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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/28290?offset=100</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/30833/dnasp-v5-a-software-for-comprehensive-analysis-of-dna-polymorphism-data</guid>
	<pubDate>Mon, 06 Feb 2017 04:45:37 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/30833/dnasp-v5-a-software-for-comprehensive-analysis-of-dna-polymorphism-data</link>
	<title><![CDATA[DnaSP v5: a software for comprehensive analysis of DNA polymorphism data]]></title>
	<description><![CDATA[<p><span>DnaSP is a software package for a comprehensive analysis of DNA polymorphism data. Version 5 implements a number of new features and analytical methods allowing extensive DNA polymorphism analyses on large datasets. Among other features, the newly implemented methods allow for: (i) analyses on multiple data files; (ii) haplotype phasing; (iii) analyses on insertion/deletion polymorphism data; (iv) visualizing sliding window results integrated with available genome annotations in the UCSC browser.</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>Jit</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36880/jvarkit-java-utilities-for-bioinformatics</guid>
	<pubDate>Fri, 08 Jun 2018 09:31:55 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36880/jvarkit-java-utilities-for-bioinformatics</link>
	<title><![CDATA[Jvarkit : Java utilities for Bioinformatics]]></title>
	<description><![CDATA[Collection of Java tool kits for bioinformatics works:

Jvarkit : Java utilities for Bioinformatics<p>Address of the bookmark: <a href="http://lindenb.github.io/jvarkit/" rel="nofollow">http://lindenb.github.io/jvarkit/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/34916/bioinformatics-tools-developed-for-oxford-nanopore-data-analysis</guid>
	<pubDate>Wed, 27 Dec 2017 20:47:30 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/34916/bioinformatics-tools-developed-for-oxford-nanopore-data-analysis</link>
	<title><![CDATA[Bioinformatics tools developed for Oxford Nanopore data analysis !]]></title>
	<description><![CDATA[<p><span>MinION is the only portable real-time device for DNA and RNA&nbsp;</span><span>sequencing</span><span>. Each consumable flow cell can now generate 10&ndash;20 Gb of DNA&nbsp;</span><span>sequence</span><span>&nbsp;data. Ultra-</span><span>long read lengths are possible (hundreds of kb) as you can choose your fragment length.&nbsp;</span>One of the technical advantages of ONT data is the read length, which offers great prospects for genome assembly. Generally, assemblers are based on several different types of algorithms, such as greedy, overlap-layout-consensus (OLC), de Bruijn graph (DBG), and string graph.</p><p><span>List of analysis tools developed for Oxford Nanopore data</span></p><p>BWA <br />Fast nanopore data tuned alignment tool <br />https://github.com/lh3/bwa</p><p>GraphMap<br />Mapper for long and error-prone reads<br />https://github.com/isovic/graphmap</p><p>LAST<br />Nanopore tuned alignment tool<br />http://last.cbrc.jp/</p><p>LINKS<br />Software tool for long read scaffolding <br />https://github.com/warrenlr/LINKS/</p><p>marginAlign<br />Tools to align nanopore reads to a reference<br />https://github.com/benedictpaten/marginAlign</p><p>minoTour<br />Real time analysis tools<br />http://minotour.nottingham.ac.uk/</p><p>nanoCORR<br />Error-correction tool for nanopore sequence data<br />https://github.com/jgurtowski/nanocorr</p><p>NanoOK<br />Software for nanopore data, quality and error profiles<br />https://documentation.tgac.ac.uk/display/NANOOK/NanoOK</p><p>Nanopolish<br />Nanopore analysis and genome assembly software<br />https://github.com/jts/nanopolish</p><p>nanopore<br />Variant-detection tool for nanopore sequence data<br />https://github.com/mitenjain/nanopore</p><p>Nanocorrect<br />Error-correction tool for nanopore sequence data<br />https://github.com/jts/nanocorrect/</p><p>npReader<br />Real-time conversion and analysis of nanopore reads<br />https://github.com/mdcao/npReader</p><p>poRe<br />Tool for analyzing and visualizing nanopore data<br />https://sourceforge.net/p/rpore/wiki/Home/</p><p>PoreSeq<br />Error-correction and variant-calling software<br />https://github.com/tszalay/poreseq</p><p>Poretools<br />Nanopore sequence analysis and visualization software <br />https://github.com/arq5x/poretools</p><p>SSPACE-LongRead<br />Genome scaffolding tool <br />http://www.baseclear.com/genomics/bioinformatics/basetools/SSPACE-longread</p><p>SMIS<br />Genome scaffolding tool <br />https://sourceforge.net/projects/phusion2/files/smis/</p><p>&nbsp;</p><p>List of assemblers for Oxford Nanopore MinION long reads</p><p>LQS<br />DALIGNER, Celera OLC Nanocorrect, <br />Nanopolish corrector<br />https://github.com/jts/nanopolish</p><p>PBcR<br />HGAP or BLASR, Celera OLC <br />PBcR corrector<br />http://wgs-assembler.sourceforge.net/wiki/index.php/PBcR<br /> &ndash;<br />Canu<br />MHAP, Celera OLC <br />Canu corrector<br />https://github.com/marbl/canu</p><p>Falcon<br />String graph, Celera OLC <br />Falcon corrector<br />https://github.com/PacificBiosciences/falcon</p><p>Miniasm <br />OLC<br />https://github.com/lh3/miniasm</p><p>ra-integrate<br />OLC<br />https://github.com/mariokostelac/ra-integrate/</p><p>ALLPATHS-LG<br />de Bruijn graph <br />ALLPATHS-L corrector<br />https://www.broadinstitute.org/software/allpaths-lg/blog/?page_id=12</p><p>SPAdes <br />de Bruijn graph <br />SPAdes corrector<br />http://bioinf.spbau.ru/spades</p>]]></description>
	<dc:creator>biogeek</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44716/exploring-rna-sequence-analysis-tools-for-every-bioinformatician</guid>
	<pubDate>Fri, 13 Dec 2024 04:03:04 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44716/exploring-rna-sequence-analysis-tools-for-every-bioinformatician</link>
	<title><![CDATA[Exploring RNA Sequence Analysis: Tools for Every Bioinformatician]]></title>
	<description><![CDATA[<p>RNA sequence analysis has become an essential part of modern biological research. From RNA-seq pipelines to specialized tools for specific RNA types, here's a comprehensive guide to tools you can use to make sense of RNA data.</p><h4><strong>1. RNA-Seq Analysis Pipelines</strong></h4><p>RNA-seq is one of the most popular techniques for studying RNA. These tools streamline processing raw sequence data:</p><ul>
<li><strong>FASTQC</strong>: For quality control of raw RNA-seq reads.</li>
<li><strong>Trimmomatic</strong>: For trimming and filtering RNA-seq reads.</li>
<li><strong>HISAT2/STAR</strong>: High-performance aligners for RNA-seq reads.</li>
<li><strong>FeatureCounts</strong>: For quantifying gene expression.</li>
<li><strong>DESeq2/EdgeR</strong>: For differential expression analysis.</li>
</ul><h4><strong>2. Transcriptome Assembly and Annotation</strong></h4><p>For analyzing transcriptomes from non-model organisms or assembling novel transcripts:</p><ul>
<li><strong>Trinity</strong>: For de novo transcriptome assembly.</li>
<li><strong>StringTie</strong>: For transcript assembly and quantification from RNA-seq alignments.</li>
<li><strong>TransDecoder</strong>: To predict coding regions within assembled transcripts.</li>
<li><strong>TAU</strong>: Tools for annotating non-coding and coding RNAs.</li>
</ul><h4><strong>3. Exploring Non-Coding RNA (ncRNA)</strong></h4><p>Non-coding RNAs play critical regulatory roles. Dedicated tools for studying them include:</p><ul>
<li><strong>Infernal</strong>: For identifying ncRNA sequences based on covariance models.</li>
<li><strong>Rfam</strong>: Database and tools for ncRNA families.</li>
