<?xml version='1.0'?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:georss="http://www.georss.org/georss" xmlns:atom="http://www.w3.org/2005/Atom" >
<channel>
	<title><![CDATA[BOL: WinPCA]]></title>
	<link>https://bioinformaticsonline.com/bookmarks/view/44906/winpca?</link>
	<atom:link href="https://bioinformaticsonline.com/bookmarks/view/44906/winpca?" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44906/winpca</guid>
	<pubDate>Tue, 23 Sep 2025 03:58:13 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44906/winpca</link>
	<title><![CDATA[WinPCA]]></title>
	<description><![CDATA[<p><span>A package for windowed principal component analysis. WinPCA performs principal component analyses (PCA) in sliding windows along chromosomes. Both hard-called genotypes (input: VCF or TSV) or genotype likelihoods (input: VCF, TSV or BEAGLE) encoding&nbsp;</span><span>biallellic SNPs</span><span>&nbsp;are accepted. WinPCA uses&nbsp;</span><a href="https://scikit-allel.readthedocs.io/en/stable/stats/decomposition.html?highlight=pca">scikit-allel</a><span>&nbsp;to perfom PCAs on genotype data and&nbsp;</span><a href="https://github.com/Rosemeis/pcangsd">PCAngsd</a><span>&nbsp;methods for genotype likelihood (GL, PL) data.</span></p><p>Address of the bookmark: <a href="https://github.com/MoritzBlumer/winpca" rel="nofollow">https://github.com/MoritzBlumer/winpca</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>

</channel>
</rss>