scholarly journals Mitochondrial mRNA is a stable and convenient reference for the normalization of RNA-seq data

2021 ◽  
Author(s):  
Yusheng Liu ◽  
Yiwei Zhang ◽  
Falong Lu ◽  
Jiaqiang Wang
Keyword(s):  
Sequencing Depth ◽  
Rna Seq ◽  
Batch Effects ◽  
Normalization Strategy ◽  
Normalization Methods ◽  
Sequencing Library ◽  
Endogenous Reference ◽  
Sequencing Library Preparation ◽  
Gene Expression Levels ◽  
Better Than

AbstractThe normalization of high-throughput RNA sequencing (RNA-seq) data is needed to accurately analyze gene expression levels. Traditional normalization methods can either correct the differences in sequencing depth, or correct both the sequencing depth and other unwanted variations introduced during sequencing library preparation through exogenous spike-ins1-4. However, the exogenous spike-ins are prone to variation5,6. Therefore, a better normalization approach with a more appropriate reference is an ongoing demand. In this study, we demonstrated that mitochondrial mRNA (mRNA encoded by mitochondria genome) can serve as a steady endogenous reference for RNA-seq data analysis, and performs better than exogenous spike-ins. We also found that using mitochondrial mRNA as a reference can reduce batch effects for RNA-seq data. These results provide a simple and practical normalization strategy for RNA-seq data, which will serve as a valuable tool widely applicable to transcriptomic studies.

scholarly journals Evaluation of RNA-seq normalization methods using challenging datasets

2018 ◽  
Author(s):  
Severin Uebbing
Keyword(s):  
Gene Expression ◽  
Rna Seq ◽  
Quantitative Gene Expression ◽  
Expression Levels ◽  
Normalization Strategy ◽  
Normalization Methods ◽  
Gene Expression Levels

AbstractRNA-seq is a powerful tool for both discovery and experimentation. Most RNA-seq studies rely on library normalization to compare samples or to reliably estimate quantitative gene expression levels. Over the years a number of RNA-seq normalization methods have been proposed. Review studies testing these methods have provided evidence that commonly used methods perform well in simple normalization tasks, but their performance in challenging normalization tasks has yet to be evaluated. Here I test RNA-seq normalization methods using two challenging normalization scenarios. My assessment reveals surprising shortcomings of some commonly used methods and identifies an underappreciated method as the most promising normalization strategy for common, yet challenging RNA-seq experiments.

scholarly journals Methyltransferase-directed orthogonal tagging and sequencing of miRNAs and bacterial small RNAs

BMC Biology ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Milda Mickutė ◽  
Kotryna Kvederavičiūtė ◽  
Aleksandr Osipenko ◽  
Raminta Mineikaitė ◽  
Saulius Klimašauskas ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Regulatory Networks ◽  
Library Preparation ◽  
Rna Seq ◽  
Basic Principles ◽  
Cofactor Binding ◽  
Sequencing Library ◽  
Sequencing Library Preparation ◽  
Target Rna

Abstract Background Targeted installation of designer chemical moieties on biopolymers provides an orthogonal means for their visualisation, manipulation and sequence analysis. Although high-throughput RNA sequencing is a widely used method for transcriptome analysis, certain steps, such as 3′ adapter ligation in strand-specific RNA sequencing, remain challenging due to structure- and sequence-related biases introduced by RNA ligases, leading to misrepresentation of particular RNA species. Here, we remedy this limitation by adapting two RNA 2′-O-methyltransferases from the Hen1 family for orthogonal chemo-enzymatic click tethering of a 3′ sequencing adapter that supports cDNA production by reverse transcription of the tagged RNA. Results We showed that the ssRNA-specific DmHen1 and dsRNA-specific AtHEN1 can be used to efficiently append an oligonucleotide adapter to the 3′ end of target RNA for sequencing library preparation. Using this new chemo-enzymatic approach, we identified miRNAs and prokaryotic small non-coding sRNAs in probiotic Lactobacillus casei BL23. We found that compared to a reference conventional RNA library preparation, methyltransferase-Directed Orthogonal Tagging and RNA sequencing, mDOT-seq, avoids misdetection of unspecific highly-structured RNA species, thus providing better accuracy in identifying the groups of transcripts analysed. Our results suggest that mDOT-seq has the potential to advance analysis of eukaryotic and prokaryotic ssRNAs. Conclusions Our findings provide a valuable resource for studies of the RNA-centred regulatory networks in Lactobacilli and pave the way to developing novel transcriptome and epitranscriptome profiling approaches in vitro and inside living cells. As RNA methyltransferases share the structure of the AdoMet-binding domain and several specific cofactor binding features, the basic principles of our approach could be easily translated to other AdoMet-dependent enzymes for the development of modification-specific RNA-seq techniques.

