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8,068 results for “Transcriptome analysis”
Figure 5 in Transcriptomic analysis of Bursaphelenchus xylophilus treated by a potential phytonematicide, punicalagin
Figure 5: Expression of six differentially expressed genes by (A) RNA-Seq, (B) qRT-PCR and (C) their correlation.
Figure A2 in Transcriptomic analysis of Bursaphelenchus xylophilus treated by a potential phytonematicide, punicalagin
Figure A2: Annotated KEGG pathway of Endocytosis (A), Peroxisome (B) and MAPK signaling pathways (C) about differentially expressed genes. Genes in blue frames with red borders were up-regulated, genes in blue frames with yellow borders were down-regulated and genes in blue frames with sky-blue borders were simultaneously up-regulated and down-regulated.
Figure 4 in Transcriptomic analysis of Bursaphelenchus xylophilus treated by a potential phytonematicide, punicalagin
Figure 4: Annotation of differentially expressed genes (DEGs). (A) Column diagram of DEGs using GO annotation. The bottom X-axis indicates the number of genes annotated on different GO terms. The X-axis indicates the ratios of genes annotated on different GO terms to all terms used for the GO annotation. (B) Scatter diagram of DEGs with GO enrichment. (C) KOG functional classification of DEGs.
Figure 3 in Transcriptomic analysis of Bursaphelenchus xylophilus treated by a potential phytonematicide, punicalagin
Figure 3: Functional annotation of unigenes. (A) GO functional annotation statistics on level 2. (B) KOG annotation statistics. (C) KEGG pathway annotation statistics. A. Cellular processes. B. Environmental information processing. C. Genetic information processing. D. metabolism. E. Organismal systems.
Fig. 6 in Comparative transcriptome analysis of three Bactrocera dorsalis (Diptera: Tephritidae) organs to identify functional genes in the male accessory glands and ejaculatory duct
Fig. 6. Four examples of the tissue expression profiling of unknown distinct unigenes (>500 bp) expressed highly in male accessory glands and ejaculatory duct tissue of Bactrocera dorsalis. Relative expression levels were determined as described in Fig. 5.
Fig. 4 in Comparative transcriptome analysis of three Bactrocera dorsalis (Diptera: Tephritidae) organs to identify functional genes in the male accessory glands and ejaculatory duct
Fig. 4. Kyoto encyclopedia of gene and genomes (KEGG) analysis of unigenes expressed highly in male accessory glands and ejaculatory duct tissue of Bactrocera dorsalis. Each category contains more than 1 unigene sequences.
Fig. 2 in Comparative transcriptome analysis of three Bactrocera dorsalis (Diptera: Tephritidae) organs to identify functional genes in the male accessory glands and ejaculatory duct
Fig. 2. Clusters of orthologous groups (COG) functional classification of unigenes expressed highly and specifically in male accessory glands and ejaculatory duct tissue of Bactrocera dorsalis.
Fig. 1 in Comparative transcriptome analysis of three Bactrocera dorsalis (Diptera: Tephritidae) organs to identify functional genes in the male accessory glands and ejaculatory duct
Fig. 1. Statistics of sequences expressed specifically in each analyzed tissue of Bactrocera dorsalis.
Fig. 5 in Comparative transcriptome analysis of three Bactrocera dorsalis (Diptera: Tephritidae) organs to identify functional genes in the male accessory glands and ejaculatory duct
Fig. 5. Six examples of the tissue expression profiling of predicted distinct unigenes (>500 bp) expressed highly in male accessory glands and ejaculatory duct tissue of Bactrocera dorsalis. Relative expression levels were determined by qRT-PCR in head (HE), thorax (TH), abdomen (AB), midgut (MG), fat body (FB), Malpighian tubules (MT), testes (TE), and male accessory glands and ejaculatory duct (MAG) samples from B. dorsalis males. Relative expression levels were calculated based on the value in head, which was ascribed an arbitrary value of 1. Different letters above the bars indicate significant differences based on Tukey's test (P ≤ 0.05).
