scholarly journals Management, Analyses, and Distribution of the MaizeCODE Data on the Cloud

2019 ◽  
Author(s):  
Liya Wang ◽  
Zhenyuan Lu ◽  
Melissa delaBastide ◽  
Peter Van Buren ◽  
Xiaofei Wang ◽  
...  
Keyword(s):  
Small Rna ◽  
Functional Data ◽  
Initial Phase ◽  
Genomic Data ◽  
Scientific Workflows ◽  
Small Rna Sequencing ◽  
Maize Genome ◽  
Rna Seq ◽  
Functional Elements ◽  
Data Page

MaizeCODE is a project aimed at identifying and analyzing functional elements in the maize genome. In its initial phase, MaizeCODE assayed up to five tissues from four maize strains (B73, NC350, W22, TIL11) by RNA-Seq, Chip-Seq, RAMPAGE, and small RNA sequencing. To facilitate reproducible science and provide both human and machine access to the MaizeCODE data, we enhanced SciApps, a cloud-based portal, for analysis and distribution of both raw data and analysis results. Based on the SciApps workflow platform, we generated new components to support the complete cycle of MaizeCODE data management. These include publicly accessible scientific workflows for the reproducible and shareable analysis of various functional data, a RESTful API for batch processing and distribution of data and metadata, a searchable data page that lists each MaizeCODE experiment as a reproducible workflow, and integrated JBrowse genome browser tracks linked with workflows and metadata. The SciApps portal is a flexible platform that allows the integration of new analysis tools, workflows, and genomic data from multiple projects. Through metadata and a ready-to-compute cloud-based platform, the portal experience improves access to the MaizeCODE data and facilitates its analysis.

scholarly journals Small RNA-Sequencing: Approaches and Considerations for miRNA Analysis

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 964
Author(s):  
Sarka Benesova ◽  
Mikael Kubista ◽  
Lukas Valihrach
Keyword(s):  
Rna Sequencing ◽  
Small Rna ◽  
High Sensitivity ◽  
Small Rna Sequencing ◽  
Rna Seq ◽  
Liquid Biopsies ◽  
Comprehensive Overview ◽  
Rna Molecules ◽  
Novel Mirna ◽  
The Many

MicroRNAs (miRNAs) are a class of small RNA molecules that have an important regulatory role in multiple physiological and pathological processes. Their disease-specific profiles and presence in biofluids are properties that enable miRNAs to be employed as non-invasive biomarkers. In the past decades, several methods have been developed for miRNA analysis, including small RNA sequencing (RNA-seq). Small RNA-seq enables genome-wide profiling and analysis of known, as well as novel, miRNA variants. Moreover, its high sensitivity allows for profiling of low input samples such as liquid biopsies, which have now found applications in diagnostics and prognostics. Still, due to technical bias and the limited ability to capture the true miRNA representation, its potential remains unfulfilled. The introduction of many new small RNA-seq approaches that tried to minimize this bias, has led to the existence of the many small RNA-seq protocols seen today. Here, we review all current approaches to cDNA library construction used during the small RNA-seq workflow, with particular focus on their implementation in commercially available protocols. We provide an overview of each protocol and discuss their applicability. We also review recent benchmarking studies comparing each protocol’s performance and summarize the major conclusions that can be gathered from their usage. The result documents variable performance of the protocols and highlights their different applications in miRNA research. Taken together, our review provides a comprehensive overview of all the current small RNA-seq approaches, summarizes their strengths and weaknesses, and provides guidelines for their applications in miRNA research.

scholarly journals xMD-miRNA-seq to generate near in vivo miRNA expression estimates in colon epithelial cells

2018 ◽  
Author(s):  
Avi Z. Rosenberg ◽  
Carrie Wright ◽  
Karen Fox-Talbot ◽  
Anandita Rajpurohit ◽  
Courtney Williams ◽  
...  
Keyword(s):  
Epithelial Cells ◽  
Small Rna ◽  
Mirna Expression ◽  
Low Cost ◽  
Expression Patterns ◽  
Small Rna Sequencing ◽  
Rna Seq ◽  
Cell Type

