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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/37536?offset=50</link>
	<atom:link href="https://bioinformaticsonline.com/related/37536?offset=50" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32485/bacterial-genome-assembly</guid>
	<pubDate>Fri, 05 May 2017 06:11:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32485/bacterial-genome-assembly</link>
	<title><![CDATA[Bacterial genome assembly !!]]></title>
	<description><![CDATA[<p>This tutorial will serve as an example of how to use free and open-source genome assembly and secondary scaffolding tools to generate high quality assemblies of&nbsp;bacterial sequence data. The bacterial sample used in this tutorial will be referred&nbsp;to simply&nbsp;as &ldquo;Species&rdquo; since it is&nbsp;live data. This data is paired-end data, meaning that there are forward and reverse reads, which we will designate as Sample_R1.fastq and Sample_R2.fastq, respectively.</p>
<p>https://github.com/jennomics/WorkflowPaper/blob/master/Genome%20Assembly%20and%20Annotation.md</p><p>Address of the bookmark: <a href="http://bioinformatics.uconn.edu/bacterial-genome-assembly-tutorial/" rel="nofollow">http://bioinformatics.uconn.edu/bacterial-genome-assembly-tutorial/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38449/koala-keggs-internal-annotation-tool-for-k-number-assignment-of-kegg-genes-using-ssearch-computation</guid>
	<pubDate>Wed, 12 Dec 2018 09:16:55 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38449/koala-keggs-internal-annotation-tool-for-k-number-assignment-of-kegg-genes-using-ssearch-computation</link>
	<title><![CDATA[KOALA: KEGG&#039;s internal annotation tool for K number assignment of KEGG GENES using SSEARCH computation]]></title>
	<description><![CDATA[<p>KOALA (KEGG Orthology And Links Annotation) is KEGG's internal annotation tool for&nbsp;<a href="https://www.kegg.jp/kegg/ko.html">K number</a>&nbsp;assignment of KEGG GENES using SSEARCH computation. BlastKOALA and GhostKOALA assign K numbers to the user's sequence data by&nbsp;<a href="http://www.ncbi.nlm.nih.gov/blast/">BLAST</a>&nbsp;and&nbsp;<a href="http://www.bi.cs.titech.ac.jp/ghostx/">GHOSTX</a>&nbsp;searches, respectively, against a nonredundant set of KEGG GENES. Annotate Sequence in KEGG Mapper and Pathogen Checker in KEGG Pathogen are special interfaces to the BlastKOALA server and can be executed in an interactive mode. &nbsp;&nbsp; See&nbsp;<a href="https://www.kegg.jp/blastkoala/help_blastkoala.html" target="_blastkoala">Step-by-step Instructions</a>.</p>
<div>Reference: Kanehisa, M., Sato, Y., and Morishima, K. (2016) BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J. Mol. Biol. 428, 726-731. [<a href="http://www.ncbi.nlm.nih.gov/pubmed/26585406">pubmed</a>] [<a href="https://doi.org/10.1016/j.jmb.2015.11.006">pdf</a>]</div><p>Address of the bookmark: <a href="https://www.kegg.jp/blastkoala/" rel="nofollow">https://www.kegg.jp/blastkoala/</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44731/exploring-bacterial-comparative-genomics-a-bioinformatics-approach</guid>
	<pubDate>Sat, 14 Dec 2024 12:31:14 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44731/exploring-bacterial-comparative-genomics-a-bioinformatics-approach</link>
	<title><![CDATA[Exploring Bacterial Comparative Genomics: A Bioinformatics Approach]]></title>
	<description><![CDATA[<p>In the world of microbiology, bacteria have long fascinated scientists for their diversity, adaptability, and crucial roles in ecosystems and human health. Comparative genomics&mdash;a field that involves analyzing and comparing the genomes of different organisms&mdash;has revolutionized our understanding of bacterial evolution, adaptation, and pathogenicity. By leveraging bioinformatics tools and techniques, researchers can uncover genomic insights that were once hidden. This blog delves into the principles, methodologies, and applications of bacterial comparative genomics from a bioinformatics perspective.</p><h4><strong>What is Bacterial Comparative Genomics?</strong></h4><p>Comparative genomics involves the systematic comparison of genomes across different bacterial species or strains. This approach allows scientists to:</p><ul>
<li>
