scholarly journals AgriSeqDB: an online RNA-Seq database for functional studies in agriculturally relevant plant species

2018 ◽  
Author(s):  
Andrew J. Robinson ◽  
Muluneh Tamiru ◽  
Rachel Salby ◽  
Clayton Bolitho ◽  
Andrew Williams ◽  
...  
Keyword(s):  
Developmental Stages ◽  
Data Access ◽  
Cell Types ◽  
Data Reuse ◽  
Tissue Cell ◽  
Agricultural Crops ◽  
Transcriptome Data ◽  
Rna Seq ◽  
Functional Studies ◽  
Genomic Studies

AbstractBackgroundThe genome-wide expression profile of genes in different tissues/cell types and developmental stages is a vital component of many functional genomic studies. Transcriptome data obtained by RNA-sequencing (RNA-Seq) is often deposited in public databases that are made available via data portals. Data visualization is one of the first steps in assessment and hypothesis generation. However, these databases do not typically include visualization tools and establishing one is not trivial for users who are not computational experts. This, as well as the various formats in which data is commonly deposited, makes the processes of data access, sharing and utility more difficult. Our goal was to provide a simple and user-friendly repository that meets these needs for datasets from major agricultural crops.DescriptionAgriSeqDB (https://expression.latrobe.edu.au/agriseqdb), is a database for viewing, analysing and interpreting developmental and tissue/cell-specific transcriptome data from several species, including major agricultural crops such as wheat, rice, maize, barley and tomato. The disparate manner in which public transcriptome data is often warehoused and the challenge of visualizing raw data are both major hurdles to data reuse. The popular eFP browser does an excellent job of presenting transcriptome data in an easily interpretable view, but previous implementation has been mostly on a case-by-case basis. Here we present an integrated visualisation database of transcriptome datasets from six species that did not previously have public-facing visualisations. We combine the eFP browser, for gene-by-gene investigation, with the Degust browser, which enables visualisation of all transcripts across multiple samples. The two visualisation interfaces launch from the same point, enabling users to easily switch between analysis modes. The tools allow users, even those without bioinformatics expertise, to mine into datasets and understand the behaviour of transcripts of interest across samples and time. We have also incorporated an additional graphic download option to simplify incorporation into presentations or publications.ConclusionPowered by eFP and Degust browsers, AgriSeqDB is a quick and easy-to-use platform for data analysis and visualization in five crops and Arabidopsis. Furthermore, it provides a tool that makes it easy for researchers to share their datasets, promoting research collaborations and dataset reuse.

scholarly journals Bulk Tissue Cell Type Deconvolution with Multi-Subject Single-Cell Expression Reference

2018 ◽  
Author(s):  
Xuran Wang ◽  
Jihwan Park ◽  
Katalin Susztak ◽  
Nancy R. Zhang ◽  
Mingyao Li
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Tissue Cell ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Cell Type Specific ◽  
Cell Expression

AbstractWe present MuSiC, a method that utilizes cell-type specific gene expression from single-cell RNA sequencing (RNA-seq) data to characterize cell type compositions from bulk RNA-seq data in complex tissues. When applied to pancreatic islet and whole kidney expression data in human, mouse, and rats, MuSiC outperformed existing methods, especially for tissues with closely related cell types. MuSiC enables characterization of cellular heterogeneity of complex tissues for identification of disease mechanisms.

scholarly journals Fast and powerful statistical method for context-specific QTL mapping in multi-context genomic studies

2021 ◽  
Author(s):  
Andrew Lu ◽  
Mike Thompson ◽  
M Grace Gordon ◽  
Andy Dahl ◽  
Chun Jimmie Ye ◽  
...  
Keyword(s):  
Fold Increase ◽  
Cell Types ◽  
Specific Gene ◽  
Data Sets ◽  
Rna Seq ◽  
Computationally Efficient ◽  
Genomic Studies ◽  
Cell Type Specific ◽  
Context Specific ◽  
The Relationship

