Tool:shinyheatmap: Ultra fast low memory heatmap web interface for big data genomics
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7.0 years ago

Dear All:

I am pleased to announce the world’s fastest interactive heatmap visualization software, presented as a user-friendly, open-access web server. Prior publications in the biological heatmap visualization field have focused predominantly on the production of static heatmaps, which do not constitute a computational challenge and are relatively simple to make using limited computational resources. In contrast, interactive heatmaps present a unique software engineering challenge due to the extensive memory requirements needed to operate efficiently on big datasets (heatmap rendering speed, zooming speed, hover speed, etc.). Since transcriptomics, metabolomics, metagenomics, and other various next-generation sequencing (-omics) fields are known for their production of large datasets, visualizing such big data requires a technological breakthrough.

shinyheatmap is an ultra fast 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. shinyheatmap features a built-in high performance web plug-in, called 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. We tested shinyheatmap on gene expression datasets ranging from 10,000 rows to 300,000 rows and achieved speeds >100,000 faster than previous state-of-the-art interactive heatmap software. We performed these benchmarks on a standard Windows desktop machine (64-bit Windows 10 Pro desktop machine with 16.0 GB of RAM and an Intel(R) Core(TM) i7-5820K CPU at 3.30 GHz), a common workstation employed in academic biology labs, implying that even better performance is achievable on more powerful workstations.

Here is a link to the paper and software: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0176334

Best regards,

Bohdan Khomtchouk

RNA-Seq ChIP-Seq • 1.9k views
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