PlantNexus: A Gene Co-expression Network Database and Visualization Tool for Barley and Sorghum

AbstractGlobal gene co-expression networks (GCNs) are powerful tools for functional genomics whereby putative functions and regulatory mechanisms can be inferred by gene co-expression. With the recent accumulation of RNA-seq data sets, the construction of RNA-seq-based GCNs has now become possible. Cereal crops, such as Hordeum vulgare (barley) and Sorghum bicolor (sorghum), are among the most important plants to humanity and contribute significantly to our food supply. However, co-expression network tools for these plants are outdated or lacking. In this study, we constructed global GCNs for barley and sorghum using 500 and 774 RNA-seq data sets, respectively. In addition, we curated the meta-information of these RNA-seq data sets and categorized them into four main tissue types, leaf, root, shoot, and flower/seed, and built tissue-specific GCNs. To enable GCN searching and visualization, we implemented a website and database named PlantNexus, offering an immersive environment for the exploration and visualization of gene expressions and co-expressions of barley and sorghum at the global and tissue-specific levels. PlantNexus is freely available at https://plantnexus.ohio.edu/.

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Tissue Resolved, Gene Structure Refined Equine Transcriptome

10.1101/061952 ◽

2016 ◽

Cited By ~ 1

Author(s):

T. A. Mansour ◽

E. Y. Scott ◽

C. J. Finno ◽

R. R. Bellone ◽

M. J. Mienaltowski ◽

...

Keyword(s):

Gene Expression ◽

Gene Structure ◽

Specific Information ◽

Data Sets ◽

Rna Seq ◽

Tissue Specific ◽

Specificity And Sensitivity ◽

Four Levels ◽

Equine Transcriptome ◽

Accurate Quantification

AbstractBackgroundTranscriptome interpretation relies on a good-quality reference transcriptome for accurate quantification of gene expression as well as functional analysis of genetic variants. The current annotation of the horse genome lacks the specificity and sensitivity necessary to assess gene expression especially at the isoform level, and suffers from insufficient annotation of untranslated regions (UTR). We built an annotation pipeline for horse and used it to integrate 1.9 billion reads from multiple RNA-seq data sets into a new refined transcriptome.ResultsThis equine transcriptome integrates eight different tissues from 59 individuals and improves gene structure and isoform resolution while providing considerable tissue-specific information. We utilized four levels of transcript filtration in our pipeline, aimed at producing several transcriptome versions that are suitable for different downstream analyses. Our most refined transcriptome includes 36,876 genes and 76,125 isoforms, with 6474 candidate transcriptional loci novel to the equine transcriptome.ConclusionsWe have employed a variety of descriptive statistics and figures that demonstrate the quality and content of the transcriptome. The equine transcriptomes that are provided by this pipeline show the best tissue-specific resolution of any equine transcriptome to date and can serve several types of downstream analyses.

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Somatic genetic mosaicism in the apolipoprotein E-null mouse aorta

Thrombosis and Haemostasis ◽

10.1055/a-1414-4840 ◽

2021 ◽

Author(s):

María del Pilar Valencia-Morales ◽

Alejandro Sanchez-Flores ◽

Dannia Colín-Castelán ◽

Yolanda Alvarado-Caudillo ◽

Nicolás Fragoso-Bargas ◽

...

Keyword(s):

