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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/32154?offset=80</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30214/megamerge-a-tool-to-merge-assembled-contigs-long-reads-from-metagenomic-sequencing-runs</guid>
	<pubDate>Mon, 19 Dec 2016 09:42:15 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30214/megamerge-a-tool-to-merge-assembled-contigs-long-reads-from-metagenomic-sequencing-runs</link>
	<title><![CDATA[MeGAMerge: A tool to merge assembled contigs, long reads from metagenomic sequencing runs]]></title>
	<description><![CDATA[<p>MeGAMerge</p>
<p>MeGAMerge (A tool to merge assembled contigs, long reads from metagenomic sequencing runs)</p>
<p>Description</p>
<p>MeGAMerge is a perl based wrapper/tool that can accept any number of sequence (FASTA) files containing assembled contigs of any length in Multi-FASTA format to produce an improved contig set based on OLC based assembly. All overlap parameters (Minimum Overlap Length, Identity, etc) are user-declarable at runtime. It is written to run on Linux.</p>
<p>Requirements:</p>
<p>You will need to have the following tools installed and in $PATH, or added to $binpath in the tool:</p>
<p>Newbler (specifically runAssembly)<br>Minimus2 (part of AMOS, also requires MUMmer)</p><p>Address of the bookmark: <a href="https://github.com/LANL-Bioinformatics/MeGAMerge" rel="nofollow">https://github.com/LANL-Bioinformatics/MeGAMerge</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31105/understanding-pacbio</guid>
	<pubDate>Fri, 24 Feb 2017 10:17:36 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31105/understanding-pacbio</link>
	<title><![CDATA[Understanding PacBio]]></title>
	<description><![CDATA[<p>This tutorial includes resources for learning more about PacBio data and bioinformatics analysis, and includes content suitable for both beginners and experts. Below are links to training modules (webinars and PowerPoint presentations) to help you get started with your data processing, as well as information for specialized applications.</p>
<p>Training Resources:</p>
<ul>
<li><a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/Bioinformatics-Workshop">Bioinformatics Workshop (Webinars)</a></li>
<li><a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/Bioinformatics-Training-Slides">Bioinformatics Training Slides</a></li>
</ul>
<p>Specialized Applications:</p>
<ul>
<li><a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/De-Novo-Assembly">De Novo Assembly</a></li>
<li><a href="https://github.com/PacificBiosciences/cDNA_primer/wiki">Transcriptome analysis</a></li>
<li><a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/Base-modification-analysis">Base Modification Analysis</a></li>
<li><a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/Barcoding">Barcoding</a></li>
<li><a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/Data-Analysis-Tools">Data Analysis Tools</a></li>
<li><a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/Minor-Variants-and-Phasing-Analysis">Minor Variants and Phasing Analysis</a></li>
</ul><p>Address of the bookmark: <a href="https://github.com/PacificBiosciences/Bioinformatics-Training/wiki" rel="nofollow">https://github.com/PacificBiosciences/Bioinformatics-Training/wiki</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30973/abacas</guid>
	<pubDate>Thu, 16 Feb 2017 12:15:55 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30973/abacas</link>
	<title><![CDATA[ABACAS]]></title>
	<description><![CDATA[<p><span>ABACAS is intended to rapidly contiguate (align, order, orientate) , visualize and design primers to close gaps on shotgun assembled contigs based on a reference sequence. It uses MUMmer to find alignment positions and identify syntenies of assembly contigs against the reference. The output is then processed to generate a pseudomolecule taking overlaping contigs and gaps in to account. MUMmer's alignment generating programs, Nucmer and Promer are used followed by the 'delta-filter' utility function. Users could also run tblastx on contigs that are not used to generate the pseudomolecule.&nbsp;</span></p><p>Address of the bookmark: <a href="http://abacas.sourceforge.net/Manual.html#9._Colour_code" rel="nofollow">http://abacas.sourceforge.net/Manual.html#9._Colour_code</a></p>]]></description>
