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	<link>https://bioinformaticsonline.com/related/44559?</link>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/32713/salzberg-lab</guid>
  <pubDate>Mon, 15 May 2017 05:14:01 -0500</pubDate>
  <link></link>
  <title><![CDATA[Salzberg lab]]></title>
  <description><![CDATA[
<p>We are a computational biology lab that develops novel methods for analysis of DNA and RNA sequences. Our research includes software for aligning and assembling RNA-seq data, whole-genome assembly, and microbiome analysis. We work closely with biomedical scientists to apply these methods to current problems arising in a broad spectrum of biological and medical research areas. We’re also part of the Center for Computational Biology, a group of 20+ faculty members and their labs at Johns Hopkins working on computational, statistical, and mathematical methods that can turn massive genomic data sets into biologically and clinically useful information.</p>

<p>https://salzberg-lab.org/</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44616/basics-of-blast-programs</guid>
	<pubDate>Fri, 26 Jul 2024 06:04:26 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44616/basics-of-blast-programs</link>
	<title><![CDATA[Basics of BLAST Programs !]]></title>
	<description><![CDATA[<p>The Basic Local Alignment Search Tool (BLAST) is a powerful bioinformatics program used to compare an input sequence (such as DNA, RNA, or protein sequences) against a database of sequences to find regions of similarity. Developed by the National Center for Biotechnology Information (NCBI), BLAST is widely used for identifying species, finding functional and evolutionary relationships between sequences, and predicting the function of novel sequences.</p><p>Key Features of BLAST:<br />1. Sequence Comparison: BLAST searches for local alignments between the query sequence and sequences in a database. It identifies regions of similarity, which can help infer functional and evolutionary relationships.</p><p>2. Speed and Efficiency: BLAST uses heuristic algorithms, making it faster than exhaustive search methods, suitable for large-scale database searches.</p><p>3. Versatility: There are several versions of BLAST for different types of sequence comparisons:<br /> - blastn: Compares a nucleotide query sequence against a nucleotide sequence database.<br /> - blastp: Compares a protein query sequence against a protein sequence database.<br /> - blastx: Compares a nucleotide query sequence translated in all reading frames against a protein sequence database.<br /> - tblastn: Compares a protein query sequence against a nucleotide sequence database translated in all reading frames.<br /> - tblastx: Compares the six-frame translations of a nucleotide query sequence against the six-frame translations of a nucleotide sequence database.</p><p>4. Scoring and E-value: BLAST results are scored based on the quality and length of the alignments. The E-value (expect value) indicates the number of alignments one can expect to find by chance, with lower E-values representing more significant matches.</p><p>5. Output Formats: BLAST provides results in various formats, including plain text, HTML, XML, and JSON, making it adaptable for different types of analyses and integrations with other tools.</p><p>Applications of BLAST:<br />- Genomic Research: Identifying genes, understanding genetic diversity, and mapping genome sequences.<br />- Protein Function Prediction: Inferring the function of unknown proteins by comparing them to known protein sequences.<br />- Evolutionary Studies: Exploring evolutionary relationships between organisms by comparing their genetic material.<br />- Medical Research: Identifying pathogens, understanding disease mechanisms, and developing treatments by comparing sequences of interest.</p><p>Overall, BLAST is an essential tool in bioinformatics, offering a reliable and efficient way to analyze and interpret biological sequence data.</p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40856/3d-de-novo-assembly-3d-dna-pipeline</guid>
	<pubDate>Sun, 02 Feb 2020 13:41:55 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40856/3d-de-novo-assembly-3d-dna-pipeline</link>
	<title><![CDATA[3D de novo assembly (3D DNA) pipeline]]></title>
	<description><![CDATA[<p>For a detailed description of the pipeline and how it integrates with other tools designed by the Aiden Lab see&nbsp;<a href="http://aidenlab.org/assembly/manual_180322.pdf">Genome Assembly Cookbook</a>&nbsp;on&nbsp;<a href="http://aidenlab.org/assembly">http://aidenlab.org/assembly</a>.</p>
<p>For the original version of the pipeline and to reproduce the Hs2-HiC and the AaegL4 genomes reported in&nbsp;<a href="http://science.sciencemag.org/content/356/6333/92">(Dudchenko et al.,&nbsp;<em>Science</em>, 2017)</a>&nbsp;see the&nbsp;<a href="https://github.com/theaidenlab/3d-dna/tree/745779bdf64db6e55bddb70c24e9b58825938c33">original commit</a>.</p>
<p>For the detailed description of the merge section see&nbsp;<a href="https://github.com/theaidenlab/AGWG-merge">https://github.com/theaidenlab/AGWG-merge</a>.</p><p>Address of the bookmark: <a href="https://github.com/theaidenlab/3d-dna" rel="nofollow">https://github.com/theaidenlab/3d-dna</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27076/ale-a-generic-assembly-likelihood-evaluation-framework-for-assessing-the-accuracy-of-genome-and-metagenome-assemblies</guid>