Recent studies suggest that context-specific eQTLs underlie genetic risk factors for complex diseases. However, methods for identifying them are still nascent, limiting their comprehensive characterization and downstream interpretation of disease-associated variants. Here, we introduce FastGxC, a method to efficiently and powerfully map context-specific eQTLs by leveraging the correlation structure of multi-context studies. We first show via simulations that FastGxC is orders of magnitude more powerful and computationally efficient than previous approaches, making previously year-long computations possible in minutes. We next apply FastGxC to bulk multi-tissue and single-cell RNA-seq data sets to produce the most comprehensive tissue- and cell-type-specific eQTL maps to date. We then validate these maps by establishing that context-specific eQTLs are enriched in corresponding functional genomic annotations. Finally, we examine the relationship between context-specific eQTLs and human disease and show that FastGxC context-specific eQTLs provide a three-fold increase in precision to identify relevant tissues and cell types for GWAS variants than standard eQTLs. In summary, FastGxC enables the construction of context-specific eQTL maps that can be used to understand the context-specific gene regulatory mechanisms underlying complex human diseases.

scholarly journals Reannotation of the cultivated strawberry genome and establishment of a strawberry genome database

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Tianjia Liu ◽  
Muzi Li ◽  
Zhongchi Liu ◽  
Xiaoyan Ai ◽  
Yongping Li
Keyword(s):  
Developmental Stages ◽  
Expression Profiles ◽  
Hybrid Origin ◽  
Rna Seq ◽  
Crop Species ◽  
Genome Database ◽  
Functional Studies ◽  
New Genes ◽  
Alternatively Spliced ◽  
Current Annotation

AbstractCultivated strawberry (Fragaria × ananassa) is an important fruit crop species whose fruits are enjoyed by many worldwide. An octoploid of hybrid origin, the complex genome of this species was recently sequenced, serving as a key reference genome for cultivated strawberry and related species of the Rosaceae family. The current annotation of the F. ananassa genome mainly relies on ab initio predictions and, to a lesser extent, transcriptome data. Here, we present the structure and functional reannotation of the F. ananassa genome based on one PacBio full-length RNA library and ninety-two Illumina RNA-Seq libraries. This improved annotation of the F. ananassa genome, v1.0.a2, comprises a total of 108,447 gene models, with 97.85% complete BUSCOs. The models of 19,174 genes were modified, 360 new genes were identified, and 11,044 genes were found to have alternatively spliced isoforms. Additionally, we constructed a strawberry genome database (SGD) for strawberry gene homolog searching and annotation downloading. Finally, the transcriptome of the receptacles and achenes of F. ananassa at four developmental stages were reanalyzed and qualified, and the expression profiles of all the genes in this annotation are also provided. Together, this study provides an updated annotation of the F. ananassa genome, which will facilitate genomic analyses across the Rosaceae family and gene functional studies in cultivated strawberry.

scholarly journals CellView: Interactive exploration of high dimensional single cell RNA-seq data

2017 ◽  
Author(s):  
Mohan T. Bolisetty ◽  
Michael L. Stitzel ◽  
Paul Robson
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Web Application ◽  
Cell Types ◽  
High Dimensional ◽  
Transcriptome Data ◽  
Rna Seq ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
Measure Gene Expression

Advances in high-throughput single cell transcriptomics technologies have revolutionized the study of complex tissues. It is now possible to measure gene expression across thousands of individual cells to define cell types and states. While powerful computational and statistical frameworks are emerging to analyze these complex datasets, a gap exists between this data and a biologist’s insight. The CellView web application fills this gap by providing easy and intuitive exploration of single cell transcriptome data.

scholarly journals Accurate estimation of molecular counts in droplet-based single-cell RNA-seq experiments

2017 ◽  
Author(s):  
Viktor Petukhov ◽  
Jimin Guo ◽  
Ninib Baryawno ◽  
Nicolas Severe ◽  
David Scadden ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Biological Tissues ◽  
Droplet Microfluidics ◽  
Accurate Estimation ◽  
Transcriptome Data ◽  
Rna Seq ◽  
Sequencing Errors ◽  
Powerful Means

