scholarly journals De Novo Assembly and Characterization of the Seed Germination Transcriptomes of Sicyos angulatus

2021 ◽  
Vol 26 (01) ◽  
pp. 31-38
Author(s):  
Helong Si
Keyword(s):  
Signal Transduction ◽  
Seed Germination ◽  
Invasive Plant ◽  
De Novo ◽  
Expression Profiles ◽  
Signal Transduction Pathway ◽  
Pairwise Comparison ◽  
Rna Seq ◽  
Sequencing Data ◽  
Meristem Development

Sicyos angulatus has become an important invasive plant exhibiting good ecological adaptability and strong competitive ability. However, studies on this plant at the molecular level are limited by a lack of sequencing data. The present study obtained transcriptome sequences and gene expression profiles using RNA-Seq during S. angulatus seed germination. In total, RNA-Seq generated 491,967,468 reads, which were de novo assembled and 127,874 unigenes with N50 length of 807 bp. About 34.9% of the unigenes (44,660) were annotated against the protein databases, and 35,176 coding sequences were determined. During S. angulatus seed germination, over 127,860 unigenes were expressed and 66,664 unigenes differentially expressed genes (DEGs), among which 8919 DEGs were similar in pairwise comparison. Gene Ontology (GO) analysis of DEGs revealed that genes related to post-embryonic development, meristem development, and photosynthesis were enriched. In addition, the GO term “plant hormone signal transduction pathway” was also enriched in the DEGs. Important changes in genes expression related to auxin and gibberellin signal transduction might possibly be associated with S. angulatus seed germination. The findings of this study provide a foundation for research on S. angulatus that may contribute to prevent further invasion of this plant, consequently protecting the environment. © 2021 Friends Science Publishers

scholarly journals TrancriptomeReconstructoR, A Data-Driven Annotation of Complex Transcriptomes

2020 ◽  
Author(s):  
Maxim Ivanov ◽  
Albin Sandelin ◽  
Sebastian Marquardt
Keyword(s):  
De Novo ◽  
Gene Annotation ◽  
R Package ◽  
Sequence Information ◽  
Rna Seq ◽  
Sequencing Data ◽  
Gene Model ◽  
Preparation Methods ◽  
Downstream Analysis

Abstract Background: The quality of gene annotation determines the interpretation of results obtained in transcriptomic studies. The growing number of genome sequence information calls for experimental and computational pipelines for de novo transcriptome annotation. Ideally, gene and transcript models should be called from a limited set of key experimental data. Results: We developed TranscriptomeReconstructoR, an R package which implements a pipeline for automated transcriptome annotation. It relies on integrating features from independent and complementary datasets: i) full-length RNA-seq for detection of splicing patterns and ii) high-throughput 5' and 3' tag sequencing data for accurate definition of gene borders. The pipeline can also take a nascent RNA-seq dataset to supplement the called gene model with transient transcripts.We reconstructed de novo the transcriptional landscape of wild type Arabidopsis thaliana seedlings as a proof-of-principle. A comparison to the existing transcriptome annotations revealed that our gene model is more accurate and comprehensive than the two most commonly used community gene models, TAIR10 and Araport11. In particular, we identify thousands of transient transcripts missing from the existing annotations. Our new annotation promises to improve the quality of A.thaliana genome research.Conclusions: Our proof-of-concept data suggest a cost-efficient strategy for rapid and accurate annotation of complex eukaryotic transcriptomes. We combine the choice of library preparation methods and sequencing platforms with the dedicated computational pipeline implemented in the TranscriptomeReconstructoR package. The pipeline only requires prior knowledge on the reference genomic DNA sequence, but not the transcriptome. The package seamlessly integrates with Bioconductor packages for downstream analysis.

scholarly journals Transcriptome Analysis Reveals Key Seed-Development Genes in Common Buckwheat (Fagopyrum esculentum)

2019 ◽  
Vol 20 (17) ◽  
pp. 4303 ◽  
Author(s):  
Hongyou Li ◽  
Qiuyu Lv ◽  
Jiao Deng ◽  
Juan Huang ◽  
Fang Cai ◽  
...  
Keyword(s):  
Signal Transduction ◽  
Seed Development ◽  
Seed Size ◽  
Molecular Mechanisms ◽  
Signal Transduction Pathway ◽  
Fagopyrum Esculentum ◽  
Rna Seq ◽  
Common Buckwheat ◽  
Size Change ◽  
Key Genes