	<pubDate>Tue, 26 Apr 2016 03:38:43 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27076/ale-a-generic-assembly-likelihood-evaluation-framework-for-assessing-the-accuracy-of-genome-and-metagenome-assemblies</link>
	<title><![CDATA[ALE: a Generic Assembly Likelihood Evaluation Framework for Assessing the Accuracy of Genome and Metagenome Assemblies]]></title>
	<description><![CDATA[<p>Assembly Likelihood Evaluation (ALE) framework that overcomes these limitations, systematically evaluating the accuracy of an assembly in a reference-independent manner using rigorous statistical methods. This framework is comprehensive, and integrates read quality, mate pair orientation and insert length (for paired-end reads), sequencing coverage, read alignment and k-mer frequency. ALE pinpoints synthetic errors in both single and metagenomic assemblies, including single-base errors, insertions/deletions, genome rearrangements and chimeric assemblies presented in metagenomes. At the genome level with real-world data, ALE identifies three large misassemblies from the Spirochaeta smaragdinae finished genome, which were all independently validated by Pacific Biosciences sequencing. At the single-base level with Illumina data, ALE recovers 215 of 222 (97%) single nucleotide variants in a training set from a GC-rich Rhodobacter sphaeroides genome. Using real Pacific Biosciences data, ALE identifies 12 of 12 synthetic errors in a Lambda Phage genome, surpassing even Pacific Biosciences' own variant caller, EviCons. In summary, the ALE framework provides a comprehensive, reference-independent and statistically rigorous measure of single genome and metagenome assembly accuracy, which can be used to identify misassemblies or to optimize the assembly process.</p>
<p>More at&nbsp;http://www.ncbi.nlm.nih.gov/pubmed/23303509</p><p>Address of the bookmark: <a href="http://sc932.github.io/ALE/about.html" rel="nofollow">http://sc932.github.io/ALE/about.html</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/27438/hagfish-assess-an-assembly-through-creative-use-of-coverage-plots</guid>
	<pubDate>Fri, 20 May 2016 19:08:17 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/27438/hagfish-assess-an-assembly-through-creative-use-of-coverage-plots</link>
	<title><![CDATA[Hagfish - assess an assembly through creative use of coverage plots]]></title>
	<description><![CDATA[<p>Hagfish is a tool that is to be used in data analysis of Next Generation Sequencing (NGS) experiments. Hagfish builds on the concept of coverage plots and aims to assist (amongst others) in quality control of&nbsp;<em style="font-size: 12.8px;">de novo</em>&nbsp;genome assembly or identification of structural variation in a genome re-sequencing experiment.</p>
<p>Hagfish requires a reference sequence and a&nbsp;<span>paired end</span>&nbsp;re-sequencing data set. Hagfish has more power the larger the insert size of the paired end library is.</p>
<p>Quick links:&nbsp;<a href="https://github.com/mfiers/hagfish/wiki/Install">Installation</a>,<a href="https://github.com/mfiers/hagfish/wiki/Operation">Operation</a>,&nbsp;<a href="https://github.com/mfiers/hagfish/wiki/ReadMappers">Read mappers</a>,&nbsp;<a href="https://github.com/mfiers/hagfish/wiki/Scripts">Hagfish scripts</a>,&nbsp;<a href="https://github.com/mfiers/hagfish/wiki/Plots">Hagfish plots</a></p><p>Address of the bookmark: <a href="https://github.com/mfiers/hagfish" rel="nofollow">https://github.com/mfiers/hagfish</a></p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42359/dnasp-dna-sequence-polymorphism-is-a-software-package-for-the-analysis-of-dna-polymorphisms</guid>
	<pubDate>Wed, 25 Nov 2020 19:51:38 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42359/dnasp-dna-sequence-polymorphism-is-a-software-package-for-the-analysis-of-dna-polymorphisms</link>
	<title><![CDATA[DnaSP: DNA Sequence Polymorphism, is a software package for the analysis of DNA polymorphisms]]></title>
	<description><![CDATA[<p><span>DnaSP, DNA Sequence Polymorphism, is a software package for the analysis of DNA polymorphisms using data from a single locus (a multiple sequence aligned -MSA data), or from several loci (a Multiple-MSA data, such as formats generated by some assembler RAD-seq software). DnaSP can estimate several measures of DNA sequence variation within and between populations in noncoding, synonymous or nonsynonymous sites, or in various sorts of codon positions), as well as linkage disequilibrium, recombination, gene flow and gene conversion parameters.</span></p><p>Address of the bookmark: <a href="http://www.ub.edu/dnasp/" rel="nofollow">http://www.ub.edu/dnasp/</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42645/mmseqs2-ultra-fast-and-sensitive-sequence-search-and-clustering-suite</guid>
	<pubDate>Mon, 18 Jan 2021 10:47:56 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42645/mmseqs2-ultra-fast-and-sensitive-sequence-search-and-clustering-suite</link>
	<title><![CDATA[MMseqs2: ultra fast and sensitive sequence search and clustering suite]]></title>