AbstractAccurate, RNA-seq based, microRNA (miRNA) expression estimates from primary cells have recently been described. However, this in vitro data is mainly obtained from cell culture, which is known to alter cell maturity/differentiation status, significantly changing miRNA levels. What is needed is a robust method to obtain in vivo miRNA expression values directly from cells. We introduce expression microdissection miRNA small RNA sequencing (xMD-miRNA-seq), a method to isolate cells directly from formalin fixed paraffin-embedded (FFPE) tissues. xMD-miRNA-seq is a low-cost, high-throughput, immunohistochemistry-based method to capture any cell type of interest. As a proof-of-concept, we isolated colon epithelial cells from two specimens and performed low-input small RNA-seq. We generated up to 600,000 miRNA reads from the samples. Isolated epithelial cells, had abundant epithelial-enriched miRNA expression (miR-192; miR-194; miR-200b; miR-200c; miR-215; miR-375) and overall similar miRNA expression patterns to other epithelial cell populations (colonic enteroids and flow-isolated colon epithelium). xMD-derived epithelial cells were generally not contaminated by other adjacent cells of the colon as noted by t-SNE analysis. xMD-miRNA-seq allows for simple, economical, and efficient identification of cell-specific miRNA expression estimates. Further development will enhance rapid identification of cell-specific miRNA expression estimates in health and disease for nearly any cell type using archival FFPE material.

scholarly journals Identification and validation of miRNA reference genes in poplar under pathogen stress

2021 ◽  
Author(s):  
Lichun Zhang ◽  
Xiaoqian Yang ◽  
Yiyi Yin ◽  
Jinxing Wang ◽  
Yanwei Wang
Keyword(s):  
Gene Expression ◽  
Reference Gene ◽  
Small Rna ◽  
Reference Genes ◽  
Expression Profiles ◽  
Small Rna Sequencing ◽  
Rna Seq ◽  
Internal Standards ◽  
Qrt Pcr ◽  
Canker Pathogen

Abstract Quantitative real time polymerase chain reaction (qRT-PCR) is a common method to analyze gene expression. Due to differences in RNA quantity, quality, and reverse transcription efficiency between qRT-PCR samples, reference genes are used as internal standards to normalize gene expression. However, few universal genes especially miRNAs have been identified as reference so far. Therefore, it is essential to identify reference genes that can be used across various experimental conditions, stress treatments, or tissues. In this study, 14 microRNAs (miRNAs) and 5.8S rRNA were assessed for expression stability in poplar trees infected with canker pathogen. Using three reference gene analysis programs, we found that miR156g and miR156a exhibited stable expression throughout the infection process. miR156g and miR156a were then tested as internal standards to measure the expression of miR1447 and miR171c, and the results were compared to small RNA sequencing (RNA-seq) data. We found that when miR156a was used as the reference gene, the expression of miR1447 and miR171c were consistent with the small RNA-seq expression profiles. Therefore, miR156a was the most stable miRNAs examined in this study, and could be used as a reference gene in poplar under canker pathogen stress, which should enable comprehensive comparisons of miRNAs expression and avoid the bias caused by different lenth between detected miRNAs and traditional referece genes. The present study has expanded the miRNA reference genes available for gene expression studies in trees under biotic stress.

scholarly journals Small RNA sequencing evaluation of renal microRNA biomarkers in dogs with X-linked hereditary nephropathy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Candice P. Chu ◽  
Shiguang Liu ◽  
Wenping Song ◽  
Ethan Y. Xu ◽  
Mary B. Nabity
Keyword(s):  
Small Rna ◽  
Target Genes ◽  
Differentially Expressed ◽  
Small Rna Sequencing ◽  
Rna Seq ◽  
Ckd Progression ◽  
Critical Pathways ◽  
Qrt Pcr ◽  
Hereditary Nephropathy ◽  
Differentially Expressed Mirnas

AbstractDogs with X-linked hereditary nephropathy (XLHN) are an animal model for Alport syndrome in humans and progressive chronic kidney disease (CKD). Using mRNA sequencing (mRNA-seq), we have characterized the gene expression profile affecting the progression of XLHN; however, the microRNA (miRNA, miR) expression remains unknown. With small RNA-seq and quantitative RT-PCR (qRT-PCR), we used 3 small RNA-seq analysis tools (QIAGEN OmicSoft Studio, miRDeep2, and CPSS 2.0) to profile differentially expressed renal miRNAs, top-ranked miRNA target genes, and enriched biological processes and pathways in CKD progression. Twenty-three kidney biopsies were collected from 5 dogs with XLHN and 4 age-matched, unaffected littermates at 3 clinical time points (T1: onset of proteinuria, T2: onset of azotemia, and T3: advanced azotemia). We identified up to 23 differentially expressed miRNAs at each clinical time point. Five miRNAs (miR-21, miR-146b, miR-802, miR-142, miR-147) were consistently upregulated in affected dogs. We identified miR-186 and miR-26b as effective reference miRNAs for qRT-PCR. This study applied small RNA-seq to identify differentially expressed miRNAs that might regulate critical pathways contributing to CKD progression in dogs with XLHN.

