scholarly journals Comprehensive Transcriptome Study to Develop Molecular Resources of the CopepodCalanus sinicusfor Their Potential Ecological Applications

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Qing Yang ◽  
Fanyue Sun ◽  
Zhi Yang ◽  
Hongjun Li
Keyword(s):  
Gene Annotation ◽  
Average Length ◽  
Rapid Development ◽  
Northwest Pacific ◽  
Nucleotide Polymorphisms ◽  
Rna Seq ◽  
Sequencing Data ◽  
Illumina Hiseq ◽  
Northwest Pacific Ocean ◽  
Snp Validation

Calanus sinicusBrodsky (Copepoda, Crustacea) is a dominant zooplanktonic species widely distributed in the margin seas of the Northwest Pacific Ocean. In this study, we utilized an RNA-Seq-based approach to develop molecular resources forC. sinicus. Adult samples were sequenced using the Illumina HiSeq 2000 platform. The sequencing data generated 69,751 contigs from 58.9 million filtered reads. The assembled contigs had an average length of 928.8 bp. Gene annotation allowed the identification of 43,417 unigene hits against the NCBI database. Gene ontology (GO) and KEGG pathway mapping analysis revealed various functional genes related to diverse biological functions and processes. Transcripts potentially involved in stress response and lipid metabolism were identified among these genes. Furthermore, 4,871 microsatellites and 110,137 single nucleotide polymorphisms (SNPs) were identified in theC. sinicustranscriptome sequences. SNP validation by the melting temperature (Tm)-shift method suggested that 16 primer pairs amplified target products and showed biallelic polymorphism among 30 individuals. The present work demonstrates the power of Illumina-based RNA-Seq for the rapid development of molecular resources in nonmodel species. The validated SNP set from our study is currently being utilized in an ongoing ecological analysis to support a future study ofC. sinicuspopulation genetics.

scholarly journals Advancing clinical genomics and precision medicine with GVViZ: FAIR bioinformatics platform for variable gene-disease annotation, visualization, and expression analysis

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Zeeshan Ahmed ◽  
Eduard Gibert Renart ◽  
Saman Zeeshan ◽  
XinQi Dong
Keyword(s):  
Data Analysis ◽  
Patient Care ◽  
Expression Analysis ◽  
High Throughput ◽  
Gene Annotation ◽  
Next Generation Sequencing Data ◽  
Rna Seq ◽  
Sequencing Data ◽  
Complex Disorders ◽  
Transcriptomics Data

Abstract Background Genetic disposition is considered critical for identifying subjects at high risk for disease development. Investigating disease-causing and high and low expressed genes can support finding the root causes of uncertainties in patient care. However, independent and timely high-throughput next-generation sequencing data analysis is still a challenge for non-computational biologists and geneticists. Results In this manuscript, we present a findable, accessible, interactive, and reusable (FAIR) bioinformatics platform, i.e., GVViZ (visualizing genes with disease-causing variants). GVViZ is a user-friendly, cross-platform, and database application for RNA-seq-driven variable and complex gene-disease data annotation and expression analysis with a dynamic heat map visualization. GVViZ has the potential to find patterns across millions of features and extract actionable information, which can support the early detection of complex disorders and the development of new therapies for personalized patient care. The execution of GVViZ is based on a set of simple instructions that users without a computational background can follow to design and perform customized data analysis. It can assimilate patients’ transcriptomics data with the public, proprietary, and our in-house developed gene-disease databases to query, easily explore, and access information on gene annotation and classified disease phenotypes with greater visibility and customization. To test its performance and understand the clinical and scientific impact of GVViZ, we present GVViZ analysis for different chronic diseases and conditions, including Alzheimer’s disease, arthritis, asthma, diabetes mellitus, heart failure, hypertension, obesity, osteoporosis, and multiple cancer disorders. The results are visualized using GVViZ and can be exported as image (PNF/TIFF) and text (CSV) files that include gene names, Ensembl (ENSG) IDs, quantified abundances, expressed transcript lengths, and annotated oncology and non-oncology diseases. Conclusions We emphasize that automated and interactive visualization should be an indispensable component of modern RNA-seq analysis, which is currently not the case. However, experts in clinics and researchers in life sciences can use GVViZ to visualize and interpret the transcriptomics data, making it a powerful tool to study the dynamics of gene expression and regulation. Furthermore, with successful deployment in clinical settings, GVViZ has the potential to enable high-throughput correlations between patient diagnoses based on clinical and transcriptomics data.

