Gene Expression Data Explorer
Info Gene counts are sourced from ARCHS4, which provides uniform alignment of GEO samples. You can learn more about ARCHS4 and its pipeline here.
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GROUP CONDITION SAMPLES
THP-1 macrophages H37Ra
GSM2912657
GSM2912658
GSM2912659
GSM2912656
GSM2912655
THP-1 macrophages H37Rv
GSM2912662
GSM2912663
GSM2912664
GSM2912661
GSM2912660
THP-1 macrophages none
GSM2912652
GSM2912653
GSM2912649
GSM2912654
GSM2912651
GSM2912650
Description

Submission Date: Jan 04, 2018

Summary: Purpose: The goals of this study are to obtain the NGS-derived transcriptome profiling (RNA-seq) for THP-1 macrophages response to Mycobacterium tuberculosis (H37Rv and H37Ra)

Methods: mRNA and long noncoding RNA profiles of THP-1 macrophages infected with H37Rv and H37Ra for 1, 4, 12, 24, 48 hours were generated by deep sequencing, using Illumina Hiseq3000. The sequence reads that passed quality filters were first mapped to the latest UCSC transcript set using Bowtie2 (version 2.1.0). Then the gene expression level was estimated using RSEM (RNA-Seq by Expectation Maximization, v1.2.15), for lncRNA analysis, reads were mapped to lncRNA transcript set from LNCipedia.org. The sequence reads were normalized with TMM (trimmed mean of M-values) to identify differentially expressed genes (DEGs) using the edgeR package edgeR. qRT–PCR validation was performed using SYBR Green assays.

Results: Using an optimized data analysis workflow, we mapped about 20 million sequence reads per sample to the human genome (GRCh38/hg38) and identified 25,343 mRNA and 47877 long non-coding RNA transcripts.

Conclusions: Our study represents the detailed analysis of transcriptomes for THP-1 macrophages response to H37Rv and H37Ra, generated by RNA-seq technology.

GEO Accession ID: GSE108731

PMID: No Pubmed ID

Description

Submission Date: Jan 04, 2018

Summary: Purpose: The goals of this study are to obtain the NGS-derived transcriptome profiling (RNA-seq) for THP-1 macrophages response to Mycobacterium tuberculosis (H37Rv and H37Ra)

Methods: mRNA and long noncoding RNA profiles of THP-1 macrophages infected with H37Rv and H37Ra for 1, 4, 12, 24, 48 hours were generated by deep sequencing, using Illumina Hiseq3000. The sequence reads that passed quality filters were first mapped to the latest UCSC transcript set using Bowtie2 (version 2.1.0). Then the gene expression level was estimated using RSEM (RNA-Seq by Expectation Maximization, v1.2.15), for lncRNA analysis, reads were mapped to lncRNA transcript set from LNCipedia.org. The sequence reads were normalized with TMM (trimmed mean of M-values) to identify differentially expressed genes (DEGs) using the edgeR package edgeR. qRT–PCR validation was performed using SYBR Green assays.

Results: Using an optimized data analysis workflow, we mapped about 20 million sequence reads per sample to the human genome (GRCh38/hg38) and identified 25,343 mRNA and 47877 long non-coding RNA transcripts.

Conclusions: Our study represents the detailed analysis of transcriptomes for THP-1 macrophages response to H37Rv and H37Ra, generated by RNA-seq technology.

GEO Accession ID: GSE108731

PMID: No Pubmed ID

Visualize Samples

Info Visualizations are precomputed using the Python package scanpy on the top 5000 most variable genes.

Precomputed Differential Gene Expression

Info Differential expression signatures are automatically computed using the limma R package. More options for differential expression are available to compute below.

Signatures:

No precomputed signatures are currently available for this study. You can compute differential gene expression on the fly below:

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Control Condition

Perturbation Condition

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Differential Gene Expression Analysis
Info Differential expression signatures can be computed using DESeq2 or characteristic direction.
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Bulk RNA-seq Appyter

This pipeline enables you to analyze and visualize your bulk RNA sequencing datasets with an array of downstream analysis and visualization tools. The pipeline includes: PCA analysis, Clustergrammer interactive heatmap, library size analysis, differential gene expression analysis, enrichment analysis, and L1000 small molecule search.