scholarly journals sRNAbench and sRNAtoolbox 2019: intuitive fast small RNA profiling and differential expression

2019 ◽  
Vol 47 (W1) ◽  
pp. W530-W535 ◽  
Author(s):  
Ernesto Aparicio-Puerta ◽  
Ricardo Lebrón ◽  
Antonio Rueda ◽  
Cristina Gómez-Martín ◽  
Stavros Giannoukakos ◽  
...  
Keyword(s):  
Differential Expression ◽  
Rna Sequencing ◽  
Small Rna ◽  
Pcr Amplification ◽  
Small Rna Sequencing ◽  
User Friendliness ◽  
Rna Profiling ◽  
Sequencing Studies ◽  
Microrna Sequencing ◽  
Downstream Analysis

Abstract Since the original publication of sRNAtoolbox in 2015, small RNA research experienced notable advances in different directions. New protocols for small RNA sequencing have become available to address important issues such as adapter ligation bias, PCR amplification artefacts or to include internal controls such as spike-in sequences. New microRNA reference databases were developed with different foci, either prioritizing accuracy (low number of false positives) or completeness (low number of false negatives). Additionally, other small RNA molecules as well as microRNA sequence and length variants (isomiRs) have continued to gain importance. Finally, the number of microRNA sequencing studies deposited in GEO nearly triplicated from 2014 (280) to 2018 (764). These developments imply that fast and easy-to-use tools for expression profiling and subsequent downstream analysis of miRNA-seq data are essential to many researchers. Key features in this sRNAtoolbox release include addition of all major RNA library preparation protocols to sRNAbench and improvements in sRNAde, a tool that summarizes several aspects of small RNA sequencing studies including the detection of consensus differential expression. A special emphasis was put on the user-friendliness of the tools, for instance sRNAbench now supports parallel launching of several jobs to improve reproducibility and user time efficiency.

scholarly journals DEUS: an R package for accurate small RNA profiling based on differential expression of unique sequences

2019 ◽  
Vol 35 (22) ◽  
pp. 4834-4836
Author(s):  
Tim Jeske ◽  
Peter Huypens ◽  
Laura Stirm ◽  
Selina Höckele ◽  
Christine M Wurmser ◽  
...  
Keyword(s):  
Differential Expression ◽  
Small Rna ◽  
Sequence Similarity ◽  
Differential Expression Analysis ◽  
R Package ◽  
Supplementary Information ◽  
Small Rna Sequencing ◽  
Sequencing Data ◽  
Rna Sequences ◽  
Rna Profiling

Abstract Summary Despite their fundamental role in various biological processes, the analysis of small RNA sequencing data remains a challenging task. Major obstacles arise when short RNA sequences map to multiple locations in the genome, align to regions that are not annotated or underwent post-transcriptional changes which hamper accurate mapping. In order to tackle these issues, we present a novel profiling strategy that circumvents the need for read mapping to a reference genome by utilizing the actual read sequences to determine expression intensities. After differential expression analysis of individual sequence counts, significant sequences are annotated against user defined feature databases and clustered by sequence similarity. This strategy enables a more comprehensive and concise representation of small RNA populations without any data loss or data distortion. Availability and implementation Code and documentation of our R package at http://ibis.helmholtz-muenchen.de/deus/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals RNA-sequencing of human post-mortem hypothalamus and nucleus accumbens identifies expression profiles associated with obesity.

2022 ◽  
Author(s):  
Christian Wake ◽  
Julie A. Schneider ◽  
Thor D. Stein ◽  
Joli Bregu ◽  
Adam Labadorf ◽  
...  
Keyword(s):  
Immune System ◽  
Nucleus Accumbens ◽  
Rna Sequencing ◽  
Small Rna ◽  
Brain Region ◽  
Expression Profiles ◽  
Cytokine Signaling ◽  
Small Rna Sequencing ◽  
Increased Risk ◽  
Sequencing Studies