Seed development is an essential and complex process, which is involved in seed size change and various nutrients accumulation, and determines crop yield and quality. Common buckwheat (Fagopyrum esculentum Moench) is a widely cultivated minor crop with excellent economic and nutritional value in temperate zones. However, little is known about the molecular mechanisms of seed development in common buckwheat (Fagopyrum esculentum). In this study, we performed RNA-Seq to investigate the transcriptional dynamics and identify the key genes involved in common buckwheat seed development at three different developmental stages. A total of 4619 differentially expressed genes (DEGs) were identified. Based on the results of Gene Ontology (GO) and KEGG analysis of DEGs, many key genes involved in the seed development, including the Ca2+ signal transduction pathway, the hormone signal transduction pathways, transcription factors (TFs), and starch biosynthesis-related genes, were identified. More importantly, 18 DEGs were identified as the key candidate genes for seed size through homologous query using the known seed size-related genes from different seed plants. Furthermore, 15 DEGs from these identified as the key genes of seed development were selected to confirm the validity of the data by using quantitative real-time PCR (qRT-PCR), and the results show high consistency with the RNA-Seq results. Taken together, our results revealed the underlying molecular mechanisms of common buckwheat seed development and could provide valuable information for further studies, especially for common buckwheat seed improvement.

scholarly journals Gill Transcriptome Sequencing and De Novo Annotation of Acanthogobius ommaturus in Response to Salinity Stress

Genes ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 631
Author(s):  
Zhicheng Sun ◽  
Fangrui Lou ◽  
Yuan Zhang ◽  
Na Song
Keyword(s):  
Signal Transduction ◽  
Salinity Stress ◽  
De Novo ◽  
Enrichment Analysis ◽  
Gill Tissue ◽  
Control Group ◽  
Rna Seq ◽  
Illumina Hiseq ◽  
Kegg Pathways ◽  
Pathways Analysis

Acanthogobius ommaturus is a euryhaline fish widely distributed in coastal, bay and estuarine areas, showing a strong tolerance to salinity. In order to understand the mechanism of adaptation to salinity stress, RNA-seq was used to compare the transcriptome responses of Acanthogobius ommaturus to the changes of salinity. Four salinity gradients, 0 psu, 15 psu (control), 30 psu and 45 psu were set to conduct the experiment. In total, 131,225 unigenes were obtained from the gill tissue of A. ommaturus using the Illumina HiSeq 2000 platform (San Diego, USA). Compared with the gene expression profile of the control group, 572 differentially expressed genes (DEGs) were screened, with 150 at 0 psu, 170 at 30 psu, and 252 at 45 psu. Additionally, among these DEGs, Gene Ontology (GO) analysis indicated that binding, metabolic processes and cellular processes were significantly enriched. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis detected 3, 5 and 8 pathways related to signal transduction, metabolism, digestive and endocrine systems at 0 psu, 30 psu and 45 psu, respectively. Based on GO enrichment analysis and manual literature searches, the results of the present study indicated that A. ommaturus mainly responded to energy metabolism, ion transport and signal transduction to resist the damage caused by salinity stress. Eight DEGs were randomly selected for further validation by quantitative real-time PCR (qRT-PCR) and the results were consistent with the RNA-seq data.

scholarly journals NASA GeneLab RNA-Seq Consensus Pipeline: Standardized Processing of Short-Read RNA-Seq Data

2020 ◽  
Author(s):  
Eliah G. Overbey ◽  
Amanda M. Saravia-Butler ◽  
Zhe Zhang ◽  
Komal S. Rathi ◽  
Homer Fogle ◽  
...  
Keyword(s):  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Rna Seq ◽  
Sequencing Data ◽  
Analysis Pipeline ◽  
Short Read ◽  
Gene Quantification ◽  
Working Groups ◽  
Using Data ◽  
Data Analysis Pipeline

SummaryWith the development of transcriptomic technologies, we are able to quantify precise changes in gene expression profiles from astronauts and other organisms exposed to spaceflight. Members of NASA GeneLab and GeneLab-associated analysis working groups (AWGs) have developed a consensus pipeline for analyzing short-read RNA-sequencing data from spaceflight-associated experiments. The pipeline includes quality control, read trimming, mapping, and gene quantification steps, culminating in the detection of differentially expressed genes. This data analysis pipeline and the results of its execution using data submitted to GeneLab are now all publicly available through the GeneLab database. We present here the full details and rationale for the construction of this pipeline in order to promote transparency, reproducibility and reusability of pipeline data, to provide a template for data processing of future spaceflight-relevant datasets, and to encourage cross-analysis of data from other databases with the data available in GeneLab.