Fig. 3 in Comparative transcriptome analysis of three Bactrocera dorsalis (Diptera: Tephritidae) organs to identify functional genes in the male accessory glands and ejaculatory duct
Fig. 3. Gene ontology (GO) classification of unigenes expressed highly in male accessory glands and ejaculatory duct tissue of Bactrocera dorsalis.
Supplemental Results for Assembly, Annotation, and Analysis from HiFi reads of Gulf Toadfish Genome and Transcriptome fOpsBet2.1
<p>This repository contains gzipped tarballs of the results of the various assembly, annotation, and analysis steps performed during the assembly of the fOpsBet2.1 genome assembly for Opsanus beta at the University of Miami Rosenstiel School of Marine, Atmospheric, and Earth Science for the McDonald Toadfish Lab. These results are too numerous to include as supplemental data for a journal publication and so are available here for review. In this repository you will find results for:</p><p>Scripts:</p><p>-all bash and LSF scheduler job scripts used as part of the analysis, both exploratory and final. </p><p>QC:</p><p>-GenomeScope2 estimation of genome metrics from HiFi Reads</p><p>-QUAST genome statistics for each assembly step</p><p>-BUSCO completeness assessments for each assembly step </p><p>-inspector logs for polishing of initial assembly</p><p>-logs from Kraken2 contaminant screen</p><p>Assembly and Scaffolding:</p><p>-ntLINKS logs and intermediates for initial scaffolding</p><p>-ragtag logs and metrics for super-scaffolding to the ThaAma1.1 T. amazonica reference assembly</p><p>-mitoHIFI results for mitogenome assembly from HiFi reads, primary assembly, and purged alternate assembly</p><p>Annotation:</p><p>-PASA directory with full input and output for SQLite PASA assembly of transcriptome for gene predictors</p><p>-Results folder for Funannotate::annotate for gene models, annotations, and CDS/mRNA/protein fastas</p><p>-InterProSCan5 results for protein annotation used as input into Funannotate</p><p>-ghostKOALA KEGG assignment results for predicted proteins from funannotate results</p><p>Repetitive Elements:</p><p>-tidk telomere repeat analysis results</p><p>-TRAH satellite DNA analysis with subsequent analysis with HiCAT and StainedGlass</p><p>-RepeatModeler results for de novo TE prediction</p><p>-repclassifier results for TE curation</p><p>Comparative Analysis:</p><p>-OrthoFinder ortholog search for O. beta to several other vertebrates</p><p>-CAFE5 gene family expansion and contraction of Orthogroups from OrthoFinder results</p><p> </p>
Hepatic transcriptomic analysis reveals differential regulation of metabolic and immune pathways in three strains of chickens with distinct growth rate exposed to mixed parasites infections
<p><span>This dataset was generated from the study investigating hepatic gene expression in three strains of chickens: Ross-308 (R), Lohmann Brown Plus (LB), and Lohmann Dual (LD), 2 weeks after either an experimental infection (n = 18) with both <em>A. galli</em> and <em>H. gallinarum or kept as uninfected control (n = 12)</em>. </span></p>
Genome and transcriptome analysis of the beet armyworm Spodoptera exigua reveals targets for pest control
<p>The genus <i>Spodoptera</i> (Lepidoptera: Noctuidae) includes some of the most infamous insect pests of cultivated plants including <i>Spodoptera frugiperda</i>, <i>Spodoptera litura</i> and <i>Spodoptera exigua</i>. To effectively develop targeted pest control strategies for diverse <i>Spodoptera</i> species, genomic resources are highly desired. To this aim, we provide the genome assembly and developmental transcriptome comprising all major life stages of <i>S. exigua</i>, the beet armyworm. <i>Spodoptera exigua</i> is a polyphagous herbivore that can feed from > 130 host plants including several economically important crops.</p> <p>The 419 Mb beet armyworm genome was sequenced from a female <i>S. exigua</i> pupa. Using a hybrid genome sequencing approach (Nanopore long read data and Illumina short read), a high-quality genome assembly was achieved (N50=1.1 Mb). An official gene set (OGS, 18,477 transcripts) was generated by automatic annotation and by using transcriptomic RNA-seq data sets of 18 <i>S. exigua</i> samples as supporting evidence. In-depth analyses of developmental stage-specific expression in combination with gene tree analyses of identified homologous genes across Lepidoptera genomes revealed four potential genes of interest (three of them <i>Spodoptera</i>-specific) upregulated during 1<sup>st</sup> and 3<sup>rd</sup> instar larval stages for targeted pest-outbreak management.