ExpressionBlast: mining large, unstructured expression databases

Zinman, Guy; Naiman, Shoshana; Kanfi, Yariv; Cohen, Haim Y.; Bar‐Joseph, Ziv

doi:10.1038/nmeth.2630

Cited by 30 publications

(25 citation statements)

References 5 publications

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“…While replicate experiments have been widely used in high-throughput analysis studies, they have been utilized to a much lesser extent when using the same technology to study time series data (Zinman et al, 2013). While it is hard to determine the exact causes for this practice, it is very likely that budget and sample quantity constraints have played a role.…”

Section: Discussionmentioning

confidence: 99%

“…However, in practice the number of time points that are used in a study is usually very small (Zinman et al, 2013). The main limiting factor for most experiments is budget.…”

Section: Introductionmentioning

confidence: 99%

“…When resource scarcity is an issue, even one replicate for each time point cuts the total number of points that can be profiled by half, and this can have a large impact on our ability to accurately reconstruct the trajectories of the biological data being profiled. Indeed, when examining the time-series datasets deposited in GEO, we observe that in many cases replicates have not been used in these studies (Zinman et al, 2013). …”

Section: Introductionmentioning

confidence: 99%

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Tradeoffs between Dense and Replicate Sampling Strategies for High-Throughput Time Series Experiments

2016

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An important experimental design question for high throughout time series studies is the number of replicates required for accurate reconstruction of the profiles. Due to budget and sample availability constraints, more replicates imply fewer time points and vice versa. We analyze the performance of dense and replicate sampling by developing a theoretical framework that focuses on a restricted yet expressive set of possible curves over a wide range of noise levels and by analyzing real expression data. For both the theoretical analysis and experimental data we observe that under reasonable noise levels, autocorrelations in the time series data allow dense sampling to better determine the correct levels of non-sampled points when compared to replicate sampling. A Java implementation of our framework can be used to determine the best replicate strategy given the expected noise. These results provide theoretical support to the large number of high throughput time series experiments that do not use replicates.

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Section: Discussionmentioning

confidence: 99%

“…However, in practice the number of time points that are used in a study is usually very small (Zinman et al, 2013). The main limiting factor for most experiments is budget.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tradeoffs between Dense and Replicate Sampling Strategies for High-Throughput Time Series Experiments

2016

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“…In order to validate the robustness of our methods, two more datasets were retrieved, one published in 2005 (17) and one in 2010 (18), where the second dataset was obtained through the ExpressionBlast tool (31). Unfortunately, we were not able to identify any dataset with the exact cancer subtypes as in the Bhattacharjee et al study, and thus a head-to-head comparison of our results was not possible.…”

Section: Discussionmentioning

confidence: 99%

Hidden Treasures in â€œAncientâ€ Microarrays: Gene-Expression Portrays Biology and Potential Resistance Pathways of Major Lung Cancer Subtypes and Normal Tissue

et al. 2014

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Objective: Novel statistical methods and increasingly more accurate gene annotations can transform “old” biological data into a renewed source of knowledge with potential clinical relevance. Here, we provide an in silico proof-of-concept by extracting novel information from a high-quality mRNA expression dataset, originally published in 2001, using state-of-the-art bioinformatics approaches.Methods: The dataset consists of histologically defined cases of lung adenocarcinoma (AD), squamous (SQ) cell carcinoma, small-cell lung cancer, carcinoid, metastasis (breast and colon AD), and normal lung specimens (203 samples in total). A battery of statistical tests was used for identifying differential gene expressions, diagnostic and prognostic genes, enriched gene ontologies, and signaling pathways.Results: Our results showed that gene expressions faithfully recapitulate immunohistochemical subtype markers, as chromogranin A in carcinoids, cytokeratin 5, p63 in SQ, and TTF1 in non-squamous types. Moreover, biological information with putative clinical relevance was revealed as potentially novel diagnostic genes for each subtype with specificity 93–100% (AUC = 0.93–1.00). Cancer subtypes were characterized by (a) differential expression of treatment target genes as TYMS, HER2, and HER3 and (b) overrepresentation of treatment-related pathways like cell cycle, DNA repair, and ERBB pathways. The vascular smooth muscle contraction, leukocyte trans-endothelial migration, and actin cytoskeleton pathways were overexpressed in normal tissue.Conclusion: Reanalysis of this public dataset displayed the known biological features of lung cancer subtypes and revealed novel pathways of potentially clinical importance. The findings also support our hypothesis that even old omics data of high quality can be a source of significant biological information when appropriate bioinformatics methods are used.

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“…NLP techniques have previously been used to design automated text mining methods that automatically identify disease-related experiments in GEO (6) and, more recently, NLP of text from GEO series was used to classify presence or absence of a disease signature, including classification of control vs. treatment samples based on metadata profiles (7). Additionally, tools to compare and contrast gene expression profiles based on automatic curation and NLP analysis of GEO records have also been developed (8). However, a multi-omics data integration support system for cross-linking GEO records by metadata similarity has not yet been devised.…”

Section: Introductionmentioning

confidence: 99%

Biochat: a database for natural language processing of Gene Expression Omnibus data

Dyomkin²,

Ka³

et al. 2018

Preprint

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A biological dataset's metadata profile (e.g., study description, organism name, sequencing type, etc.) typically contains terse but descriptive textual information that can be used to link it with other similar biological datasets for the purpose of integrating omics data of different types to inform hypotheses and biological questions. Here we present Biochat, a database containing a multi-omics data integration support system to aid in cross-linking Gene Expression Omnibus (GEO) records to each other by metadata similarity through a user-friendly web application. Biochat is publicly available at:

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ExpressionBlast: mining large, unstructured expression databases

Cited by 30 publications

References 5 publications

Tradeoffs between Dense and Replicate Sampling Strategies for High-Throughput Time Series Experiments

Tradeoffs between Dense and Replicate Sampling Strategies for High-Throughput Time Series Experiments

Hidden Treasures in â€œAncientâ€ Microarrays: Gene-Expression Portrays Biology and Potential Resistance Pathways of Major Lung Cancer Subtypes and Normal Tissue

Biochat: a database for natural language processing of Gene Expression Omnibus data

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ExpressionBlast: mining large, unstructured expression databases

Cited by 30 publications

References 5 publications

Tradeoffs between Dense and Replicate Sampling Strategies for High-Throughput Time Series Experiments

Tradeoffs between Dense and Replicate Sampling Strategies for High-Throughput Time Series Experiments

Hidden Treasures in â€œAncientâ€ Microarrays: Gene-Expression Portrays Biology and Potential Resistance Pathways of Major Lung Cancer Subtypes and Normal Tissue

Biochat: a database for natural language processing of Gene Expression Omnibus data

Contact Info

Product

Resources

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Hidden Treasures in â€œAncientâ€ Microarrays: Gene-Expression Portrays Biology and Potential Resistance Pathways of Major Lung Cancer Subtypes and Normal Tissue