<li><strong>miRDeep</strong>: For identifying microRNAs in RNA-seq datasets.</li>
</ul><h4><strong>4. RNA Structure and Motif Analysis</strong></h4><p>Structural biology of RNA helps in understanding its function:</p><ul>
<li><strong>RNAfold (ViennaRNA)</strong>: Predicts secondary structures from RNA sequences.</li>
<li><strong>RNAstructure</strong>: Tools for RNA secondary structure prediction and analysis.</li>
<li><strong>MEME Suite</strong>: For identifying motifs in RNA sequences.</li>
<li><strong>IntaRNA</strong>: For RNA-RNA interaction prediction.</li>
</ul><h4><strong>5. RNA Editing and Modifications</strong></h4><p>Epitranscriptomics is a growing field focusing on RNA modifications:</p><ul>
<li><strong>REDItools</strong>: For RNA editing analysis.</li>
<li><strong>m6Aboost</strong>: For identifying m6A modifications in RNA.</li>
</ul><h4><strong>6. Long-Read RNA Sequencing Analysis</strong></h4><p>Long-read technologies like Nanopore and PacBio are transforming RNA research:</p><ul>
<li><strong>FLAIR</strong>: For isoform-level analysis of long-read RNA-seq data.</li>
<li><strong>NanoMod</strong>: For detecting modifications in RNA from Nanopore sequencing.</li>
</ul><h4><strong>7. RNA-Protein Interactions</strong></h4><p>To study RNA-protein interactions and complexes:</p><ul>
<li><strong>RBPmap</strong>: For identifying RNA-binding protein motifs.</li>
<li><strong>PARalyzer</strong>: For analyzing PAR-CLIP data.</li>
</ul><h4><strong>8. Functional Enrichment Analysis</strong></h4><p>Understanding biological functions and pathways from RNA-seq data:</p><ul>
<li><strong>getENRICH</strong>: A tool designed for pathway enrichment analysis of non-model organisms (hypergeometric P-value calculation with FDR correction).</li>
<li><strong>ClusterProfiler</strong>: For GO and KEGG pathway enrichment analysis.</li>
</ul><h4><strong>9. Visualization and Data Sharing</strong></h4><p>Presenting and sharing RNA sequence analysis results effectively:</p><ul>
<li><strong>IGV</strong>: Genome browser for visualizing RNA-seq alignments.</li>
<li><strong>Circos</strong>: Circular visualization of RNA-seq data.</li>
<li><strong>DashBio</strong>: A Python library for creating bioinformatics visualizations.</li>
</ul><h4><strong>Conclusion</strong></h4><p>The bioinformatics landscape for RNA sequence analysis is vast, with tools catering to specific needs. Whether you&rsquo;re studying coding RNAs, non-coding RNAs, or exploring RNA-protein interactions, the right tools can transform your data into biological insights.</p>]]></description>
	<dc:creator>Neel</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/926/list-of-popular-bioinformatics-softwaretools</guid>
	<pubDate>Tue, 16 Jul 2013 14:30:30 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/926/list-of-popular-bioinformatics-softwaretools</link>
	<title><![CDATA[List of popular bioinformatics software/tools]]></title>
	<description><![CDATA[<p><a href="http://samtools.sourceforge.net/swlist.shtml">I</a>n current genome era, our day to day work is to handle the huge geneome sequences, expression data, several other datasets. This link provide a comprehensive list of commonly used sofware/tools.</p><p>Address of the bookmark: <a href="http://samtools.sourceforge.net/swlist.shtml" rel="nofollow">http://samtools.sourceforge.net/swlist.shtml</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40531/shasta-long-read-assembler</guid>
	<pubDate>Tue, 14 Jan 2020 06:47:07 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40531/shasta-long-read-assembler</link>
	<title><![CDATA[Shasta long read assembler]]></title>
	<description><![CDATA[<p>The goal of the Shasta long read assembler is to rapidly produce accurate assembled sequence using as input DNA reads generated by&nbsp;<a href="https://nanoporetech.com/">Oxford Nanopore</a>&nbsp;flow cells.</p>