</p> <p>The beet armyworm genome sequence and developmental transcriptome covering all major developmental stages provides critical insights into the biology of this devastating polyphagous insect pest species with a worldwide distribution. In addition, comparative genomic analyses across Lepidoptera significantly advance our knowledge to further control other invasive <i>Spodoptera</i> species and reveals potential lineage-specific target genes for pest control strategies.</p>
QTL mapping and transcriptome analysis of Sclerotinia-resistance in the wild cabbage species Brassica oleracea var. villosa [Main code]
<p>This is the main code supplement for my computational analysis for the manuscript: "QTL mapping and transcriptome analysis of Sclerotinia-resistance in the wild cabbage species <em>Brassica oleracea </em>var<em>. villosa".</em> The main code is availabe in separate html-files. DOI will be added if available.</p>
Comparative Analysis of Droplet- vs. Microwell-based Whole Transcriptome Single-Cell Sequencing Technologies in Complex Human Tissues
<p>In the past decade, high-dimensional single-cell omics tools have enabled scientists to study the tumor microenvironment (TME) in unprecedented detail. However, recent investigations suggest that each technique has its unique strengths but also technology-inherent limitations. Here we directly compared two commercially available high-throughput single-cell RNA sequencing (scRNA-seq) technologies - droplet-based 10X Chromium <em>vs.</em> microwell-based BD Rhapsody - using paired samples from patients with localized prostate cancer (PCa) undergoing a radical prostatectomy.</p> <p>Although high technical consistency was observed in unraveling the whole transcriptome, the relative abundance of detectable cell populations differed. This could in part be ascribed to differences in the performance to recover cells with low-mRNA content. Hence, immune cells such as neutrophils are underrepresented in data generated with the widely used droplet-based scRNA-seq protocol, highlighting the importance of considering platform limitations in low mRNA content cell recovery. In contrast, droplet-based scRNA-seq demonstrated superiority in terms of recovering cells of epithelial origin. Moreover, we discovered platform-dependent variabilities in mRNA quantification and cell-type marker annotation, affecting the composition of identified tissue profiles and the exploratory value of the generated datasets. Overall, our study emphasizes the importance of carefully selecting the appropriate scRNA-seq platform to improve cell type representation and obtain a more comprehensive and accurate understanding of the TME.</p>
Transcriptome analysis of anuran breeding glands reveals a surprisingly high expression and diversity of NNMT-like genes
<p><strong>Abstract</strong></p> <p>In many amphibians, males have sexually dimorphic breeding glands, which can produce proteinaceous or volatile pheromones, used for intraspecific communication. In this study we analyse two types of glands in the Mexican treefrog species <em>Ptychohyla macrotympanum </em>(Hylidae) – large ventrolateral glands and small nuptial pads on their fingers – using histology, whole-transcriptome sequencing and phylogenetic analyses. We found strong differences in glandular tissue composition and gene expression patterns between the two breeding gland types. In both glands we only found low expression of protein pheromone candidates. Instead, in the ventrolateral glands, gene expression was strikingly dominated by nicotinamide N-methyltransferase (NNMT)-like genes. Diversity of these genes was remarkably high, with at least 68 distinct NNMT-like genes. Our phylogenetic comparative analysis of the diversity of NNMT-like genes across vertebrates indicates that the extreme diversity of this gene is largely a frog-specific phenomenon and can be traced to large numbers of relatively recent gene duplications occurring independently in many lineages. The strong dominance