scholarly journals MicroRNA-Seq Data Analysis Pipeline to Identify Blood Biomarkers for Alzheimer's Disease from Public Data

2015 ◽  
Vol 10 ◽  
pp. BMI.S25132 ◽  
Author(s):  
Jun-ichi Satoh ◽  
Yoshihiro Kino ◽  
Shumpei Niida
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Data Analysis ◽  
Small Rna ◽  
Small Rna Sequencing ◽  
Rna Seq ◽  
Analysis Pipeline ◽  
Web Based ◽  
Public Data ◽  
Data Analysis Pipeline

Background Alzheimer's disease (AD) is the most common cause of dementia with no curative therapy currently available. Establishment of sensitive and non-invasive biomarkers that promote an early diagnosis of AD is crucial for the effective administration of disease-modifying drugs. MicroRNAs (miRNAs) mediate posttranscriptional repression of numerous target genes. Aberrant regulation of miRNA expression is implicated in AD pathogenesis, and circulating miRNAs serve as potential biomarkers for AD. However, data analysis of numerous AD-specific miRNAs derived from small RNA-sequencing (RNA-Seq) is most often laborious. Methods To identify circulating miRNA biomarkers for AD, we reanalyzed a publicly available small RNA-Seq dataset, composed of blood samples derived from 48 AD patients and 22 normal control (NC) subjects, by a simple web-based miRNA data analysis pipeline that combines omiRas and DIANA miRPath. Results By using omiRas, we identified 27 miRNAs expressed differentially between both groups, including upregulation in AD of miR-26b-3p, miR-28–3p, miR-30c-5p, miR-30d-5p, miR-148b-5p, miR-151a-3p, miR-186–5p, miR-425–5p, miR-550a-5p, miR-1468, miR-4781–3p, miR-5001–3p, and miR-6513–3p and downregulation in AD of let-7a-5p, let-7e-5p, let-7f-5p, let-7g-5p, miR-15a-5p, miR-17–3p, miR-29b-3p, miR-98–5p, miR-144–5p, miR-148a-3p, miR-502–3p, miR-660–5p, miR-1294, and miR-3200–3p. DIANA miRPath indicated that miRNA-regulated pathways potentially down– regulated in AD are linked with neuronal synaptic functions, while those upregulated in AD are implicated in cell survival and cellular communication. Conclusions The simple web-based miRNA data analysis pipeline helps us to effortlessly identify candidates for miRNA biomarkers and pathways of AD from the complex small RNA–Seq data.

scholarly journals WIND (Workflow for pIRNAs aNd beyonD): a strategy for in-depth analysis of small RNA-seq data

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1
Author(s):  
Konstantinos Geles ◽  
Domenico Palumbo ◽  
Assunta Sellitto ◽  
Giorgio Giurato ◽  
Eleonora Cianflone ◽  
...  
Keyword(s):  
Small Rna ◽  
Differential Expression Analysis ◽  
Small Rna Sequencing ◽  
Rna Seq ◽  
Sequencing Data ◽  
Transcript Quantification ◽  
Annotation Track ◽  
Depth Analysis ◽  
Exploratory Data ◽  
Downstream Analysis

Current bioinformatics workflows for PIWI-interacting RNA (piRNA) analysis focus primarily on germline-derived piRNAs and piRNA-clusters. Frequently, they suffer from outdated piRNA databases, questionable quantification methods, and lack of reproducibility. Often, pipelines specific to miRNA analysis are used for the piRNA research in silico. Furthermore, the absence of a well-established database for piRNA annotation, as for miRNA, leads to uniformity issues between studies and generates confusion for data analysts and biologists. For these reasons, we have developed WIND (Workflow for pIRNAs aNd beyonD), a bioinformatics workflow that addresses the crucial issue of piRNA annotation, thereby allowing a reliable analysis of small RNA sequencing data for the identification of piRNAs and other small non-coding RNAs (sncRNAs) that in the past have been incorrectly classified as piRNAs. WIND allows the creation of a comprehensive annotation track of sncRNAs combining information available in RNAcentral, with piRNA sequences from piRNABank, the first database dedicated to piRNA annotation. WIND was built with Docker containers for reproducibility and integrates widely used bioinformatics tools for sequence alignment and quantification. In addition, it includes Bioconductor packages for exploratory data and differential expression analysis. Moreover, WIND implements a "dual" approach for the evaluation of sncRNAs expression level quantifying the aligned reads to the annotated genome and carrying out an alignment-free transcript quantification using reads mapped to the transcriptome. Therefore, a broader range of piRNAs can be annotated, improving their quantification and easing the subsequent downstream analysis. WIND performance has been tested with several small RNA-seq datasets, demonstrating how our approach can be a useful and comprehensive resource to analyse piRNAs and other classes of sncRNAs.

