scholarly journals MetaRNA-Seq: An Interactive Tool to Browse and Annotate Metadata from RNA-Seq Studies

2015 ◽  
Vol 2015 ◽  
pp. 1-6
Author(s):  
Pankaj Kumar ◽  
Anna Halama ◽  
Shahina Hayat ◽  
Anja M. Billing ◽  
Manish Gupta ◽  
...  
Keyword(s):  
Time Series ◽  
Gene Expression Omnibus ◽  
Case Control ◽  
Rna Seq ◽  
Web Interface ◽  
Level Information ◽  
Drug Treatments ◽  
Multiple Samples ◽  
Significant Effort ◽  
Project Level

The number of RNA-Seq studies has grown in recent years. The design of RNA-Seq studies varies from very simple (e.g., two-condition case-control) to very complicated (e.g., time series involving multiple samples at each time point with separate drug treatments). Most of these publically available RNA-Seq studies are deposited in NCBI databases, but their metadata are scattered throughout four different databases: Sequence Read Archive (SRA), Biosample, Bioprojects, and Gene Expression Omnibus (GEO). Although the NCBI web interface is able to provide all of the metadata information, it often requires significant effort to retrieve study- or project-level information by traversing through multiple hyperlinks and going to another page. Moreover, project- and study-level metadata lack manual or automatic curation by categories, such as disease type, time series, case-control, or replicate type, which are vital to comprehending any RNA-Seq study. Here we describe “MetaRNA-Seq,” a new tool for interactively browsing, searching, and annotating RNA-Seq metadata with the capability of semiautomatic curation at the study level.

No association between vulvovaginitis-bacterial vaginosis, related drug treatments of pregnant women, and congenital abnormalities in their offspring — A population-based case-control study

Open Medicine ◽  
2008 ◽  
Vol 3 (3) ◽  
pp. 332-340 ◽  
Author(s):  
Nándor Ács ◽  
Ferenc Bánhidy ◽  
Erzsébet Puhó ◽  
Andrew Czeizel
Keyword(s):  
Bacterial Vaginosis ◽  
Pregnant Women ◽  
Congenital Abnormality ◽  
Congenital Abnormalities ◽  
Population Based ◽  
Case Control ◽  
Data Set ◽  
Appropriate Treatment ◽  
Drug Treatments ◽  
Related Drug

AbstractThe possible association between prospectively and medically recorded vulvovaginitis-bacterial vaginosis (VV-BV) and different congenital abnormalities (CA) has not been studied. The data set of the population-based Hungarian Case-Control Surveillance of Congenital Abnormalities between 1980 and 1996 were evaluated, i.e. 22,843 pregnant women who had newborns or fetuses with congenital abnormality (cases) and 38,151 pregnant women who delivered newborn babies without any congenital abnormality (controls). The main outcome measures were different congenital abnormalities. Of 22,843 cases with CA, 1,536 (6.7%) had mothers with VV-BV, while of 38,151 matched controls without CA, 2,698 (7.1%) had mothers with VV-BV in the second and/or third gestational month of pregnancy. Nearly all pregnant women with VV-BV were treated during pregnancy, but a higher risk for the total group of CAs (adjusted POR with 95% CI: 0.95, 0.89–1.02) or any CA group was not found. In addition, the risk for total CAs was significantly lower in cases born to mothers with VV-BV and appropriate treatment than born to mothers with VV-BV but without treatment. Thus maternal VV-BV needs treatment during pregnancy as well, because it helps reduce the rate of preterm birth without a risk for CAs.

scholarly journals RNA-seq analysis of an apical meristem time series reveals a critical point in Arabidopsis thaliana flower initiation

BMC Genomics ◽  
2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Anna V. Klepikova ◽  
Maria D. Logacheva ◽  
Sergey E. Dmitriev ◽  
Aleksey A. Penin
Keyword(s):  
Time Series ◽  
Arabidopsis Thaliana ◽  
Critical Point ◽  
Apical Meristem ◽  
Flower Initiation ◽  
Rna Seq

scholarly journals Time Series RNA-seq in Pigeonpea Revealed the Core Genes in Metabolic Pathways under Aluminum Stress

Genes ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 380 ◽  
Author(s):  
Zhaoxu Gao ◽  
Biying Dong ◽  
Hongyan Cao ◽  
Hang He ◽  
Qing Yang ◽  
...  
Keyword(s):  
Time Series ◽  
Metabolic Pathways ◽  
Expression Patterns ◽  
Plant Stress ◽  
Regulation Mechanism ◽  
Rna Seq ◽  
Aluminum Stress ◽  
Biological Regulation ◽  
Al Stress ◽  
The Impact

