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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/42405?offset=30</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36935/assemblytics-delta-file-to-analyze-alignments-of-an-assembly-to-another-assembly-or-a-reference-genome</guid>
	<pubDate>Thu, 14 Jun 2018 07:31:00 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36935/assemblytics-delta-file-to-analyze-alignments-of-an-assembly-to-another-assembly-or-a-reference-genome</link>
	<title><![CDATA[assemblytics: delta file to analyze alignments of an assembly to another assembly or a reference genome]]></title>
	<description><![CDATA[Download and install MUMmer
Align your assembly to a reference genome using nucmer (from MUMmer package)
$ nucmer -maxmatch -l 100 -c 500 REFERENCE.fa ASSEMBLY.fa -prefix OUT
Consult the MUMmer manual if you encounter problems

Optional: Gzip the delta file to speed up upload (usually 2-4X faster)
$ gzip OUT.delta
Then use the OUT.delta.gz file for upload.
Upload the .delta or delta.gz file (view example) to Assemblytics
Important: Use only contigs rather than scaffolds from the assembly. This will prevent false positives when the number of Ns in the scaffolded sequence does not match perfectly to the distance in the reference.

The unique sequence length required represents an anchor for determining if a sequence is unique enough to safely call variants from, which is an alternative to the mapping quality filter for read alignment.

