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	<title><![CDATA[BOL: Poonam Mahapatra's bookmarks]]></title>
	<link>https://bioinformaticsonline.com/bookmarks/owner/poonam?offset=0</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41825/hnadock-a-nucleic-acid-docking-server-for-modeling-rnadna%E2%80%93rnadna-3d-complex-structures</guid>
	<pubDate>Thu, 04 Jun 2020 23:19:07 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41825/hnadock-a-nucleic-acid-docking-server-for-modeling-rnadna%E2%80%93rnadna-3d-complex-structures</link>
	<title><![CDATA[HNADOCK: a nucleic acid docking server for modeling RNA/DNA–RNA/DNA 3D complex structures]]></title>
	<description><![CDATA[<p><span>The HNADOCK server is to predict the binding complex structure between two nucleic acid molecules through a hierarchical docking algorihtm of an FFT-based global search strategy and an intrinsic scoring function for nucleic acid interactions. Users are required to provide the three-dimensional (3D) structures of the two molecules to be docked.&nbsp;</span></p><p>Address of the bookmark: <a href="http://huanglab.phys.hust.edu.cn/hnadock/" rel="nofollow">http://huanglab.phys.hust.edu.cn/hnadock/</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39875/lrsday-long-read-sequencing-data-analysis-for-yeasts</guid>
	<pubDate>Mon, 26 Aug 2019 18:07:33 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39875/lrsday-long-read-sequencing-data-analysis-for-yeasts</link>
	<title><![CDATA[LRSDAY: Long-read Sequencing Data Analysis for Yeasts]]></title>
	<description><![CDATA[<p><span>Long-read sequencing technologies have become increasingly popular in genome projects due to their strengths in resolving complex genomic regions. As a leading model organism with small genome size and great biotechnological importance, the budding yeast,&nbsp;</span><em>Saccharomyces cerevisiae</em><span>, has many isolates currently being sequenced with long reads.&nbsp;</span></p><p>Address of the bookmark: <a href="https://github.com/yjx1217/LRSDAY" rel="nofollow">https://github.com/yjx1217/LRSDAY</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39872/miropeats-discovers-regions-of-sequence-similarity-amongst-any-set-of-dna-sequences</guid>
	<pubDate>Mon, 26 Aug 2019 17:55:24 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39872/miropeats-discovers-regions-of-sequence-similarity-amongst-any-set-of-dna-sequences</link>
	<title><![CDATA[Miropeats: discovers regions of sequence similarity amongst any set of DNA sequences]]></title>
	<description><![CDATA[<p><span>Miropeats discovers regions of sequence similarity amongst any set of DNA sequences and then presents this similarity information graphically. Sequence similarity searching is a very general tool that forms the basis of many different biological sequence analyses but it is limited by the verbosity of traditional alignment presentation styles. Miropeats enhances the utility of conventional DNA sequence comparisons when looking at long lengths of sequence similarity by summarizing extensive large scale sequence similarities on a single page of graphics. The latest version of Miropeats can be used as a general pairwise alignment program or in its traditional role sorting out a big mess of overlapping or similar regions.</span></p><p>Address of the bookmark: <a href="http://www.littlest.co.uk/software/bioinf/old_packages/miropeats/" rel="nofollow">http://www.littlest.co.uk/software/bioinf/old_packages/miropeats/</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36974/many-to-many-pairwise-alignments-of-two-sequence-sets</guid>
	<pubDate>Tue, 19 Jun 2018 08:34:15 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36974/many-to-many-pairwise-alignments-of-two-sequence-sets</link>
	<title><![CDATA[Many-to-many pairwise alignments of two sequence sets]]></title>
	<description><![CDATA[needleall reads a set of input sequences and compares them all to one or more sequences, writing their optimal global sequence alignments to file. It uses the Needleman-Wunsch alignment algorithm to find the optimum alignment (including gaps) of two sequences along their entire length. The algorithm uses a dynamic programming method to ensure the alignment is optimum, by exploring all possible alignments and choosing the best. A scoring matrix is read that contains values for every possible residue or nucleotide match. Needleall finds the alignment with the maximum possible score where the score of an alignment is equal to the sum of the matches taken from the scoring matrix, minus penalties arising from opening and extending gaps in the aligned sequences. The substitution matrix and gap opening and extension penalties are user-specified.<p>Address of the bookmark: <a href="http://emboss.sourceforge.net/apps/release/6.6/emboss/apps/needleall.html" rel="nofollow">http://emboss.sourceforge.net/apps/release/6.6/emboss/apps/needleall.html</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36857/%E2%80%9Cone-code-to-find-them-all%E2%80%9D-a-perl-tool-to-conveniently-parse-repeatmasker-output-files</guid>
	<pubDate>Mon, 04 Jun 2018 03:45:15 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36857/%E2%80%9Cone-code-to-find-them-all%E2%80%9D-a-perl-tool-to-conveniently-parse-repeatmasker-output-files</link>
	<title><![CDATA[“One code to find them all”: a perl tool to conveniently parse RepeatMasker output files]]></title>
	<description><![CDATA[One code to find them all is a set of perl scripts to extract useful information from RepeatMasker about transposable elements, retrieve their sequences and get some quantitative information.

