scholarly journals Granatum: a graphical single-cell RNA-Seq analysis pipeline for genomics scientists

2017 ◽  
Author(s):  
Xun Zhu ◽  
Thomas Wolfgruber ◽  
Austin Tasato ◽  
David G. Garmire ◽  
Lana X Garmire
Keyword(s):  
Single Cell ◽  
Enrichment Analysis ◽  
Graphical Interface ◽  
Analysis Pipeline ◽  
Web Based ◽  
Cell Clustering ◽  
Gene Filtering ◽  
Study Heterogeneity ◽  
Differential Gene ◽  
Cell Series

AbstractBackgroundSingle-cell RNA sequencing (scRNA-Seq) is an increasingly popular platform to study heterogeneity at the single-cell level.Computational methods to process scRNA-Seq have limited accessibility to bench scientists as they require significant amounts of bioinformatics skills.ResultsWe have developed Granatum, a web-based scRNA-Seq analysis pipeline to make analysis more broadly accessible to researchers. Without a single line of programming code, users can click through the pipeline, setting parameters and visualizing results via the interactive graphical interface Granatum conveniently walks users through various steps of scRNA-Seq analysis. It has a comprehensive list of modules, including plate merging and batch-effect removal, outlier-sample removal, gene filtering, geneexpression normalization, cell clustering, differential gene expression analysis, pathway/ontology enrichment analysis, protein-networ interaction visualization, and pseudo-time cell series construction.ConclusionsGranatum enables broad adoption of scRNA-Seq technology by empowering the bench scientists with an easy-to-use graphical interface for scRNA-Seq data analysis. The package is freely available for research use athttp://garmiregroup.org/granatum/app

scholarly journals ASAP 2020 update: an open, scalable and interactive web-based portal for (single-cell) omics analyses

2020 ◽  
Vol 48 (W1) ◽  
pp. W403-W414
Author(s):  
Fabrice P A David ◽  
Maria Litovchenko ◽  
Bart Deplancke ◽  
Vincent Gardeux
Keyword(s):  
Big Data ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Omics Data ◽  
Cell Analysis ◽  
Rna Seq ◽  
Analysis Pipeline ◽  
Web Based ◽  
Data Analyses ◽  
The Web

Abstract Single-cell omics enables researchers to dissect biological systems at a resolution that was unthinkable just 10 years ago. However, this analytical revolution also triggered new demands in ‘big data’ management, forcing researchers to stay up to speed with increasingly complex analytical processes and rapidly evolving methods. To render these processes and approaches more accessible, we developed the web-based, collaborative portal ASAP (Automated Single-cell Analysis Portal). Our primary goal is thereby to democratize single-cell omics data analyses (scRNA-seq and more recently scATAC-seq). By taking advantage of a Docker system to enhance reproducibility, and novel bioinformatics approaches that were recently developed for improving scalability, ASAP meets challenging requirements set by recent cell atlasing efforts such as the Human (HCA) and Fly (FCA) Cell Atlas Projects. Specifically, ASAP can now handle datasets containing millions of cells, integrating intuitive tools that allow researchers to collaborate on the same project synchronously. ASAP tools are versioned, and researchers can create unique access IDs for storing complete analyses that can be reproduced or completed by others. Finally, ASAP does not require any installation and provides a full and modular single-cell RNA-seq analysis pipeline. ASAP is freely available at https://asap.epfl.ch.

scholarly journals Functional annotation-driven unsupervised clustering of single-cell transcriptomes

2021 ◽  
Author(s):  
Keita Iida ◽  
Jumpei Kondo ◽  
Masahiro Inoue ◽  
Mariko Okada
Keyword(s):  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Resistance Mechanisms ◽  
Analysis Pipeline ◽  
Lung Cancers ◽  
Cell Clustering ◽  
Healthy Donors ◽  
Biological Interpretation ◽  
Functional States

