Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Accurate Adapter Information Is Crucial for Reproducibility and Reusability in Small RNA Seq Studies.

ABSTRACT: A necessary pre-processing data analysis step is the removal of adapter sequences from the raw reads. While most adapter trimming tools require adapter sequence as an essential input, adapter information is often incomplete or missing. This can impact quantification of features, reproducibility of the study and might even lead to erroneous conclusions. Here, we provide examples to highlight the importance of specifying the adapter sequence by demonstrating the effect of using similar but different adapter sequences and identify additional potential sources of errors in the adapter trimming step. Finally, we propose solutions by which users can ensure their small RNA-seq data is fully annotated with adapter information.

SUBMITTER: Zhong X

PROVIDER: S-EPMC6958438 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Accurate Adapter Information Is Crucial for Reproducibility and Reusability in Small RNA Seq Studies.

Zhong Xiangfu X Heinicke Fatima F Lie Benedicte A BA Rayner Simon S

Non-coding RNA 20191028 4

A necessary pre-processing data analysis step is the removal of adapter sequences from the raw reads. While most adapter trimming tools require adapter sequence as an essential input, adapter information is often incomplete or missing. This can impact quantification of features, reproducibility of the study and might even lead to erroneous conclusions. Here, we provide examples to highlight the importance of specifying the adapter sequence by demonstrating the effect of using similar but differe ...[more]

PMID: 31661777

Similar Datasets

Small RNA-seq: The RNA 5'-end adapter ligation problem and how to circumvent it.

Project description:The preparation of small RNA cDNA sequencing libraries depends on the unbiased ligation of adapters to the RNA ends. Small RNA with 5' recessed ends are poor substrates for enzymatic adapter ligation, but this 5' adapter ligation problem can go undetected if the library preparation steps are not monitored. Here we illustrate the severity of the 5' RNA end ligation problem using several pre-miRNA-like hairpins that allow us to expand the definition of the problem to include 5' ends close to a hairpin stem, whether recessed or in a short extension. The ribosome profiling method can avoid a difficult 5' adapter ligation, but the enzyme typically used to circularize the cDNA has been reported to be biased, calling into question the benefit of this workaround. Using the TS2126 RNA ligase 1 (a.k.a. CircLigase) as the circularizing enzyme, we devised a bias test for the circularization of first strand cDNA. All possible dinucleotides were circle-ligated with similar efficiency. To re-linearize the first strand cDNA in the ribosome profiling approach, we introduce an improved method wherein a single ribonucleotide is placed between the sequencing primer binding sites in the reverse transcriptase primer, which later serves as the point of re-linearization by RNase A. We incorporate this step into the ribosomal profiling method and describe a complete improved library preparation method, Coligo-seq, for the sequencing of small RNA with secondary structure close to the 5' end. This method accepts a variety of 5' modified RNA, including 5' monophosphorylated RNA, as demonstrated by the construction of a HeLa cell microRNA cDNA library.

| S-EPMC6507418 | biostudies-literature

FindAdapt: A python package for fast and accurate adapter detection in small RNA sequencing.

Project description:Adapter trimming is an essential step for analyzing small RNA sequencing data, where reads are generally longer than target RNAs ranging from 18 to 30 bp. Most adapter trimming tools require adapter information as input. However, adapter information is hard to access, specified incorrectly, or not provided with publicly available datasets, hampering their reproducibility and reusability. Manual identification of adapter patterns from raw reads is labor-intensive and error-prone. Moreover, the use of randomized adapters to reduce ligation biases during library preparation makes adapter detection even more challenging. Here, we present FindAdapt, a Python package for fast and accurate detection of adapter patterns without relying on prior information. We demonstrated that FindAdapt was far superior to existing approaches. It identified adapters successfully in 180 simulation datasets with diverse read structures and 3,184 real datasets covering a variety of commercial and customized small RNA library preparation kits. FindAdapt is stand-alone software that can be easily integrated into small RNA sequencing analysis pipelines.

| S-EPMC10833567 | biostudies-literature

Current RNA-seq methodology reporting limits reproducibility.