scholarly journals OMGene: Mutual improvement of gene models through optimisation of evolutionary conservation

2017 ◽  
Author(s):  
Michael P. Dunne ◽  
Steven Kelly
Keyword(s):  
Experimental Data ◽  
De Novo ◽  
Gene Prediction ◽  
Expression Profiles ◽  
Accurate Determination ◽  
Subcellular Localisation ◽  
Rna Seq ◽  
Gene Model ◽  
Specific Expression ◽  
Gene Models

AbstractBackgroundThe accurate determination of the genomic coordinates for a given gene – its gene model – is of vital importance to the utility of its annotation, and the accuracy of bioinformatic analyses derived from it. Currently-available methods of computational gene prediction, while on the whole successful, often disagree on the model for a given predicted gene, with some or all of the variant gene models failing to match the biologically observed structure. Many prediction methods can be bolstered by using experimental data such as RNA-seq and mass spectrometry. However, these resources are not always available, and rarely give a comprehensive portrait of an organism’s transcriptome due to temporal and tissue-specific expression profiles.ResultsOrthology between genes provides evolutionary evidence to guide the construction of gene models. OMGene (Optimise My Gene) aims to optimise gene models in the absence of experimental data by optimising the derived amino acid alignments for gene models within orthogroups. Using RNA-seq data sets from plants and fungi, considering intron/exon junction representation and exon coverage, and assessing the intra-orthogroup consistency of subcellular localisation predictions, we demonstrate the utility of OMGene for improving gene models in annotated genomes.ConclusionsWe show that significant improvements in the accuracy of gene model annotations can be made in both established and de novo annotated genomes by leveraging information from multiple species.

scholarly journals TrancriptomeReconstructoR: data-driven annotation of complex transcriptomes

2020 ◽  
Author(s):  
Maxim Ivanov ◽  
Albin Sandelin ◽  
Sebastian Marquardt
Keyword(s):  
De Novo ◽  
Gene Annotation ◽  
R Package ◽  
Sequence Information ◽  
Rna Seq ◽  
Sequencing Data ◽  
Gene Model ◽  
Preparation Methods ◽  
Downstream Analysis

AbstractBackgroundThe quality of gene annotation determines the interpretation of results obtained in transcriptomic studies. The growing number of genome sequence information calls for experimental and computational pipelines for de novo transcriptome annotation. Ideally, gene and transcript models should be called from a limited set of key experimental data.ResultsWe developed TranscriptomeReconstructoR, an R package which implements a pipeline for automated transcriptome annotation. It relies on integrating features from independent and complementary datasets: i) full-length RNA-seq for detection of splicing patterns and ii) high-throughput 5’ and 3’ tag sequencing data for accurate definition of gene borders. The pipeline can also take a nascent RNA-seq dataset to supplement the called gene model with transient transcripts.We reconstructed de novo the transcriptional landscape of wild type Arabidopsis thaliana seedlings as a proof-of-principle. A comparison to the existing transcriptome annotations revealed that our gene model is more accurate and comprehensive than the two most commonly used community gene models, TAIR10 and Araport11. In particular, we identify thousands of transient transcripts missing from the existing annotations. Our new annotation promises to improve the quality of A.thaliana genome research.ConclusionsOur proof-of-concept data suggest a cost-efficient strategy for rapid and accurate annotation of complex eukaryotic transcriptomes. We combine the choice of library preparation methods and sequencing platforms with the dedicated computational pipeline implemented in the TranscriptomeReconstructoR package. The pipeline only requires prior knowledge on the reference genomic DNA sequence, but not the transcriptome. The package seamlessly integrates with Bioconductor packages for downstream analysis.

scholarly journals Transcriptome Analysis of Ovarian and Uterine Clear Cell Malignancies

2020 ◽  
Vol 10 ◽  
Author(s):  
Jill Alldredge ◽  
Leslie Randall ◽  
Gabriela De Robles ◽  
Anshu Agrawal ◽  
Dan Mercola ◽  
...  
Keyword(s):  
Cellular Proliferation ◽  
Immune Cell ◽  
Clear Cell ◽  
Early Stage ◽  
Expression Profiles ◽  
Uterine Cancer ◽  
Cell Populations ◽  
Rna Seq ◽  
Sequencing Data ◽  
Ovarian Cancers