scholarly journals Method for improved Illumina sequencing library preparation using NuGEN Ovation RNA-Seq System

BioTechniques ◽  
2011 ◽  
Vol 50 (3) ◽  
pp. 177-181 ◽  
Author(s):  
Steven R. Head ◽  
H.Kiyomi Komori ◽  
G.Traver Hart ◽  
John Shimashita ◽  
Lana Schaffer ◽  
...  
Keyword(s):  
Illumina Sequencing ◽  
Library Preparation ◽  
Rna Seq ◽  
Sequencing Library ◽  
Sequencing Library Preparation

scholarly journals High heterogeneity undermines generalization of differential expression results in RNA-Seq analysis

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Weitong Cui ◽  
Huaru Xue ◽  
Lei Wei ◽  
Jinghua Jin ◽  
Xuewen Tian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Small Sample ◽  
Differentially Expressed ◽  
Cancer Type ◽  
Rna Seq ◽  
Sample Sizes ◽  
Large Sample ◽  
Expression Levels ◽  
Gene Expression Levels

Abstract Background RNA sequencing (RNA-Seq) has been widely applied in oncology for monitoring transcriptome changes. However, the emerging problem that high variation of gene expression levels caused by tumor heterogeneity may affect the reproducibility of differential expression (DE) results has rarely been studied. Here, we investigated the reproducibility of DE results for any given number of biological replicates between 3 and 24 and explored why a great many differentially expressed genes (DEGs) were not reproducible. Results Our findings demonstrate that poor reproducibility of DE results exists not only for small sample sizes, but also for relatively large sample sizes. Quite a few of the DEGs detected are specific to the samples in use, rather than genuinely differentially expressed under different conditions. Poor reproducibility of DE results is mainly caused by high variation of gene expression levels for the same gene in different samples. Even though biological variation may account for much of the high variation of gene expression levels, the effect of outlier count data also needs to be treated seriously, as outlier data severely interfere with DE analysis. Conclusions High heterogeneity exists not only in tumor tissue samples of each cancer type studied, but also in normal samples. High heterogeneity leads to poor reproducibility of DEGs, undermining generalization of differential expression results. Therefore, it is necessary to use large sample sizes (at least 10 if possible) in RNA-Seq experimental designs to reduce the impact of biological variability and DE results should be interpreted cautiously unless soundly validated.

scholarly journals Impact of RNA-seq data analysis algorithms on gene expression estimation and downstream prediction

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Li Tong ◽  
◽  
Po-Yen Wu ◽  
John H. Phan ◽  
Hamid R. Hassazadeh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Disease Outcome ◽  
Rna Seq ◽  
Next Generation Sequencing Technology ◽  
Normalization Methods ◽  
The Us ◽  
Sequencing Quality ◽  
Improved Accuracy ◽  
The Impact

Abstract To use next-generation sequencing technology such as RNA-seq for medical and health applications, choosing proper analysis methods for biomarker identification remains a critical challenge for most users. The US Food and Drug Administration (FDA) has led the Sequencing Quality Control (SEQC) project to conduct a comprehensive investigation of 278 representative RNA-seq data analysis pipelines consisting of 13 sequence mapping, three quantification, and seven normalization methods. In this article, we focused on the impact of the joint effects of RNA-seq pipelines on gene expression estimation as well as the downstream prediction of disease outcomes. First, we developed and applied three metrics (i.e., accuracy, precision, and reliability) to quantitatively evaluate each pipeline’s performance on gene expression estimation. We then investigated the correlation between the proposed metrics and the downstream prediction performance using two real-world cancer datasets (i.e., SEQC neuroblastoma dataset and the NIH/NCI TCGA lung adenocarcinoma dataset). We found that RNA-seq pipeline components jointly and significantly impacted the accuracy of gene expression estimation, and its impact was extended to the downstream prediction of these cancer outcomes. Specifically, RNA-seq pipelines that produced more accurate, precise, and reliable gene expression estimation tended to perform better in the prediction of disease outcome. In the end, we provided scenarios as guidelines for users to use these three metrics to select sensible RNA-seq pipelines for the improved accuracy, precision, and reliability of gene expression estimation, which lead to the improved downstream gene expression-based prediction of disease outcome.