<p>Computational methods used by the Shasta assembler include:</p>
<ul>
<li>Using a&nbsp;<a href="https://en.wikipedia.org/wiki/Run-length_encoding">run-length</a>&nbsp;representation of the read sequence. This makes the assembly process more resilient to errors in homopolymer repeat counts, which are the most common type of errors in Oxford Nanopore reads.</li>
<li>Using in some phases of the computation a representation of the read sequence based on&nbsp;<em>markers</em>, a fixed subset of short k-mers (k &asymp; 10).</li>
</ul>
<p>More at&nbsp;<a href="https://chanzuckerberg.github.io/shasta/index.html">https://chanzuckerberg.github.io/shasta/index.html</a></p><p>Address of the bookmark: <a href="https://github.com/chanzuckerberg/shasta" rel="nofollow">https://github.com/chanzuckerberg/shasta</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/8504/update-genome-workbench-2715-released</guid>
	<pubDate>Wed, 26 Feb 2014 16:12:17 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/8504/update-genome-workbench-2715-released</link>
	<title><![CDATA[Update Genome Workbench 2.7.15 released]]></title>
	<description><![CDATA[<p>NCBI Genome Workbench is an integrated application for viewing and analyzing sequence data. With Genome Workbench, you can view data in publically available sequence databases at NCBI, and mix this data with your own private data.</p><p><img src="http://www.ncbi.nlm.nih.gov/core/assets/gbench/images/firstscreen_still.gif" alt="Introductory screen shot" style="border: 0px; border: 0px;"></p><p>Genome Workbench can display sequence data in many ways, including graphical sequence views, various alignment views, phylogenetic tree views, and tabular views of data. It can also align your private data to data in public databases, display your data in the context of public data, and retrieve BLAST results.</p><p>Genome Workbench is built on the NCBI C++ ToolKit and uses cross-platform APIs for graphics. It runs on your local machine, and is available for Windows 2000/XP, Linux, MacOS X, and various flavors of Unix.</p><p>NCBI Genome Workbench is an integrated application for viewing and analyzing sequence data. Genome Workbench was developed entirely in-house at NCBI and makes use of the NCBI C++ ToolKit. The C++ ToolKit provides a convenient and flexible cross-platform API for managing system internals, database connections, network sockets, and the NCBI data model. In addition, the C++ ToolKit provides the Object Manager, which abstracts handling of sequences and sequence-related objects.</p><p>&nbsp;New Features in Genome Workbench 2.7.15 <br /><br /></p><ul>
<li>Multiple Alignment View: implemented adaptive feature display when zooming in</li>
<li>Active Objects Inspector replaces Selection Inspector. New View should offer an improved selection context examination. See Using Active Objects Inspector tutorial for more details.</li>
<li>Binary packages for Linux OpenSUSE 13.1 are now available</li>
</ul><p><br />Bug Fixes and Improvements in Genome Workbench 2.7.15 <br /><br /></p><ul>
<li>Fixed major issue with OpenGL overlay/scrolling. Could cause crashes or view scrolling irregularities</li>
<li>Multiple Pane View: fixed crash on loading BLAST results</li>
<li>Graphical Sequence View: fixed crash on zooming in and out, related to SNP track</li>
<li>Graphical Sequence View: fixed Go To Position dialog to give better diagnostics in case of a user error</li>
<li>Graphical Sequence View: PDF export fixed rendering of Markers with commas in the name</li>
<li>Text View / Flat File: fixed Mac OS rendering issues</li>
<li>Text View / Flat File: performance optimization, extended capabilities of real-time rendering of molecules to tens of thousands</li>
<li>File Import: optimization improvement to speed up load of files containing multiple project items</li>