Obesity, the accumulation of body fat to excess, may cause serious negative health effects, including increased risk of heart disease, type 2 diabetes, stroke and certain cancers. The biology of obesity is complex and not well understood, involving both environmental and genetic factors and affecting metabolic and endocrine mechanisms in tissues of the gut, adipose, and brain. Previous RNA sequencing studies have identified transcripts associated with obesity and body mass index in blood and fat, often using animal models, but RNA sequencing studies in human brain tissue related to obesity have not been previously undertaken. We conducted both large and small RNA sequencing of hypothalamus (207 samples) and nucleus accumbens (276 samples) from individuals defined as consistently obese (124 samples), consistently normal weight as controls (148 samples) or selected without respect to BMI and falling within neither case nor control definition (211 samples), based on longitudinal BMI measures. The samples were provided by three cohort studies with brain donation programs; the Framingham Heart Study (FHS), the Religious Orders Study (ROS) and the Rush Memory and Aging Project (MAP). For each brain region and large/small RNA sequencing set, differential expression of obesity, BMI, brain region and sex was performed. Analyses were done transcriptome-wide as well as with a priori defined sets of obesity or BMI-associated mRNAs and microRNAs (miRNAs). There are sixteen mRNAs and five microRNAs that are differentially expressed (adjusted p < 0.05) by obesity or BMI in these tissues, several of which were validated with qPCR data. The results include many that are BMI-associated, such as APOBR and CES1, as well as many associated with the immune system and some with addiction, such as the gene sets 'cytokine signaling in immune system' and 'opioid signaling'. In spite of the relatively large number of samples, our study was likely under-powered to detect other transcripts or miRNA with relevant but smaller effects.

scholarly journals Human Colon Mucosal Biofilms and Murine Host Communicate via Altered mRNA and microRNA Expression during Cancer

mSystems ◽  
2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Sarah Tomkovich ◽  
Raad Z. Gharaibeh ◽  
Christine M. Dejea ◽  
Jillian L. Pope ◽  
Jinmai Jiang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Microbial Communities ◽  
Rna Sequencing ◽  
Small Rna ◽  
Intestinal Bacteria ◽  
Distal Colon ◽  
Human Colon ◽  
Small Rna Sequencing ◽  
Microrna Sequencing

ABSTRACT Disrupted interactions between host and intestinal bacteria are implicated in colorectal cancer (CRC) development. However, activities derived from these bacteria and their interplay with the host are unclear. Here, we examine this interplay by performing mouse and microbiota RNA sequencing on colon tissues and 16S and small RNA sequencing on stools from germfree (GF) and gnotobiotic ApcMinΔ850/+;Il10−/− mice associated with microbes from biofilm-positive human CRC tumor (BF+T) and biofilm-negative healthy (BF-bx) tissues. The bacteria in BF+T mice differentially expressed (DE) >2,900 genes, including genes related to bacterial secretion, virulence, and biofilms but affected only 62 host genes. Small RNA sequencing of stools from these cohorts revealed eight significant DE host microRNAs (miRNAs) based on biofilm status and several miRNAs that correlated with bacterial taxon abundances. Additionally, computational predictions suggest that some miRNAs preferentially target bacterial genes while others primarily target mouse genes. 16S rRNA sequencing of mice that were reassociated with mucosa-associated communities from the initial association revealed a set of 13 bacterial genera associated with cancer that were maintained regardless of whether the reassociation inoculums were initially obtained from murine proximal or distal colon tissues. Our findings suggest that complex interactions within bacterial communities affect host-derived miRNA, bacterial composition, and CRC development. IMPORTANCE Bacteria and bacterial biofilms have been implicated in colorectal cancer (CRC), but it is still unclear what genes these microbial communities express and how they influence the host. MicroRNAs regulate host gene expression and have been explored as potential biomarkers for CRC. An emerging area of research is the ability of microRNAs to impact growth and gene expression of members of the intestinal microbiota. This study examined the bacteria and bacterial transcriptome associated with microbes derived from biofilm-positive human cancers that promoted tumorigenesis in a murine model of CRC. The murine response to different microbial communities (derived from CRC patients or healthy people) was evaluated through RNA and microRNA sequencing. We identified a complex interplay between biofilm-associated bacteria and the host during CRC in mice. These findings may lead to the development of new biomarkers and therapeutics for identifying and treating biofilm-associated CRCs.

scholarly journals Assessment of microRNA differential expression and detection in multiplexed small RNA sequencing data

