scholarly journals The ISMARA client

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2851 ◽  
Author(s):  
Panu Artimo ◽  
Séverine Duvaud ◽  
Mikhail Pachkov ◽  
Vassilios Ioannidis ◽  
Erik van Nimwegen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Client Application ◽  
Rna Seq ◽  
State Data ◽  
Regulatory Interactions ◽  
Link Type ◽  
Major Bottleneck ◽  
High Throughput Gene Expression ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

ISMARA (ismara.unibas.ch) automatically infers the key regulators and regulatory interactions from high-throughput gene expression or chromatin state data. However, given the large sizes of current next generation sequencing (NGS) datasets, data uploading times are a major bottleneck. Additionally, for proprietary data, users may be uncomfortable with uploading entire raw datasets to an external server. Both these problems could be alleviated by providing a means by which users could pre-process their raw data locally, transferring only a small summary file to the ISMARA server. We developed a stand-alone client application that pre-processes large input files (RNA-seq or ChIP-seq data) on the user's computer for performing ISMARA analysis in a completely automated manner, including uploading of small processed summary files to the ISMARA server. This reduces file sizes by up to a factor of 1000, and upload times from many hours to mere seconds. The client application is available from ismara.unibas.ch/ISMARA/client.

scholarly journals Basal Contamination of Sequencing: Lessons from the GTEx dataset

2019 ◽  
Author(s):  
Tim O. Nieuwenhuis ◽  
Stephanie Yang ◽  
Rohan X. Verma ◽  
Vamsee Pillalamarri ◽  
Dan E. Arking ◽  
...  
Keyword(s):  
Gene Expression ◽  
Tissue Expression ◽  
Rna Seq ◽  
Data Set ◽  
Low Level ◽  
Allelic Differences ◽  
Highly Expressed Genes ◽  
Next Generation Sequencing Ngs ◽  
Cell Data ◽  
Generation Sequencing

AbstractOne of the challenges of next generation sequencing (NGS) is read contamination. We used the Genotype-Tissue Expression (GTEx) project, a large, diverse, and robustly generated dataset, to understand the factors that contribute to contamination. We obtained GTEx datasets and technical metadata and validating RNA-Seq from other studies. Of 48 analyzed tissues in GTEx, 26 had variant co-expression clusters of four known highly expressed and pancreas-enriched genes (PRSS1, PNLIP, CLPS, and/or CELA3A). Fourteen additional highly expressed genes from other tissues also indicated contamination. Sample contamination by non-native genes was associated with a sample being sequenced on the same day as a tissue that natively expressed those genes. This was highly significant for pancreas and esophagus genes (linear model, p=9.5e-237 and p=5e-260 respectively). Nine SNPs in four genes shown to contaminate non-native tissues demonstrated allelic differences between DNA-based genotypes and contaminated sample RNA-based genotypes, validating the contamination. Low-level contamination affected 4,497 (39.6%) samples (defined as 10 PRSS1 TPM). It also led ≥ to eQTL assignments in inappropriate tissues among these 18 genes. We note this type of contamination occurs widely, impacting bulk and single cell data set analysis. In conclusion, highly expressed, tissue-enriched genes basally contaminate GTEx and other datasets impacting analyses. Awareness of this process is necessary to avoid assigning inaccurate importance to low-level gene expression in inappropriate tissues and cells.

scholarly journals XenoCP: Cloud-based BAM cleansing tool for RNA and DNA from Xenograft

2019 ◽  
Author(s):  
Michael Rusch ◽  
Liang Ding ◽  
Sasi Arunachalam ◽  
Andrew Thrasher ◽  
Hongjian Jin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Tumor Heterogeneity ◽  
Next Generation Sequencing Data ◽  
Rna Seq ◽  
Sequencing Data ◽  
Link Type ◽  
Gene Expression Quantification ◽  
Expression Quantification ◽  
Generation Sequencing ◽  
Rna And Dna

ABSTRACTSummaryXenografts are important models for cancer research and the presence of mouse reads in xenograft next generation sequencing data can potentially confound interpretation of experimental results. We present an efficient, cloud-based BAM-to-BAM cleaning tool called XenoCP to remove mouse reads from xenograft BAM files. We show application of XenoCP in obtaining accurate gene expression quantification in RNA-seq and tumor heterogeneity in WGS of xenografts derived from brain and solid tumors.Availability and ImplementationSt. Jude Cloud (https://pecan.stjude.cloud/permalink/xenocp) and St. Jude Github (https://github.com/stjude/XenoCP)

scholarly journals OnTAD: hierarchical domain structure reveals the divergence of activity among TADs and boundaries

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Lin An ◽  
Tao Yang ◽  
Jiahao Yang ◽  
Johannes Nuebler ◽  
Guanjue Xiang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Domain Structure ◽  
High Frequency ◽  
Spatial Organization ◽  
Structural Units ◽  
Regulatory Interactions ◽  
Link Type ◽  
Topologically Associating Domains

