scholarly journals DEApp: an interactive web interface for differential expression analysis of next generation sequence data

2017 ◽  
Vol 12 (1) ◽  
Author(s):  
Yan Li ◽  
Jorge Andrade
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Sequence Data ◽  
Differential Expression Analysis ◽  
Next Generation ◽  
Web Interface

scholarly journals A Systematic Comparison of Differential Analysis Methods for CyTOF Data

2021 ◽  
Author(s):  
Lis Arend ◽  
Judith Bernett ◽  
Quirin Manz ◽  
Melissa Klug ◽  
Olga Lazareva ◽  
...  
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
Differential Analysis ◽  
Web Interface ◽  
Analysis Methods ◽  
Shiny App ◽  
R Shiny ◽  
User Friendly

Cytometry techniques are widely used to discover cellular characteristics at single-cell resolution. Many data analysis methods for cytometry data focus solely on identifying subpopulations via clustering and testing for differential cell abundance. For differential expression analysis of markers between conditions, only few tools exist. These tools either reduce the data distribution to medians, discarding valuable information, or have underlying assumptions that may not hold for all expression patterns. Here, we systematically evaluated existing and novel approaches for differential expression analysis on real and simulated CyTOF data. We found that methods using median marker expressions compute fast and reliable results when the data is not strongly zero-inflated. Methods using all data detect changes in strongly zero-inflated markers, but partially suffer from overprediction or cannot handle big datasets. We present a new method, CyEMD, based on calculating the Earth Mover's Distance between expression distributions that can handle strong zero-inflation without being too sensitive. Additionally, we developed CYANUS, a user-friendly R Shiny App allowing the user to analyze cytometry data with state-of-the-art tools, including well-performing methods from our comparison. A public web interface is available at https://exbio.wzw.tum.de/cyanus/.

scholarly journals BiSEK: a platform for a reliable differential expression analysis

2021 ◽  
Author(s):  
Roni Haas ◽  
Dean Light ◽  
Yahav Festinger ◽  
Neta Friedman ◽  
Ayelet T. Lamm
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Myeloid Leukemia ◽  
Differential Expression Analysis ◽  
Biological Sequence ◽  
Web Interface ◽  
Sequencing Data ◽  
Input Material ◽  
Experimental Findings

ABSTRACTDifferential Expression Analysis (DEA) of RNA-sequencing data is frequently performed for detecting key genes, affected across different conditions. Although DEA-workflows are well established, preceding reliability-testing of the input material, which is crucial for consistent and strong results, is challenging and less straightforward. Here we present Biological Sequence Expression Kit (BiSEK), a graphical user interface-based platform for DEA, dedicated to a reliable inquiry. BiSEK is based on a novel algorithm to track discrepancies between the data and the statistical model design. Moreover, BiSEK enables differential-expression analysis of groups of genes, to identify affected pathways, without relying on the significance of genes comprising them. Using BiSEK, we were able to improve previously conducted analysis, aimed to detect genes affected by FUBP1 depletion in chronic myeloid leukemia cells of mice bone-marrow. We found affected genes that are related to the regulation of apoptosis, supporting in-vivo experimental findings. We further tested the host response following SARS-CoV-2 infection. We identified a substantial interferon-I reaction and low expression levels of TLR3, an inducer of interferon-III (IFN-III) production, upon infection with SARS-CoV-2 compared to other respiratory viruses. This finding may explain the low IFN-III response upon SARS-CoV-2 infection. BiSEK is open-sourced, available as a web-interface.

scholarly journals Best practices on the differential expression analysis of multi-species RNA-seq

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Matthew Chung ◽  
Vincent M. Bruno ◽  
David A. Rasko ◽  
Christina A. Cuomo ◽  
José F. Muñoz ◽  
...  
Keyword(s):  
Best Practices ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Single Species ◽  
Rna Seq ◽  
Species Analysis ◽  
Differential Gene ◽  
Multiple Species ◽  
Downstream Analysis

AbstractAdvances in transcriptome sequencing allow for simultaneous interrogation of differentially expressed genes from multiple species originating from a single RNA sample, termed dual or multi-species transcriptomics. Compared to single-species differential expression analysis, the design of multi-species differential expression experiments must account for the relative abundances of each organism of interest within the sample, often requiring enrichment methods and yielding differences in total read counts across samples. The analysis of multi-species transcriptomics datasets requires modifications to the alignment, quantification, and downstream analysis steps compared to the single-species analysis pipelines. We describe best practices for multi-species transcriptomics and differential gene expression.

scholarly journals The long and the short of it: unlocking nanopore long-read RNA sequencing data with short-read differential expression analysis tools

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Xueyi Dong ◽  
Luyi Tian ◽  
Quentin Gouil ◽  
Hasaru Kariyawasam ◽  
Shian Su ◽  
...  
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Transcriptomic Analysis ◽  
Statistical Testing ◽  
Rna Seq ◽  
Sequencing Data ◽  
Short Read ◽  
Sequencing Platform ◽  
Long Read

Abstract Application of Oxford Nanopore Technologies’ long-read sequencing platform to transcriptomic analysis is increasing in popularity. However, such analysis can be challenging due to the high sequence error and small library sizes, which decreases quantification accuracy and reduces power for statistical testing. Here, we report the analysis of two nanopore RNA-seq datasets with the goal of obtaining gene- and isoform-level differential expression information. A dataset of synthetic, spliced, spike-in RNAs (‘sequins’) as well as a mouse neural stem cell dataset from samples with a null mutation of the epigenetic regulator Smchd1 was analysed using a mix of long-read specific tools for preprocessing together with established short-read RNA-seq methods for downstream analysis. We used limma-voom to perform differential gene expression analysis, and the novel FLAMES pipeline to perform isoform identification and quantification, followed by DRIMSeq and limma-diffSplice (with stageR) to perform differential transcript usage analysis. We compared results from the sequins dataset to the ground truth, and results of the mouse dataset to a previous short-read study on equivalent samples. Overall, our work shows that transcriptomic analysis of long-read nanopore data using long-read specific preprocessing methods together with short-read differential expression methods and software that are already in wide use can yield meaningful results.

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