scholarly journals Characterization of a de novo assembled transcriptome of the Common Blackbird (Turdus merula)

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e4045 ◽  
Author(s):  
Sven Koglin ◽  
Daronja Trense ◽  
Michael Wink ◽  
Hedwig Sauer-Gürth ◽  
Dieter Thomas Tietze
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Reference Genome ◽  
De Novo ◽  
Next Generation ◽  
Model Species ◽  
Bioinformatics Tools ◽  
The Common ◽  
Common Blackbird

Background In recent years, next generation high throughput sequencing technologies have proven to be useful tools for investigations concerning the genomics or transcriptomics also of non-model species. Consequently, ornithologists have adopted these technologies and the respective bioinformatics tools to survey the genomes and transcriptomes of a few avian non-model species. The Common Blackbird is one of the most common bird species living in European cities, which has successfully colonized urban areas and for which no reference genome or transcriptome is publicly available. However, to target questions like genome wide gene expression analysis, a reference genome or transcriptome is needed. Methods Therefore, in this study two Common Blackbirds were sacrificed, their mRNA was isolated and analyzed by RNA-Seq to de novo assemble a transcriptome and characterize it. Illumina reads (125 bp paired-end) and a Velvet/Oases pipeline led to 162,158 transcripts. For the annotation (using Blast+), an unfiltered protein database was used. SNPs were identified using SAMtools and BCFtools. Furthermore, mRNA from three single tissues (brain, heart and liver) of the same two Common Blackbirds were sequenced by Illumina (75 bp single-end reads). The draft transcriptome and the three single tissues were compared by their BLAST hits with the package VennDiagram in R. Results Following the annotation against protein databases, we found evidence for 15,580 genes in the transcriptome (all well characterized hits after annotation). On 18% of the assembled transcripts, 144,742 SNPs were identified which are, consequently, 0.09% of all nucleotides in the assembled transcriptome. In the transcriptome and in the single tissues (brain, heart and liver), 10,182 shared genes were found. Discussion Using a next-generation technology and bioinformatics tools, we made a first step towards the genomic investigation of the Common Blackbird. The de novo assembled transcriptome is usable for downstream analyses such as differential gene expression analysis and SNP identification. This study shows the importance of the approach to sequence single tissues to understand functions of tissues, proteins and the phenotype.

Identification of regulatory pathways across different cell lines (NHBE and PBMC) in COVID-19 patients using transcriptome analysis v1

2021 ◽  
Author(s):  
Lavanya not provided C ◽  
Vidya Niranjan ◽  
Aajnaa not provided Upadhyaya ◽  
Arpita not provided Guha Neogi
Keyword(s):  
Gene Expression ◽  
Cell Line ◽  
Cell Lines ◽  
Expression Analysis ◽  
Pathway Analysis ◽  
Gene Expression Analysis ◽  
Reference Genome ◽  
Host Cells ◽  
Human Reference Genome ◽  
Regulatory Pathways

The Sars-CoV-2 virus is a previously uncharacterized coronavirus and causative agent of the COVID-19 pandemic. Gene expression analysis followed by pathway analysis helps researchers to find possible key targets present in biological pathways of host cells that are targeted by the SARS-CoV-2 virus. This review considers the peripheral blood mononuclear cell line (PBMC) and the normal human bronchial epithelial (NHBE) cell line, both of which support SARS-CoV-2 viral replication. Pathway analysis between the healthy and patient samples of the respective cell lines shall provide useful insights on the COVID-19 disease. Initially, the datasets from the respective cell lines were collected from the NCBI databank. These datasets underwent further analysis and were mapped and aligned to the human reference genome. This outputs the file in the BAM format. The BAM files along with the human reference genome in the GFF format were uploaded to an open-source software called OmicsBox 2.0 for differential gene expression analysis. This resulted in the generation of a table containing the differentially expressed genes which were upregulated and downregulated. These gene lists were uploaded to various pathway analyzers that map the significant genes to the most significant pathways. In this project, KOBAS 3.0 and Enrichr were used for pathway analysis. The pathways obtained from the above-mentioned pathway analyzers were further narrowed down by manual comparison. It was observed that many pathways were similar between the NHBE and PBMC cell lines. However, they were also different in terms of their overall nature. In this project, many patterns were seen through the pathways obtained, however, further optimization and functionality studies must be performed in order to establish conclusive results on the scope of the COVID-19 disease. Expanding research on the scope of the disease by going back to the basics will generate new and valuable information about the virus. This knowledge will help us combat the disease in a better and appropriate manner.

Differential gene expression analysis between anagen and telogen of Capra hircus skin based on the de novo assembled transcriptome sequence

Gene ◽  
2013 ◽  
Vol 520 (1) ◽  
pp. 30-38 ◽  
Author(s):  
Teng Xu ◽  
Xudong Guo ◽  
Hui Wang ◽  
Fei Hao ◽  
Xiaoyuan Du ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
De Novo ◽  
Capra Hircus ◽  
Differential Gene Expression Analysis ◽  
Differential Gene

scholarly journals Rapid parallel adaptation despite gene flow in silent crickets

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Xiao Zhang ◽  
Jack G. Rayner ◽  
Mark Blaxter ◽  
Nathan W. Bailey
Keyword(s):  
Gene Expression ◽  
Gene Flow ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
De Novo ◽  
Parallel Evolution ◽  
De Novo Mutation ◽  
Genome Scans ◽  
Wing Structures ◽  
In The Wild