<p>Identify conserved and unique genes.</p>
</li>
<li>
<p>Explore genetic determinants of pathogenicity.</p>
</li>
<li>
<p>Understand bacterial evolution and phylogenetics.</p>
</li>
<li>
<p>Investigate horizontal gene transfer and its role in antibiotic resistance.</p>
</li>
</ul><p>Bioinformatics is central to these analyses, enabling the processing and interpretation of large-scale genomic data.</p><h4><strong>Key Steps in Bacterial Comparative Genomics</strong></h4><ol>
<li>
<p><strong>Genome Sequencing and Assembly</strong>: The process begins with obtaining high-quality bacterial genome sequences. Advances in next-generation sequencing (NGS) technologies have made it faster and more affordable to sequence bacterial genomes. Tools such as SPAdes and Velvet are commonly used for genome assembly.</p>
</li>
<li>
<p><strong>Genome Annotation</strong>: Annotating a genome involves identifying genes, regulatory elements, and other genomic features. Automated tools like Prokka and RAST provide functional annotations, allowing researchers to predict the roles of genes and proteins.</p>
</li>
<li>
<p><strong>Genome Alignment</strong>: Aligning genomes is crucial for identifying conserved regions, single-nucleotide polymorphisms (SNPs), and structural variations. Tools like Mauve and progressiveMauve are commonly employed for whole-genome alignments.</p>
</li>
<li>
<p><strong>Comparative Analyses</strong>:</p>
<ul>
<li>
<p><strong>Core and Pan-genome Analysis</strong>: The core genome consists of genes shared across all strains of a species, while the pan-genome includes all genes found in any strain. Software like Roary and BPGA can perform core and pan-genome analyses.</p>
</li>
<li>
<p><strong>Phylogenetic Analysis</strong>: Comparative genomics often involves reconstructing evolutionary relationships. Tools such as MEGA and IQ-TREE facilitate phylogenetic tree construction based on genomic data.</p>
</li>
<li>
<p><strong>Functional Enrichment Analysis</strong>: To understand the biological significance of unique or shared genes, functional enrichment analysis using databases like GO (Gene Ontology) and KEGG is essential.</p>
</li>
</ul>
</li>
</ol><div>&nbsp;<strong style="font-size: 1em;">Recommended Bioinformatics Tools for Comparative Genomics</strong></div><p>Here are some additional bioinformatics tools that can aid bacterial comparative genomics:</p><ul>
<li>
<p><strong>OrthoFinder</strong>: For accurate ortholog identification across multiple genomes.</p>
</li>
<li>
<p><strong>PanOCT</strong>: Specifically designed for pan-genome clustering and annotation.</p>
</li>
<li>
<p><strong>FASTANI</strong>: A tool for calculating Average Nucleotide Identity (ANI) for microbial genome comparisons.</p>
</li>
<li>
<p><strong>CIRCOS</strong>: For visually comparing genomic data through circular genome plots.</p>
</li>
<li>
<p><strong>Galaxy Platform</strong>: A user-friendly web-based platform offering numerous genomic analysis tools.</p>
</li>
<li>
<p><strong>BLAST</strong>: Essential for sequence alignment and similarity searches.</p>
</li>
<li>
<p><strong>PhyloSift</strong>: Focused on phylogenetic analysis of microbial genomes using marker genes.</p>
</li>
</ul><p>These tools, in combination with the methods discussed, provide a robust framework for conducting comprehensive comparative genomic studies.</p><h4><strong>Applications of Bacterial Comparative Genomics</strong></h4><ol>
<li>
<p><strong>Understanding Pathogenicity</strong>: Comparative genomics helps identify virulence factors that distinguish pathogenic strains from non-pathogenic relatives. For instance, comparing genomes of <em>Escherichia coli</em> strains has revealed key genetic determinants of pathogenicity in enterohemorrhagic strains.</p>
</li>
<li>
<p><strong>Antibiotic Resistance Research</strong>: The spread of antibiotic resistance genes through horizontal gene transfer is a major global concern. Comparative analyses can trace the origins and dissemination of resistance genes, aiding in the development of countermeasures.</p>
</li>
<li>
<p><strong>Microbial Ecology and Evolution</strong>: By studying genomic variations, researchers can understand how bacteria adapt to different environments. This is particularly relevant for extremophiles and symbiotic bacteria.</p>