Somatic Mutations ◽

Epigenetic Inheritance ◽

Null Mouse ◽

Data Sets ◽

Rna Seq ◽

Somatic Variation ◽

Cvd Risk ◽

Tissue Specific ◽

Sibling Pairs ◽

Genetic Mosaicism

In addition to genetic and epigenetic inheritance, somatic variation may contribute to cardiovascular disease (CVD) risk. CVD-associated somatic mutations have been reported in human clonal haematopoiesis, but evidence in the atheroma is lacking. To probe for somatic variation in atherosclerosis, we sought single-nucleotide private variants (PVs) in whole-exome sequencing (WES) data of aorta, liver and skeletal muscle of two C57BL/6J coisogenic male ApoE-null/WT sibling pairs, and RNA-seq data of one of the two pairs. Relative to the C57BL/6 reference genome, we identified 9 and 11 ApoE-null aorta- and liver-specific PVs that were shared by all WES and RNA-seq data sets. Corresponding PVs in WT sibling aorta and liver were 1 and 0, respectively, and not overlapping with ApoE-null PVs. Pyrosequencing analysis of 4 representative PVs in 17 ApoE-null aortas and livers confirmed tissue-specific shifts towards the alternative allele, in addition to significant deviations from Mendelian allele ratios. Notably, all aorta and liver PVs were present in the dbSNP database and were predominantly transition mutations within atherosclerosis-related genes. The majority of PVs were in discrete clusters ~3 Mb and 65-73 Mb away from hypermutable immunoglobin loci in chromosome 6. These features were largely shared with previously reported CVD-associated somatic mutations in human clonal haematopoiesis. The observation that SNPs exhibit tissue-specific somatic DNA mosaicism in ApoE-null mice is potentially relevant for genetic association study design. The proximity of PVs to hypermutable loci suggests testable mechanistic hypotheses.

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Identification and analysis of house-keeping and tissue-specific genes based on RNA-seq data sets across 15 mouse tissues

Gene ◽

10.1016/j.gene.2015.11.003 ◽

2016 ◽

Vol 576 (1) ◽

pp. 560-570 ◽

Cited By ~ 10

Author(s):

Jingyao Zeng ◽

Shoucheng Liu ◽

Yuhui Zhao ◽

Xinyu Tan ◽

Hasan Awad Aljohi ◽

...

Keyword(s):

Data Sets ◽

Rna Seq ◽

Tissue Specific ◽

Mouse Tissues

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Comparative Methylation and RNA-Seq Expression Analysis in CpG Context to Identify Genes Involved in Backfat vs Liver Diversification in Nanchukmacdon Pig

10.21203/rs.3.rs-164446/v1 ◽

2021 ◽

Author(s):

Devender Arora ◽

Jong-Eun Park ◽

Dajeong Lim ◽

Bong-Hwan Choi ◽

In-Cheol Cho ◽

...

Keyword(s):

Cpg Island ◽

Cholesterol Biosynthesis ◽

Bmp Signaling ◽

Salt Transport ◽

Regulatory Mechanisms ◽

Transcriptional Level ◽

Rna Seq ◽

Motif Analysis ◽

Tissue Specific ◽

Differentially Methylated Genes

Abstract Background: DNA methylation and demethylation at CpG island is one of the main regulatory mechanisms at the transcriptional level that give cells the possibility to respond to different stimuli. These regulatory mechanisms help in developing tissue without affecting the genomic composition or undergone selection. Liver and Backfat play important role in regulating lipid metabolism and control various pathways involved in reproductive performance, meat quality, and immunity. Genes inside these tissue stores plethora of information and their understanding are required to enhance tissue characteristics in the future generation. Results: In this study, to understand the differentiation mechanism we have performed whole-genome bisulfite sequencing (WGBS) and RNA-seq analysis and identified 16 CpG islands were involved in differentially methylation regions (DMRs) as well differentially expressed genes (DEGs) between liver and backfat. Among the identified differentially-methylated genes (C7orf50, ACTB, MLC1) in backfat and (TNNT3, SIX2, SDK1, CLSTN3, LTBP4, CFAP74, SLC22A23, FOXC1, GMDS, GSC, GATA4, SEMA5A, HOXA5) in the liver were identified. Motif analysis for DMRs was also performed to understand the major role of methylated motif for tissue-specific differentiation. Gene ontology studies revealed the association with collagen fibril organization, BMP signaling pathway in backfat and Cholesterol biosynthesis, bile acid and bile salt transport, immunity-related pathways in methylated genes expressed in the liver. Conclusion: Our finding could help in understanding how methylation on certain genes plays an important role and can be used as biomarkers to study tissue specific characteristics.