AbstractGene flow is predicted to impede parallel adaptation via de novo mutation, because it can introduce pre-existing adaptive alleles from population to population. We test this using Hawaiian crickets (Teleogryllus oceanicus) in which ‘flatwing’ males that lack sound-producing wing structures recently arose and spread under selection from an acoustically-orienting parasitoid. Morphometric and genetic comparisons identify distinct flatwing phenotypes in populations on three islands, localized to different loci. Nevertheless, we detect strong, recent and ongoing gene flow among the populations. Using genome scans and gene expression analysis we find that parallel evolution of flatwing on different islands is associated with shared genomic hotspots of adaptation that contain the gene doublesex, but the form of selection differs among islands and corresponds to known flatwing demographics in the wild. We thus show how parallel adaptation can occur on contemporary timescales despite gene flow, indicating that it could be less constrained than previously appreciated.

scholarly journals gEAR: gene Expression Analysis Resource portal for community-driven, multi-omic data exploration

2020 ◽  
Author(s):  
Joshua Orvis ◽  
Brian Gottfried ◽  
Jayaram Kancherla ◽  
Ricky S. Adkins ◽  
Yang Song ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
De Novo ◽  
Marker Gene ◽  
Rna Seq ◽  
Data Types ◽  
Multiple Species ◽  
User Friendly ◽  
The Brain

ABSTRACTThe gEAR portal (gene Expression Analysis Resource, umgear.org) is an open access community-driven tool for multi-omic and multi-species data visualization, analysis and sharing. The gEAR supports visualization of multiple RNA-seq data types (bulk, sorted, single cell/nucleus) and epigenomics data, from multiple species, time points and tissues in a single-page, user-friendly browsable format. An integrated scRNA-seq workbench provides access to raw data of scRNA-seq datasets for de novo analysis, as well as marker-gene and cluster comparisons of pre-assigned clusters. Users can upload, view, analyze and privately share their own data in the context of previously published datasets. Short, permanent URLs can be generated for dissemination of individual or collections of datasets in published manuscripts. While the gEAR is currently curated for auditory research with over 90 high-value datasets organized in thematic profiles, the gEAR also supports the BRAIN initiative (via nemoanalytics.org) and is easily adaptable for other research domains.

scholarly journals A consensus-based ensemble approach to improve de novo transcriptome assembly

2020 ◽  
Author(s):  
Adam Voshall ◽  
Sairam Behera ◽  
Xiangjun Li ◽  
Xiao-Hong Yu ◽  
Kushagra Kapil ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Reference Genome ◽  
De Novo ◽  
Transcriptome Assembly ◽  
Model Organisms ◽  
Pathway Reconstruction ◽  
Rnaseq Data ◽  
Metabolic Pathway Reconstruction ◽  
Assembly Performance

AbstractSystems-level analyses, such as differential gene expression analysis, co-expression analysis, and metabolic pathway reconstruction, depend on the accuracy of the transcriptome. Multiple tools exist to perform transcriptome assembly from RNAseq data. However, assembling high quality transcriptomes is still not a trivial problem. This is especially the case for non-model organisms where adequate reference genomes are often not available. Different methods produce different transcriptome models and there is no easy way to determine which are more accurate. Furthermore, having alternative splicing events could exacerbate such difficult assembly problems. While benchmarking transcriptome assemblies is critical, this is also not trivial due to the general lack of true reference transcriptomes. In this study, we provide a pipeline to generate a set of the benchmark transcriptome and corresponding RNAseq data. Using the simulated benchmarking datasets, we compared the performance of various transcriptome assembly approaches including genome-guided, de novo, and ensemble methods. The results showed that the assembly performance deteriorates significantly when the reference is not available from the same genome (for genome-guided methods) or when alternative transcripts (isoforms) exist. We demonstrated the value of consensus between de novo assemblers in transcriptome assembly. Leveraging the overlapping predictions between the four de novo assemblers, we further present ConSemble, a consensus-based de novo ensemble transcriptome assembly pipeline. Without using a reference genome, ConSemble achieved an accuracy up to twice as high as any de novo assemblers we compared. It matched or exceeded the best performing genome-guided assemblers even when the transcriptomes included isoforms. The RNAseq simulation pipeline, the benchmark transcriptome datasets, and the ConSemble pipeline are all freely available from: http://bioinfolab.unl.edu/emlab/consemble/.Author summaryObtaining the accurate representation of the gene expression is critical in many analyses, such as differential gene expression analysis, co-expression analysis, and metabolic pathway reconstruction. The state of the art high-throughput RNA-sequencing (RNAseq) technologies can be used to sequence the set of all transcripts in a cell, the transcriptome. Although many computational tools are available for transcriptome assembly from RNAseq data, assembling high-quality transcriptomes is difficult especially for non-model organisms. Different methods often produce different transcriptome models and there is no easy way to determine which are more accurate. In this study, we present an approach to evaluate transcriptome assembly performance using simulated benchmarking read sets. The results showed that the assembly performance of genome-guided assembly methods deteriorates significantly when the adequate reference genome is not available. The assembly performance of all methods is affected when alternative transcripts (isoforms) exist. We further demonstrated the value of consensus among assemblers in improving transcriptome assembly. Leveraging the overlapping predictions between the four de novo assemblers, we present ConSemble. Without using a reference genome, ConSemble achieved a much higher accuracy than any de novo assemblers we compared. It matched or exceeded the best performing genome-guided assemblers even when the transcriptomes included isoforms.

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