</li>
<li>
<p><strong>Vaccine Development</strong>: Identifying conserved antigens across pathogenic strains is critical for vaccine design. Comparative genomics has been instrumental in developing vaccines against pathogens like <em>Neisseria meningitidis</em>.</p>
</li>
<li>
<p><strong>Biotechnology Applications</strong>: Comparative studies can uncover unique metabolic pathways in bacteria, paving the way for applications in bioremediation, synthetic biology, and industrial microbiology.</p>
</li>
</ol><h4><strong>Challenges in Bacterial Comparative Genomics</strong></h4><p>While the field has made significant strides, several challenges remain:</p><ul>
<li>
<p><strong>Data Overload</strong>: The rapid growth of sequencing data requires robust computational infrastructure and efficient algorithms.</p>
</li>
<li>
<p><strong>Genome Plasticity</strong>: High rates of horizontal gene transfer and genome rearrangements in bacteria complicate comparative analyses.</p>
</li>
<li>
<p><strong>Annotation Accuracy</strong>: Automated annotation tools are not infallible, and manual curation is often needed for high-confidence results.</p>
</li>
<li>
<p><strong>Interpreting Non-Coding Regions</strong>: Understanding the functional significance of non-coding genomic regions remains a challenge.</p>
</li>
</ul><h4><strong>Future Directions</strong></h4><p>The integration of bacterial comparative genomics with other &lsquo;omics&rsquo; approaches&mdash;such as transcriptomics, proteomics, and metabolomics&mdash;promises a more comprehensive understanding of bacterial biology. Additionally, advancements in machine learning and artificial intelligence are likely to further enhance bioinformatics analyses, enabling the prediction of complex phenotypes from genomic data.</p><h4><strong>Conclusion</strong></h4><p>Bacterial comparative genomics, driven by bioinformatics, continues to unravel the complexities of bacterial life. From combating antibiotic resistance to uncovering the secrets of microbial evolution, this interdisciplinary field holds immense potential for addressing pressing challenges in microbiology and beyond. As technology advances, so too will our ability to harness the power of comparative genomics for scientific and societal benefit.</p>]]></description>
	<dc:creator>LEGE</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/38551/gupta-lab</guid>
  <pubDate>Sat, 29 Dec 2018 13:18:31 -0600</pubDate>
  <link></link>
  <title><![CDATA[Gupta Lab]]></title>
  <description><![CDATA[
<p>Work include (i) understanding the evolutionary relationships among different prokaryotic and eukaryotic organisms; (ii) Understanding the cellular functions of these lineage-specific signature proteins as well as lineage-specific conserved inserts and deletions in important housekeeping proteins by genetic and biochemical studies; (iii) Development of novel diagnostic methods (PCR based and immunological) for identification of different groups of organisms based upon these signature proteins and conserved indels; (iv) The use of these lineage-specific probes with predicitive ability to identify/explore the presence of different groups of organisms in metagenomic sequences from various environments.</p>

<p>https://fhs.mcmaster.ca/gupta-lab/index.html</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/44640/new-blast-core-nucleotide-database-core-nt</guid>
	<pubDate>Tue, 13 Aug 2024 07:12:53 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/44640/new-blast-core-nucleotide-database-core-nt</link>
	<title><![CDATA[New BLAST Core Nucleotide Database (core_nt)]]></title>
	<description><![CDATA[<p><span>The Core Nucleotide Database (core_nt) is now the default nucleotide BLAST database. Core_nt is also available on the command line. You get faster searches &amp; more focused results.</span></p><p><span><span>Core_nt contains the same eukaryotic transcript and gene-related sequences as nt. The core_nt database is nt without most eukaryotic chromosome sequences. Most nucleotide BLAST searches with core_nt will be similar to the nt database. However, core_nt is better than nt for accomplishing your most common BLAST search goals, such as identifying gene-related sequences like transcript sequences and complete bacterial chromosomes. This is because, in recent years, nt has acquired more low-relevance, non-annotated, and non-gene&nbsp;<span>content.&nbsp;</span></span></span></p><p><span> Learn more:&nbsp;https://ncbiinsights.ncbi.nlm.nih.gov/2024/07/18/new-blast-core-nucleotide-database/</span></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32134/lifemap</guid>