<li>File Import: remapping stage now shows accession.version and description of molecules, instead of plain GI numbers</li>
<li>Mac OS: improved tooltips for toolbar buttons</li>
<li>Phylogenetic Tree Builder Tool: improved diagnostics of errors</li>
<li>Multiple Alignment View: optimizations to avoid main GUI freezes</li>
<li>Open Dialog: removed duplicate elements in table of genomes (load Genome)</li>
<li>PDF export: fixed issue with XREF table errors</li>
<li>Tree View: fixed issues with showing Force Layout progress on Mac OS</li>
<li>Tree View: PDF export fixed issues for showing labels of collapsed nodes</li>
<li>Tree View: added an option to stop layout</li>
<li>Tree View: broadcasting mechanism fixed not to accumulate selected nodes</li>
</ul><p>Reference:</p><p>NCBI news</p><p>http://www.ncbi.nlm.nih.gov/tools/gbench/</p>]]></description>
	<dc:creator>Surabhi Chaudhary</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/36398/tools-for-protein-protein-docking</guid>
	<pubDate>Wed, 25 Apr 2018 05:15:53 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/36398/tools-for-protein-protein-docking</link>
	<title><![CDATA[Tools for Protein-Protein Docking !]]></title>
	<description><![CDATA[<p>Predicting the structure of protein&ndash;protein complexes using docking approaches is a difficult problem whose major challenges include identifying correct solutions, and properly dealing with molecular flexibility and conformational changes. Following are the tools to predict&nbsp;<span>the structure of protein&ndash;protein complexes:</span></p><p><a href="http://www.sbg.bio.ic.ac.uk/docking/index.html" target="_blank">3D-Dock Suite</a></p><p>Global rigid search: FFTShape complementarity and electrostatics</p><p>Re-scoring and clustering. Refinement of interface side-chains</p><p><a href="http://www.sbg.bio.ic.ac.uk/~3dgarden/" target="_blank">3D-Garden</a></p><p>Global rigid search in ensamble</p><p>Shape complementarity and Lennard&ndash;Jones potential</p><p>Side chain and backbone dihedral refinement</p><p><a href="http://www.sdsc.edu/CCMS/DOT/" target="_blank">DOT</a></p><p>Global rigid search: FFTShape complementarity, electrostatics and VDWNone</p><p><a href="http://users.unimi.it/~ddl/escherng/index.htm" target="_blank">Escher NG</a></p><p>Global rigid searchShape complementarity, hydrogen bonds and electrostatic</p><p>Integrated in&nbsp;<a href="http://users.unimi.it/~ddl/vega/download.htm" target="_blank">VEGA</a></p><p><a href="http://vakser.bioinformatics.ku.edu/resources/gramm/gramm1" target="_blank">GRAMM</a>&nbsp;</p><p>Global rigid search: FFT. smooth protein surface representation for soft docking</p><p>Shape complementarity and Lennard-Jones potential</p><p>Clustering of conformations</p><p><a href="http://vakser.bioinformatics.ku.edu/resources/gramm/grammx/" target="_blank">GRAMM-X</a>&nbsp;</p><p>Global rigid search: FFT. smooth protein surface representation for soft docking</p><p>Shape complementarity and Lennard-Jones potentialminimization and re-scoring with multiple filters</p><p><a href="http://www.loria.fr/~ritchied/hex_server/" target="_blank">HEX</a></p><p>Global rigid search: Fourier correlation of spherical harmonics</p><p>Shape complementarity</p><p><a href="http://www.csd.abdn.ac.uk/hex/" target="_blank"></a><a href="http://haddock.chem.uu.nl/Haddock/haddock.php" target="_blank">HADDOCK</a></p><p>Global rigid searchElectrostatic ,VDW and desolvation energy termsMD simulated annealing refinement . Filtering based on external data.