scholarly journals WIND (Workflow for pIRNAs aNd beyonD): a strategy for in-depth analysis of small RNA-seq data

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1
Author(s):  
Konstantinos Geles ◽  
Domenico Palumbo ◽  
Assunta Sellitto ◽  
Giorgio Giurato ◽  
Eleonora Cianflone ◽  
...  
Keyword(s):  
Small Rna ◽  
Differential Expression Analysis ◽  
Small Rna Sequencing ◽  
Rna Seq ◽  
Sequencing Data ◽  
Transcript Quantification ◽  
Annotation Track ◽  
Depth Analysis ◽  
Exploratory Data ◽  
Downstream Analysis

Current bioinformatics workflows for PIWI-interacting RNA (piRNA) analysis focus primarily on germline-derived piRNAs and piRNA-clusters. Frequently, they suffer from outdated piRNA databases, questionable quantification methods, and lack of reproducibility. Often, pipelines specific to miRNA analysis are used for the piRNA research in silico. Furthermore, the absence of a well-established database for piRNA annotation, as for miRNA, leads to uniformity issues between studies and generates confusion for data analysts and biologists. For these reasons, we have developed WIND (Workflow for pIRNAs aNd beyonD), a bioinformatics workflow that addresses the crucial issue of piRNA annotation, thereby allowing a reliable analysis of small RNA sequencing data for the identification of piRNAs and other small non-coding RNAs (sncRNAs) that in the past have been incorrectly classified as piRNAs. WIND allows the creation of a comprehensive annotation track of sncRNAs combining information available in RNAcentral, with piRNA sequences from piRNABank, the first database dedicated to piRNA annotation. WIND was built with Docker containers for reproducibility and integrates widely used bioinformatics tools for sequence alignment and quantification. In addition, it includes Bioconductor packages for exploratory data and differential expression analysis. Moreover, WIND implements a "dual" approach for the evaluation of sncRNAs expression level quantifying the aligned reads to the annotated genome and carrying out an alignment-free transcript quantification using reads mapped to the transcriptome. Therefore, a broader range of piRNAs can be annotated, improving their quantification and easing the subsequent downstream analysis. WIND performance has been tested with several small RNA-seq datasets, demonstrating how our approach can be a useful and comprehensive resource to analyse piRNAs and other classes of sncRNAs.

scholarly journals MiR&moRe2: A Bioinformatics Tool to Characterize microRNAs and microRNA-Offset RNAs from Small RNA-Seq Data

2020 ◽  
Vol 21 (5) ◽  
pp. 1754 ◽  
Author(s):  
Enrico Gaffo ◽  
Michele Bortolomeazzi ◽  
Andrea Bisognin ◽  
Piero Di Battista ◽  
Federica Lovisa ◽  
...  
Keyword(s):  
Small Rna ◽  
Small Rnas ◽  
Cell Types ◽  
Small Rna Sequencing ◽  
Rna Seq ◽  
Sequencing Data ◽  
Research Findings ◽  
Human Blood Cell ◽  
And Function ◽  
Comprehensive Study

MicroRNA-offset RNAs (moRNAs) are microRNA-like small RNAs generated by microRNA precursors. To date, little is known about moRNAs and bioinformatics tools to inspect their expression are still missing. We developed miR&moRe2, the first bioinformatics method to consistently characterize microRNAs, moRNAs, and their isoforms from small RNA sequencing data. To illustrate miR&moRe2 discovery power, we applied it to several published datasets. MoRNAs identified by miR&moRe2 were in agreement with previous research findings. Moreover, we observed that moRNAs and new microRNAs predicted by miR&moRe2 were downregulated upon the silencing of the microRNA-biogenesis pathway. Further, in a sizeable dataset of human blood cell populations, tens of novel miRNAs and moRNAs were discovered, some of them with significantly varied expression levels among the cell types. Results demonstrate that miR&moRe2 is a valid tool for a comprehensive study of small RNAs generated from microRNA precursors and could help to investigate their biogenesis and function.