AbstractThe spatial organization of chromatin in the nucleus has been implicated in regulating gene expression. Maps of high-frequency interactions between different segments of chromatin have revealed topologically associating domains (TADs), within which most of the regulatory interactions are thought to occur. TADs are not homogeneous structural units but appear to be organized into a hierarchy. We present OnTAD, an optimized nested TAD caller from Hi-C data, to identify hierarchical TADs. OnTAD reveals new biological insights into the role of different TAD levels, boundary usage in gene regulation, the loop extrusion model, and compartmental domains. OnTAD is available at https://github.com/anlin00007/OnTAD.

scholarly journals Uncovering the Complexity of Transcriptomes with RNA-Seq

2010 ◽  
Vol 2010 ◽  
pp. 1-19 ◽  
Author(s):  
Valerio Costa ◽  
Claudia Angelini ◽  
Italia De Feis ◽  
Alfredo Ciccodicola
Keyword(s):  
Massively Parallel Sequencing ◽  
Point Of View ◽  
Rna Seq ◽  
Specific Expression ◽  
Allele Specific ◽  
Data Files ◽  
Sequencing Platforms ◽  
New Applications ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

In recent years, the introduction of massively parallel sequencing platforms for Next Generation Sequencing (NGS) protocols, able to simultaneously sequence hundred thousand DNA fragments, dramatically changed the landscape of the genetics studies. RNA-Seq for transcriptome studies, Chip-Seq for DNA-proteins interaction, CNV-Seq for large genome nucleotide variations are only some of the intriguing new applications supported by these innovative platforms. Among them RNA-Seq is perhaps the most complex NGS application. Expression levels of specific genes, differential splicing, allele-specific expression of transcripts can be accurately determined by RNA-Seq experiments to address many biological-related issues. All these attributes are not readily achievable from previously widespread hybridization-based or tag sequence-based approaches. However, the unprecedented level of sensitivity and the large amount of available data produced by NGS platforms provide clear advantages as well as new challenges and issues. This technology brings the great power to make several new biological observations and discoveries, it also requires a considerable effort in the development of new bioinformatics tools to deal with these massive data files. The paper aims to give a survey of the RNA-Seq methodology, particularly focusing on the challenges that this application presents both from a biological and a bioinformatics point of view.

scholarly journals BioSankey: Visualizing microbial communities and gene expression data over time

2017 ◽  
Author(s):  
Alexander Platzer ◽  
Julia Polzin ◽  
Ping Penny Han ◽  
Klaus Rembart ◽  
Thomas Nussbaumer
Keyword(s):  
Gene Expression ◽  
Full Potential ◽  
Rna Seq ◽  
Quantitative Measurements ◽  
Visualization Tools ◽  
Data Elements ◽  
Or Genes ◽  
Interesting Species ◽  
Generation Sequencing ◽  
Over Time

AbstractMetagenomics, RNA-seq, WGS (Whole Genome Sequencing) and other types of next-generation sequencing techniques provide quantitative measurements for single strains and genes over time. To obtain a global overview of the experiment and to explore the full potential of a given dataset, intuitive and interactive visualization tools are needed. Therefore, we established BioSankey, which allows to visualize microbial species in microbiome studies and gene expression over time as a Sankey diagram. These diagrams are embedded into a project-specific HTML page, that contains all information as provided during the installation process. BioSankey can be easily applied to analyse bacterial communities in time-series datasets. Furthermore, it can be used to analyse the fluctuations of differentially expressed genes (DEG). The output of BioSankey is a project-specific HTML page, which depends only on JavaScript to enable searches of interesting species or genes of interest without requiring a web server or connection to a database to exchange results among collaboration partners. BioSankey is a tool to visualize different data elements from single and dual RNA-seq datasets as well as from metagenomes studies.

scholarly journals A Framework for the RNA-Seq Based Classification and Prediction of Disease

2019 ◽  
Author(s):  
Naiyar Iqbal ◽  
Pradeep Kumar
Keyword(s):  
Microarray Gene Expression Data ◽  
Biological Data ◽  
Disease Classification ◽  
Rna Seq ◽  
Microarray Gene Expression ◽  
Early Detection Of Disease ◽  
Medical Practitioners ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

Disease classification based on biological data is an important area in bioinformatics and biomedical research. It helps the doctors and medical practitioners for the early detection of disease and support them as a computer-aided diagnostic tool for accurate diagnosis, prognosis, and treatment of disease. Earlier Microarray gene expression data have wide application for the classification of disease, but now Next-generation sequencing (NGS) has replaced the Microarray technology. From the last few years, RNA sequence (RNA-Seq) data are widely used for the transcriptomic analysis. Hence, RNA-Seq based classification of disease is in its infancy. In this article, we present a general framework for the classification of disease constructed on RNA-Seq data. This framework will guide the researchers to process RNA-Seq, extract relevant features and apply the appropriate classifier to classify any kind of disease.

scholarly journals GEO2RNAseq: An easy-to-use R pipeline for complete pre-processing of RNA-seq data