&nbsp;</p><p><a href="http://www.molsoft.com/docking.html">ICM</a></p><p>Global rigid search: Monte CarloEmpirical scoring function</p><p>Clustering and selection of conformations. Refinement of interface side-chains and re-scoring</p><p><a href="http://www.weizmann.ac.il/Chemical_Research_Support/molfit/" target="_blank">MolFit&nbsp;</a></p><p>Global rigid search: FFTShape complementarity</p><p>Clustering of good solutions, filtering using&nbsp;<em>a priori&nbsp;</em>information and small, local rigid rotations around selected conformations</p><p><a href="http://bioinfo3d.cs.tau.ac.il/PatchDock/" target="_blank">PatchDock</a></p><p>Global rigid searchShape complementarity and atomic desolvation energy</p><p>Clustering of conformations</p><p><a href="http://inb.bsc.es/gn6/PyDock" target="_blank">PyDock</a></p><p>Global rigid search:FFTShape complementarity</p><p>rescoring by binding electrostatics and desolvation energy</p><p><a href="http://bioinfo3d.cs.tau.ac.il/PatchDock/" target="_blank"></a><a href="http://rosettadock.graylab.jhu.edu/" target="_blank">RosettaDock</a></p><p>Local rigid search: Monte Carlo with low and high resolution structure representation levels</p><p>Different scoring parameters for the different resolutions&nbsp;</p><p><a href="http://zlab.bu.edu/zdock/" target="_blank">ZDOCK</a></p><p>Global rigid search: FFTShape complementarity, desolvation energy, and electrostatics.</p><p>Energy minimization and re-scoringFree for academics</p><p>&nbsp;</p><p>Point to note:</p><p>The proper treatment of flexibility in protein&ndash;protein docking is still an active field of research. You first should analyzed your proteins in order to define their conformational space and then choose the most suitable method for your docking problem.</p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38743/molinspiration-broad-range-of-cheminformatics-software-tools-supporting-molecule-manipulation</guid>
	<pubDate>Sun, 20 Jan 2019 05:32:40 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38743/molinspiration-broad-range-of-cheminformatics-software-tools-supporting-molecule-manipulation</link>
	<title><![CDATA[molinspiration: broad range of cheminformatics software tools supporting molecule manipulation]]></title>
	<description><![CDATA[<p><span>Molinspiration offers&nbsp;</span><a href="https://www.molinspiration.com/products.html">broad range of cheminformatics software tools</a><span>&nbsp;supporting molecule manipulation and processing, including SMILES and SDfile conversion, normalization of molecules, generation of tautomers, molecule fragmentation, calculation of various molecular properties needed in QSAR, molecular modelling and drug design, high quality molecule depiction, molecular database tools supporting substructure and similarity searches. Our products support also fragment-based virtual screening, bioactivity prediction and data visualization. Molinspiration tools are written in Java, therefore can be used practically on any computer platform.</span></p><p>Address of the bookmark: <a href="https://www.molinspiration.com/" rel="nofollow">https://www.molinspiration.com/</a></p>]]></description>
	<dc:creator>BioJoker</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/11249/how-to-sequence-the-human-genome-mark-j-kiel</guid>
	<pubDate>Fri, 30 May 2014 13:24:11 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/11249/how-to-sequence-the-human-genome-mark-j-kiel</link>
	<title><![CDATA[How to sequence the human genome - Mark J. Kiel]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/MvuYATh7Y74" frameborder="0" allowfullscreen></iframe>View full lesson: http://ed.ted.com/lessons/how-to-sequence-the-human-genome-mark-j-kiel

Your genome, every human's genome, consists of a unique DNA sequence of A's, T's, C's and G's that tell your cells how to operate. Thanks to technological advances, scientists are now able to know the sequence of letters that makes up an individual genome relatively quickly and inexpensively. Mark J. Kiel takes an in-depth look at the science behind the sequence.

Lesson by Mark J. Kiel, animation by Marc Christoforidis.]]></description>
	
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