SigCom LINCS: data and metadata search engine for a million gene expression signatures

Evangelista, John Erol; Clarke, Daniel J B; Xie, Zhuorui; Lachmann, Alexander; Jeon, Minji; Chen, Kerwin; Jagodnik, Kathleen M.; Jenkins, Sherry L.; Kuleshov, Maxim V.; Wojciechowicz, Megan L.; Schürer, Stephan C.; Medvedovic, Mario; Ma’ayan, Avi

doi:10.1093/nar/gkac328

Cited by 48 publications

(68 citation statements)

References 83 publications

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“…A total of 135 aging signatures were created. These signatures are made available for download from SigCom LINCS [ 15 ]. To minimize sample size differences, control and case samples were randomly selected for each comparison such that the number of samples representing both groups was equal.…”

Section: Resultsmentioning

confidence: 99%

Transforming L1000 profiles to RNA-seq-like profiles with deep learning

et al. 2022

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The L1000 technology, a cost-effective high-throughput transcriptomics technology, has been applied to profile a collection of human cell lines for their gene expression response to > 30,000 chemical and genetic perturbations. In total, there are currently over 3 million available L1000 profiles. Such a dataset is invaluable for the discovery of drug and target candidates and for inferring mechanisms of action for small molecules. The L1000 assay only measures the mRNA expression of 978 landmark genes while 11,350 additional genes are computationally reliably inferred. The lack of full genome coverage limits knowledge discovery for half of the human protein coding genes, and the potential for integration with other transcriptomics profiling data. Here we present a Deep Learning two-step model that transforms L1000 profiles to RNA-seq-like profiles. The input to the model are the measured 978 landmark genes while the output is a vector of 23,614 RNA-seq-like gene expression profiles. The model first transforms the landmark genes into RNA-seq-like 978 gene profiles using a modified CycleGAN model applied to unpaired data. The transformed 978 RNA-seq-like landmark genes are then extrapolated into the full genome space with a fully connected neural network model. The two-step model achieves 0.914 Pearson’s correlation coefficients and 1.167 root mean square errors when tested on a published paired L1000/RNA-seq dataset produced by the LINCS and GTEx programs. The processed RNA-seq-like profiles are made available for download, signature search, and gene centric reverse search with unique case studies.

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Section: Resultsmentioning

confidence: 99%

Transforming L1000 profiles to RNA-seq-like profiles with deep learning

et al. 2022

Self Cite

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“…For SigCom LINCS analysis, we follow the instructions of the web tool to enter the list of up- and down-regulated genes and select the signature that provides 431 Datasets for scoring. 44 The top 30 results were ranked by the sum of Z scores [Z score (up) + Z score (down)]. For enriched gene-set comparison, the compareCluster function in clusterProfiler was used and the enrichKEGG, enrichWP and enrichDO functions were employed to analyze the enrichment scores and p-values for each gene set.…”

Section: Methodsmentioning

confidence: 99%

Assessing the Global Impact on the Mouse Kidney After Traumatic Brain Injury: A Transcriptomic Study

Chan¹,

Hsu²,

Cheng³

et al. 2022

JIR

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In this study, we use animal models combined with bioinformatics strategies to investigate the potential changes in overall renal transcriptional expression after traumatic brain injury. Methods: Microarray analysis was performed after kidney acquisition using unilateral controlled cortical impact as the primary mouse TBI model. Multi-oriented gene set enrichment analysis was performed for differentially expressed genes. Results:The results showed that TBI affected the gene set associated with mitochondria function in kidney cells, and a negative enrichment of gene sets associated with immune cell migration and epidermal development was also observed. Analysis of the disease phenotype gene set revealed that differential expression of mitochondria-related genes was associated with lactate metabolism. Alternatively, activation and adhesion of immune cells associated with the complement system may promote autoinflammation in kidney tissue. The simulated immune cell infiltration analysis showed an increase in the proportion of activated memory CD4 T cells and a decrease in the proportion of resting memory CD4 T cells, suggesting that activated memory CD4 T cell infiltration may be involved in the inflammation of renal tissue and cause damage to renal cells, such as principal cells, mesangial cells and loops of Henle cells. Conclusion:This study is the first to reveal the effects of brain trauma on the kidney. TBI may affect the expression of mitochondria function-related gene sets in renal cells by increasing lactate. It may also affect renal mesangial cells by inducing increased infiltration of immune cells through mechanisms related to complement system activation or autoimmune antibodies.

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“…The signature-searchable platform Sigcom LINCS ( (accessed on 18 June 2021)) contains 1,536,533 gene expression signatures integrated from the Library of Integrated Network-Based Cellular Signatures (LINCS), the Genotype-Tissue Expression (GTEx), and GEO [ 15 ]. Up-down enrichment mode was adopted for signature enrichment analysis, and then different cancer entities were screened in the automatic human GEO RNA-seq signature mimickers, namely matching signatures with a positive z-score (enrichment score).…”

Section: Methodsmentioning

confidence: 99%

Deep View of HCC Gene Expression Signatures and Their Comparison with Other Cancers

Qian

Itzel

Ebert

et al. 2022

Cancers

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Background: Gene expression signatures correlate genetic alterations with specific clinical features, providing the potential for clinical usage. A plethora of HCC-dependent gene signatures have been developed in the last two decades. However, none of them has made its way into clinical practice. Thus, we investigated the specificity of public gene signatures to HCC by establishing a comparative transcriptomic analysis, as this may be essential for clinical applications. Methods: We collected 10 public HCC gene signatures and evaluated them by utilizing four different (commercial and non-commercial) gene expression profile comparison tools: Oncomine Premium, SigCom LINCS, ProfileChaser (modified version), and GENEVA, which can assign similar pre-analyzed profiles of patients with tumors or cancer cell lines to our gene signatures of interests. Among the query results of each tool, different cancer entities were screened. In addition, seven breast and colorectal cancer gene signatures were included in order to further challenge tumor specificity of gene expression signatures. Results: Although the specificity of the evaluated HCC gene signatures varied considerably, none of the gene signatures showed strict specificity to HCC. All gene signatures exhibited potential significant specificity to other cancers, particularly for colorectal and breast cancer. Since signature specificity proved challenging, we furthermore investigated common core genes and overlapping enriched pathways among all gene signatures, which, however, showed no or only very little overlap, respectively. Conclusion: Our study demonstrates that specificity, independent validation, and clinical use of HCC genetic signatures solely relying on gene expression remains challenging. Furthermore, our work made clear that standards in signature generation and statistical methods but potentially also in tissue preparation are urgently needed.

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SigCom LINCS: data and metadata search engine for a million gene expression signatures

Cited by 48 publications

References 83 publications

Transforming L1000 profiles to RNA-seq-like profiles with deep learning

Transforming L1000 profiles to RNA-seq-like profiles with deep learning

Assessing the Global Impact on the Mouse Kidney After Traumatic Brain Injury: A Transcriptomic Study

Deep View of HCC Gene Expression Signatures and Their Comparison with Other Cancers

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