng ◽

Andres Stucky ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Head And Neck ◽

Squamous Cell ◽

Neck Squamous Cell Carcinoma ◽

Superior Performance ◽

Clinical Samples ◽

Virus Species ◽

Human Tissues ◽

Rna Seq

AbstractNext generation sequencing (NGS) provides an opportunity to detect viral species from RNA-seq data on human tissues, but existing computational approaches do not perform optimally on clinical samples. We developed a bioinformatics method called VirTect for detecting viruses in neoplastic human tissues using RNA-seq data. Here, we used VirTect to analyze RNA-seq data from 363 HNSCC (head and neck squamous cell carcinoma) patients and identified 22 HPV-induced HNSCCs. These predictions were validated by manual review of pathology reports on histopathologic specimens. Compared to two existing prediction methods, VirusFinder and VirusSeq, VirTect demonstrated superior performance with many fewer false positives and false negatives. The majority of HPV carcinogenesis studies thus far have been performed on cervical cancer and generalized to HNSCC. Our results suggest that HPV-induced HNSCC involves unique mechanisms of carcinogenesis, so understanding these molecular mechanisms will have a significant impact on therapeutic approaches and outcomes. In summary, VirTect can be an effective solution for the detection of viruses with NGS data, and can facilitate the clinicopathologic characterization of various types of cancers with broad applications for oncology.Significance StatementWe developed a new bioinformatics tool, and reported the new inside of HPV carcinogenesis mechanism in HPV-induced head and neck squamous cell carcinoma (HNSCC). This novel bioin-formatics tool and the new knowledge of HPV-induced HNSCC will facilitate the development of target therapies for treating HNSCC.

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Identification of Tissue-Specific Protein-Coding and Noncoding Transcripts across 14 Human Tissues Using RNA-seq

Scientific Reports ◽

10.1038/srep28400 ◽

2016 ◽

Vol 6 (1) ◽

Cited By ~ 20

Author(s):

Jinhang Zhu ◽

Geng Chen ◽

Sibo Zhu ◽

Suqing Li ◽

Zhuo Wen ◽

...

Keyword(s):

Specific Protein ◽

Human Tissues ◽

Rna Seq ◽

Protein Coding ◽

Tissue Specific

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Thousands of exon skipping events differentiate among splicing patterns in sixteen human tissues

F1000Research ◽

10.12688/f1000research.2-188.v1 ◽

2013 ◽

Vol 2 ◽

pp. 188 ◽

Cited By ~ 131

Author(s):

Liliana Florea ◽

Li Song ◽

Steven L Salzberg

Keyword(s):

Alternative Splicing ◽

Gene Diversity ◽

Exon Skipping ◽

Human Tissues ◽

Rna Seq ◽

Gene Splicing ◽

Link Type ◽

Normal Human ◽

Splicing Patterns

Alternative splicing is widely recognized for its roles in regulating genes and creating gene diversity. However, despite many efforts, the repertoire of gene splicing variation is still incompletely characterized, even in humans. Here we describe a new computational system, ASprofile, and its application to RNA-seq data from Illumina’s Human Body Map project (>2.5 billion reads). Using the system, we identified putative alternative splicing events in 16 different human tissues, which provide a dynamic picture of splicing variation across the tissues. We detected 26,989 potential exon skipping events representing differences in splicing patterns among the tissues. A large proportion of the events (>60%) were novel, involving new exons (~3000), new introns (~16000), or both. When tracing these events across the sixteen tissues, only a small number (4-7%) appeared to be differentially expressed (‘switched’) between two tissues, while 30-45% showed little variation, and the remaining 50-65% were not present in one or both tissues compared. Novel exon skipping events appeared to be slightly less variable than known events, but were more tissue-specific. Our study represents the first effort to build a comprehensive catalog of alternative splicing in normal human tissues from RNA-seq data, while providing insights into the role of alternative splicing in shaping tissue transcriptome differences. The catalog of events and the ASprofile software are freely available from the Zenodo repository(http://zenodo.org/record/7068; doi:10.5281/zenodo.7068) and from our web site http://ccb.jhu.edu/software/ASprofile.

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Discovery and characterization of variance QTLs in human induced pluripotent stem cells

10.1101/424192 ◽

2018 ◽

Cited By ~ 1

Author(s):

Abhishek K. Sarkar ◽

Po-Yuan Tung ◽

John D. Blischak ◽

Jonathan E. Burnett ◽

Yang I. Li ◽

...