PurposeOvarian and uterine clear cell carcinomas (CCCs) are rare but associated with poor prognosis. This study explored RNA transcription patterns characteristic of these tumors.Experimental DesignRNA sequencing (RNA-seq) of 11 ovarian CCCs and five uterine CCCs was performed and compared to publicly available data from high grade serous ovarian cancers (HGSOCs). Ingenuity Pathway Analyses were performed. CIBERSORT analyses estimated relative fractions of 22 immune cell types in each RNA-seq sample. Sequencing data was correlated with PD-L1 immunohistochemical expression.ResultsRNA-seq revealed 1,613 downregulated and 1,212 upregulated genes (corrected p < 0.05, |FC |≥10) in ovarian CCC versus HGSOC. Two subgroups were identified in the ovarian CCC, characterized by ethnicity and expression differences in ARID1A. There were 3,252 differentially expressed genes between PD-L1+/− ovarian CCCs, revealing immune response, cell death, and DNA repair networks, negatively correlated with PD-L1 expression, whereas cellular proliferation networks positively correlated with expression. In clear cell ovarian versus clear cell uterine cancer, 1,607 genes were significantly upregulated, and 109 genes were significantly downregulated (corrected p < 0.05, |FC|≥10). Comparative pathway analysis of late and early stage ovarian CCCs revealed unique metabolic and PTEN pathways, whereas uterine CCCs had unique Wnt/Ca+, estrogen receptor, and CCR5 signaling. CIBERSORT analysis revealed that activated mast cells and regulatory T cell populations were relatively enriched in uterine CCCs. The PD-L1+ ovarian CCCs had enriched resting NK cells and memory B cell populations, while PD-L1− had enriched CD8 T-cells, monocytes, eosinophils, and activated dendritic cells.ConclusionsUnique transcriptional expression profiles distinguish clear cell uterine and ovarian cancers from each other and from other more common histologic subtypes. These insights may aid in devising novel therapeutics.

scholarly journals Prion Infection of Mouse Brain Reveals Multiple New Upregulated Genes Involved in Neuroinflammation or Signal Transduction

2014 ◽  
Vol 89 (4) ◽  
pp. 2388-2404 ◽  
Author(s):  
James A. Carroll ◽  
James F. Striebel ◽  
Brent Race ◽  
Katie Phillips ◽  
Bruce Chesebro
Keyword(s):  
Signal Transduction ◽  
Inflammatory Responses ◽  
Prion Diseases ◽  
Expression Profiles ◽  
Signal Transduction Pathway ◽  
Gene Expression Profiles ◽  
Observable Effect ◽  
Prion Infection ◽  
Scrapie Infection ◽  
Scrapie Strains

ABSTRACTGliosis is often a preclinical pathological finding in neurodegenerative diseases, including prion diseases, but the mechanisms facilitating gliosis and neuronal damage in these diseases are not understood. To expand our knowledge of the neuroinflammatory response in prion diseases, we assessed the expression of key genes and proteins involved in the inflammatory response and signal transduction in mouse brain at various times after scrapie infection. In brains of scrapie-infected mice at pre- and postclinical stages, we identified 15 previously unreported differentially expressed genes related to inflammation or activation of the STAT signal transduction pathway. Levels for the majority of differentially expressed genes increased with time postinfection. In quantitative immunoblotting experiments of STAT proteins, STAT1α, phosphorylated-STAT1α (pSTAT1α), and pSTAT3 were increased between 94 and 131 days postinfection (p.i.) in brains of mice infected with strain 22L. Furthermore, a select group of STAT-associated genes was increased preclinically during scrapie infection, suggesting early activation of the STAT signal transduction pathway. Comparison of inflammatory markers between mice infected with scrapie strains 22L and RML indicated that the inflammatory responses and gene expression profiles in the brains were strikingly similar, even though these scrapie strains infect different brain regions. The endogenous interleukin-1 receptor antagonist (IL-1Ra), an inflammatory marker, was newly identified as increasing preclinically in our model and therefore might influence scrapie pathogenesisin vivo. However, in IL-1Ra-deficient or overexpressor transgenic mice inoculated with scrapie, neither loss nor overexpression of IL-1Ra demonstrated any observable effect on gliosis, protease-resistant prion protein (PrPres) formation, disease tempo, pathology, or expression of the inflammatory genes analyzed.IMPORTANCEPrion infection leads to PrPres deposition, gliosis, and neuroinflammation in the central nervous system before signs of clinical illness. Using a scrapie mouse model of prion disease to assess various time points postinoculation, we identified 15 unreported genes that were increased in the brains of scrapie-infected mice and were associated with inflammation and/or JAK-STAT activation. Comparison of mice infected with two scrapie strains (22L and RML), which have dissimilar neuropathologies, indicated that the inflammatory responses and gene expression profiles in the brains were similar. Genes that increased prior to clinical signs might be involved in controlling scrapie infection or in facilitating damage to host tissues. We tested the possible role of the endogenous IL-1Ra, which was increased at 70 days p.i. In scrapie-infected mice deficient in or overexpressing IL-1Ra, there was no observable effect on gliosis, PrPres formation, disease tempo, pathology, or expression of inflammatory genes analyzed.

