An open RNA-Seq data analysis pipeline tutorial with an example of reprocessing data from a recent Zika virus study

RNA-seq analysis is becoming a standard method for global gene expression profiling. However, open and standard pipelines to perform RNA-seq analysis by non-experts remain challenging due to the large size of the raw data files and the hardware requirements for running the alignment step. Here we introduce a reproducible open source RNA-seq pipeline delivered as an IPython notebook and a Docker image. The pipeline uses state-of-the-art tools and can run on various platforms with minimal configuration overhead. The pipeline enables the extraction of knowledge from typical RNA-seq studies by generating interactive principal component analysis (PCA) and hierarchical clustering (HC) plots, performing enrichment analyses against over 90 gene set libraries, and obtaining lists of small molecules that are predicted to either mimic or reverse the observed changes in mRNA expression. We apply the pipeline to a recently published RNA-seq dataset collected from human neuronal progenitors infected with the Zika virus (ZIKV). In addition to confirming the presence of cell cycle genes among the genes that are downregulated by ZIKV, our analysis uncovers significant overlap with upregulated genes that when knocked out in mice induce defects in brain morphology. This result potentially points to the molecular processes associated with the microcephaly phenotype observed in newborns from pregnant mothers infected with the virus. In addition, our analysis predicts small molecules that can either mimic or reverse the expression changes induced by ZIKV. The IPython notebook and Docker image are freely available at: http://nbviewer.jupyter.org/github/maayanlab/Zika-RNAseq-Pipeline/blob/master/Zika.ipynb and https://hub.docker.com/r/maayanlab/zika/.

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MicroScope: ChIP-seq and RNA-seq software analysis suite for gene expression heatmaps

10.1101/034694 ◽

2015 ◽

Cited By ~ 1

Author(s):

Bohdan B. Khomtchouk ◽

James R. Hennessy ◽

Claes Wahlestedt

Keyword(s):

Web Application ◽

Differential Expression Analysis ◽

Dynamic Network ◽

Principal Component ◽

Rna Seq ◽

Software Suite ◽

Software Analysis ◽

Link Type ◽

R Shiny ◽

User Friendly

AbstractWe propose a user-friendly ChIP-seq and RNA-seq software suite for the interactive visualization and analysis of genomic data, including integrated features to support differential expression analysis, interactive heatmap production, principal component analysis, gene ontology analysis, and dynamic network analysis.MicroScope is hosted online as an R Shiny web application based on the D3 JavaScript library: http://microscopebioinformatics.org/. The methods are implemented in R, and are available as part of the MicroScope project at: https://github.com/Bohdan-Khomtchouk/Microscope.

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Subtyping Diffuse Large B-Cell Lymphoma with Cell-of-Origin Using 32-Gene Expression Assay in Formalin-Fixed Paraffin-Embedded Tissue

Blood ◽

10.1182/blood-2019-126629 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 5337-5337

Author(s):

Xiangnan Jiang ◽

Wanhui Yan ◽

Yifeng Sun ◽

Qinghua Xu ◽

Xiaoyan Zhou ◽

...

Keyword(s):

Gene Expression ◽

B Cell ◽

Gene Expression Profiling ◽

Expression Profiling ◽

Gold Standard ◽

B Cell Lymphoma ◽

Global Gene Expression ◽

Rna Seq ◽

Gene Assay ◽

Global Gene Expression Profiling

Introduction Diffuse large B-cell lymphoma (DLBCL) is a group of heterogeneous disease with distinct molecular subtypes. The most established subtyping algorithm, the Cell-of-Origin (COO) model, categorizes DLBCL into activated B-cell (ABC) and germinal center B-cell (GCB)-like subgroups through gene expression profiling. COO subtyping is mandatory for every newly diagnosed DLBCL patients, as it is critical for determining the therapeutic and surveillance strategies. We evaluated a newly developed assay using 32-gene expression profiling to determine the COO of DLBCL with formalin-fixed paraffin-embedded (FFPE) tissue. Methods The DLBCL-COO Test is a qPCR-based 32-gene expression assay for COO determination in FFPE samples. Biopsy of DLBCL patients with paired FFPE and fresh tissue were identified to assign COO, based on the immunohistochemistry (IHC) algorithm (Han's algorithm), DLBCL-COO qPCR assay and global gene expression profiling with RNA-seq, respectively. The global gene expression profiling with RNA-seq was taken as the "gold standard" for reference. Clinical information including the survival data were collected. Results 160 cases of DLBCL with evaluable COO assignments with IHC, DLBCL-COO 32-gene assay and global gene expression profiling with RNA-seq were identified. Comparing with the 77.5% concordance between IHC algorithm and gold standard, there is 91.9% concordance between DLBCL-COO 32-gene assay and gold standard (P =0.005). 72 patients assigned as ABC subtype and 14 patients assigned as Type-3 subtype demonstrated a significantly inferior overall survival than 42 patients assigned as GCB subtype using DLBCL-COO assay (P =0.023). However, COO based the IHC algorithm failed to provide the predictive value regarding overall survival (P =0.09). Conclusions DLBCL-COO assay provides flexibility and accuracy in DLBCL subtype characterization. These subtype distinctions should help guide disease prognosis and treatment options within DLBCL clinical practice. Disclosures Sun: Canhelp Genomics: Employment. Xu:Canhelp Genomics: Employment.