Single-cell RNA sequencing (scRNA-seq) analysis has significantly advanced our knowledge of functional states of cells. By analyzing scRNA-seq data, we can deconvolve individual cell states into thousands of gene expression profiles, allowing us to perform cell clustering, and identify significant genes for each cluster. However, interpreting these results remains challenging. Here, we present a novel scRNA-seq analysis pipeline named ASURAT, which simultaneously performs unsupervised cell clustering and biological interpretation in semi-automatic manner, in terms of cell type and various biological functions. We validate the reliable clustering performance of ASURAT by comparing it with existing methods, using six published scRNA-seq datasets from healthy donors and cancer patients. Furthermore, we applied ASURAT to patient-derived scRNA-seq datasets including small cell lung cancers, finding some putative cancer subpopulations showing different resistance mechanisms. ASURAT is expected to open new means of scRNA-seq analysis, focusing more on "biological meaning" than conventional gene-based analyses.

scholarly journals Unambiguous detection of SARS-CoV-2 subgenomic mRNAs with single cell RNA sequencing

2021 ◽  
Author(s):  
Phillip Cohen ◽  
Emma J DeGrace ◽  
Oded Danziger ◽  
Roosheel Patel ◽  
Brad R Rosenberg
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Enrichment Analysis ◽  
Viral Particles ◽  
Single Cell Rna Sequencing ◽  
Active Viral Replication ◽  
Sequencing Strategy ◽  
Infected Cells ◽  
Differential Gene ◽  
Unambiguous Detection

Single cell RNA sequencing (scRNAseq) studies have provided critical insight into the pathogenesis of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), the causative agent of COronaVIrus Disease 2019 (COVID-19). scRNAseq workflows are generally designed for the detection and quantification of eukaryotic host mRNAs and not viral RNAs. The performance of different scRNAseq methods to study SARS-CoV-2 RNAs has not been thoroughly evaluated. Here, we compare different scRNAseq methods for their ability to quantify and detect SARS-CoV-2 RNAs with a focus on subgenomic mRNAs (sgmRNAs), which are produced only during active viral replication and not present in viral particles. We present a data processing strategy, single cell CoronaVirus sequencing (scCoVseq), which quantifies reads unambiguously assigned to sgmRNAs or genomic RNA (gRNA). Compared to standard 10X Genomics Chromium Next GEM Single Cell 3′ (10X 3′) and Chromium Next GEM Single Cell V(D)J (10X 5′) sequencing, we find that 10X 5′ with an extended R1 sequencing strategy maximizes the unambiguous detection of sgmRNAs by increasing the number of reads spanning leader-sgmRNA junction sites. Differential gene expression testing and KEGG enrichment analysis of infected cells compared with bystander or mock cells showed an enrichment for COVID19-associated genes, supporting the ability of our method to accurately identify infected cells. Our method allows for quantification of coronavirus sgmRNA expression at single-cell resolution, and thereby supports high resolution studies of the dynamics of coronavirus RNA synthesis.

scholarly journals Computational cell cycle analysis of single cell RNA-Seq data

2020 ◽  
Author(s):  
Marmar Moussa ◽  
Ion I. Măndoiu
Keyword(s):  
Cell Cycle ◽  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Analysis Tool ◽  
Rna Seq ◽  
Cell Type ◽  
Computational Cell ◽  
Analysis Pipeline ◽  
Web Based

AbstractThe variation in gene expression profiles of cells captured in different phases of the cell cycle can interfere with cell type identification and functional analysis of single cell RNA-Seq (scRNA-Seq) data. In this paper, we introduce SC1CC (SC1 Cell Cycle analysis tool), a computational approach for clustering and ordering single cell transcriptional profiles according to their progression along cell cycle phases. We also introduce a new robust metric, Gene Smoothness Score (GSS) for assessing the cell cycle based order of the cells. SC1CC is available as part of the SC1 web-based scRNA-Seq analysis pipeline, publicly accessible at https://sc1.engr.uconn.edu/.

scholarly journals Zeros in scRNA-seq data: good or bad? How to embrace or tackle zeros in scRNA-seq data analysis?