Assemble RepeatMasker hits into complete TE copies, including LTR-retrotransposon
Retrieve corresponding TE sequences, and flanking sequences, from the local fasta files
Compute summary statistics for each TE family (number of TE copies, genome coverage...)
Ambiguous cases such as nested TE can be assembled into copies automatically or manually
Allow for working with a TE user-defined library
Allow for working with only a user-chosen set of TE families


http://doua.prabi.fr/software/one-code-to-find-them-all<p>Address of the bookmark: <a href="http://doua.prabi.fr/software/one-code-to-find-them-all" rel="nofollow">http://doua.prabi.fr/software/one-code-to-find-them-all</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36852/mcmctree-a-phylogenetic-program-for-bayesian-estimation-of-species-divergence-times</guid>
	<pubDate>Sat, 02 Jun 2018 07:40:06 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36852/mcmctree-a-phylogenetic-program-for-bayesian-estimation-of-species-divergence-times</link>
	<title><![CDATA[MCMCTREE: a phylogenetic program for Bayesian estimation of species divergence times]]></title>
	<description><![CDATA[<p><a href="http://abacus.gene.ucl.ac.uk/software/paml.html" target="_blank">MCMCTREE</a><span>&nbsp;is a phylogenetic program for Bayesian estimation of species divergence times using soft fossil constraints under various molecular clock models. This is part of the&nbsp;</span><a href="http://abacus.gene.ucl.ac.uk/software/paml.html" target="_blank">PAML</a><span>&nbsp;package. In this tutorial I will analyze an easy example modified from dataset of&nbsp;</span><a href="http://www.ncbi.nlm.nih.gov/pubmed/20551041" target="_blank">Inoue et al. (2010)</a><span>. Here we conduct a commonly used time estimation method, "Approximate Likelihood Method", for the datasets including more than 10 species.</span></p><p>Address of the bookmark: <a href="http://www.fish-evol.com/mcmctreeExampleVert6/text1Eng.html" rel="nofollow">http://www.fish-evol.com/mcmctreeExampleVert6/text1Eng.html</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36849/glean-an-unsupervised-learning-system-to-integrate-disparate-sources-of-gene-structure-evidence</guid>
	<pubDate>Sat, 02 Jun 2018 07:38:33 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36849/glean-an-unsupervised-learning-system-to-integrate-disparate-sources-of-gene-structure-evidence</link>
	<title><![CDATA[GLEAN: an unsupervised learning system to integrate disparate sources of gene structure evidence]]></title>
	<description><![CDATA[<p><span>GLEAN is an unsupervised learning system to integrate disparate sources of gene structure evidence (gene model predictions, EST/protein genomic sequence alignments, SAGE/peptide tags, etc) to produce a consensus gene prediction, without prior training.</span></p><p>Address of the bookmark: <a href="https://sourceforge.net/projects/glean-gene/" rel="nofollow">https://sourceforge.net/projects/glean-gene/</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36846/gblocks-eliminates-poorly-aligned-positions-and-divergent-regions-of-a-dna-or-protein-alignment</guid>
	<pubDate>Sat, 02 Jun 2018 07:36:05 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36846/gblocks-eliminates-poorly-aligned-positions-and-divergent-regions-of-a-dna-or-protein-alignment</link>
	<title><![CDATA[Gblocks: eliminates poorly aligned positions and divergent regions of a DNA or protein alignment]]></title>
