scholarly journals unitas: the universal tool for annotation of small RNAs

2017 ◽  
Author(s):  
Daniel Gebert ◽  
Charlotte Hewel ◽  
David Rosenkranz
Keyword(s):  
Small Rna ◽  
Small Rnas ◽  
High Throughput Sequencing ◽  
Ease Of Use ◽  
Sequence Information ◽  
High Quality ◽  
Non Coding Rna ◽  
Wide Range ◽  
Rna Biology ◽  
User Friendly

AbstractBackgroundNext generation sequencing is a key technique in small RNA biology research that has led to the discovery of functionally different classes of small non-coding RNAs in the past years. However, reliable annotation of the extensive amounts of small non-coding RNA data produced by high-throughput sequencing is time-consuming and requires robust bioinformatics expertise. Moreover, existing tools have a number of shortcomings including a lack of sensitivity under certain conditions, limited number of supported species or detectable sub-classes of small RNAs.ResultsHere we introduce unitas, an out-of-the-box ready software for complete annotation of small RNA sequence datasets, supporting the wide range of species for which non-coding RNA reference sequences are available in the Ensembl databases (currently more than 800). unitas combines high quality annotation and numerous analysis features in a user-friendly manner. A complete annotation can be started with one simple shell command, making unitas particularly useful for researchers not having access to a bioinformatics facility. Noteworthy, the algorithms implemented in unitas are on par or even outperform comparable existing tools for small RNA annotation that base on available ncRNA sequence information.Conclusionsunitas brings together annotation and analysis features that hitherto required the installation of numerous different bioinformatics tools which can pose a challenge for the non-expert user. With this, unitas overcomes the problem of read normalization. Moreover, the high quality of sequence annotation and analysis, paired with the ease of use, make unitas a valuable tool for researchers in all fields connected to small RNA biology.

scholarly journals Ligation bias is a major contributor to nonstoichiometric abundances of secondary siRNAs and impacts analyses of microRNAs

2020 ◽  
Author(s):  
Patricia Baldrich ◽  
Saleh Tamim ◽  
Sandra Mathioni ◽  
Blake Meyers
Keyword(s):  
Small Rna ◽  
Small Rnas ◽  
High Throughput Sequencing ◽  
Library Construction ◽  
Construction Methods ◽  
Wide Range ◽  
Secondary Sirnas ◽  
Two Stages ◽  
Material Library ◽  
Small Rna Library

ABSTRACTPlant small RNAs are a diverse and complex set of molecules, ranging in length from 21 to 24 nt, involved in a wide range of essential biological processes. High-throughput sequencing is used for the discovery and quantification of small RNAs. However, several biases can occur during the preparation of small RNA libraries, especially using low input RNA. We used two stages of maize anthers to evaluate the performance of seven commercially-available methods for small RNA library construction, using different RNA input amounts. We show that when working with plant material, library construction methods have differing capabilities to capture small RNAs, and that different library construction methods provide better results when applied to the detection of microRNAs, phasiRNAs, or tRNA-derived fragment. We also observed that ligation bias occurs at both ends of miRNAs and phasiRNAs, suggesting that the biased compositions observed in small RNA populations, including nonstoichiometric levels of phasiRNAs within a locus, may reflect a combination of biological and technical influences.

scholarly journals Small RNA sequencing reveals distinct nuclear microRNAs in pig granulosa cells during ovarian follicle growth

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Derek Toms ◽  
Bo Pan ◽  
Yinshan Bai ◽  
Julang Li
Keyword(s):  
Granulosa Cells ◽  
Small Rna ◽  
High Throughput Sequencing ◽  
Ovarian Function ◽  
Ovarian Follicle ◽  
Small Rna Sequencing ◽  
Cell Fractionation ◽  
Steroid Synthesis ◽  
Non Coding Rna ◽  
Dna Binding Sites