	<pubDate>Mon, 10 Apr 2017 05:42:37 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32134/lifemap</link>
	<title><![CDATA[Lifemap]]></title>
	<description><![CDATA[<p><strong>Lifemap</strong> is an interactive tool to explore the WHOLE NCBI TAXONOMY. The concept used in <strong>Lifemap</strong> is similar to the one used in cartography with tools like Google Maps&copy; or Open Street Maps: exploring is done by zooming and panning.</p>
<div>
<p>&nbsp;The current tree contains ALL species present in NCBI taxonomy as of <span style="text-decoration: underline;">October 18th, 2016</span>: 1,135,169 species including 10,545 Archaea, 418,777 Bacteria and 705,847 Eukaryotes. The Lifemap tree is updated every two weeks.</p>
</div>
<p>&nbsp;All the nodes in the tree are clickable. This displays various information and options:</p>
<ul>
<li>The species name (and the associated common name if there is one)</li>
<li>The rank (kingdom, family, class, species...)</li>
<li>Ability to go to the corresponding node/species on NCBI web site (displayed in a new window)</li>
<li>Possibility to download the corresponding subtree in newick extended format</li>
<li>Possibilty to get the whole lineage from the current node/tip to the root of the tree.</li>
</ul><p>Address of the bookmark: <a href="http://lifemap-ncbi.univ-lyon1.fr/" rel="nofollow">http://lifemap-ncbi.univ-lyon1.fr/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/2261/best-book-titles-for-learning-bionformatics</guid>
	<pubDate>Tue, 13 Aug 2013 17:31:51 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/2261/best-book-titles-for-learning-bionformatics</link>
	<title><![CDATA[Best book Titles for Learning Bionformatics]]></title>
	<description><![CDATA[<p>Nothing can add to our intellect more than reading a book. &nbsp;In books, we can experience new things that we would not normally be able to experience. It is proved that books can change our lives and other people&rsquo;s lives. Reading can make us more intelligent, updated, imaginative. Without reading we wouldn&rsquo;t know anything that we know today. There are several book, online and offile to read and I can't mentioned all of them here in the list. Therefore, I mentioned some bioinformatics and its related books in subgroups. Hope you will like the list.&nbsp;</p><p>Sequence Analysis and General Bioinformatics</p><ul>
<li>BLAST, Ian Korf, Mark Yandell, Joseph Bedell, 2003, O'Reilly</li>
<li>Sequence Analysis in a Nutshell: A Guide to Common Tools and Databases, Scott Markel, Darryl Leon, 2003, O'Reilly</li>
<li>Bioinformatics for Geneticists, Michael Barnes, Ian C Gray (Editors), 2003, John Wiley &amp; Sons</li>
<li>Bioinformatics for Dummies, Jean-Michel Claverie, Cedric Notredame, 2003, John Wiley &amp; Sons</li>
<li>Mathematics of Genome Analysis, Jerome K. Percus, 2002, Cambridge Univ Press</li>
<li>Bioinformatics Computing, Bryan P. Bergeron, 2002, Prentice Hall</li>
<li>Evolutionary Computation in Bioinformatics, Gary B. Fogel, David W. Corne (Editors), 2002, Morgan Kaufmann</li>
<li>Introduction to Bioinformatics, Arthur M. Lesk, 2002, Oxford University Press</li>
<li>Instant Notes in Bioinformatics, D.R. Westhead, J. H. Parish, R.M. Twyman, 2002, Bios Scientific Pub</li>
<li>Fundamental Concepts of Bioinformatics, Dan E. Krane, Michael L. Raymer, Michaeel L. Raymer, Elaine Nicpon Marieb, 2002, Benjamin/Cummings</li>
<li>Essentials of Genomics and Bioinformatics, C. W. Sensen (Editor), 2002, John Wiley &amp; Sons</li>
<li>Current Topics in Computational Molecular Biology (Computational Molecular Biology), Tao Jiang, Ying Xu, Michael Zhang (Editors), 2002, MIT Press</li>
<li>Hidden Markov Models for Bioinformatics, Timo Koski, Timo Koskinen, 2001, Kluwer Academic Publishers</li>
<li>Bioinformatics: From Genomes to Drugs, Thomas Lengauer (Editor), 2001, John Wiley &amp; Sons</li>