Project description:Ribonucleic acid sequencing (RNA-seq) identifies and quantifies RNA molecules from a biological sample. Transformation from raw sequencing data to meaningful gene or isoform counts requires an in silico bioinformatics pipeline. Such pipelines are modular in nature, built using selected software and biological references. Software is usually chosen and parameterized according to the sequencing protocol and biological question. However, while biological and technical noise is alleviated through replicates, biases due to the pipeline and choice of biological references are often overlooked. Here, we show that the current standard practice prevents reproducibility in RNA-seq studies by failing to specify required methodological information. Peer-reviewed articles are intended to apply currently accepted scientific and methodological standards. Inasmuch as the bias-less and optimal RNA-seq pipeline is not perfectly defined, methodological information holds a meaningful role in defining the results. This work illustrates the need for a standardized and explicit display of methodological information in RNA-seq experiments.

| S-EPMC7820846 | biostudies-literature

A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium.

Project description:We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the US Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for junction discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with >80% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed for all examined platforms, including qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling. The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings.

| S-EPMC4321899 | biostudies-literature

How data analysis affects power, reproducibility and biological insight of RNA-seq studies in complex datasets.

Project description:The sequencing of the full transcriptome (RNA-seq) has become the preferred choice for the measurement of genome-wide gene expression. Despite its widespread use, challenges remain in RNA-seq data analysis. One often-overlooked aspect is normalization. Despite the fact that a variety of factors or 'batch effects' can contribute unwanted variation to the data, commonly used RNA-seq normalization methods only correct for sequencing depth. The study of gene expression is particularly problematic when it is influenced simultaneously by a variety of biological factors in addition to the one of interest. Using examples from experimental neuroscience, we show that batch effects can dominate the signal of interest; and that the choice of normalization method affects the power and reproducibility of the results. While commonly used global normalization methods are not able to adequately normalize the data, more recently developed RNA-seq normalization can. We focus on one particular method, RUVSeq and show that it is able to increase power and biological insight of the results. Finally, we provide a tutorial outlining the implementation of RUVSeq normalization that is applicable to a broad range of studies as well as meta-analysis of publicly available data.

| S-EPMC4652761 | biostudies-literature

Fostering reproducibility, reusability, and technology transfer in health informatics.

Project description:Computational methods can transform healthcare. In particular, health informatics with artificial intelligence has shown tremendous potential when applied in various fields of medical research and has opened a new era for precision medicine. The development of reusable biomedical software for research or clinical practice is time-consuming and requires rigorous compliance with quality requirements as defined by international standards. However, research projects rarely implement such measures, hindering smooth technology transfer into the research community or manufacturers as well as reproducibility and reusability. Here, we present a guideline for quality management systems (QMS) for academic organizations incorporating the essential components while confining the requirements to an easily manageable effort. It provides a starting point to implement a QMS tailored to specific needs effortlessly and greatly facilitates technology transfer in a controlled manner, thereby supporting reproducibility and reusability. Ultimately, the emerging standardized workflows can pave the way for an accelerated deployment in clinical practice.

| S-EPMC8282945 | biostudies-literature

Accurate measurements of dynamics and reproducibility in small genetic networks.

Project description:Quantification of gene expression has become a central tool for understanding genetic networks. In many systems, the only viable way to measure protein levels is by immunofluorescence, which is notorious for its limited accuracy. Using the early Drosophila embryo as an example, we show that careful identification and control of experimental error allows for highly accurate gene expression measurements. We generated antibodies in different host species, allowing for simultaneous staining of four Drosophila gap genes in individual embryos. Careful error analysis of hundreds of expression profiles reveals that less than ?20% of the observed embryo-to-embryo fluctuations stem from experimental error. These measurements make it possible to extract not only very accurate mean gene expression profiles but also their naturally occurring fluctuations of biological origin and corresponding cross-correlations. We use this analysis to extract gap gene profile dynamics with ?1?min accuracy. The combination of these new measurements and analysis techniques reveals a twofold increase in profile reproducibility owing to a collective network dynamics that relays positional accuracy from the maternal gradients to the pair-rule genes.

| S-EPMC3564256 | biostudies-literature

Watchdog 2.0: New developments for reusability, reproducibility, and workflow execution.