http://assemblytics.com/<p>Address of the bookmark: <a href="http://assemblytics.com/" rel="nofollow">http://assemblytics.com/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37674/qualimap2-evaluating-next-generation-sequencing-alignment-data</guid>
	<pubDate>Tue, 11 Sep 2018 04:44:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37674/qualimap2-evaluating-next-generation-sequencing-alignment-data</link>
	<title><![CDATA[Qualimap2: Evaluating next generation sequencing alignment data]]></title>
	<description><![CDATA[<p><strong>Qualimap 2</strong><span>&nbsp;is a platform-independent application written in Java and R that provides both a Graphical User Inteface (GUI) and a command-line interface to facilitate the quality control of alignment sequencing data and its derivatives like feature counts.&nbsp;</span><br><br><span>Supported types of experiments include:</span></p>
<ul>
<li>Whole-genome sequencing</li>
<li>Whole-exome sequencing</li>
<li>RNA-seq (speical mode available)</li>
<li>ChIP-seq</li>
</ul><p>Address of the bookmark: <a href="http://qualimap.bioinfo.cipf.es/" rel="nofollow">http://qualimap.bioinfo.cipf.es/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41910/the-wavefront-alignment-wfa-algorithm</guid>
	<pubDate>Sun, 28 Jun 2020 10:17:50 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41910/the-wavefront-alignment-wfa-algorithm</link>
	<title><![CDATA[The wavefront alignment (WFA) algorithm]]></title>
	<description><![CDATA[<p><span>The wavefront alignment (WFA) algorithm is an exact gap-affine algorithm that takes advantage of</span><br><span>homologous regions between the sequences to accelerate the alignment process. As opposed to traditional dynamic programming algorithms that run in quadratic time, the WFA runs in time O(ns), proportional to the read length n and the alignment score s, using O(s^2) memory. Moreover, the WFA exhibits simple data dependencies that can be easily vectorized, even by the automatic features of modern compilers, for different architectures, without the need to adapt the code.</span></p><p>Address of the bookmark: <a href="https://github.com/smarco/WFA" rel="nofollow">https://github.com/smarco/WFA</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44887/alfapang-alignment-free-algorithm-for-pangenome-graph-construction</guid>
	<pubDate>Thu, 28 Aug 2025 02:56:35 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44887/alfapang-alignment-free-algorithm-for-pangenome-graph-construction</link>
	<title><![CDATA[AlfaPang: alignment free algorithm for pangenome graph construction]]></title>
	<description><![CDATA[<p><span>AlfaPang constructs variation graphs, leveraging its alignment-free and reference-free approach, based solely on intrinsic sequence properties. This design allows AlfaPang's runtime and memory usage to scale linearly with the size of input sequences, enabling it to handle significantly larger genome sets compared to other methods.</span></p><p>Address of the bookmark: <a href="https://github.com/AdamCicherski/AlfaPang" rel="nofollow">https://github.com/AdamCicherski/AlfaPang</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33506/bedops-v2426-high-performance-genomic-feature-operations</guid>
	<pubDate>Mon, 12 Jun 2017 10:11:01 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33506/bedops-v2426-high-performance-genomic-feature-operations</link>
	<title><![CDATA[BEDOPS v2.4.26: high-performance genomic feature operations]]></title>
	<description><![CDATA[<p><strong>BEDOPS v2.4.26</strong> is a suite of tools to address common questions raised in genomic studies &mdash; mostly with regard to overlap and proximity relationships between data sets. It aims to be scalable and flexible, facilitating the efficient and accurate analysis and management of large-scale genomic data.</p>
<p>The <a href="https://bedops.readthedocs.io/en/latest/content/overview.html#overview">overview</a> section of the <strong>BEDOPS v2.4.26</strong> documentation summarizes the toolkit, functionality and performance enhancements. The <a href="https://bedops.readthedocs.io/en/latest/index.html#reference">reference</a> table offers documentation for all applications and scripts.</p><p>Address of the bookmark: <a href="https://github.com/bedops/bedops" rel="nofollow">https://github.com/bedops/bedops</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41694/mercator-multiple-whole-genome-orthology-map-construction</guid>
	<pubDate>Tue, 19 May 2020 16:46:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41694/mercator-multiple-whole-genome-orthology-map-construction</link>
	<title><![CDATA[Mercator: Multiple Whole-Genome Orthology Map Construction]]></title>
	<description><![CDATA[<p><span>Whole-genome homology maps attempt to identify the evolutionary relationships between and within multiple genomes. The term "syntenic" is often used to describe regions of multiple genomes that are believed to have evolved from the same region in an ancestral genome. However, it has been pointed out that this use of the term is incorrect (</span><a href="https://www.biostat.wisc.edu/~cdewey/mercator/#refSynteny">Passarge et al. 1999</a><span>) and thus we will use the terms "homologous", "orthologous", and "paralogous" instead. Ideally, given K genomes, we would like to identify all orthologous genomic regions as well as paralogous regions within each genome and hypothetical ancestral genome. Maps listing these relationships are extremely valuable to researchers performing comparative analyses of genomic sequence. Here we present our initial work in the form a program called&nbsp;</span><em>Mercator</em><span>&nbsp;that constructs orthology maps between multiple whole genomes.</span></p><p>Address of the bookmark: <a href="https://www.biostat.wisc.edu/~cdewey/mercator/" rel="nofollow">https://www.biostat.wisc.edu/~cdewey/mercator/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38208/anitools-web-a-web-tool-for-fast-genome-comparison-within-multiple-bacterial-strains</guid>
	<pubDate>Wed, 14 Nov 2018 04:34:23 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38208/anitools-web-a-web-tool-for-fast-genome-comparison-within-multiple-bacterial-strains</link>
	<title><![CDATA[ANItools web: a web tool for fast genome comparison within multiple bacterial strains]]></title>
	<description><![CDATA[<p><span>ANItools is a software package written by PERL scripts that can be run in a Linux/Unix system. If you want to compare bacterial genomes and calculate their average nucleotide identity (ANI), you could download and run this program directly. Or you could send us the genome sequence by email. Then we will do the analysis work for you.</span></p>
<p><span>https://academic.oup.com/database/article/doi/10.1093/database/baw084/2630454</span></p><p>Address of the bookmark: <a href="http://ani.mypathogen.cn/" rel="nofollow">http://ani.mypathogen.cn/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44663/svbyeye-r-package-to-visualize-alignments-between-two-or-multiple-dna-sequences</guid>
	<pubDate>Tue, 17 Sep 2024 02:34:57 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44663/svbyeye-r-package-to-visualize-alignments-between-two-or-multiple-dna-sequences</link>
	<title><![CDATA[SVbyEye: R Package to visualize alignments between two or multiple DNA sequences]]></title>
	<description><![CDATA[<p dir="auto">R Package to visualize alignments between two or multiple DNA sequences including<br>a number of functionalities to facilitate processing of alignments in PAF format.</p>
<p dir="auto"><span>SVbyEye, an open-source R package to visualize and annotate sequence-to-sequence alignments along with various functionalities to process alignments in PAF format. The tool facilitates the characterization of complex SVs in the context of sequence homology helping resolve the mechanisms underlying their formation. Availability and implementation SVbyEye is available at https://github.com/daewoooo/SVbyEye.</span></p>
<p dir="auto">Author: David Porubsky</p><p>Address of the bookmark: <a href="https://github.com/daewoooo/SVbyEye" rel="nofollow">https://github.com/daewoooo/SVbyEye</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/4943/molecular-genetics-lecture</guid>
	<pubDate>Fri, 27 Sep 2013 04:24:45 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/4943/molecular-genetics-lecture</link>
	<title><![CDATA[Molecular Genetics Lecture]]></title>
	<description><![CDATA[<p><span>"Robert Sapolsky makes interdisciplinary connections between behavioral biology and molecular genetic influences. He relates protein synthesis and point mutations to microevolutionary change, and discusses conflicting theories of gradualism and punctuated equilibrium and the influence of epigenetics on development theories."&nbsp;</span></p>
<p><span>"<span><strong>Robert Sapolsky</strong> is an American neuroendocrinologist, professor of biology, neuroscience, and neurosurgery at Stanford University, researcher and author" ----Wikipedia</span></span></p><p>Address of the bookmark: <a href="http://www.youtube.com/watch?v=_dRXA1_e30o" rel="nofollow">http://www.youtube.com/watch?v=_dRXA1_e30o</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36752/minmax-a-versatile-tool-for-calculating-and-comparing-synonymous-codon-usage-and-its-impact-on-protein-folding</guid>
	<pubDate>Thu, 24 May 2018 02:53:31 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36752/minmax-a-versatile-tool-for-calculating-and-comparing-synonymous-codon-usage-and-its-impact-on-protein-folding</link>
	<title><![CDATA[%MinMax: A versatile tool for calculating and comparing synonymous codon usage and its impact on protein folding.]]></title>
	<description><![CDATA[%MM calculates whether a given gene sequence encodes amino acids using the most common codons possible, the least common codons possible, or (most typically) some combination of these extremes. See our PLoS ONE paper for more details on how the %MinMax algorithm works. 

%MinMax results are averaged over an 18-codon sliding window; hence the result for "codon window = 1" is the average codon usage for codons 1-18, codon window 2 = codons 2-19, etc.<p>Address of the bookmark: <a href="http://www.codons.org/" rel="nofollow">http://www.codons.org/</a></p>]]></description>
	<dc:creator>Surabhi Chaudhary</dc:creator>
</item>

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