scholarly journals Normalization of generalized transcript degradation improves accuracy in RNA-seq analysis

2018 ◽  
Author(s):  
Bin Xiong ◽  
Yiben Yang ◽  
Frank R. Fineis ◽  
Ji-Ping Wang
Keyword(s):  
High Throughput ◽  
Simulated Data ◽  
Sequencing Depth ◽  
Rna Seq ◽  
Effective Performance ◽  
High Throughput Assay ◽  
In Cells

AbstractRNA-seq is a high-throughput assay to profile transcriptional activities in cells. Here we show that transcript degradation is gene-/sample-specific and presents a common and major source that may substantially bias the results in RNA-seq analysis. Most existing global normalization approaches are ineffective to correct for the degradation bias. We propose a novel pipeline named DegNorm (stands for degradation normalization) to adjust read counts for transcript degradation heterogeneity on a gene-by-gene basis while simultaneously controlling the sequencing depth. The robust and effective performance of this method is demonstrated in an extensive set of real RNA-seq data and simulated data.

scholarly journals Model based normalization improves differential expression calling in low-depth RNA-seq

2015 ◽  
Author(s):  
Pavel Zakharov ◽  
Alexey Sergushichev ◽  
Alexander Predeus ◽  
Maxim Artyomov
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
High Throughput ◽  
Differential Expression Analysis ◽  
Sequencing Depth ◽  
Rna Seq ◽  
Optimal Balance ◽  
Gene Count ◽  
Count Distribution

RNA-seq is a powerful tool for gene expression profiling and differential expression analysis. Its power depends on sequencing depth which limits its high-throughput potential, with 10-15 million reads considered as optimal balance between quality of differential expression calling and cost per sample. We observed, however, that some statistical features of the data, e.g. gene count distribution, are preserved well below 10-15M reads, and found that they improve differential expression analysis at low sequencing depths when distribution statistics is estimated by pooling individual samples to a combined higher-depth library. Using a novel gene-by-gene scaling technique, based on the fact that gene counts obey Pareto-like distribution, we re-normalize samples towards bigger sequencing depth and show that this leads to significant improvement in differential expression calling, with only a marginal increase in false positive calls. This makes differential expression calling from 3-4M reads comparable to 10-15M reads, improving high-throughput of RNA-sequencing 3-4 fold.

scholarly journals K-nearest neighbor smoothing for high-throughput single-cell RNA-Seq data

2017 ◽  
Author(s):  
Florian Wagner ◽  
Yun Yan ◽  
Itai Yanai
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Nearest Neighbor ◽  
Expression Profiles ◽  
Simulated Data ◽  
T Cell Subsets ◽  
Cell Populations ◽  
Rna Seq ◽  
K Nearest Neighbor ◽  
Heterogeneous Tissues

High-throughput single-cell RNA-Seq (scRNA-Seq) is a powerful approach for studying heterogeneous tissues and dynamic cellular processes. However, compared to bulk RNA-Seq, single-cell expression profiles are extremely noisy, as they only capture a fraction of the transcripts present in the cell. Here, we propose the k-nearest neighbor smoothing (kNN-smoothing) algorithm, designed to reduce noise by aggregating information from similar cells (neighbors) in a computationally efficient and statistically tractable manner. The algorithm is based on the observation that across protocols, the technical noise exhibited by UMI-filtered scRNA-Seq data closely follows Poisson statistics. Smoothing is performed by first identifying the nearest neighbors of each cell in a step-wise fashion, based on partially smoothed and variance-stabilized expression profiles, and then aggregating their transcript counts. We show that kNN-smoothing greatly improves the detection of clusters of cells and co-expressed genes, and clearly outperforms other smoothing methods on simulated data. To accurately perform smoothing for datasets containing highly similar cell populations, we propose the kNN-smoothing 2 algorithm, in which neighbors are determined after projecting the partially smoothed data onto the first few principal components. We show that unlike its predecessor, kNN-smoothing 2 can accurately distinguish between cells from different T cell subsets, and enables their identification in peripheral blood using unsupervised methods. Our work facilitates the analysis of scRNA-Seq data across a broad range of applications, including the identification of cell populations in heterogeneous tissues and the characterization of dynamic processes such as cellular differentiation. Reference implementations of our algorithms can be found at https://github.com/yanailab/knn-smoothing.

scholarly journals SUPPA2 provides fast, accurate, and uncertainty-aware differential splicing analysis across multiple conditions

2016 ◽  
Author(s):  
Juan L. Trincado ◽  
Juan C. Entizne ◽  
Gerald Hysenaj ◽  
Babita Singh ◽  
Miha Skalic ◽  
...  
Keyword(s):  
Alternative Splicing ◽  
Intron Retention ◽  
Simulated Data ◽  
Cost Effective ◽  
Sequencing Depth ◽  
Read Length ◽  
Rna Seq ◽  
Differential Splicing ◽  
The Many ◽  
Multiple Conditions

AbstractDespite the many approaches to study differential splicing from RNA-seq, many challenges remain unsolved, including computing capacity and sequencing depth requirements. Here we present SUPPA2, a new method for differential splicing analysis that addresses these challenges and enables streamlined analysis across multiple conditions taking into account biological variability. Using experimental and simulated data SUPPA2 achieves higher accuracy compared to other methods; especially at low sequencing depth and short read length, with important implications for cost-effective use of RNA-seq for splicing; and was able to identify novel Transformer2-regulated exons. We further analyzed two differentiation series to support the applicability of SUPPA2 beyond binary comparisons. This identified clusters of alternative splicing events enriched in microexons induced during differentiation of bipolar neurons, and a cluster enriched in intron retention events that are present at late stages during erythroblast differentiation. Our data suggest that SUPPA2 is a valuable tool for the robust investigation of the biological complexity of alternative splicing.

scholarly journals Assessing Reproducibility of High-throughput Experiments in the Case of Missing Data.