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Development of genic KASP SNP markers from RNA-Seq data for map-based cloning and marker-assisted selection in maize

BMC Plant Biology ◽

10.1186/s12870-021-02932-8 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Zhengjie Chen ◽

Dengguo Tang ◽

Jixing Ni ◽

Peng Li ◽

Le Wang ◽

...

Keyword(s):

Marker Assisted Selection ◽

Inbred Lines ◽

Average Density ◽

Snp Markers ◽

Data Sets ◽

Rna Seq ◽

Specific Pcr ◽

Maize Inbred Lines ◽

Allele Specific ◽

Allele Specific Pcr

Abstract Background Maize is one of the most important field crops in the world. Most of the key agronomic traits, including yield traits and plant architecture traits, are quantitative. Fine mapping of genes/ quantitative trait loci (QTL) influencing a key trait is essential for marker-assisted selection (MAS) in maize breeding. However, the SNP markers with high density and high polymorphism are lacking, especially kompetitive allele specific PCR (KASP) SNP markers that can be used for automatic genotyping. To date, a large volume of sequencing data has been produced by the next generation sequencing technology, which provides a good pool of SNP loci for development of SNP markers. In this study, we carried out a multi-step screening method to identify kompetitive allele specific PCR (KASP) SNP markers based on the RNA-Seq data sets of 368 maize inbred lines. Results A total of 2,948,985 SNPs were identified in the high-throughput RNA-Seq data sets with the average density of 1.4 SNP/kb. Of these, 71,311 KASP SNP markers (the average density of 34 KASP SNP/Mb) were developed based on the strict criteria: unique genomic region, bi-allelic, polymorphism information content (PIC) value ≥0.4, and conserved primer sequences, and were mapped on 16,161 genes. These 16,161 genes were annotated to 52 gene ontology (GO) terms, including most of primary and secondary metabolic pathways. Subsequently, the 50 KASP SNP markers with the PIC values ranging from 0.14 to 0.5 in 368 RNA-Seq data sets and with polymorphism between the maize inbred lines 1212 and B73 in in silico analysis were selected to experimentally validate the accuracy and polymorphism of SNPs, resulted in 46 SNPs (92.00%) showed polymorphism between the maize inbred lines 1212 and B73. Moreover, these 46 polymorphic SNPs were utilized to genotype the other 20 maize inbred lines, with all 46 SNPs showing polymorphism in the 20 maize inbred lines, and the PIC value of each SNP was 0.11 to 0.50 with an average of 0.35. The results suggested that the KASP SNP markers developed in this study were accurate and polymorphic. Conclusions These high-density polymorphic KASP SNP markers will be a valuable resource for map-based cloning of QTL/genes and marker-assisted selection in maize. Furthermore, the method used to develop SNP markers in maize can also be applied in other species.

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MUREN: a robust and multi-reference approach of RNA-seq transcript normalization

BMC Bioinformatics ◽

10.1186/s12859-021-04288-0 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Yance Feng ◽

Lei M. Li

Keyword(s):