AbstractSingle-cell RNA-seq protocols provide powerful means for examining the gamut of cell types and transcriptional states that comprise complex biological tissues. Recently-developed approaches based on droplet microfluidics, such as inDrop or Drop-seq, use massively multiplexed barcoding to enable simultaneous measurements of transcriptomes for thousands of individual cells. The increasing complexity of such data also creates challenges for subsequent computational processing and troubleshooting of these experiments, with few software options currently available. Here we describe a flexible pipeline for processing droplet-based transcriptome data that implements barcode corrections, classification of cell quality, and diagnostic information about the droplet libraries. We introduce advanced methods for correcting composition bias and sequencing errors affecting cellular and molecular barcodes to provide more accurate estimates of molecular counts in individual cells.

scholarly journals Molecular Basis of Urostyle Development: Genes and Gene Regulation Underlying an Evolutionary Novelty

2021 ◽  
Author(s):  
Gayani Senevirathne ◽  
Neil H. Shubin
Keyword(s):  
Vertebral Column ◽  
Developmental Stages ◽  
Stress Hormones ◽  
Ectopic Expression ◽  
Rna Seq ◽  
Evolutionary Novelty ◽  
T Box ◽  
Functional Studies ◽  
Laser Capture ◽  
Matrix Metallopeptidases

Evolutionary novelties entail the origin of morphologies that enable new functions. These features can arise through changes to gene function and regulation. One important novelty is the fused rod at the end of the vertebral column in anurans, the urostyle. This feature is composed of a coccyx and an ossifying hypochord, and both structures ossify during metamorphosis. We used Laser Capture Micro-dissection of these identified tissues and subjected them to RNA-seq and ATAC-seq analyses at three developmental stages in tadpoles of Xenopus tropicalis. These experiments reveal that the coccyx and hypochord have two different molecular signatures. ATAC-seq data reveals potential regulatory regions that are observed in proximity to candidate genes identified from RNA-seq. Neuronal (TUBB3) and muscle markers (MYH3) are upregulated in coccygeal tissues, whereas T-box genes (TBXT, TBXT.2), corticosteroid stress hormones (CRCH.1), and matrix metallopeptidases (MMP1, MMP8, MMP13) are upregulated in the hypochord. Even though an ossifying hypochord is only present in anurans, this ossification between the vertebral column and the notochord appears to resemble a congenital vertebral anomaly seen prenatally in humans, caused by an ectopic expression of the TBXT/TBXT.2 gene. This work opens the way to functional studies that help us better elucidate anuran bauplan evolution.

scholarly journals Mouse Aging Cell Atlas Analysis Reveals Global and Cell Type Specific Aging Signatures Revision 1

2019 ◽  
Author(s):  
Martin Jinye Zhang ◽  
Angela Oliveira Pisco ◽  
Spyros Darmanis ◽  
James Zou
Keyword(s):  
Cell Types ◽  
Tissue Cell ◽  
Rna Seq ◽  
Cell Type ◽  
Cellular Processes ◽  
Age Related ◽  
Cell Type Specific ◽  
Global Aging ◽  
Mammalian System ◽  
Aging Cell

ABSTRACTAging is associated with complex molecular and cellular processes that are poorly understood. Here we leveraged the Tabula Muris Senis single-cell RNA-seq dataset to systematically characterize gene expression changes during aging across diverse cell types in the mouse. We identified aging-dependent genes in 76 tissue-cell types from 23 tissues and characterized both shared and tissue-cell-specific aging behaviors. We found that the aging-related genes shared by multiple tissue-cell types also change their expression congruently in the same direction during aging in most tissue-cell types, suggesting a coordinated global aging behavior at the organismal level. Scoring cells based on these shared aging genes allowed us to contrast the aging status of different tissues and cell types from a transcriptomic perspective. In addition, we identified genes that exhibit age-related expression changes specific to each functional category of tissue-cell types. All together, our analyses provide one of the most comprehensive and systematic characterizations of the molecular signatures of aging across diverse tissue-cell types in a mammalian system.

scholarly journals Mouse aging cell atlas analysis reveals global and cell type-specific aging signatures