	<description><![CDATA[<p><span>MMseqs2 (Many-against-Many sequence searching) is a software suite to search and cluster huge protein and nucleotide sequence sets. MMseqs2 is open source GPL-licensed software implemented in C++ for Linux, MacOS, and (as beta version, via cygwin) Windows. The software is designed to run on multiple cores and servers and exhibits very good scalability. MMseqs2 can run 10000 times faster than BLAST. At 100 times its speed it achieves almost the same sensitivity. It can perform profile searches with the same sensitivity as PSI-BLAST at over 400 times its speed.</span></p><p>Address of the bookmark: <a href="https://github.com/soedinglab/MMseqs2" rel="nofollow">https://github.com/soedinglab/MMseqs2</a></p>]]></description>
	<dc:creator>Manisha Mishra</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/2791/ncbi-psi-blast-tutorial</guid>
	<pubDate>Fri, 23 Aug 2013 02:25:02 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/2791/ncbi-psi-blast-tutorial</link>
	<title><![CDATA[NCBI PSI-BLAST Tutorial]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/T3kHEieyylk" frameborder="0" allowfullscreen></iframe>http:--www.biotechnology.jhu.edu-
Tutorial for PSI-BLAST, an extension of BLAST that uses matrix algebra. BLAST is a cornerstone bioinformatics tool at NCBI. BLAST is the
Basic Local Alignment Search tool and will protein and DNA sequences that
are related to a sequence that the user provides.]]></description>
	
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30831/fsa-fast-statistical-alignment</guid>
	<pubDate>Mon, 06 Feb 2017 04:26:01 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30831/fsa-fast-statistical-alignment</link>
	<title><![CDATA[FSA: Fast Statistical Alignment]]></title>
	<description><![CDATA[<p><span>FSA is a probabilistic multiple sequence alignment algorithm which uses a "distance-based" approach to aligning homologous protein, RNA or DNA sequences. Much as distance-based phylogenetic reconstruction methods like Neighbor-Joining build a phylogeny using only pairwise divergence estimates, FSA builds a multiple alignment using only pairwise estimations of homology. This is made possible by the sequence annealing technique for constructing a multiple alignment from pairwise comparisons, developed by Ariel Schwartz in&nbsp;</span><a href="http://www.eecs.berkeley.edu/Pubs/TechRpts/2007/EECS-2007-39.html">"Posterior Decoding Methods for Optimization and Control of Multiple Alignments</a><span>."</span></p>
<p>FSA brings the high accuracies previously available only for small-scale analyses of proteins or RNAs to large-scale problems such as aligning thousands of sequences or megabase-long sequences. FSA introduces several novel methods for constructing better alignments:</p>
<ul>
<li>FSA uses machine-learning techniques to estimate gap and substitution parameters on the fly for each set of input sequences. This "query-specific learning" alignment method makes FSA very robust: it can produce superior alignments of sets of homologous sequences which are subject to very different evolutionary constraints.</li>
<li>FSA is capable of aligning hundreds or even thousands of sequences using a randomized inference algorithm to reduce the computational cost of multiple alignment. This randomized inference can be over ten times faster than a direct approach with little loss of accuracy.</li>
<li>FSA can quickly align very long sequences using the "anchor annealing" technique for resolving anchors and projecting them with transitive anchoring. It then stitches together the alignment between the anchors using the methods described above.</li>
<li>The included GUI, MAD (Multiple Alignment Display), can display the intermediate alignments produced by FSA, where each character is colored according to the probability that it is correctly aligned (see the picture and&nbsp;<a href="http://fsa.sourceforge.net/images/Suchard_SIV.fsa.mov">movie</a>&nbsp;at the top of the page).</li>
</ul>
<p><span>You can see more information on the&nbsp;</span><a href="http://fsa.sourceforge.net/FAQ.html">FAQ</a><span>.&nbsp;</span></p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="http://fsa.sourceforge.net/" rel="nofollow">http://fsa.sourceforge.net/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38304/lordfast-sensitive-and-fast-alignment-search-tool-for-long-noisy-read-sequencing-data</guid>
	<pubDate>Tue, 27 Nov 2018 04:43:57 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38304/lordfast-sensitive-and-fast-alignment-search-tool-for-long-noisy-read-sequencing-data</link>
	<title><![CDATA[lordFAST: sensitive and Fast Alignment Search Tool for LOng noisy Read sequencing Data]]></title>
	<description><![CDATA[<p><span>lordFAST is a sensitive tool for mapping long reads with high error rates. lordFAST is specially designed for aligning reads from PacBio sequencing technology but provides the user the ability to change alignment parameters depending on the reads and application.</span></p>
<p>lordFAST, a novel long-read mapper that is specifically designed to align reads generated by PacBio and potentially other SMS technologies to a reference. lordFAST not only has higher sensitivity than the available alternatives, it is also among the fastest and has a very low memory footprint.</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="https://github.com/vpc-ccg/lordfast" rel="nofollow">https://github.com/vpc-ccg/lordfast</a></p>]]></description>
	<dc:creator>BioJoker</dc:creator>
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