{"id":14863,"date":"2017-09-11T19:00:25","date_gmt":"2017-09-11T18:00:25","guid":{"rendered":"https:\/\/blogs.nature.com\/naturejobs\/?p=14863"},"modified":"2017-09-11T23:37:15","modified_gmt":"2017-09-11T22:37:15","slug":"techblog-hipiler-simplifies-chromatin-structure-analysis","status":"publish","type":"post","link":"https:\/\/blogs.nature.com\/naturejobs\/2017\/09\/11\/techblog-hipiler-simplifies-chromatin-structure-analysis\/","title":{"rendered":"TechBlog: HiPiler simplifies chromatin structure analysis"},"content":{"rendered":"

\"Screen<\/a><\/p>\n

For my recent Toolbox on 3D genome\u00a0visualization tools<\/a>, Nils Gehlenborg<\/a> at Harvard Medical School clued\u00a0me\u00a0into two interesting pieces of software. One,\u00a0HiGlass<\/a>, was included in my article; a related tool, HiPiler<\/a>, was not. But that doesn’t mean it’s not\u00a0worth talking about.<\/p>\n

<\/p>\n

HiPiler, says Gehlenborg, “is a tool to visualize individual features in [Hi-C] maps.” Think of it like a digital photo application that\u00a0can find and extract\u00a0all the faces in\u00a0your image\u00a0library*.<\/p>\n

Tools like HiGlass allow researchers to navigate and explore chromatin contact matrices, data from experiments such as Hi-C that indirectly reveals the folding of chromosomes. Sometimes, though, researchers need to focus on specific structural features, such as chromosomal loops or domains. Using bioinformatic algorithms, researchers can query chromatin-contact\u00a0datasets to find all such structures. But these algorithms may return thousands of hits. How is\u00a0a researcher to study them?<\/p>\n

Enter HiPiler.<\/p>\n

Developed by Gehlenborg’s lab in collaboration\u00a0with the lab of Hanspeter Pfister at the John A. Paulson School of Engineering and Applied Sciences at Harvard, HiPiler excises these features and presents them on a canvas as miniature snippets — basically tiny segments of the full-sized contact map, which users can then organize, sort, and cluster based on parameters such as noise or location. Among other things, users can stack related snippets into a pile (hence the tool’s name) and study them either in aggregate or individually.<\/p>\n

“We\u2019re no longer bound to this matrix and having to navigate from one part of the matrix to another part,” Gehlenborg explains. “You can just grab whatever part you\u2019re interested in, extract it, literally cut it out, and then look at these cutouts.”<\/p>\n

The software maintains the link between each\u00a0snippet and its origin using an integrated HiGlass viewer, which presents the snippets in the context of the original\u00a0data matrix.<\/p>\n

According to Gehlenborg, HiPiler fills a void in the Hi-C data analysis toolbox. Previously, researchers who wanted to compare, say, the performance of different feature-finding algorithms had to manually generate and compare hundreds or\u00a0thousands of static screenshots. “In that effort,” he says, “HiPiler is extremely useful, because they can directly get all the tools they need to inspect whatever regions they\u2019re identifying and whatever algorithm they might be developing.”<\/p>\n

HiPiler’s\u00a0strength, Gehlenborg continues, lies in revealing\u00a0unexpected patterns. “There are certainly things that we might not be aware of, simply because we\u2019ve never really had a view like the one that HiPiler is providing. And that\u2019s where we\u2019re now working with our collaborators on applying this approach to additional datasets.”<\/p>\n

Bioinformatician Leonid Mirny at the Massachusetts Institute of Technology in Cambridge, is one such collaborator. Mirny uses HiPiler in his research into chromatin folding, a process that involves the\u00a0protein CTCF. Using ChIP-seq, which maps the locations of DNA-binding proteins to the genome, Mirny can identify all the genomic locations at which CTCF is found. He can use HiPiler to collect snippets for every location and aggregate them to see what the ‘average’ CTCF site looks like — the equivalent, he says, of asking\u00a0Google Maps what the typical\u00a0beach looks like.<\/p>\n

He also can study the snippets individually and group them into classes, in order to discover the features that make certain sites unique — like finding\u00a0that while some beaches face west, others north, south, or east.<\/p>\n

“That’s what HiPiler would show you: what kind of structures are there that you don’t see in the average map.”<\/p>\n

Gehlenborg’s team have created a HiPiler demo at hipiler.higlass.io<\/a>, as well as a video<\/a> explaining how the software works. Their paper<\/a> on HiPiler was accepted for publication in IEEE Transactions on Visualization and Computer Graphics<\/em>, and will be presented at a\u00a0data visualization conference<\/a> in Phoenix, Arizona, in October.<\/p>\n

 <\/p>\n

*That’s not a perfect\u00a0analogy, as HiPiler cannot actually find features; users have to tell it what regions to extract. But, you get the idea.<\/em><\/p>\n

 <\/p>\n

Jeffrey Perkel<\/strong> is Technology Editor<\/em>, Nature.<\/p>\n

 <\/p>\n

Image: screenshot\/Jeffrey Perkel<\/em><\/p>\n

Update (2017-09-11): The post has been updated to reflect the fact that HiPiler was a collaboration between Gehlenborg’s lab and that of\u00a0Hanspeter Pfister.
\n<\/em><\/p>\n

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Mike Goodstadt: A circuitous route to bioinformatics<\/a><\/p>\n

Building synthetic circuits from RNA<\/a><\/p>\n

Jupyter joins the Galaxy<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

For my recent Toolbox on 3D genome\u00a0visualization tools, Nils Gehlenborg at Harvard Medical School clued\u00a0me\u00a0into two interesting pieces of software. One,\u00a0HiGlass,was included in my article; a related tool, HiPiler, was not. But that doesn\u2019t mean it\u2019s not\u00a0worth talking about.  Read more<\/a> Continue reading →<\/span><\/a><\/p>\n","protected":false},"author":104777,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[192,200],"tags":[72611,8164397,8254881,8254883,5945961,8254879,6677357,75],"class_list":["post-14863","post","type-post","status-publish","format-standard","hentry","category-blog-2","category-technology-2","tag-bioinformatics","tag-epigenetics","tag-higlass","tag-hipiler","tag-jeffrey-perkel","tag-nils-gehlenborg","tag-techblog","tag-technology"],"_links":{"self":[{"href":"https:\/\/blogs.nature.com\/naturejobs\/wp-json\/wp\/v2\/posts\/14863","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.nature.com\/naturejobs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.nature.com\/naturejobs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.nature.com\/naturejobs\/wp-json\/wp\/v2\/users\/104777"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.nature.com\/naturejobs\/wp-json\/wp\/v2\/comments?post=14863"}],"version-history":[{"count":0,"href":"https:\/\/blogs.nature.com\/naturejobs\/wp-json\/wp\/v2\/posts\/14863\/revisions"}],"wp:attachment":[{"href":"https:\/\/blogs.nature.com\/naturejobs\/wp-json\/wp\/v2\/media?parent=14863"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.nature.com\/naturejobs\/wp-json\/wp\/v2\/categories?post=14863"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.nature.com\/naturejobs\/wp-json\/wp\/v2\/tags?post=14863"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}