2020 ◽  
Author(s):  
Ruochen Jiang ◽  
Tianyi Sun ◽  
Dongyuan Song ◽  
Jingyi Jessica Li
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Single Cell ◽  
Biomedical Sciences ◽  
Data Types ◽  
Excess Zeros ◽  
Cell Clustering ◽  
Open Questions ◽  
Genome Wide ◽  
Differential Gene

AbstractSingle-cell RNA sequencing (scRNA-seq) technologies have revolutionized biomedical sciences by enabling genome-wide profiling of gene expression levels at an unprecedented single-cell resolution. A distinct characteristic of scRNA-seq data is the vast proportion of zeros unseen in bulk RNA-seq data. Researchers view these zeros differently: some regard zeros as biological signals representing no or low gene expression, while others regard zeros as false signals or missing data to be corrected. As a result, the scRNA-seq field faces much controversy regarding how to handle zeros in data analysis. In this paper, we first discuss the origins of biological and non-biological zeros in scRNA-seq data. Second, we clarify the definitions of several commonly-used but ambiguous terms, including “dropouts,” “excess zeros,” and “zero inflation.” Third, we evaluate the impacts of non-biological zeros on cell clustering and differential gene expression analysis. Fourth, we summarize the advantages, disadvantages, and suitable users of three input data types: original counts, imputed counts, and binarized counts. Finally, we discuss the open questions regarding non-biological zeros, the need for benchmarking, and the importance of transparent analysis.

scholarly journals VolcanoR - web service to produce volcano plots and do basic enrichment analysis

2017 ◽  
Author(s):  
Vladimir Naumov ◽  
Ivan Balashov ◽  
Vadim Lagutin ◽  
Pavel Borovikov ◽  
Alexey Alexeev
Keyword(s):  
Web Service ◽  
Input Data ◽  
Enrichment Analysis ◽  
P Value ◽  
Web Based ◽  
Link Type ◽  
Volcano Plots ◽  
R Shiny ◽  
Differential Gene ◽  
Publication Quality

AbstractSummary: We introduce VolcanoR - web based tool to analyse results of differential gene expression. It takes a table containing gene name p-value and foldChange as input data. It can produce publication quality volcano plots, apply different p-value and fold change thresholds and do basic GeneOntology and KEGG enrichment analysis with selected gene set. For now it supports H.sapiens, R.norvegicus and M.musclus.Availability and Implementation: VolcanoR is wtitten using R Shiny framework. It is publically available at http://volcanor.bioinf.su or stand-alone application, that can be downloaded at https://github.com/vovalive/volcanoRContact: [email protected]

Identifying Breast Cancer-induced gene perturbations and its application in guiding drug repurposing

2020 ◽  
Vol 15 ◽  
Author(s):  
Jujuan Zhuang ◽  
Shuang Dai ◽  
Lijun Zhang ◽  
Pan Gao ◽  
Yingmin Han ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Differential Expression ◽  
Enrichment Analysis ◽  
Cancer Tissue ◽  
Connectivity Analysis ◽  
Cancer Drugs ◽  
Normal Tissues ◽  
Differential Gene ◽  
Differential Modules ◽  
Differential Connectivity

Background: Breast cancer is a complex disease with high prevalence in women, the molecular mechanisms of which are still unclear at present. Most transcriptomic studies on breast cancer focus on differential expression of each gene between tumor and the adjacent normal tissues, while the other perturbations induced by breast cancer including the gene regulation variations, the changes of gene modules and the pathways, which might be critical to the diagnosis, treatment and prognosis of breast cancer are more or less ignored. Objective: We presented a complete process to study breast cancer from multiple perspectives, including differential expression analysis, constructing gene co-expression networks, modular differential connectivity analysis, differential gene connectivity analysis, gene function enrichment analysis key driver analysis. In addition, we prioritized the related anti-cancer drugs based on enrichment analysis between differential expression genes and drug perturbation signatures. Methods: The RNA expression profiles of 1109 breast cancer tissue and 113 non-tumor tissues were downloaded from The Cancer Genome Atlas (TCGA) database. Differential expression of RNAs was identified using the “DESeq2” bioconductor package in R, and gene co-expression networks was constructed using the weighted gene co-expression network analysis (WGCNA). To compare the module changes and gene co-expression variations between tumor and the adjacent normal tissues, modular differential connectivity (MDC) analysis and differential gene connectivity analysis (DGCA) were performed. Results: Top differential genes like MMP11 and COL10A1 were known to be associated with breast cancer. And we found 23 modules in the tumor network had significantly different co-expression patterns. The top differential modules were enriched in Goterms related to breast cancer like MHC protein complex, leukocyte activation, regulation of defense response and so on. In addition, key genes like UBE2T driving the top differential modules were significantly correlated with the patients’ survival. Finally, we predicted some potential breast cancer drugs, such as Eribulin, Taxane, Cisplatin and Oxaliplatin. Conclusion: As an indication, this framework might be useful in understanding the molecular pathogenesis of diseases like breast cancer and inferring useful drugs for personalized medication

