HeatmapGenerator: high performance RNAseq and microarray visualization software suite to examine differential gene expression levels using an R and C++ hybrid computational pipeline

Khomtchouk, Bohdan B.; Booven, Derek J. Van; Wahlestedt, Claes

doi:10.1186/s13029-014-0030-2

Cited by 55 publications

(38 citation statements)

References 6 publications

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“…Reads were mapped to the panel and expression values determined. R Software version R-3.2.3 was used to generate heatmap (29). Microarray data have been deposited in a NCBI GEO database (accession number: GSE83930) and website address of the database is: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?token=ahgtwusgflmxnah&acc=GSE83930…”

Section: Methodsmentioning

confidence: 99%

MicroRNA-17 Suppresses TNF-α Signaling by Interfering with TRAF2 and cIAP2 Association in Rheumatoid Arthritis Synovial Fibroblasts

Akhtar

Singh

Ahmed

2016

The Journal of Immunology

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TNF-α is a major cytokine implicated in rheumatoid arthritis (RA). TNF-α expression is shown to be regulated at transcriptional as well as posttranscriptional levels. However, the impact of changes in microRNA (miRNA) expression on posttranslational processes involved in TNF-α signaling networks is not well-defined in RA. Here we evaluated the effect of miR-17, a member of miR-17-92~ cluster, on TNF-α signaling pathway in human RA synovial fibroblasts (RASFs). We demonstrated that miR-17 expression was significantly low in RA serum, SFs, and synovial tissues as well as in the serum and joints of adjuvant-induced arthritis rats. RNA sequencing analysis showed modulation of 664 genes by pre-miR-17 in human RASFs. Ingenuity pathway analysis of RNA sequencing data identified the ubiquitin proteasome system (UPS) in TNF-α signaling pathway as a primary target of miR-17. Western blot analysis confirmed the reduction of TRAF2, cIAP1, cIAP2, USP2, and PSMD13 expression by miR-17 in TNF-α-stimulated RASFs. Immunoprecipitation assays showed that miR-17 restoration increased the K48-linked polyubiquitination of TRAF2, cIAP1, and cIAP2 in TNF-α-stimulated RASFs. Thus, destabilization of TRAF2 by miR-17 reduced the ability of TRAF2 to associate with cIAP2, thereby resulting in the downregulation of TNF-α-induced NF-κBp65, c-Jun, and STAT3 nuclear translocation and the production of IL-6, IL-8, MMP-1, and MMP-13 in human RASFs. In conclusion, this study provides evidence for the role of miR-17 as a negative regulator of TNF-α signaling by modulating the protein ubiquitin processes in RASFs.

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Section: Methodsmentioning

confidence: 99%

MicroRNA-17 Suppresses TNF-α Signaling by Interfering with TRAF2 and cIAP2 Association in Rheumatoid Arthritis Synovial Fibroblasts

Akhtar

Singh

Ahmed

2016

The Journal of Immunology

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“…The results of accuracy and performance tests with real data are shown in Table 6 and Figure 6 are used to plot the quantitative differences in accuracy score of each data set. Heatmaps are two dimensional graphical representations of data with a pre-defined colormap to display values of a matrix [52]. Heatmaps can be used to understand what parameters affect the accuracy of the classification model.…”

Section: Commonly Used Classification Data Setsmentioning

confidence: 99%

Classification with boosting of extreme learning machine over arbitrarily partitioned data

Çatak

2015

Soft Comput

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Machine learning based computational intelligence methods are widely used to analyze large scale data sets in this age of big data. Extracting useful predictive modeling from these types of data sets is a challenging problem due to their high complexity. Analyzing large amount of streaming data that can be leveraged to derive business value is another complex problem to solve. With high levels of data availability (i.e. Big Data) automatic classification of them has become an important and complex task. Hence, we explore the power of applying MapReduce based Distributed AdaBoosting of Extreme Learning Machine (ELM) to build a predictive bag of classification models. Accordingly, (i) data set ensembles are created; (ii) ELM algorithm is used to build weak learners (classifier functions); and (iii) builds a strong learner from a set of weak learners. We applied this training model to the benchmark knowledge discovery and data mining data sets.

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“…Heatmap software can be generally classified into two categories: static heatmap software [30,29,49,25,14,7,8,16,19] and interactive heatmap software [32,4,18,51,23,34,48,1,17,9,52]. Static heatmaps are pictorially frozen snapshots of genomic activity displayed as colored images generated from the underlying data.…”

Section: Introductionmentioning

confidence: 99%

shinyheatmap: ultra fast low memory heatmap web interface for big data genomics

Khomtchouk

Hennessy

Wahlestedt

2016

Preprint

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Background: Transcriptomics, metabolomics, metagenomics, and other various next-generation sequencing (-omics) fields are known for their production of large datasets, especially across single-cell sequencing studies. Visualizing such big data has posed technical challenges in biology, both in terms of available computational resources as well as programming acumen. Since heatmaps are used to depict high-dimensional numerical data as a colored grid of cells, efficiency and speed have often proven to be critical considerations in the process of successfully converting data into graphics. For example, rendering interactive heatmaps from large input datasets (e.g., 100k+ rows) has been computationally infeasible on both desktop computers and web browsers. In addition to memory requirements, programming skills and knowledge have frequently been barriers-to-entry for creating highly customizable heatmaps.Results: We propose shinyheatmap: an advanced user-friendly heatmap software suite capable of efficiently creating highly customizable static and interactive biological heatmaps in a web browser. shinyheatmap is a low memory footprint program, making it particularly well-suited for the interactive visualization of extremely large datasets that cannot typically be computed in-memory due to size restrictions. Also, shinyheatmap features a built-in high performance web plug-in, fastheatmap, for rapidly plotting interactive heatmaps of datasets as large as 10 5 -10 7 rows within seconds, effectively shattering previous performance benchmarks of heatmap rendering speed.Conclusions: shinyheatmap is hosted online as a freely available web server with an intuitive graphical user interface: http://shinyheatmap.com. The methods are implemented in R, and are available as part of the shinyheatmap project at: https://github.com/Bohdan-Khomtchouk/shinyheatmap. Users can access fastheatmap directly from within the shinyheatmap web interface, and all source code has been made publicly available on Github: https://github.com/Bohdan-Khomtchouk/fastheatmap.

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HeatmapGenerator: high performance RNAseq and microarray visualization software suite to examine differential gene expression levels using an R and C++ hybrid computational pipeline

Cited by 55 publications

References 6 publications

MicroRNA-17 Suppresses TNF-α Signaling by Interfering with TRAF2 and cIAP2 Association in Rheumatoid Arthritis Synovial Fibroblasts

MicroRNA-17 Suppresses TNF-α Signaling by Interfering with TRAF2 and cIAP2 Association in Rheumatoid Arthritis Synovial Fibroblasts

Classification with boosting of extreme learning machine over arbitrarily partitioned data

shinyheatmap: ultra fast low memory heatmap web interface for big data genomics

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