AbstractNuclear small RNAs have emerged as an important subset of non-coding RNA species that are capable of regulating gene expression. A type of small RNA, microRNA (miRNA) have been shown to regulate development of the ovarian follicle via canonical targeting and translational repression. Little has been done to study these molecules at a subcellular level. Using cell fractionation and high throughput sequencing, we surveyed cytoplasmic and nuclear small RNA found in the granulosa cells of the pig ovarian antral preovulatory follicle. Bioinformatics analysis revealed a diverse network of small RNA that differ in their subcellular distribution and implied function. We identified predicted genomic DNA binding sites for nucleus-enriched miRNAs that may potentially be involved in transcriptional regulation. The small nucleolar RNA (snoRNA) SNORA73, known to be involved in steroid synthesis, was also found to be highly enriched in the cytoplasm, suggesting a role for snoRNA species in ovarian function. Taken together, these data provide an important resource to study the small RNAome in ovarian follicles and how they may impact fertility.

scholarly journals Accurate detection for a wide range of mutation and editing sites of microRNAs from small RNA high-throughput sequencing profiles

2016 ◽  
Vol 44 (14) ◽  
pp. e123-e123 ◽  
Author(s):  
Yun Zheng ◽  
Bo Ji ◽  
Renhua Song ◽  
Shengpeng Wang ◽  
Ting Li ◽  
...  
Keyword(s):  
High Throughput ◽  
Small Rna ◽  
High Throughput Sequencing ◽  
Accurate Detection ◽  
Wide Range

scholarly journals Oasis2.0: improved online analysis of small RNA-seq data

2017 ◽  
Author(s):  
Raza-Ur Rahman ◽  
Abhivyakti Gautam ◽  
Jörn Bethune ◽  
Abdul Sattar ◽  
Maksims Fiosins ◽  
...  
Keyword(s):  
Deep Sequencing ◽  
Small Rna ◽  
Small Rnas ◽  
Web Application ◽  
The Novel ◽  
Sequencing Data ◽  
Essential Information ◽  
Deep Sequencing Data ◽  
Small Rna Deep Sequencing ◽  
User Friendly

AbstractOasis 2 is a new main release of the Oasis web application for the detection, differential expression, and classification of small RNAs in deep sequencing data. Compared to its predecessor Oasis, Oasis 2 features a novel and speed-optimized sRNA detection module that supports the identification of small RNAs in any organism with higher accuracy. Next to the improved detection of small RNAs in a target organism, the software now also recognizes potential cross-species miRNAs and viral and bacterial sRNAs in infected samples. In addition, novel miRNAs can now be queried and visualized interactively, providing essential information for over 700 high-quality miRNA predictions across 14 organisms. Robust biomarker signatures can now be obtained using the novel enhanced classification module. Oasis 2 enables biologists and medical researchers to rapidly analyze and query small RNA deep sequencing data with improved precision, recall, and speed, in an interactive and user-friendly environment.Availability and Implementation: Oasis 2 is implemented in Java, J2EE, mysql, Python, R, PHP and JavaScript. It is freely available at http://oasis.dzne.de

scholarly journals Different classes of small RNAs are essential for head regeneration in the planarian Dugesia japonica

BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhonghong Cao ◽  
David Rosenkranz ◽  
Suge Wu ◽  
Hongjin Liu ◽  
Qiuxiang Pang ◽  
...  
Keyword(s):  
Small Rna ◽  
Small Rnas ◽  
High Throughput Sequencing ◽  
Expression Profiles ◽  
Evolutionary Conservation ◽  
Body Parts ◽  
Promising Candidate ◽  
Dugesia Japonica ◽  
Head Regeneration

Abstract Background Planarians reliably regenerate all body parts after injury, including a fully functional head and central nervous system. But until now, the expression dynamics and functional role of miRNAs and other small RNAs during the process of head regeneration are not well understood. Furthermore, little is known about the evolutionary conservation of the relevant small RNAs pathways, rendering it difficult to assess whether insights from planarians will apply to other taxa. Results In this study, we applied high throughput sequencing to identify miRNAs, tRNA fragments and piRNAs that are dynamically expressed during head regeneration in Dugesia japonica. We further show that knockdown of selected small RNAs, including three novel Dugesia-specific miRNAs, during head regeneration induces severe defects including abnormally small-sized eyes, cyclopia and complete absence of eyes. Conclusions Our findings suggest that a complex pool of small RNAs takes part in the process of head regeneration in Dugesia japonica and provide novel insights into global small RNA expression profiles and expression changes in response to head amputation. Our study reveals the evolutionary conserved role of miR-124 and brings further promising candidate small RNAs into play that might unveil new avenues for inducing restorative programs in non-regenerative organisms via small RNA mimics based therapies.