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dropClust2: An R package for resource efficient analysis of large scale single cell RNA-Seq data

10.1101/596924 ◽

2019 ◽

Author(s):

Debajyoti Sinha ◽

Pradyumn Sinha ◽

Ritwik Saha ◽

Sanghamitra Bandyopadhyay ◽

Debarka Sengupta

Keyword(s):

Single Cell ◽

Programming Languages ◽

Large Scale ◽

Principal Component ◽

Cell Types ◽

R Package ◽

Locality Sensitive Hashing ◽

Rna Seq ◽

Link Type ◽

Component Selection

ABSTRACTDropClust leverages Locality Sensitive Hashing (LSH) to speed up clustering of large scale single cell expression data. It makes ingenious use of structure persevering sampling and modality based principal component selection to rescue minor cell types. Existing implementation of dropClust involves interfacing with multiple programming languagesviz. R, python and C, hindering seamless installation and portability. Here we present dropClust2, a complete R package that’s not only fast but also minimally resource intensive. DropClust2 features a novel batch effect removal algorithm that allows integrative analysis of single cell RNA-seq (scRNA-seq) datasets.Availability and implementationdropClust2 is freely available athttps://debsinha.shinyapps.io/dropClust/as an online web service and athttps://github.com/debsin/dropClustas an R package.

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Faculty Opinions recommendation of Global gene expression profiling of the asymptomatic bacteriuria Escherichia coli strain 83972 in the human urinary tract.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1032465.373112 ◽

2006 ◽

Author(s):

Stephen Spiro

Keyword(s):

Gene Expression ◽

Escherichia Coli ◽

Urinary Tract ◽

Gene Expression Profiling ◽

Expression Profiling ◽

Asymptomatic Bacteriuria ◽

Global Gene Expression ◽

Coli Strain ◽

Global Gene Expression Profiling

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Effects of eight neuropsychiatric copy number variants on human brain structure

Translational Psychiatry ◽

10.1038/s41398-021-01490-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Claudia Modenato ◽

Kuldeep Kumar ◽

Clara Moreau ◽

Sandra Martin-Brevet ◽

Guillaume Huguet ◽

...

Keyword(s):

Copy Number ◽

Copy Number Variants ◽

Principal Component ◽

Brain Morphology ◽

Brain Anatomy ◽

Brain Morphometry ◽

Supplementary Motor Cortex ◽

Psychiatric Conditions ◽

Latent Dimension ◽

Mirror Effects

AbstractMany copy number variants (CNVs) confer risk for the same range of neurodevelopmental symptoms and psychiatric conditions including autism and schizophrenia. Yet, to date neuroimaging studies have typically been carried out one mutation at a time, showing that CNVs have large effects on brain anatomy. Here, we aimed to characterize and quantify the distinct brain morphometry effects and latent dimensions across 8 neuropsychiatric CNVs. We analyzed T1-weighted MRI data from clinically and non-clinically ascertained CNV carriers (deletion/duplication) at the 1q21.1 (n = 39/28), 16p11.2 (n = 87/78), 22q11.2 (n = 75/30), and 15q11.2 (n = 72/76) loci as well as 1296 non-carriers (controls). Case-control contrasts of all examined genomic loci demonstrated effects on brain anatomy, with deletions and duplications showing mirror effects at the global and regional levels. Although CNVs mainly showed distinct brain patterns, principal component analysis (PCA) loaded subsets of CNVs on two latent brain dimensions, which explained 32 and 29% of the variance of the 8 Cohen’s d maps. The cingulate gyrus, insula, supplementary motor cortex, and cerebellum were identified by PCA and multi-view pattern learning as top regions contributing to latent dimension shared across subsets of CNVs. The large proportion of distinct CNV effects on brain morphology may explain the small neuroimaging effect sizes reported in polygenic psychiatric conditions. Nevertheless, latent gene brain morphology dimensions will help subgroup the rapidly expanding landscape of neuropsychiatric variants and dissect the heterogeneity of idiopathic conditions.

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4,7-Disubstituted 7H-Pyrrolo[2,3-d]pyrimidines and Their Analogs as Antiviral Agents against Zika Virus

Molecules ◽

10.3390/molecules26133779 ◽

2021 ◽

Vol 26 (13) ◽

pp. 3779

Author(s):

Ruben Soto-Acosta ◽

Eunkyung Jung ◽

Li Qiu ◽

Daniel J. Wilson ◽

Robert J. Geraghty ◽

...

Keyword(s):

Antiviral Activity ◽

Dengue Virus ◽

Small Molecules ◽

Zika Virus ◽

Antiviral Agents ◽

Structural Features ◽

Molecular Target ◽

Core Structure ◽

Human Pathogens ◽

Important Group

Discovery of compound 1 as a Zika virus (ZIKV) inhibitor has prompted us to investigate its 7H-pyrrolo[2,3-d]pyrimidine scaffold, revealing structural features that elicit antiviral activity. Furthermore, we have demonstrated that 9H-purine or 1H-pyrazolo[3,4-d]pyrimidine can serve as an alternative core structure. Overall, we have identified 4,7-disubstituted 7H-pyrrolo[2,3-d]pyrimidines and their analogs including compounds 1, 8 and 11 as promising antiviral agents against flaviviruses ZIKV and dengue virus (DENV). While the molecular target of these compounds is yet to be elucidated, 4,7-disubstituted 7H-pyrrolo[2,3-d]pyrimidines and their analogs are new chemotypes in the design of small molecules against flaviviruses, an important group of human pathogens.

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