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Martin Jinye Zhang ◽  
Angela Oliveira Pisco ◽  
Spyros Darmanis ◽  
James Zou
Keyword(s):  
Cell Types ◽  
Tissue Cell ◽  
Rna Seq ◽  
Data Set ◽  
Cellular Processes ◽  
Age Related ◽  
Cell Type Specific ◽  
Global Aging ◽  
Mammalian System ◽  
Aging Cell

Aging is associated with complex molecular and cellular processes that are poorly understood. Here we leveraged the Tabula Muris Senis single-cell RNA-seq data set to systematically characterize gene expression changes during aging across diverse cell types in the mouse. We identified aging-dependent genes in 76 tissue-cell types from 23 tissues and characterized both shared and tissue-cell-specific aging behaviors. We found that the aging-related genes shared by multiple tissue-cell types also change their expression congruently in the same direction during aging in most tissue-cell types, suggesting a coordinated global aging behavior at the organismal level. Scoring cells based on these shared aging genes allowed us to contrast the aging status of different tissues and cell types from a transcriptomic perspective. In addition, we identified genes that exhibit age-related expression changes specific to each functional category of tissue-cell types. Altogether, our analyses provide one of the most comprehensive and systematic characterizations of the molecular signatures of aging across diverse tissue-cell types in a mammalian system.

scholarly journals Transcriptome Analysis of Ginkgo biloba L. Leaves across Late Developmental Stages Based on RNA-Seq and Co-Expression Network

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 315
Author(s):  
Hailin Liu ◽  
Xin Han ◽  
Jue Ruan ◽  
Lian Xu ◽  
Bing He
Keyword(s):  
Ginkgo Biloba ◽  
Leaf Development ◽  
Developmental Stages ◽  
Leaf Growth ◽  
Rna Seq ◽  
Final Size ◽  
Dioecious Species ◽  
Related Factors ◽  
New Genes ◽  
Gene Modules

The final size of plant leaves is strictly controlled by environmental and genetic factors, which coordinate cell expansion and cell cycle activity in space and time; however, the regulatory mechanisms of leaf growth are still poorly understood. Ginkgo biloba is a dioecious species native to China with medicinally and phylogenetically important characteristics, and its fan-shaped leaves are unique in gymnosperms, while the mechanism of G. biloba leaf development remains unclear. In this study we studied the transcriptome of G. biloba leaves at three developmental stages using high-throughput RNA-seq technology. Approximately 4167 differentially expressed genes (DEGs) were obtained, and a total of 12,137 genes were structure optimized together with 732 new genes identified. More than 50 growth-related factors and gene modules were identified based on DEG and Weighted Gene Co-expression Network Analysis. These results could remarkably expand the existing transcriptome resources of G. biloba, and provide references for subsequent analysis of ginkgo leaf development.

scholarly journals Transcriptional and morphological profiling of parvalbumin interneuron subpopulations in the mouse hippocampus

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lin Que ◽  
David Lukacsovich ◽  
Wenshu Luo ◽  
Csaba Földy
Keyword(s):  
Large Scale ◽  
Cell Types ◽  
Rna Seq ◽  
Neuronal Identity ◽  
Parvalbumin Interneurons ◽  
Different Types ◽  
Parvalbumin Interneuron ◽  
Cam Profile ◽  
Developmental Domains

AbstractThe diversity reflected by >100 different neural cell types fundamentally contributes to brain function and a central idea is that neuronal identity can be inferred from genetic information. Recent large-scale transcriptomic assays seem to confirm this hypothesis, but a lack of morphological information has limited the identification of several known cell types. In this study, we used single-cell RNA-seq in morphologically identified parvalbumin interneurons (PV-INs), and studied their transcriptomic states in the morphological, physiological, and developmental domains. Overall, we find high transcriptomic similarity among PV-INs, with few genes showing divergent expression between morphologically different types. Furthermore, PV-INs show a uniform synaptic cell adhesion molecule (CAM) profile, suggesting that CAM expression in mature PV cells does not reflect wiring specificity after development. Together, our results suggest that while PV-INs differ in anatomy and in vivo activity, their continuous transcriptomic and homogenous biophysical landscapes are not predictive of these distinct identities.

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