	<description><![CDATA[<p><a href="http://molevol.cmima.csic.es/castresana/Gblocks.html">Gblocks</a><span>&nbsp;eliminates poorly aligned positions and divergent regions of a DNA or protein alignment so that it becomes more suitable for phylogenetic analysis. This server implements the most important features of the Gblocks program to make its use as simple as possible without loosing the functionality that it is necessary in most of the cases. Other options can be changed in the stand-alone program. You can see here an&nbsp;</span><a href="http://molevol.cmima.csic.es/castresana/Gblocks_server/nad3.pir-gb.htm">example output file</a><span>&nbsp;showing the blocks selected from a protein alignment. Further information can be found in the&nbsp;</span><a href="http://molevol.cmima.csic.es/castresana/Gblocks/Gblocks_documentation.html">online documentation</a><span>.&nbsp;</span></p><p>Address of the bookmark: <a href="http://molevol.cmima.csic.es/castresana/Gblocks_server.html" rel="nofollow">http://molevol.cmima.csic.es/castresana/Gblocks_server.html</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36746/soap2-short-oligonucleotide-analysis-package-2</guid>
	<pubDate>Wed, 23 May 2018 10:09:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36746/soap2-short-oligonucleotide-analysis-package-2</link>
	<title><![CDATA[SOAP2 : Short Oligonucleotide Analysis Package 2]]></title>
	<description><![CDATA[SOAPaligner/soap2 is a member of the SOAP (Short Oligonucleotide Analysis Package). It is an updated version of SOAP software for short oligonucleotide alignment. The new program features in super fast and accurate alignment for huge amounts of short reads generated by Illumina/Solexa Genome Analyzer. Compared to soap v1, it is one order of magnitude faster. It require only 2 minutes aligning one million single-end reads onto the human reference genome. Another remarkable improvement of SOAPaligner is that it now supports a wide range of the read length.

SOAPaligner benefitted in time and space efficiency by a revolution in the basic data structures and algorithms used.The core algorithms and the indexing data structures (2way-BWT) are developed by the algorithms research group of the Department of Computer Science, the University of Hong Kong (T.W. Lam, Alan Tam, Simon Wong, Edward Wu and S.M. Yiu).<p>Address of the bookmark: <a href="http://soap.genomics.org.cn/soapaligner.html" rel="nofollow">http://soap.genomics.org.cn/soapaligner.html</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36644/tacoa-taxonomic-classification-of-environmental-genomic-fragments-using-a-kernelized-nearest-neighbor-approach</guid>
	<pubDate>Tue, 15 May 2018 09:52:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36644/tacoa-taxonomic-classification-of-environmental-genomic-fragments-using-a-kernelized-nearest-neighbor-approach</link>
	<title><![CDATA[TACOA: Taxonomic classification of environmental genomic fragments using a kernelized nearest neighbor approach]]></title>
	<description><![CDATA[TACOA is a software that can accurately predict the taxonomic origin of genomic fragments from metagenomic data sets by combining the advantages of the k -NN approach with a smoothing kernel function. 

TACOA can be easily installed and run on a desktop computer, therefore allowing researchers to locally analyze their metagenomic sequence data or integrate it into their pipelines.<p>Address of the bookmark: <a href="http://www.cebitec.uni-bielefeld.de/index.php/2-uncategorised/99-tacoa" rel="nofollow">http://www.cebitec.uni-bielefeld.de/index.php/2-uncategorised/99-tacoa</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
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