RNA ◽  
2014 ◽  
Vol 21 (2) ◽  
pp. 164-171 ◽  
Author(s):  
Joshua D. Campbell ◽  
Gang Liu ◽  
Lingqi Luo ◽  
Ji Xiao ◽  
Joseph Gerrein ◽  
...  
Keyword(s):  
Differential Expression ◽  
Rna Sequencing ◽  
Small Rna ◽  
Small Rna Sequencing ◽  
Sequencing Data

scholarly journals Statistical Assessment of Depth Normalization for Small RNA Sequencing

2020 ◽  
pp. 567-582 ◽  
Author(s):  
Li-Xuan Qin ◽  
Jian Zou ◽  
Jiejun Shi ◽  
Ann Lee ◽  
Aleksandra Mihailovic ◽  
...  
Keyword(s):  
Differential Expression ◽  
Rna Sequencing ◽  
Small Rna ◽  
Objective Assessment ◽  
Small Rna Sequencing ◽  
Test Bed ◽  
Data Set ◽  
Computational Tools ◽  
Benchmark Data ◽  
Normalization Methods

PURPOSE Methods for depth normalization have been assessed primarily with simulated data or cell-line–mixture data. There is a pressing need for benchmark data enabling a more realistic and objective assessment, especially in the context of small RNA sequencing. METHODS We collected a unique pair of microRNA sequencing data sets for the same set of tumor samples; one data set was collected with and the other without uniform handling and balanced design. The former provided a benchmark for evaluating evidence of differential expression and the latter served as a test bed for normalization. Next, we developed a data perturbation algorithm to simulate additional data set pairs. Last, we assembled a set of computational tools to visualize and quantify the assessment. RESULTS We validated the quality of the benchmark data and showed the need for normalization of the test data. For illustration, we applied the data and tools to assess the performance of 9 existing normalization methods. Among them, trimmed mean of M-values was a better scaling method, whereas the median and the upper quartiles were consistently the worst performers; one variation of remove unwanted variation had the best chance of capturing true positives but at the cost of increased false positives. In general, these methods were, at best, moderately helpful when the level of differential expression was extensive and asymmetric. CONCLUSION Our study (1) provides the much-needed benchmark data and computational tools for assessing depth normalization, (2) shows the dependence of normalization performance on the underlying pattern of differential expression, and (3) calls for continued research efforts to develop more effective normalization methods.

Small RNA Sequencing to Discover Circulating MicroRNA Biomarkers of Testicular Toxicity in Dogs

2020 ◽  
pp. 109158182096151
Author(s):  
Jennifer C. Shing ◽  
Kai Schaefer ◽  
Shaun E. Grosskurth ◽  
Andy H. Vo ◽  
Tatiana Sharapova ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Small Rna ◽  
Exploratory Study ◽  
Quantitative Polymerase Chain Reaction ◽  
Small Rna Sequencing ◽  
Circulating Microrna ◽  
Testicular Toxicity ◽  
Potential Candidate ◽  
Drug Induced ◽  
Organ Systems

Predictive indicators of testicular toxicity could improve drug development by allowing early in-life screening for this adverse effect before it becomes severe. We hypothesized that circulating microRNAs (miRNAs) could serve as testicular toxicity biomarkers in dogs. Herein, we describe the results of an exploratory study conducted to discover biomarkers of drug-induced testicular injury. Following a dose-selection study using the testicular toxicant ethylene glycol monomethyl ether (EGME), we chose a dose of 50 mg/kg/d EGME to avoid systemic toxicity and treated 2 groups of dogs (castrated, non-castrated) for 14 to 28 days. Castrated animals were used as negative controls to identify biomarkers specific for testicular toxicity because EGME can cause toxicity to organ systems in addition to the testis. Blood was collected daily during the dosing period, followed by recovery for 29 to 43 days with less frequent sampling. Dosing was well tolerated, resulting in mild-to-moderate degeneration in testes and epididymides. Global profiling of serum miRNAs at selected dosing and recovery time points was completed by small RNA sequencing. Bioinformatics data analysis using linear modeling demonstrated several circulating miRNAs that were differentially abundant during the dosing period compared with baseline and/or castrated control samples. Confirmatory reverse transcription quantitative polymerase chain reaction data in these animals was unable to detect sustained alterations of miRNAs in serum, except for 1 potential candidate cfa-miR-146b. Taken together, we report the results of a comprehensive exploratory study and suggest future directions for follow-up research to address the challenge of developing diagnostic biomarkers of testicular toxicity.

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.

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