scholarly journals miRSeq: A User-Friendly Standalone Toolkit for Sequencing Quality Evaluation and miRNA Profiling

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Cheng-Tsung Pan ◽  
Kuo-Wang Tsai ◽  
Tzu-Min Hung ◽  
Wei-Chen Lin ◽  
Chao-Yu Pan ◽  
...  
Keyword(s):  
Small Rna ◽  
Mirna Expression ◽  
Biological Activities ◽  
Expression Profiles ◽  
Read Length ◽  
Wide Range ◽  
Mirna Expression Profiles ◽  
Sequencing Quality ◽  
User Friendly ◽  
Ngs Data

MicroRNAs (miRNAs) present diverse regulatory functions in a wide range of biological activities. Studies on miRNA functions generally depend on determining miRNA expression profiles between libraries by using a next-generation sequencing (NGS) platform. Currently, several online web services are developed to provide small RNA NGS data analysis. However, the submission of large amounts of NGS data, conversion of data format, and limited availability of species bring problems. In this study, we developed miRSeq to provide alternatives. To test the performance, we had small RNA NGS data from four species, including human, rat, fly, and nematode, analyzed with miRSeq. The alignments results indicate that miRSeq can precisely evaluate the sequencing quality of samples regarding percentage of self-ligation read, read length distribution, and read category. miRSeq is a user-friendly standalone toolkit featuring a graphical user interface (GUI). After a simple installation, users can easily operate miRSeq on a PC or laptop by using a mouse. Within minutes, miRSeq yields useful miRNA data, including miRNA expression profiles, 3′ end modification patterns, and isomiR forms. Moreover, miRSeq supports the analysis of up to 105 animal species, providing higher flexibility.

scholarly journals SeqPlots - Interactive software for exploratory data analyses, pattern discovery and visualization in genomics

2016 ◽  
Vol 1 ◽  
pp. 14 ◽  
Author(s):  
Przemyslaw Stempor ◽  
Julie Ahringer
Keyword(s):  
Gene Expression ◽  
Chromatin Immunoprecipitation ◽  
High Throughput Sequencing ◽  
Ease Of Use ◽  
Data Exploration ◽  
Interactive Software ◽  
File Formats ◽  
Visualization Software ◽  
Exploratory Data ◽  
User Friendly

Experiments involving high-throughput sequencing are widely used for analyses of chromatin function and gene expression. Common examples are the use of chromatin immunoprecipitation for the analysis of chromatin modifications or factor binding, enzymatic digestions for chromatin structure assays, and RNA sequencing to assess gene expression changes after biological perturbations. To investigate the pattern and abundance of coverage signals across regions of interest, data are often visualized as profile plots of average signal or stacked rows of signal in the form of heatmaps. We found that available plotting software was either slow and laborious or difficult to use by investigators with little computational training, which inhibited wide data exploration. To address this need, we developed SeqPlots, a user-friendly exploratory data analysis (EDA) and visualization software for genomics. After choosing groups of signal and feature files and defining plotting parameters, users can generate profile plots of average signal or heatmaps clustered using different algorithms in a matter of seconds through the graphical user interface (GUI) controls. SeqPlots accepts all major genomic file formats as input and can also generate and plot user defined motif densities. Profile plots and heatmaps are highly configurable and batch operations can be used to generate a large number of plots at once. SeqPlots is available as a GUI application for Mac or Windows and Linux, or as an R/Bioconductor package. It can also be deployed on a server for remote and collaborative usage. The analysis features and ease of use of SeqPlots encourages wide data exploration, which should aid the discovery of novel genomic associations.

High Throughput Sequencing of Acute Lymphoblastic Leukemia Small RNAs.