scholarly journals The genetic origin of evolidine, the first cyclopeptide discovered in plants, and related orbitides

2020 ◽  
Vol 295 (42) ◽  
pp. 14510-14521 ◽  
Author(s):  
Mark F. Fisher ◽  
Colton D. Payne ◽  
Thaveshini Chetty ◽  
Darren Crayn ◽  
Oliver Berkowitz ◽  
...  
Keyword(s):  
Disulfide Bonds ◽  
Cyclic Peptides ◽  
Cyclic Peptide ◽  
De Novo ◽  
Forest Tree ◽  
Whole Genome Sequencing Data ◽  
The Novel ◽  
Rna Seq ◽  
Sequencing Data ◽  
The Family

Cyclic peptides are reported to have antibacterial, antifungal, and other bioactivities. Orbitides are a class of cyclic peptides that are small, head-to-tail cyclized, composed of proteinogenic amino acids and lack disulfide bonds; they are also known in several genera of the plant family Rutaceae. Melicope xanthoxyloides is the Australian rain forest tree of the Rutaceae family in which evolidine, the first plant cyclic peptide, was discovered. Evolidine (cyclo-SFLPVNL) has subsequently been all but forgotten in the academic literature, so to redress this we used tandem MS and de novo transcriptomics to rediscover evolidine and decipher its biosynthetic origin from a short precursor just 48 residues in length. We also identified another six M. xanthoxyloides orbitides using the same techniques. These peptides have atypically diverse C termini consisting of residues not recognized by either of the known proteases plants use to macrocyclize peptides, suggesting new cyclizing enzymes await discovery. We examined the structure of two of the novel orbitides by NMR, finding one had a definable structure, whereas the other did not. Mining RNA-seq and whole genome sequencing data from other species of the Rutaceae family revealed that a large and diverse family of peptides is encoded by similar sequences across the family and demonstrates how powerful de novo transcriptomics can be at accelerating the discovery of new peptide families.

scholarly journals Alignment-free filtering for cfNA fusion fragments

2019 ◽  
Vol 35 (14) ◽  
pp. i225-i232 ◽  
Author(s):  
Xiao Yang ◽  
Yasushi Saito ◽  
Arjun Rao ◽  
Hyunsung John Kim ◽  
Pranav Singh ◽  
...  
Keyword(s):  
Nucleic Acid ◽  
Cell Line ◽  
De Novo ◽  
High Sensitivity ◽  
Detection Methods ◽  
Rna Seq ◽  
Sequencing Data ◽  
Alignment Free ◽  
Fusion Detection ◽  
High Depth

Abstract Motivation Cell-free nucleic acid (cfNA) sequencing data require improvements to existing fusion detection methods along multiple axes: high depth of sequencing, low allele fractions, short fragment lengths and specialized barcodes, such as unique molecular identifiers. Results AF4 was developed to address these challenges. It uses a novel alignment-free kmer-based method to detect candidate fusion fragments with high sensitivity and orders of magnitude faster than existing tools. Candidate fragments are then filtered using a max-cover criterion that significantly reduces spurious matches while retaining authentic fusion fragments. This efficient first stage reduces the data sufficiently that commonly used criteria can process the remaining information, or sophisticated filtering policies that may not scale to the raw reads can be used. AF4 provides both targeted and de novo fusion detection modes. We demonstrate both modes in benchmark simulated and real RNA-seq data as well as clinical and cell-line cfNA data. Availability and implementation AF4 is open sourced, licensed under Apache License 2.0, and is available at: https://github.com/grailbio/bio/tree/master/fusion.

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