and astonishing diversity of NNMT-like genes found in anurans in general, and in their sexually dimorphic breeding glands specifically, suggests an important function of NNMT-like proteins for anuran reproduction, possibly being related to volatile pheromone production.In many amphibians, males have sexually dimorphic breeding glands, which can produce proteinaceous or volatile pheromones, used for intraspecific communication. In this study we analyse two types of glands in the Mexican treefrog species <em>Ptychohyla macrotympanum </em>(Hylidae) – large ventrolateral glands and small nuptial pads on their fingers – using histology, whole-transcriptome sequencing and phylogenetic analyses. We found strong differences in glandular tissue composition and gene expression patterns between the two breeding gland types. In both glands we only found low expression of protein pheromone candidates. Instead, in the ventrolateral glands, gene expression was strikingly dominated by nicotinamide N-methyltransferase (NNMT)-like genes. Diversity of these genes was remarkably high, with at least 68 distinct NNMT-like genes. Our phylogenetic comparative analysis of the diversity of NNMT-like genes across vertebrates indicates that the extreme diversity of this gene is largely a frog-specific phenomenon and can be traced to large numbers of relatively recent gene duplications occurring independently in many lineages. The strong dominance and astonishing diversity of NNMT-like genes found in anurans in general, and in their sexually dimorphic breeding glands specifically, suggests an important function of NNMT-like proteins for anuran reproduction, possibly being related to volatile pheromone production.</p> <p> </p> <p><strong>Supplementary datasets accompanying the paper:</strong></p> <p>- final RNAseq assemblies of the ventrolateral glands and the nuptial pads of <em>Ptychohyla macrotympanum</em><br> - fasta-file of all <em>Ptychohyla</em>-NNMT-like genes found in this study</p>
Transcriptomic analysis of light-induced genes in Nasonia vitripennis: possible implications for circadian light entrainment pathways
<p class="MDPI17abstract"><span>Circadian entrainment to the environmental day-night cycle is essential for the optimal use of environmental resources. In insects, opsin-based photoreception in the compound eye and ocelli, and CRYPTOCHROME1 (CRY1) in circadian clock neurons are thought to be involved in sensing photic information, but genetic regulation of circadian light entrainment in species without light-sensitive CRY1 remains unclear. To elucidate a possible CRY1-independent light transduction cascade, we analysed light-induced gene expression through RNA-sequencing in <em>Nasonia vitripennis</em>. Entrained wasps were subjected to a light pulse in the subjective night to reset the circadian clock and light-induced changes in gene expression were characterized at four different time points in wasp heads. We used co-expression, functional annotation, and transcription factor binding motif analyses to gain insight into the molecular pathways in response to acute light stimulus and form a hypothesis about the circadian light resetting pathway. Maximal gene induction was found after 2h of light stimulation (1432 genes), including the opsin <em>opblue</em> and the core clock genes <em>cry2</em> and <em>npas2</em>. Pathway and cluster analyses revealed light activation of glutamatergic and GABA-ergic neurotransmission, including CREB and AP-1 transcription pathway signalling. This suggests that circadian photic entrainment in <em>Nasonia</em> may require pathways that are similar to mammals. We propose a model for hymenopteran circadian light resetting that involves opsin-based photoreception, glutamatergic neurotransmission, and gene induction of <em>cry2</em> and <em>npas2</em> to reset the circadian clock.</span></p>
Transcriptome-wide meta-analysis of codon usage in Escherichia coli
<p>Data generated by the CUBseq pipeline on Escherichia coli RNA-seq data.</p>
Transcriptome Analysis of Cisplatin, Cannabidiol, and Intermittent Serum Starvation Alone and in Various Combinations on Colorectal Cancer Cells