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 3650-3650
Author(s):  
Michael C Wei ◽  
Elizabeth O Osborn ◽  
Michael Cleary
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Cell Lines ◽  
High Throughput ◽  
Small Rna ◽  
Small Rnas ◽  
High Throughput Sequencing ◽  
Lymphoblastic Leukemia ◽  
Leukemia Cell ◽  
Rna Expression ◽  
Leukemia Cell Lines

Abstract Abstract 3650 MicroRNAs are small, non-coding RNAs that regulate gene expression and play key roles in cancer by modulating oncogene and tumor suppressor pathways. We are investigating the clinical and prognostic roles of miRNA expression in pediatric leukemias using high-throughput sequencing as a profiling tool. To establish the methodology, we have utilized high-throughput sequencing to quantify small RNA expression from eight acute lymphoblastic leukemia cell lines and one MLL-rearranged infant ALL patient sample. We generated sequencing libraries from these cells, conducted high-throughput sequencing using the Illumina platform, and established a custom bioinformatics pipeline for data analysis. Over 50 million individual sequence reads were analyzed. These sequences were mapped against a database of human miRNAs, and the frequency of miRNA expression among samples was enumerated. Expression of hematopoietic-specific miR-142 and miR-181 cluster miRs was found in these leukemia samples, while the liver-specific miR-122 was not expressed. miR-196b, previously reported to be over-expressed in MLL-rearranged leukemias, was expressed in 3/3 MLL-rearranged leukemia cell lines and 1/5 non-MLL cell lines. Expression of individual miRNAs was validated by quantitative PCR. Additional analysis of MLL-associated miRNAs and novel small RNAs will be presented. Our results demonstrate the feasibility and potential of high-throughput sequencing to profile the expression of small RNAs from leukemia cells, and we plan to apply these methods to additional primary patient samples to examine prognostic and clinical correlations with small RNA expression patterns. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Finding differentially expressed sRNA-Seq regions with srnadiff

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256196
Author(s):  
Matthias Zytnicki ◽  
Ignacio González
Keyword(s):  
Gene Regulation ◽  
Small Rna ◽  
Small Rnas ◽  
Differentially Expressed ◽  
External Information ◽  
Small Rna Sequencing ◽  
Small Interfering Rnas ◽  
Genomic Annotation ◽  
Wide Range ◽  
Precursor Rna

Small RNAs (sRNAs) encompass a great variety of molecules of different kinds, such as microRNAs, small interfering RNAs, Piwi-associated RNA, among others. These sRNAs have a wide range of activities, which include gene regulation, protection against virus, transposable element silencing, and have been identified as a key actor in determining the development of the cell. Small RNA sequencing is thus routinely used to assess the expression of the diversity of sRNAs, usually in the context of differentially expression, where two conditions are compared. Tools that detect differentially expressed microRNAs are numerous, because microRNAs are well documented, and the associated genes are well defined. However, tools are lacking to detect other types of sRNAs, which are less studied, and whose precursor RNA is not well characterized. We present here a new method, called srnadiff, which finds all kinds of differentially expressed sRNAs. To the extent of our knowledge, srnadiff is the first tool that detects differentially expressed sRNAs without the use of external information, such as genomic annotation or additional sequences of sRNAs.

scholarly journals seqCAT: a Bioconductor R-package for variant analysis of high throughput sequencing data

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1466 ◽  
Author(s):  
Erik Fasterius ◽  
Cristina Al-Khalili Szigyarto
Keyword(s):  
Genetic Variation ◽  
Liver Cancer ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
R Package ◽  
Ease Of Use ◽  
Sequencing Data ◽  
Dna And Rna ◽  
High Throughput Sequencing Data ◽  
Wide Range

High throughput sequencing technologies are flourishing in the biological sciences, enabling unprecedented insights into e.g. genetic variation, but require extensive bioinformatic expertise for the analysis. There is thus a need for simple yet effective software that can analyse both existing and novel data, providing interpretable biological results with little bioinformatic prowess. We present seqCAT, a Bioconductor toolkit for analysing genetic variation in high throughput sequencing data. It is a highly accessible, easy-to-use and well-documented R-package that enables a wide range of researchers to analyse their own and publicly available data, providing biologically relevant conclusions and publication-ready figures. SeqCAT can provide information regarding genetic similarities between an arbitrary number of samples, validate specific variants as well as define functionally similar variant groups for further downstream analyses. Its ease of use, installation, complete data-to-conclusions functionality and the inherent flexibility of the R programming language make seqCAT a powerful tool for variant analyses compared to already existing solutions. A publicly available dataset of liver cancer-derived organoids is analysed herein using the seqCAT package, demonstrating that the organoids are genetically stable. A previously known liver cancer-related mutation is additionally shown to be present in a sample though it was not listed in the original publication. Differences between DNA- and RNA-based variant calls in this dataset are also analysed revealing a high median concordance of 97.5%.

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