Project description:Advances in high-throughput methods have brought new challenges for biological data analysis, often requiring many interdependent steps applied to a large number of samples. To address this challenge, workflow management systems, such as Watchdog, have been developed to support scientists in the (semi-)automated execution of large analysis workflows. Here, we present Watchdog 2.0, which implements new developments for module creation, reusability, and documentation and for reproducibility of analyses and workflow execution. Developments include a graphical user interface for semi-automatic module creation from software help pages, sharing repositories for modules and workflows, and a standardized module documentation format. The latter allows generation of a customized reference book of public and user-specific modules. Furthermore, extensive logging of workflow execution, module and software versions, and explicit support for package managers and container virtualization now ensures reproducibility of results. A step-by-step analysis protocol generated from the log file may, e.g., serve as a draft of a manuscript methods section. Finally, 2 new execution modes were implemented. One allows resuming workflow execution after interruption or modification without rerunning successfully executed tasks not affected by changes. The second one allows detaching and reattaching to workflow execution on a local computer while tasks continue running on computer clusters. Watchdog 2.0 provides several new developments that we believe to be of benefit for large-scale bioinformatics analysis and that are not completely covered by other competing workflow management systems. The software itself, module and workflow repositories, and comprehensive documentation are freely available at https://www.bio.ifi.lmu.de/watchdog.

| S-EPMC7298769 | biostudies-literature

SnowyOwl: accurate prediction of fungal genes by using RNA-Seq and homology information to select among ab initio models.

Project description:BackgroundLocating the protein-coding genes in novel genomes is essential to understanding and exploiting the genomic information but it is still difficult to accurately predict all the genes. The recent availability of detailed information about transcript structure from high-throughput sequencing of messenger RNA (RNA-Seq) delineates many expressed genes and promises increased accuracy in gene prediction. Computational gene predictors have been intensively developed for and tested in well-studied animal genomes. Hundreds of fungal genomes are now or will soon be sequenced. The differences of fungal genomes from animal genomes and the phylogenetic sparsity of well-studied fungi call for gene-prediction tools tailored to them.ResultsSnowyOwl is a new gene prediction pipeline that uses RNA-Seq data to train and provide hints for the generation of Hidden Markov Model (HMM)-based gene predictions and to evaluate the resulting models. The pipeline has been developed and streamlined by comparing its predictions to manually curated gene models in three fungal genomes and validated against the high-quality gene annotation of Neurospora crassa; SnowyOwl predicted N. crassa genes with 83% sensitivity and 65% specificity. SnowyOwl gains sensitivity by repeatedly running the HMM gene predictor Augustus with varied input parameters and selectivity by choosing the models with best homology to known proteins and best agreement with the RNA-Seq data.ConclusionsSnowyOwl efficiently uses RNA-Seq data to produce accurate gene models in both well-studied and novel fungal genomes. The source code for the SnowyOwl pipeline (in Python) and a web interface (in PHP) is freely available from http://sourceforge.net/projects/snowyowl/.

| S-EPMC4084796 | biostudies-literature

Reproducibility across single-cell RNA-seq protocols for spatial ordering analysis.

Project description:As newer single-cell protocols generate increasingly more cells at reduced sequencing depths, the value of a higher read depth may be overlooked. Using data from three different single-cell RNA-seq protocols that lend themselves to having either higher read depth (Smart-seq) or many cells (MARS-seq and 10X), we evaluate their ability to recapitulate biological signals in the context of spatial reconstruction. Overall, we find gene expression profiles after spatial reconstruction analysis are highly reproducible between datasets despite being generated by different protocols and using different computational algorithms. While UMI-based protocols such as 10X and MARS-seq allow for capturing more cells, Smart-seq's higher sensitivity and read-depth allow for analysis of lower expressed genes and isoforms. Additionally, we evaluate trade-offs for each protocol by performing subsampling analyses and find that optimizing the balance between sequencing depth and number of cells within a protocol is necessary for efficient use of resources. Our analysis emphasizes the importance of selecting a protocol based on the biological questions and features of interest.

| S-EPMC7521718 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data