Pigeonpea is an important economic crop in the world and is mainly distributed in tropical and subtropical regions. In order to further expand the scope of planting, one of the problems that must be solved is the impact of soil acidity on plants in these areas. Based on our previous work, we constructed a time series RNA sequencing (RNA-seq) analysis under aluminum (Al) stress in pigeonpea. Through a comparison analysis, 11,425 genes were found to be differentially expressed among all the time points. After clustering these genes by their expression patterns, 12 clusters were generated. Many important functional pathways were identified by gene ontology (GO) analysis, such as biological regulation, localization, response to stimulus, metabolic process, detoxification, and so on. Further analysis showed that metabolic pathways played an important role in the response of Al stress. Thirteen out of the 23 selected genes related to flavonoids and phenols were downregulated in response to Al stress. In addition, we verified these key genes of flavonoid- and phenol-related metabolism pathways by qRT-PCR. Collectively, our findings not only revealed the regulation mechanism of pigeonpea under Al stress but also provided methodological support for further exploration of plant stress regulation mechanisms.

scholarly journals Transcriptome Analysis of the Thymus in Short-Term Calorie-Restricted Mice Using RNA-seq

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Zehra Omeroğlu Ulu ◽  
Salih Ulu ◽  
Soner Dogan ◽  
Bilge Guvenc Tuna ◽  
Nehir Ozdemir Ozgenturk
Keyword(s):  
Calorie Restriction ◽  
Critical Role ◽  
Gene Expression Omnibus ◽  
Cdna Libraries ◽  
Rna Seq ◽  
Illumina Hiseq ◽  
Ncbi Gene Expression Omnibus ◽  
Important Player ◽  
Feeding Regimes ◽  
Geo Database

Calorie restriction (CR), which is a factor that expands lifespan and an important player in immune response, is an effective protective method against cancer development. Thymus, which plays a critical role in the development of the immune system, reacts to nutrition deficiency quickly. RNA-seq-based transcriptome sequencing was performed to thymus tissues of MMTV-TGF-α mice subjected to ad libitum (AL), chronic calorie restriction (CCR), and intermittent calorie restriction (ICR) diets in this study. Three cDNA libraries were sequenced using Illumina HiSeq™ 4000 to produce 100 base pair-end reads. On average, 105 million clean reads were mapped and in total 6091 significantly differentially expressed genes (DEGs) were identified (p<0.05). These DEGs were clustered into Gene Ontology (GO) categories. The expression pattern revealed by RNA-seq was validated by quantitative real-time PCR (qPCR) analysis of four important genes, which are leptin, ghrelin, Igf1, and adinopectin. RNA-seq data has been deposited in NCBI Gene Expression Omnibus (GEO) database (GSE95371). We report the use of RNA sequencing to find DEGs that are affected by different feeding regimes in the thymus.

scholarly journals Time Series Expression Analyses Using RNA-seq: A Statistical Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Sunghee Oh ◽  
Seongho Song ◽  
Gregory Grabowski ◽  
Hongyu Zhao ◽  
James P. Noonan
Keyword(s):  
Time Series ◽  
Statistical Approach ◽  
Expression Patterns ◽  
Temporal Analysis ◽  
Rna Seq ◽  
Computationally Efficient ◽  
Evolutionary Trajectory ◽  
Dynamic Methods ◽  
Series Expression ◽  
Time Lagged

RNA-seq is becoming thede factostandard approach for transcriptome analysis with ever-reducing cost. It has considerable advantages over conventional technologies (microarrays) because it allows for direct identification and quantification of transcripts. Many time series RNA-seq datasets have been collected to study the dynamic regulations of transcripts. However, statistically rigorous and computationally efficient methods are needed to explore the time-dependent changes of gene expression in biological systems. These methods should explicitly account for the dependencies of expression patterns across time points. Here, we discuss several methods that can be applied to model timecourse RNA-seq data, including statistical evolutionary trajectory index (SETI), autoregressive time-lagged regression (AR(1)), and hidden Markov model (HMM) approaches. We use three real datasets and simulation studies to demonstrate the utility of these dynamic methods in temporal analysis.