2019 ◽  
Author(s):  
Bastian Seelbinder ◽  
Thomas Wolf ◽  
Steffen Priebe ◽  
Sylvie McNamara ◽  
Silvia Gerber ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Species ◽  
Gene Expression Omnibus ◽  
Rna Seq ◽  
Sequencing Data ◽  
Interacting Species ◽  
Link Type ◽  
Fastq Format ◽  
Standard Tool ◽  
Processing Steps

ABSTRACTIn transcriptomics, the study of the total set of RNAs transcribed by the cell, RNA sequencing (RNA-seq) has become the standard tool for analysing gene expression. The primary goal is the detection of genes whose expression changes significantly between two or more conditions, either for a single species or for two or more interacting species at the same time (dual RNA-seq, triple RNA-seq and so forth). The analysis of RNA-seq can be simplified as many steps of the data pre-processing can be standardised in a pipeline.In this publication we present the “GEO2RNAseq” pipeline for complete, quick and concurrent pre-processing of single, dual, and triple RNA-seq data. It covers all pre-processing steps starting from raw sequencing data to the analysis of differentially expressed genes, including various tables and figures to report intermediate and final results. Raw data may be provided in FASTQ format or can be downloaded automatically from the Gene Expression Omnibus repository. GEO2RNAseq strongly incorporates experimental as well as computational metadata. GEO2RNAseq is implemented in R, lightweight, easy to install via Conda and easy to use, but still very flexible through using modular programming and offering many extensions and alternative workflows.GEO2RNAseq is publicly available at https://anaconda.org/xentrics/r-geo2rnaseq and https://bitbucket.org/thomas_wolf/geo2rnaseq/overview, including source code, installation instruction, and comprehensive package documentation.

scholarly journals A cloud-based learning environment for comparing RNA-seq aligners

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 888
Author(s):  
Elizabeth Baskin ◽  
Peter DeFord ◽  
Allison F. Dennis ◽  
Ian Misner ◽  
Frederick J. Tan ◽  
...  
Keyword(s):  
Rna Seq ◽  
Data Intensive ◽  
Data Analyst ◽  
Analysis Process ◽  
Wet Lab ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing ◽  
Machine Image ◽  
Made In

The rapid rise of high-throughput, data intensive experimental techniques has thrust many biologists into the role of data analyst – a role many biologists feel ill equipped to fill. Novices often struggle to find the resources and expertise they need to analyze their experimental results in a wet-lab environment. To fill this need, we developed an educational resource as part of a National Center for Biotechnology Information (NCBI) hackathon. Using RNA-seq as a model, our tutorial guides new users through the steps of data analysis, while placing an emphasis on understanding the motivation behind choices made in the process. To advance the goal of providing a deeper understanding of the analysis process, we developed a new tool, bamDiff. bamDiff allows users to compare the performance of multiple RNA-seq aligners, allowing users to select the most appropriate aligner for the data in question and experimental end-goal. Our tutorial is accessible via a GitHub wiki, with associated data and software provided on an Amazon Machine Image (AMI), which can be completed at no cost to the user through the Amazon Educate Program. Following the hackathon, our tutorial was integrated into the October 2015 offering of NCBI NOW (Next Generation Sequencing (NGS) Online Workshop) a free online experience targeting individuals new to NGS analysis.

scholarly journals Genetic approaches to study plant responses to environmental stresses

2018 ◽  
Author(s):  
Khaled Moustafa ◽  
Joanna M. Cross
Keyword(s):  
Gene Expression ◽  
Next Generation Sequencing ◽  
Systems Biology ◽  
Massively Parallel Sequencing ◽  
Environmental Stresses ◽  
Plant Responses ◽  
Rna Seq ◽  
Next Generation ◽  
Rnase Protection ◽  
Generation Sequencing

The assessment of gene expression levels is an important step toward elucidating gene functions temporally and spatially. Decades ago, typical studies were focusing on a few genes individually, whereas now researchers are able to examine whole genomes at once. The upgrade of throughput levels aided the introduction of systems biology approaches whereby cell functional networks can be scrutinized in their entireties to unravel potential functional interacting components. The birth of systems biology goes hand-in-hand with huge technological advancements and enables a fairly rapid detection of all transcripts in studied biological samples. Even so, earlier technologies that were restricted to probing single genes or a subset of genes still have their place in research laboratories. The objective here is to highlight key approaches used in gene expression analysis in plant responses to environmental stresses, or, more generally, any other condition of interest. Northern blots, RNase protection assays, and qPCR are described for their targeted detection of one or a few transcripts at a once. Differential display and serial analysis of gene expression represent non-targeted methods to evaluate expression changes of a significant number of gene transcripts. Finally, microarrays and RNA-seq (next-generation sequencing) contribute to the ultimate goal of identifying and quantifying all transcripts in a cell under conditions or stages of study. Recent examples of applications as well as principles, advantages, and drawbacks of each method are contrasted. We also suggest replacing the term "Next-Generation Sequencing (NGS)" with another less confusing synonym such as "RNA-seq", "high throughput sequencing", or "massively parallel sequencing" to avoid confusion with any future sequencing technologies.

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