Abstract 556: Highly complex DNA sequencing library preparation for cfDNA enrichment panels using a single-stranded approach

2021 ◽  
Author(s):  
Charles J. Vaske ◽  
Chris Troll ◽  
Camille Schwartz ◽  
Colin Naughton ◽  
Abdullah Mahmood Ali ◽  
...  
Keyword(s):  
Dna Sequencing ◽  
Library Preparation ◽  
Sequencing Library ◽  
Sequencing Library Preparation

scholarly journals Comparative analysis of antibody- and lipid-based multiplexing methods for single-cell RNA-seq

2020 ◽  
Author(s):  
Viacheslav Mylka ◽  
Jeroen Aerts ◽  
Irina Matetovici ◽  
Suresh Poovathingal ◽  
Niels Vandamme ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Comparative Analysis ◽  
Single Cell ◽  
Cell Lines ◽  
Clinical Studies ◽  
Clinical Samples ◽  
Rna Seq ◽  
Batch Effects ◽  
Single Cell Sequencing ◽  
Single Nucleus

ABSTRACTMultiplexing of samples in single-cell RNA-seq studies allows significant reduction of experimental costs, straightforward identification of doublets, increased cell throughput, and reduction of sample-specific batch effects. Recently published multiplexing techniques using oligo-conjugated antibodies or - lipids allow barcoding sample-specific cells, a process called ‘hashing’. Here, we compare the hashing performance of TotalSeq-A and -C antibodies, custom synthesized lipids and MULTI-seq lipid hashes in four cell lines, both for single-cell RNA-seq and single-nucleus RNA-seq. Hashing efficiency was evaluated using the intrinsic genetic variation of the cell lines. Benchmarking of different hashing strategies and computational pipelines indicates that correct demultiplexing can be achieved with both lipid- and antibody-hashed human cells and nuclei, with MULTISeqDemux as the preferred demultiplexing function and antibody-based hashing as the most efficient protocol on cells. Antibody hashing was further evaluated on clinical samples using PBMCs from healthy and SARS-CoV-2 infected patients, where we demonstrate a more affordable approach for large single-cell sequencing clinical studies, while simultaneously reducing batch effects.

scholarly journals JIND: Joint Integration and Discrimination for Automated Single-Cell Annotation

2020 ◽  
Author(s):  
Mohit Goyal ◽  
Guillermo Serrano ◽  
Ilan Shomorony ◽  
Mikel Hernaez ◽  
Idoia Ochoa
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Marker Genes ◽  
Specific Marker ◽  
Rna Seq ◽  
Batch Effects ◽  
Cell Type ◽  
Latent Space ◽  
Cell Type Specific ◽  
Low Dimensional

AbstractSingle-cell RNA-seq is a powerful tool in the study of the cellular composition of different tissues and organisms. A key step in the analysis pipeline is the annotation of cell-types based on the expression of specific marker genes. Since manual annotation is labor-intensive and does not scale to large datasets, several methods for automated cell-type annotation have been proposed based on supervised learning. However, these methods generally require feature extraction and batch alignment prior to classification, and their performance may become unreliable in the presence of cell-types with very similar transcriptomic profiles, such as differentiating cells. We propose JIND, a framework for automated cell-type identification based on neural networks that directly learns a low-dimensional representation (latent code) in which cell-types can be reliably determined. To account for batch effects, JIND performs a novel asymmetric alignment in which the transcriptomic profile of unseen cells is mapped onto the previously learned latent space, hence avoiding the need of retraining the model whenever a new dataset becomes available. JIND also learns cell-type-specific confidence thresholds to identify and reject cells that cannot be reliably classified. We show on datasets with and without batch effects that JIND classifies cells more accurately than previously proposed methods while rejecting only a small proportion of cells. Moreover, JIND batch alignment is parallelizable, being more than five or six times faster than Seurat integration. Availability: https://github.com/mohit1997/JIND.

scholarly journals Batch Effect Removal via Batch-Free Encoding

2018 ◽  
Author(s):  
Uri Shaham
Keyword(s):  
Deep Learning ◽  
Biological Properties ◽  
Batch Effect ◽  
Training Data ◽  
Rna Seq ◽  
Batch Effects ◽  
Training Time ◽  
Learning Techniques ◽  
Downstream Analysis ◽  
Biological Patterns

AbstractBiological measurements often contain systematic errors, also known as “batch effects”, which may invalidate downstream analysis when not handled correctly. The problem of removing batch effects is of major importance in the biological community. Despite recent advances in this direction via deep learning techniques, most current methods may not fully preserve the true biological patterns the data contains. In this work we propose a deep learning approach for batch effect removal. The crux of our approach is learning a batch-free encoding of the data, representing its intrinsic biological properties, but not batch effects. In addition, we also encode the systematic factors through a decoding mechanism and require accurate reconstruction of the data. Altogether, this allows us to fully preserve the true biological patterns represented in the data. Experimental results are reported on data obtained from two high throughput technologies, mass cytometry and single-cell RNA-seq. Beyond good performance on training data, we also observe that our system performs well on test data obtained from new patients, which was not available at training time. Our method is easy to handle, a publicly available code can be found at https://github.com/ushaham/BatchEffectRemoval2018.

Sign in / Sign up

Export Citation Format

Share Document