Prediction of Future Market Price for Agricultural Commodities

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Sagar Pathane ◽  
Uttam Patil ◽  
Nandini Sidnal
Keyword(s):  
Mobile Application ◽  
Market Price ◽  
Commodity Prices ◽  
Future Price ◽  
Future Market ◽  
Graphical Interface ◽  
Agricultural Commodities ◽  
Agricultural Commodity ◽  
Web Based ◽  
Agriculture Products

The agricultural commodity prices have a volatile nature which may increase or decrease inconsistently causing an adverse effect on the economy. The work carried out here for predicting prices of agricultural commodities is useful for the farmers because of which they can sow appropriate crop depending on its future price. Agriculture products have seasonal rates, these rates are spread over the entire year. If these rates are known/alerted to the farmers in advance, then it will be promising on ROI (Return on Investments). It requires that the rates of the agricultural products updated into the dataset of each state and each crop, in this application five crops are considered. The predictions are done based on neural networks Neuroph framework in java platform and also the previous years data. The results are produced on mobile application using android. Web based interface is also provided for displaying processed commodity rates in graphical interface. Agricultural experts can follow these graphs and predict market rates which can be informed to the farmers. The results will be provided based on the location of the users of this application.

scholarly journals DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics

2021 ◽  
Vol 22 (3) ◽  
pp. 1399
Author(s):  
Salim Ghannoum ◽  
Waldir Leoncio Netto ◽  
Damiano Fantini ◽  
Benjamin Ragan-Kelley ◽  
Amirabbas Parizadeh ◽  
...  
Keyword(s):  
Single Cell ◽  
Biomarker Discovery ◽  
Enrichment Analysis ◽  
Myxoid Liposarcoma ◽  
R Package ◽  
Differential Analysis ◽  
A Cell ◽  
Reproducible Analysis ◽  
Transcriptomic Level ◽  
User Friendly

The growing attention toward the benefits of single-cell RNA sequencing (scRNA-seq) is leading to a myriad of computational packages for the analysis of different aspects of scRNA-seq data. For researchers without advanced programing skills, it is very challenging to combine several packages in order to perform the desired analysis in a simple and reproducible way. Here we present DIscBIO, an open-source, multi-algorithmic pipeline for easy, efficient and reproducible analysis of cellular sub-populations at the transcriptomic level. The pipeline integrates multiple scRNA-seq packages and allows biomarker discovery with decision trees and gene enrichment analysis in a network context using single-cell sequencing read counts through clustering and differential analysis. DIscBIO is freely available as an R package. It can be run either in command-line mode or through a user-friendly computational pipeline using Jupyter notebooks. We showcase all pipeline features using two scRNA-seq datasets. The first dataset consists of circulating tumor cells from patients with breast cancer. The second one is a cell cycle regulation dataset in myxoid liposarcoma. All analyses are available as notebooks that integrate in a sequential narrative R code with explanatory text and output data and images. R users can use the notebooks to understand the different steps of the pipeline and will guide them to explore their scRNA-seq data. We also provide a cloud version using Binder that allows the execution of the pipeline without the need of downloading R, Jupyter or any of the packages used by the pipeline. The cloud version can serve as a tutorial for training purposes, especially for those that are not R users or have limited programing skills. However, in order to do meaningful scRNA-seq analyses, all users will need to understand the implemented methods and their possible options and limitations.

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