	<dc:creator>Surabhi Chaudhary</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31014/sockeye</guid>
	<pubDate>Fri, 17 Feb 2017 08:51:16 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31014/sockeye</link>
	<title><![CDATA[sockeye]]></title>
	<description><![CDATA[<p>This sockeye&nbsp;software uses the Ensembl database project to import sequence and annotation information from several eukaryotic species. A user can additionally import their own custom sequence and annotation data. Individual annotation objects are displayed in Sockeye by using custom 3D models. Ensembl-derived and imported sequences can be analyzed by using a suite of multiple and pair-wise alignment algorithms. The results of these comparative analyses are also displayed in the 3D environment of Sockeye. By using the Java3D API to visualize genomic data in a 3D environment, we are able to compactly display cross-sequence comparisons. This provides the user with a novel platform for visualizing and comparing genomic feature organization.</p><p>Address of the bookmark: <a href="http://www.bcgsc.ca/platform/bioinfo/software/sockeye/releases/1.3" rel="nofollow">http://www.bcgsc.ca/platform/bioinfo/software/sockeye/releases/1.3</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32851/anges-reconstructing-ancestral-genomes-maps</guid>
	<pubDate>Thu, 18 May 2017 05:27:08 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32851/anges-reconstructing-ancestral-genomes-maps</link>
	<title><![CDATA[ANGES: reconstructing ANcestral GEnomeS maps]]></title>
	<description><![CDATA[<p>This page contains the software ANGES 1.01, that aims at reconstucting ancestral genome maps from homologous markers in extant related genomes.</p>
<h3>Download</h3>
<ul>
<li><a href="http://paleogenomics.irmacs.sfu.ca/ANGES/anges_1.01.tar.gz">Program, version 1.01</a>&nbsp;(July 10, 2012, documentation updated in August 2014)</li>
<li><a href="http://paleogenomics.irmacs.sfu.ca/ANGES/anges_1.01_examples_with_results.tar.gz">Examples with results (featured ancestors: boreoeutherian, amniote, yeasts, Burkholderia, monocots)</a>; please refer to the documentation of the distribution above.</li>
</ul><p>Address of the bookmark: <a href="http://paleogenomics.irmacs.sfu.ca/ANGES/" rel="nofollow">http://paleogenomics.irmacs.sfu.ca/ANGES/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/36711/ancestral-sequence-reconstruction-steps</guid>
	<pubDate>Fri, 18 May 2018 08:28:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/36711/ancestral-sequence-reconstruction-steps</link>
	<title><![CDATA[Ancestral sequence reconstruction steps !]]></title>
	<description><![CDATA[<div><strong>Ancestral sequence reconstruction</strong>&nbsp;(<strong>ASR</strong>) &ndash; also known as&nbsp;<strong>ancestral gene</strong>/<strong>sequence reconstruction</strong>/<strong>resurrection</strong>&nbsp;&ndash; is a technique used in the study of&nbsp;molecular evolution. The method consists of the synthesis of an ancestral&nbsp;gene&nbsp;and expression of the corresponding ancestral&nbsp;protein.&nbsp;<a href="https://en.wikipedia.org/wiki/Ancestral_sequence_reconstruction#cite_note-thornton-1"></a>The idea of protein 'resurrection' was suggested in 1963 by Pauling and Zuckerkandl.<a href="https://en.wikipedia.org/wiki/Ancestral_sequence_reconstruction#cite_note-2"></a>&nbsp;Some early efforts were made in the eighties-nineties, led by the laboratory of&nbsp;Steven A. Benner, showing the potential of this technique &ndash; one that only started to be fulfilled in the post-genomic era.<a href="https://en.wikipedia.org/wiki/Ancestral_sequence_reconstruction#cite_note-3"></a>&nbsp;Thanks to the improvement of algorithms and of better sequencing and synthesis techniques, the method was developed further in the early 2000s to allow the resurrection of a greater variety of and much more ancient genes.