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 HTSeq - A Python framework to work with high-throughput sequencing data

2014 ◽  
Author(s):  
Simon Anders ◽  
Paul Theodor Pyl ◽  
Wolfgang Huber
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Rapid Development ◽  
Differential Expression Analysis ◽  
Rna Seq ◽  
Sequencing Data ◽  
Standard Work ◽  
Data Formats ◽  
High Throughput Sequencing Data ◽  
Python Package

Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard work flows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data such as genomic coordinates, sequences, sequencing reads, alignments, gene model information, variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability: HTSeq is released as open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index, https://pypi.python.org/pypi/HTSeq

scholarly journals Characterization of the transcriptome and EST-SSR development in Boea clarkeana, a desiccation-tolerant plant endemic to China

2016 ◽  
Author(s):  
Ying Wang ◽  
Kun Liu ◽  
De Bi ◽  
Biao Shou Zhou ◽  
Wen Jian Shao
Keyword(s):  
De Novo ◽  
Gene Annotation ◽  
Sequence Similarity ◽  
Molecular Study ◽  
Sequence Information ◽  
Sequencing Data ◽  
Protein Database ◽  
Illumina Hiseq ◽  
Significant Similarity ◽  
Assembly Technology

Background. Resurrection plants constitute a unique cadre within angiosperms. Boea clarkeana Hemsl. (Boea, Gesneriaceae) is a desiccation-tolerant dicotyledonous herb that is endemic to China. Although research on angiosperms with DT could be instructive for crops, genomic resources for B. clarkeana remain scarce. In addition, transcriptome sequencing could be an effective way to study desiccation-tolerant plants. Methods. In the present study, we used the platform Illumina HiSeqTM 2000 and de novo assembly technology to obtain leaf transcriptomes of B. clarkeana and conducted a BLASTX alignment of the sequencing data and protein databases for sequence classification and annotation. Then, based on the sequence information obtained, we developed EST-SSR markers by means of EST-SSR mining, primer design and polymorphism identification. Results. A total of 91,449 unigenes were generated from the leaf cDNA library of B. clarkeana in this study. Based on a sequence similarity search with a known protein database, 72,087 unigenes were annotated. Among the annotated unigenes, a total of 71,170 unigenes showed significant similarity to known proteins of 463 popular model species in the Nr database, and 59,962 unigenes and 32,336 unigenes were assigned to GO classifications and COG, respectively. In addition, 44,924 unigenes were mapped in 128 KEGG pathways. Furthermore, a total of 7,610 unigenes with 8,563 microsatellites were found. Seventy-four primer pairs were selected from 436 primer pairs designed for polymorphism validation. SSRs with higher polymorphism rates were concentrated on dinucleotides, pentanucleotides and hexanucleotides. Finally, 17 pairs with highly polymorphic and stable loci were selected for polymorphism screening. There were a total of 65 alleles, with 2–6 alleles at each locus. Mainly due to the unique biological characteristics of plants, the HE, HO and PIC per locus were very low, ranging from 0 to 0.196, 0.082 to 0.14 and 0 to 0.155, respectively. Discussion. A substantial fraction transcriptome sequences of B. clarkeana were generated in this study, which is the first molecular-level analysis of this plant. These sequences are valuable resources for gene annotation and discovery and molecular marker development. These sequences could also provide a valuable basis for the future molecular study of B. clarkeana.

scholarly journals Impact of Gene Annotation Choice on the Quantification of RNA-Seq Data