2021 ◽  
Author(s):  
Roopali Singh ◽  
Feipeng Zhang ◽  
Qunhua Li
Keyword(s):  
Missing Data ◽  
Single Cell ◽  
High Throughput ◽  
Latent Variable ◽  
Missing Values ◽  
Sequencing Depth ◽  
Rna Seq ◽  
Variable Approach ◽  
Operational Factors ◽  
High Throughput Experiments

High-throughput experiments are an essential part of modern biological and biomedical research. The outcomes of high-throughput biological experiments often have a lot of missing observations due to signals below detection levels. For example, most single-cell RNA-seq (scRNA-seq) protocols experience high levels of dropout due to the small amount of starting material, leading to a majority of reported expression levels being zero. Though missing data contain information about reproducibility, they are often excluded in the reproducibility assessment, potentially generating misleading assessments. In this paper, we develop a regression model to assess how the reproducibility of high-throughput experiments is affected by the choices of operational factors (e.g., platform or sequencing depth) when a large number of measurements are missing. Using a latent variable approach, we extend correspondence curve regression (CCR), a recently proposed method for assessing the effects of operational factors to reproducibility, to incorporate missing values. Using simulations, we show that our method is more accurate in detecting differences in reproducibility than existing measures of reproducibility. We illustrate the usefulness of our method using a single-cell RNA-seq dataset collected on HCT116 cells. We compare the reproducibility of different library preparation platforms and study the effect of sequencing depth on reproducibility, thereby determining the cost-effective sequencing depth that is required to achieve sufficient reproducibility.

A Functional High-Throughput Assay of Myelination in Vitro

2013 ◽  
Author(s):  
Michael J. Moore
Keyword(s):  
High Throughput ◽  
High Throughput Assay

scholarly journals Advancing clinical genomics and precision medicine with GVViZ: FAIR bioinformatics platform for variable gene-disease annotation, visualization, and expression analysis

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Zeeshan Ahmed ◽  
Eduard Gibert Renart ◽  
Saman Zeeshan ◽  
XinQi Dong
Keyword(s):  
Data Analysis ◽  
Patient Care ◽  
Expression Analysis ◽  
High Throughput ◽  
Gene Annotation ◽  
Next Generation Sequencing Data ◽  
Rna Seq ◽  
Sequencing Data ◽  
Complex Disorders ◽  
Transcriptomics Data

Abstract Background Genetic disposition is considered critical for identifying subjects at high risk for disease development. Investigating disease-causing and high and low expressed genes can support finding the root causes of uncertainties in patient care. However, independent and timely high-throughput next-generation sequencing data analysis is still a challenge for non-computational biologists and geneticists. Results In this manuscript, we present a findable, accessible, interactive, and reusable (FAIR) bioinformatics platform, i.e., GVViZ (visualizing genes with disease-causing variants). GVViZ is a user-friendly, cross-platform, and database application for RNA-seq-driven variable and complex gene-disease data annotation and expression analysis with a dynamic heat map visualization. GVViZ has the potential to find patterns across millions of features and extract actionable information, which can support the early detection of complex disorders and the development of new therapies for personalized patient care. The execution of GVViZ is based on a set of simple instructions that users without a computational background can follow to design and perform customized data analysis. It can assimilate patients’ transcriptomics data with the public, proprietary, and our in-house developed gene-disease databases to query, easily explore, and access information on gene annotation and classified disease phenotypes with greater visibility and customization. To test its performance and understand the clinical and scientific impact of GVViZ, we present GVViZ analysis for different chronic diseases and conditions, including Alzheimer’s disease, arthritis, asthma, diabetes mellitus, heart failure, hypertension, obesity, osteoporosis, and multiple cancer disorders. The results are visualized using GVViZ and can be exported as image (PNF/TIFF) and text (CSV) files that include gene names, Ensembl (ENSG) IDs, quantified abundances, expressed transcript lengths, and annotated oncology and non-oncology diseases. Conclusions We emphasize that automated and interactive visualization should be an indispensable component of modern RNA-seq analysis, which is currently not the case. However, experts in clinics and researchers in life sciences can use GVViZ to visualize and interpret the transcriptomics data, making it a powerful tool to study the dynamics of gene expression and regulation. Furthermore, with successful deployment in clinical settings, GVViZ has the potential to enable high-throughput correlations between patient diagnoses based on clinical and transcriptomics data.

Revealing the changes of IgG subclass‐specific N ‐glycosylation in colorectal cancer progression by high‐throughput assay

2021 ◽  
pp. 2000022
Author(s):  
Si Liu ◽  
Yuting Yu ◽  
Yuanyuan Liu ◽  
Jiajing Lin ◽  
Yang Fu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cancer Progression ◽  
High Throughput ◽  
Igg Subclass ◽  
Colorectal Cancer Progression ◽  
High Throughput Assay
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