<a href="https://en.wikipedia.org/wiki/Ancestral_sequence_reconstruction#cite_note-4"></a>&nbsp;Over the last decade, ancestral protein resurrection has developed as a strategy to reveal the mechanisms and dynamics of protein evolution.&nbsp;</div><div>&nbsp;</div><div>BEAST is the best way to predict the ancestral structure. but, I suggest following steps?</div><div>&nbsp;</div><div>1- Alignments "Mafft -&nbsp;<a href="https://www.researchgate.net/deref/http%3A%2F%2Fmafft.cbrc.jp%2Falignment%2Fsoftware%2Fsource.html" target="_blank">http://mafft.cbrc.jp/alignment/software/source.html</a>"</div><div>mafft --maxiterate 1000 --reorder --thread 24 --genafpair Dataset.fasta &gt; Dataset_Alig.fasta</div><div>&nbsp;</div><div>2- Your dataset has a good phylogenetic signal, is possible to perform with Tree-Puzzle "<a href="https://www.researchgate.net/deref/http%3A%2F%2Fwww.tree-puzzle.de" target="_blank">http://www.tree-puzzle.de</a>";</div><div>&nbsp;</div><div id="yui_3_14_1_1_1526649596608_1443">3 - This dataset which the saturation index, I perform with "<a href="https://www.researchgate.net/deref/http%3A%2F%2Fdambe.bio.uottawa.ca%2Fdambe.asp" target="_blank">http://dambe.bio.uottawa.ca/dambe.asp</a>";</div><div>&nbsp;</div><div>4- Has evidence of possible recombination in your dataset, the evaluate if this presence or absence, because this may to influence the grouping of clades, I perform with</div><div>---recombination</div><div>&nbsp;</div><div>4.1- Phi-test, implemented in SplitTree4"<a href="https://www.researchgate.net/deref/http%3A%2F%2Fwww.splitstree.org" target="_blank">http://www.splitstree.org</a>", (.nex file)</div><div>&nbsp;</div><div>4.2- GARD deployed in webserver in the DataMonkey "<a href="https://www.researchgate.net/deref/http%3A%2F%2Fwww.datamonkey.org%2F" target="_blank">http://www.datamonkey.org/</a>" - turning to the amino acid seaview -&gt; view proteins -&gt; save as ...) Ideally do a tree-based groups.</div><div>&nbsp;</div><div>4.3- RDP4 for download and installation on Windows in "<a href="https://www.researchgate.net/deref/http%3A%2F%2Fweb.cbio.uct.ac.za%2F~darren%2Frdp.html" target="_blank">http://web.cbio.uct.ac.za/~darren/rdp.html</a>"</div><div>&nbsp;</div><div>4.4- Hyphy (Mac, Windows, Linux) in "<a href="https://www.researchgate.net/deref/http%3A%2F%2Fhyphy.org%2Fw%2Findex.php%2FDownload" target="_blank">http://hyphy.org/w/index.php/Download</a>"</div><div>&nbsp;</div><div>4.5- Path-o-Gen (temporal structure of a tree input file -&gt; arquivo.tre)</div><div>These steps above, I call of pre-processing to inferences phylogenetic...</div><div>&nbsp;</div><div>5- Perform phylogenetic tree, used Bayesian Inference with Molecular Clock, but is necessary Clock Testing:</div><div>&nbsp;</div><div>- This step is performed with program Beast (Beauti, Beast and TreeAnnotator), and Tracer_v1.5 more FigTree to inspection.</div><div>&nbsp;</div><div>- Tutorials:&nbsp;<a href="https://www.researchgate.net/deref/http%3A%2F%2Fbeast.bio.ed.ac.uk%2Ftutorials" target="_blank">http://beast.bio.ed.ac.uk/tutorials</a></div><div>- Downloads:&nbsp;<a href="https://www.researchgate.net/deref/http%3A%2F%2Fbeast.bio.ed.ac.uk%2Fdownloads" target="_blank">http://beast.bio.ed.ac.uk/downloads</a></div>]]></description>
	<dc:creator>Surabhi Chaudhary</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31302/multi-metagenome-assembly</guid>
	<pubDate>Fri, 03 Mar 2017 10:14:18 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31302/multi-metagenome-assembly</link>
	<title><![CDATA[Multi-metagenome assembly]]></title>
	<description><![CDATA[<p>This project contains scripts and tutorials on how to assemble individual microbial genomes from metagenomes, as described in:</p>
<p>Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes<br><br>Mads Albertsen, Philip Hugenholtz, Adam Skarshewski, Gene W. Tyson, K&aring;re L. Nielsen and Per .H. Nielsen</p>