2021 ◽  
Author(s):  
David Chisanga ◽  
Yang Liao ◽  
Wei Shi
Keyword(s):  
Gene Expression ◽  
Gene Annotation ◽  
Expression Data ◽  
Refseq Gene ◽  
Rna Seq ◽  
Sequencing Data ◽  
Microarray Expression Data ◽  
Sequencing Quality ◽  
Gene Expression Quantification ◽  
Expression Quantification

Abstract Background: RNA sequencing is currently the method of choice for genome-wide profiling of gene expression. A popular approach to quantify expression levels of genes from RNA-seq data is to map reads to a reference genome and then count mapped reads to each gene. Gene annotation data, which include chromosomal coordinates of exons for tens of thousands of genes, are required for this quantification process. There are several major sources of gene annotations that can be used for quantification, such as Ensembl and RefSeq databases. However, there is very little understanding of the effect that the choice of annotation has on the accuracy of gene expression quantification in an RNA-seq analysis.Results: In this paper, we present results from our comparison of Ensembl and RefSeq human annotations on their impact on gene expression quantification using a benchmark RNA-seq dataset generated by the SEquencing Quality Control (SEQC) consortium. We show that the use of RefSeq gene annotation models led to better quantification accuracy, based on the correlation with ground truths including expression data from >800 real-time PCR validated genes, known titration ratios of gene expression and microarray expression data. We also found that the recent expansion of the RefSeq annotation has led to a decrease in its annotation accuracy. Finally, we demonstrated that the RNA-seq quantification differences observed between different annotations were not affected by the use of different normalization methods.Conclusion: In conclusion, our study found that the use of the conservative RefSeq gene annotation yields better RNA-seq quantification results than the more comprehensive Ensembl annotation. We also found that, surprisingly, the recent expansion of the RefSeq database, which was primarily driven by the incorporation of sequencing data into the gene annotation process, resulted in a reduction in the accuracy of RNA-seq quantification.

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 Integrated Full-Length Transcriptome and RNA-Seq to Identify Immune System Genes from the Skin of Sperm Whale (Physeter macrocephalus)

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 233
Author(s):  
Daling Wang ◽  
Ying Li ◽  
Reyilamu Aierken ◽  
Qi Kang ◽  
Xianyan Wang ◽  
...  
Keyword(s):  
Immune System ◽  
Molecular Mechanisms ◽  
Gene Annotation ◽  
Average Length ◽  
Enrichment Analysis ◽  
Sperm Whale ◽  
Living Environment ◽  
Full Length ◽  
Physeter Macrocephalus ◽  
Rna Seq

Cetaceans are a group of secondary aquatic mammals whose ancestors returned to the ocean from land, and during evolution, their immune systems adapted to the aquatic environment. Their skin, as the primary barrier to environmental pathogens, supposedly evolved to adapt to a new living environment. However, the immune system in the skin of cetaceans and the associated molecular mechanisms are still largely unknown. To better understand the immune system, we extracted RNA from the sperm whale’s (Physeter macrocephalus) skin and performed PacBio full-length sequencing and RNA-seq sequencing. We obtained a total of 96,350 full-length transcripts with an average length of 1705 bp and detected 5150 genes that were associated with 21 immune-related pathways by gene annotation enrichment analysis. Moreover, we found 89 encoding genes corresponding to 33 proteins were annotated in the NOD-like receptor (NLR)-signaling pathway, including NOD1, NOD2, RIP2, and NF-κB genes, which were discussed in detail and predicted to play essential roles in the immune system of the sperm whale. Furthermore, NOD1 was highly conservative during evolution by the sequence comparison and phylogenetic tree. These results provide new information about the immune system in the skin of cetaceans, as well as the evolution of immune-related genes.

scholarly journals Reliability of genomic variants across different next-generation sequencing platforms and bioinformatic processing pipelines

2020 ◽  
Author(s):  
Stephan Weißbach ◽  
Stanislav Jur`Evic Sys ◽  
Charlotte Hewel ◽  
Hristo Todorov ◽  
Susann Schweiger ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Gc Content ◽  
Nucleotide Polymorphisms ◽  
Next Generation ◽  
Sequencing Data ◽  
Illumina Hiseq ◽  
Cross Sectional ◽  
Single Nucleotide ◽  
Alu Elements ◽  
Generation Sequencing