RNASeq_similarity_matrix: visually identify sample mix-ups in RNASeq data using a ‘genomic’ sequence similarity matrix

2019 ◽  
Vol 36 (6) ◽  
pp. 1940-1941
Author(s):  
Nicolaas C Kist ◽  
Robert A Power ◽  
Andrew Skelton ◽  
Seth D Seegobin ◽  
Moira Verbelen ◽  
...  
Keyword(s):  
Genomic Sequence ◽  
Sequence Similarity ◽  
Current Method ◽  
Biological Data ◽  
Similarity Matrix ◽  
Rna Seq ◽  
Rnaseq Data ◽  
Phenotype Data ◽  
Multiple Samples ◽  
Automated Tool

Abstract Summary Mistakes in linking a patient’s biological samples with their phenotype data can confound RNA-Seq studies. The current method for avoiding such sample mix-ups is to test for inconsistencies between biological data and known phenotype data such as sex. However, in DNA studies a common QC step is to check for unexpected relatedness between samples. Here, we extend this method to RNA-Seq, which allows the detection of duplicated samples without relying on identifying inconsistencies with phenotype data. Results We present RNASeq_similarity_matrix: an automated tool to generate a sequence similarity matrix from RNA-Seq data, which can be used to visually identify sample mix-ups. This is particularly useful when a study contains multiple samples from the same individual, but can also detect contamination in studies with only one sample per individual. Availability and implementation RNASeq_similarity_matrix has been made available as a documented GPL licensed Docker image on www.github.com/nicokist/RNASeq_similarity_matrix.

scholarly journals GTRD: an integrated view of transcription regulation

2020 ◽  
Vol 49 (D1) ◽  
pp. D104-D111
Author(s):  
Semyon Kolmykov ◽  
Ivan Yevshin ◽  
Mikhail Kulyashov ◽  
Ruslan Sharipov ◽  
Yury Kondrakhin ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Data Integration ◽  
Transcription Regulation ◽  
Gene Transcription ◽  
Cell Types ◽  
Rna Seq ◽  
Web Interface ◽  
Gene Transcription Regulation ◽  
Cell Ontology ◽  
Ngs Data

Abstract The Gene Transcription Regulation Database (GTRD; http://gtrd.biouml.org/) contains uniformly annotated and processed NGS data related to gene transcription regulation: ChIP-seq, ChIP-exo, DNase-seq, MNase-seq, ATAC-seq and RNA-seq. With the latest release, the database has reached a new level of data integration. All cell types (cell lines and tissues) presented in the GTRD were arranged into a dictionary and linked with different ontologies (BRENDA, Cell Ontology, Uberon, Cellosaurus and Experimental Factor Ontology) and with related experiments in specialized databases on transcription regulation (FANTOM5, ENCODE and GTEx). The updated version of the GTRD provides an integrated view of transcription regulation through a dedicated web interface with advanced browsing and search capabilities, an integrated genome browser, and table reports by cell types, transcription factors, and genes of interest.

scholarly journals GXD’s RNA-Seq and Microarray Experiment Search: using curated metadata to reliably find mouse expression studies of interest

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Constance M Smith ◽  
James A Kadin ◽  
Richard M Baldarelli ◽  
Jonathan S Beal ◽  
Olin Blodgett ◽  
...  
Keyword(s):  
Gene Expression ◽  
Microarray Experiment ◽  
Gene Expression Omnibus ◽  
Easy Access ◽  
Free Text ◽  
Rna Seq ◽  
Endogenous Gene ◽  
Expression Studies ◽  
Text Searching ◽  
Study Type

Abstract The Gene Expression Database (GXD), an extensive community resource of curated expression information for the mouse, has developed an RNA-Seq and Microarray Experiment Search (http://www.informatics.jax.org/gxd/htexp_index). This tool allows users to quickly and reliably find specific experiments in ArrayExpress and the Gene Expression Omnibus (GEO) that study endogenous gene expression in wild-type and mutant mice. Standardized metadata annotations, curated by GXD, allow users to specify the anatomical structure, developmental stage, mutated gene, strain and sex of samples of interest, as well as the study type and key parameters of the experiment. These searches, powered by controlled vocabularies and ontologies, can be combined with free text searching of experiment titles and descriptions. Search result summaries include link-outs to ArrayExpress and GEO, providing easy access to the expression data itself. Links to the PubMed entries for accompanying publications are also included. More information about this tool and GXD can be found at the GXD home page (http://www.informatics.jax.org/expression.shtml). Database URL: http://www.informatics.jax.org/expression.shtml

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