Keyword(s):

Gene Expression ◽

Stem Cells ◽

Genetic Variation ◽

Single Cell ◽

Pluripotent Stem Cells ◽

Human Tissues ◽

Rna Seq ◽

Induced Pluripotent ◽

Expression Variance ◽

Gene Expression Levels

AbstractQuantification of gene expression levels at the single cell level has revealed that gene expression can vary substantially even across a population of homogeneous cells. However, it is currently unclear what genomic features control variation in gene expression levels, and whether common genetic variants may impact gene expression variation. Here, we take a genome-wide approach to identify expression variance quantitative trait loci (vQTLs). To this end, we generated single cell RNA-seq (scRNA-seq) data from induced pluripotent stem cells (iPSCs) derived from 53 Yoruba individuals. We collected data for a median of 95 cells per individual and a total of 5,447 single cells, and identified 241 mean expression QTLs (eQTLs) at 10% FDR, of which 82% replicate in bulk RNA-seq data from the same individuals. We further identified 14 vQTLs at 10% FDR, but demonstrate that these can also be explained as effects on mean expression. Our study suggests that dispersion QTLs (dQTLs) which could alter the variance of expression independently of the mean can have larger fold changes, but explain less phenotypic variance than eQTLs. We estimate 424 individuals as a lower bound to achieve 80% power to detect the strongest dQTLs in iPSCs. These results will guide the design of future studies on understanding the genetic control of gene expression variance.Author summaryCommon genetic variation can alter the level of average gene expression in human tissues, and through changes in gene expression have downstream consequences on cell function, human development, and human disease. However, human tissues are composed of many cells, each with its own level of gene expression. With advances in single cell sequencing technologies, we can now go beyond simply measuring the average level of gene expression in a tissue sample and directly measure cell-to-cell variance in gene expression. We hypothesized that genetic variation could also alter gene expression variance, potentially revealing new insights into human development and disease. To test this hypothesis, we used single cell RNA sequencing to directly measure gene expression variance in multiple individuals, and then associated the gene expression variance with genetic variation in those same individuals. Our results suggest that effects on gene expression variance are smaller than effects on mean expression, relative to how much the phenotypes vary between individuals, and will require much larger studies than previously thought to detect.

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Deletion of the sclerotome-enriched lncRNA PEAT augments ribosomal protein expression

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1612069113 ◽

2016 ◽

Vol 114 (1) ◽

pp. 101-106 ◽

Cited By ~ 3

Author(s):

David A. Stafford ◽

Darwin S. Dichmann ◽

Jessica K. Chang ◽

Richard M. Harland

Keyword(s):

Ribosomal Protein ◽

Noncoding Rna ◽

Target Gene ◽

Antisense Transcript ◽

Mouse Tissue ◽

Rna Seq ◽

Embryonic Mouse ◽

Morphogenetic Protein ◽

Early Markers ◽

Complete Deletion

To define a complete catalog of the genes that are activated during mouse sclerotome formation, we sequenced RNA from embryonic mouse tissue directed to form sclerotome in culture. In addition to well-known early markers of sclerotome, such as Pax1, Pax9, and the Bapx2/Nkx3-2 homolog Nkx3-1, the long-noncoding RNA PEAT (Pax1 enhancer antisense transcript) was induced in sclerotome-directed samples. Strikingly, PEAT is located just upstream of the Pax1 gene. Using CRISPR/Cas9, we generated a mouse line bearing a complete deletion of the PEAT-transcribed unit. RNA-seq on PEAT mutant embryos showed that loss of PEAT modestly increases bone morphogenetic protein target gene expression and also elevates the expression of a large subset of ribosomal protein mRNAs.

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APAlyzer: a bioinformatics package for analysis of alternative polyadenylation isoforms

Bioinformatics ◽

10.1093/bioinformatics/btaa266 ◽

2020 ◽

Vol 36 (12) ◽

pp. 3907-3909 ◽

Cited By ~ 3

Author(s):

Ruijia Wang ◽

Bin Tian

Keyword(s):

Gene Expression ◽

Alternative Polyadenylation ◽

Supplementary Information ◽

Human Tissues ◽

Bioconductor Package ◽

Supplementary Data ◽

Rna Seq ◽

Eukaryotic Genes ◽

Polyadenylation Sites

Abstract Summary Most eukaryotic genes produce alternative polyadenylation (APA) isoforms. APA is dynamically regulated under different growth and differentiation conditions. Here, we present a bioinformatics package, named APAlyzer, for examining 3′UTR APA, intronic APA and gene expression changes using RNA-seq data and annotated polyadenylation sites in the PolyA_DB database. Using APAlyzer and data from the GTEx database, we present APA profiles across human tissues. Availability and implementation APAlyzer is freely available at https://bioconductor.org/packages/release/bioc/html/APAlyzer.html as an R/Bioconductor package. Supplementary information Supplementary data are available at Bioinformatics online.

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