Biological Significance ◽

Housekeeping Genes ◽

R Package ◽

Data Sets ◽

Statistical Regression ◽

Rna Seq ◽

Least Trimmed Squares ◽

Standard Data ◽

Wide Range ◽

Multiple References

Abstract Background Normalization of RNA-seq data aims at identifying biological expression differentiation between samples by removing the effects of unwanted confounding factors. Explicitly or implicitly, the justification of normalization requires a set of housekeeping genes. However, the existence of housekeeping genes common for a very large collection of samples, especially under a wide range of conditions, is questionable. Results We propose to carry out pairwise normalization with respect to multiple references, selected from representative samples. Then the pairwise intermediates are integrated based on a linear model that adjusts the reference effects. Motivated by the notion of housekeeping genes and their statistical counterparts, we adopt the robust least trimmed squares regression in pairwise normalization. The proposed method (MUREN) is compared with other existing tools on some standard data sets. The goodness of normalization emphasizes on preserving possible asymmetric differentiation, whose biological significance is exemplified by a single cell data of cell cycle. MUREN is implemented as an R package. The code under license GPL-3 is available on the github platform: github.com/hippo-yf/MUREN and on the conda platform: anaconda.org/hippo-yf/r-muren. Conclusions MUREN performs the RNA-seq normalization using a two-step statistical regression induced from a general principle. We propose that the densities of pairwise differentiations are used to evaluate the goodness of normalization. MUREN adjusts the mode of differentiation toward zero while preserving the skewness due to biological asymmetric differentiation. Moreover, by robustly integrating pre-normalized counts with respect to multiple references, MUREN is immune to individual outlier samples.

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Expression of the genes for the ferritin H and L subunits in rat liver and heart. Evidence for tissue-specific regulations at pre- and post-translational levels

Biochemical Journal ◽

10.1042/bj2750813 ◽

1991 ◽

Vol 275 (3) ◽

pp. 813-816 ◽

Cited By ~ 38

Author(s):

G Cairo ◽

E Rappocciolo ◽

L Tacchini ◽

L Schiaffonati

Keyword(s):

Translational Regulation ◽

Specific Pattern ◽

Regulatory Mechanisms ◽

Mrna Accumulation ◽

Tissue Specific ◽

Protein Levels ◽

Ferritin Gene ◽

Ferritin H ◽

Translational Mechanisms ◽

Different Tissues

The proportion of ferritin light-chain and heavy-chain subunits (L and H) present in the ferritin multimeric shell varies between different tissues. To identify the regulatory mechanisms responsible for the greater amount of L in liver than in heart isoferritins, we analysed ferritin-gene expression at the RNA and protein levels in these two tissues of the rat. In the heart the ratio between the amount of L and H, at the level both of synthesis and accumulation, is about 1 and is the same as the ratio between their respective mRNAs. In contrast, in the liver, the ratio between the L- and H-mRNAs is approx. 2 and cannot entirely explain the large predominance of L in isoferritins in this tissue. Since in the liver the L-mRNA is neither preferentially associated with polyribosomes nor translated more efficiently than its H- counterpart, it seems that the liver-specific isoferritin profile is determined by a combination of pre- and post-translational mechanisms, whereas in heart the post-translational regulation does not seem to be relevant and the tissue-specific pattern is determined at the level of mRNA accumulation.

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GENE SELECTION USING LOGISTIC REGRESSIONS BASED ON AIC, BIC AND MDL CRITERIA

New Mathematics and Natural Computation ◽

10.1142/s179300570500007x ◽

2005 ◽

Vol 01 (01) ◽

pp. 129-145 ◽

Cited By ~ 15

Author(s):

XIAOBO ZHOU ◽

XIAODONG WANG ◽

EDWARD R. DOUGHERTY

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Logistic Regression Model ◽

Gene Selection ◽

Information Criterion ◽

Cancer Classification ◽

Data Sets ◽

Classification Methods ◽

Gene Expressions ◽

Experimental Conditions

In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables (gene expressions) and the small number of experimental conditions. Many gene-selection and classification methods have been proposed; however most of these treat gene selection and classification separately, and not under the same model. We propose a Bayesian approach to gene selection using the logistic regression model. The Akaike information criterion (AIC), the Bayesian information criterion (BIC) and the minimum description length (MDL) principle are used in constructing the posterior distribution of the chosen genes. The same logistic regression model is then used for cancer classification. Fast implementation issues for these methods are discussed. The proposed methods are tested on several data sets including those arising from hereditary breast cancer, small round blue-cell tumors, lymphoma, and acute leukemia. The experimental results indicate that the proposed methods show high classification accuracies on these data sets. Some robustness and sensitivity properties of the proposed methods are also discussed. Finally, mixing logistic-regression based gene selection with other classification methods and mixing logistic-regression-based classification with other gene-selection methods are considered.

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