<p>Nature Biotechnology 2013, doi:&nbsp;<a href="http://www.nature.com/nbt/journal/vaop/ncurrent/abs/nbt.2579.html">10.1038/nbt.2579</a></p><p>Address of the bookmark: <a href="https://github.com/MadsAlbertsen/multi-metagenome" rel="nofollow">https://github.com/MadsAlbertsen/multi-metagenome</a></p>]]></description>
	<dc:creator>Radha Agarkar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31353/concoct-clustering-contigs-with-coverage-and-composition</guid>
	<pubDate>Mon, 06 Mar 2017 04:08:16 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31353/concoct-clustering-contigs-with-coverage-and-composition</link>
	<title><![CDATA[CONCOCT: Clustering cONtigs with COverage and ComposiTion]]></title>
	<description><![CDATA[<p>A program for unsupervised binning of metagenomic contigs by using nucleotide composition, coverage data in multiple samples and linkage data from paired end reads.</p>
<p>Warning! This software is to be considered under development. Functionality and the user interface may still change significantly from one version to another. If you want to use this software, please stay up to date with the list of known issues:<a href="https://github.com/BinPro/CONCOCT/issues">https://github.com/BinPro/CONCOCT/issues</a></p><p>Address of the bookmark: <a href="https://github.com/BinPro/CONCOCT" rel="nofollow">https://github.com/BinPro/CONCOCT</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31382/seqmule-automated-human-exomegenome-variants-detection</guid>
	<pubDate>Tue, 07 Mar 2017 10:12:36 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31382/seqmule-automated-human-exomegenome-variants-detection</link>
	<title><![CDATA[SeqMule: Automated human exome/genome variants detection]]></title>
	<description><![CDATA[<p><span>SeqMule takes single-end or paird-end FASTQ or BAM files, generates a script consisting of more than 10 popular alignment, analysis tools and runs the script line by line. Users can change the pipeline or fine-tune the parameters by modifying its configuration file. SeqMule also has some built-in functions, such as pooling consensus calls from various callers, plotting a Venn diagram showing intersection among different callers, and downloading databases. SeqMule can be used for both Mendelian disease study and cancer genome study.</span></p><p>Address of the bookmark: <a href="http://seqmule.openbioinformatics.org/en/latest/" rel="nofollow">http://seqmule.openbioinformatics.org/en/latest/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31881/gbtools-interactive-visualization-of-metagenome-bins-in-r</guid>
	<pubDate>Sun, 26 Mar 2017 15:41:31 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31881/gbtools-interactive-visualization-of-metagenome-bins-in-r</link>
	<title><![CDATA[gbtools: Interactive Visualization of Metagenome Bins in R]]></title>
	<description><![CDATA[<p><span>We have developed gbtools, a software package that allows users to visualize metagenomic assemblies by plotting coverage (sequencing depth) and GC values of contigs, and also to annotate the plots with taxonomic information. Different sets of annotations, including taxonomic assignments from conserved marker genes or SSU rRNA genes, can be imported simultaneously; users can choose which annotations to plot. Bins can be manually defined from plots, or be imported from third-party binning tools and overlaid onto plots, such that results from different methods can be compared side-by-side. gbtools reports summary statistics of bins including marker gene completeness, and allows the user to add or subtract bins with each other.&nbsp;</span></p>
<p><span>Tool at&nbsp;https://github.com/kbseah/genome-bin-tools</span></p><p>Address of the bookmark: <a href="http://journal.frontiersin.org/article/10.3389/fmicb.2015.01451/full" rel="nofollow">http://journal.frontiersin.org/article/10.3389/fmicb.2015.01451/full</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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