Abstract Background Next Generation Sequencing (NGS) is the fundament of various studies, providing insights into questions from biology and medicine. Nevertheless, integrating data from different experimental backgrounds can introduce strong biases. In order to methodically investigate the magnitude of systematic errors in single nucleotide variant calls, we performed a cross-sectional observational study on a genomic cohort of 99 subjects each sequenced via (i) Illumina HiSeq X, (ii) Illumina HiSeq, and (iii) Complete Genomics and processed with the respective bioinformatic pipeline. We also repeated variant calling for the Illumina cohorts with GATK, which allowed us to investigate the effect of the bioinformatics analysis strategy separately from the sequencing platform's impact.Results The number of detected variants/variant classes per individual was highly dependent on the experimental setup. We observed a statistically significant overrepresentation of variants uniquely called by a single setup, indicating potential systematic biases. Insertion/deletion polymorphisms (InDels) were associated with decreased concordance compared to single nucleotide polymorphisms (SNPs). The discrepancies in InDel absolute numbers were particularly prominent in introns, Alu elements, simple repeats, and regions with medium GC content. Notably, reprocessing sequencing data following the best practice recommendations of GATK considerably improved concordance between the respective setups.Conclusion We provide empirical evidence of systematic heterogeneity in variant calls between alternative experimental and data analysis setups. Furthermore, our results demonstrate the benefit of reprocessing genomic data with harmonized pipelines when integrating data from different studies.

scholarly journals TrancriptomeReconstructoR: data-driven annotation of complex transcriptomes

2021 ◽  
Vol 22 (1) ◽  
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 and Saccharomyces cerevisiae cells as a proof-of-principle. A comparison to the existing transcriptome annotations revealed that our gene model is more accurate and comprehensive than the most commonly used community gene models, TAIR10 and Araport11 for A.thaliana and SacCer3 for S.cerevisiae. In particular, we identify multiple transient transcripts missing from the existing annotations. Our new annotations promise to improve the quality of A.thaliana and S.cerevisiae 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 Identification of SNPs Associated with Stress Response Traits within High Stress and Low Stress Lines of Japanese Quail

Genes ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 405
Author(s):  
Steven Shumaker ◽  
Bhuwan Khatri ◽  
Stephanie Shouse ◽  
Dongwon Seo ◽  
Seong Kang ◽  
...  
Keyword(s):  
Japanese Quail ◽  
Stress Responses ◽  
High Stress ◽  
Read Depth ◽  
Poultry Production ◽  
Nucleotide Polymorphisms ◽  
Production Traits ◽  
Sequencing Data ◽  
Illumina Hiseq ◽  
Genetic Mechanisms

Mitigation of stress is of great importance in poultry production, as chronic stress can affect the efficiency of production traits. Selective breeding with a focus on stress responses can be used to combat the effects of stress. To better understand the genetic mechanisms driving differences in stress responses of a selectively bred population of Japanese quail, we performed genomic resequencing on 24 birds from High Stress (HS) and Low Stress (LS) lines of Japanese quail using Illumina HiSeq 2 × 150 bp paired end read technology in order to analyze Single Nucleotide Polymorphisms (SNPs) within the genome of each line. SNPs are common mutations that can lead to genotypic and phenotypic variations in animals. Following alignment of the sequencing data to the quail genome, 6,364,907 SNPs were found across both lines of quail. 10,364 of these SNPs occurred in coding regions, from which 2886 unique, non-synonymous SNPs with a SNP% ≥ 0.90 and a read depth ≥ 10 were identified. Using Ingenuity Pathway Analysis, we identified genes affected by SNPs in pathways tied to immune responses, DNA repair, and neurological signaling. Our findings support the idea that the SNPs found within HS and LS lines of quail could direct the observed changes in phenotype.

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