scholarly journals Comprehensive MicroRNA Expression Profile of the Mammary Gland in Lactating Dairy Cows With Extremely Different Milk Protein and Fat Percentages

2020 ◽  
Vol 11 ◽  
Author(s):  
Xiaogang Cui ◽  
Shengli Zhang ◽  
Qin Zhang ◽  
Xiangyu Guo ◽  
Changxin Wu ◽  
...  
Keyword(s):  
Mammary Gland ◽  
Rna Sequencing ◽  
Differentially Expressed Genes ◽  
Small Rna ◽  
Milk Protein ◽  
Milk Composition ◽  
Differentially Expressed ◽  
Small Rna Sequencing ◽  
Rna Seq

A total of 31 differentially expressed genes in the mammary glands were identified in our previous study using RNA sequencing (RNA-Seq), for lactating cows with extremely high and low milk protein and fat percentages. To determine the regulation of milk composition traits, we herein investigated the expression profiles of microRNA (miRNA) using small RNA sequencing based on the same samples as in the previous RNA-Seq experiment. A total of 497 known miRNAs (miRBase, release 22.1) and 49 novel miRNAs among the reads were identified. Among these miRNAs, 71 were found differentially expressed between the high and low groups (p < 0.05, q < 0.05). Furthermore, 21 of the differentially expressed genes reported in our previous RNA-Seq study were predicted as target genes for some of the 71 miRNAs. Gene ontology and KEGG pathway analyses showed that these targets were enriched for functions such as metabolism of protein and fat, and development of mammary gland, which indicating the critical role of these miRNAs in regulating the formation of milk protein and fat. With dual luciferase report assay, we further validated the regulatory role of 7 differentially expressed miRNAs through interaction with the specific sequences in 3′UTR of the targets. In conclusion, the current study investigated the complexity of the mammary gland transcriptome in dairy cattle using small RNA-seq. Comprehensive analysis of differential miRNAs expression and the data from previous study RNA-seq provided the opportunity to identify the key candidate genes for milk composition traits.

scholarly journals WIND (Workflow for pIRNAs aNd beyonD): a strategy for in-depth analysis of small RNA-seq data

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1
Author(s):  
Konstantinos Geles ◽  
Domenico Palumbo ◽  
Assunta Sellitto ◽  
Giorgio Giurato ◽  
Eleonora Cianflone ◽  
...  
Keyword(s):  
Small Rna ◽  
Differential Expression Analysis ◽  
Small Rna Sequencing ◽  
Rna Seq ◽  
Sequencing Data ◽  
Transcript Quantification ◽  
Annotation Track ◽  
Depth Analysis ◽  
Exploratory Data ◽  
Downstream Analysis

Current bioinformatics workflows for PIWI-interacting RNA (piRNA) analysis focus primarily on germline-derived piRNAs and piRNA-clusters. Frequently, they suffer from outdated piRNA databases, questionable quantification methods, and lack of reproducibility. Often, pipelines specific to miRNA analysis are used for the piRNA research in silico. Furthermore, the absence of a well-established database for piRNA annotation, as for miRNA, leads to uniformity issues between studies and generates confusion for data analysts and biologists. For these reasons, we have developed WIND (Workflow for pIRNAs aNd beyonD), a bioinformatics workflow that addresses the crucial issue of piRNA annotation, thereby allowing a reliable analysis of small RNA sequencing data for the identification of piRNAs and other small non-coding RNAs (sncRNAs) that in the past have been incorrectly classified as piRNAs. WIND allows the creation of a comprehensive annotation track of sncRNAs combining information available in RNAcentral, with piRNA sequences from piRNABank, the first database dedicated to piRNA annotation. WIND was built with Docker containers for reproducibility and integrates widely used bioinformatics tools for sequence alignment and quantification. In addition, it includes Bioconductor packages for exploratory data and differential expression analysis. Moreover, WIND implements a "dual" approach for the evaluation of sncRNAs expression level quantifying the aligned reads to the annotated genome and carrying out an alignment-free transcript quantification using reads mapped to the transcriptome. Therefore, a broader range of piRNAs can be annotated, improving their quantification and easing the subsequent downstream analysis. WIND performance has been tested with several small RNA-seq datasets, demonstrating how our approach can be a useful and comprehensive resource to analyse piRNAs and other classes of sncRNAs.

Sign in / Sign up

Export Citation Format

Share Document