<p>* See README file for the description of data files available in this repository</p> <p>1. Study Description:</p> <p>Platinum-derived chemotherapy medications are often combined with other conventional therapies for treating different tumours, including colorectal cancer. However, the development of drug resistance and multiple adverse effects remain common in clinical settings. Thus, there is a necessity to find novel treatments and drug combinations that could effectively target colorectal cancer cells and lower the probability of disease relapse. To find potential synergistic interaction, we designed multiple different combinations between cisplatin, cannabidiol, and intermittent serum starvation on colorectal cancer cell lines. Based on the cell viability assay, we found that combinations between cannabidiol and intermittent serum starvation, cisplatin, and intermittent serum starvation, as well as cisplatin, cannabidiol and intermittent serum starvation can work in a synergistic fashion on different colorectal cancer cell lines. Furthermore, we analyzed differentially expressed genes and affected pathways in colorectal cancer cell lines to understand further the potential molecular mechanisms behind the treatments and their interactions. We found that synergistic interaction between cannabidiol and intermittent serum starvation can be related to changes in the transcription of genes responsible for cell metabolism and cancer’s stress pathways. Moreover, when we added cisplatin to the treatments, there was a strong enrichment of genes taking part in G2/M cell cycle arrest and apoptosis.</p> <p> </p> <p>2. Bioinformatics workflow:</p> <p>Initial quality control was conducted using FastQC v0.11.9 https://www.bioinformatics.babraham.ac.uk/projects/fastqc/. Sequencing reads were trimmed of adapter sequences and low-quality bases using Trimmomatic. Trimmed sequence files were examined with FastQC to verify the trimming results. Trimmed sequencing reads were mapped to Human genome (GRCh37, Ensembl) downloaded from Illumina iGenome website (<a href="https://support.illumina.com/sequencing/sequencing_software/igenome.html">https://support.illumina.com/sequencing/sequencing_software/igenome.html</a>). Mapping was done using splice aware aligner HISAT2 2.1.0. Alignment files in SAM format were converted to BAM, sorted and indexed with samtools v.1.3.1. Mapping quality and statistics were collected with QualiMap software package v.2.2.2 <a href="http://qualimap.conesalab.org/">http://qualimap.conesalab.org/</a> The counts if reads mapping to features (genes) were counted using FeatureCounts v.2.0.1 software.</p> <p>Data exploration, visualization and statistical comparisons were conducted using R language version 4.2.2. Pair-wise comparisons between experimental groups were done with DESeq2 v.2.1.36 as described in the package manual. To decrease computational time, only the genes with at least 5 reads across 3 samples were kept in the analysis. In addition to hard threshold filtering mentioned above, DESeq2 implements independent filtering based on mean of normalized count as a filter statistic.</p> <p>We used hierarchical clustering (HC) and principal components analysis (PCA) to investigate the relationship between samples and detect potential outliers. Prior to HC and PCA analysis, DESeq2 normalized values underwent variance stabilizing transformation with using vst() function from DESeq2. HC was done using hclust() function implemented in R, with the clustering method set as “complete” for the matrices of sample-to-sample distances, and “Ward.D2” in case of the sample and gene clustering based on top 500 most variable genes. The distance measure in HC analysis was set to “euclidean”. Principal components analysis (PCA), applied to top 500 highly variable genes, was conducted using prcomp() function implemented in R with default options.</p> <p>Differentially expressed genes (DEGs) were detected with DESeq2 function results() with default options. DESeq2 uses Wald test to determine significantly changed genes between groups. The independent filtering option was set to TRUE with alpha threshold (adjusted p-value) kept at 0.1. Multiple comparison adjustment was done using Bejamini-Hochberg procedure.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Thesis: Transcriptome analysis of insecticide resistant Drosophila suzukii
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