<li>Statistical Methods in Bioinformatics: An Introduction (Statistics for Biology and Health), Warren Ewens, Gregory Grant, 2001, Springer Verlag</li>
<li>Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, Second Edition, Andreas D. Baxevanis, B. F. Francis Ouellette, 2001, Wiley-Interscience</li>
<li>Bioinformatics: The Machine Learning Approach, Second Edition (Adaptive Computation and Machine Learning), Pierre Baldi, Soren Brunak, Sren Brunak, 2001, MIT Press</li>
<li>Introduction to Bioinformatics, T eresa Attwood, David Parry-Smith, 2001, Prentice Hall</li>
<li>Bioinformatics: A Primer, Charles Staben, 2001, Jones &amp; Bartlett Pub</li>
<li>Data Analysis and Classification for Bioinformatics, Arun Jagota, 2000, AKJ Academics</li>
<li>Bioinformatics: Sequence and Genome Analysis, David W. Mount, 2001, Cold Spring Harbor Laboratory Press</li>
<li>Bioinformatics: A Biologist's Guide to Biocomputing and the Internet, Stuart M. Brown, 2000, Eaton Pub Co</li>
<li>Bioinformatics: Sequence, Structure and Databanks: A Practical Approach (The Practical Approach Series, 236), Des Higgins (Editor), Willie Taylor (Editor), 2000, Oxford Univ Press</li>
<li>Neural Networks and Genome Informatics, Cathy H. Wu, Jerry W. McLarty, 2000, Elsevier Science</li>
<li>Computational Molecular Biology: An Introduction (Wiley Series in Mathematical and Computational Biology), Peter Clote and Rolf Backofen, 2000, John Wiley &amp; Sons</li>
<li>Computational Molecular Biology: An Algorithmic Approach, Pavel A. Pevzner, 2000, MIT Press</li>
<li>Post-Genome Informatics, Minoru Kanehisa, 2000, Oxford Univ Press</li>
<li>Mathematical and Computational Biology: Computational Morphogenesis, Hierarchical Complexity, and Digital Evolution, Chrystopher L. Nehaniv, 1999, American Mathematical Society</li>
<li>Pattern Discovery in Biomolecular Data: Tools, Techniques, and Applications, Jason T. L. Wang, Bruce A. Shapiro, Dennis Elliott Shasha (Editors), 1999, Oxford Univ Press</li>
<li>Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparison, David Sankoff and Joseph Kruskal (Editors), 1999, Cambridge University Press</li>
<li>Bioinformatics Basics: Applications in Biological Science and Medicine, Hooman Rashidi, 1999, CRC Press</li>
<li>Bioinformatics: Methods and Protocols (Methods in Molecular Biology, Vol 132), Stephen Misener and Stephen A. Krawetz (Editors),1999, Humana Press</li>
<li>Bioinformatics: Databases and Systems, Stanley Letovsky (Editor),1999, Kluwer Academic Publishers</li>
<li>Computational Molecular Biology, P. Green, 1998, Blackwell Science Inc.</li>
<li>Computational Methods in Molecular Biology (New Comprehensive Biochemistry, V. 32), Steven L. Salzberg, David B. Searls, Simon Kasif (Editors), 1998, Elsevier Science Ltd.</li>
<li>Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids, Richard Durbin, S. Eddy, A. Krogh, G. Mitchison, 1998, Cambridge University Press</li>
<li>Guide to Human Genome Computing, M. J. Bishop (Editor), 1998, Academic Press</li>
<li>Introduction to Computational Molecular Biology, Joao Meidanis, Joao C. Setabal, 1997, PWS Pub. Co.</li>
<li>Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology, Dan Gusfield, 1997, Cambridge University Press</li>
<li>Sequence Data Analysis Guidebook, Simon R. Swindell (Editor), 1997, Humana Press</li>
<li>High Performance Computational Methods for Biological Sequence Analysis, Tieng K. Yap, Ophir Frieder, Robert L. Martino, 1996, Kluwer Academic Pub.</li>
<li>Computer Methods for Macromolecular Sequence Analysis, Methods in Enzymology, volume 266, Russell F. Doolittle (Editor), 1996, Academic Press</li>
<li>DNA and Protein Sequence Analysis: A Practical Approach (Practical Approach Series , No 171), 1996, M. J. Bishop and C. J. Rawlings (Editors), 1996, IRL Press</li>
<li>Molecular Bioinformatics: Algorithms and Applications, Steffen Schulze-Kremer, 1995, Walter De Gruyter</li>
<li>Introduction to Computational Biology - Maps, sequences and genomes, Michael S. Waterman, 1995, Chapman &amp; Hall</li>
<li>Computer Analysis of Sequence Data, Annette M. Griffin and Hugh G. Griffin (Editors), 1994, Humana Press</li>
<li>Artificial Intelligence and Molecular Biology, Lawrence Hunter (Editor), 1993, AAAI Press</li>
<li>Sequence Analysis Primer, Michael Gribskov and John Devereux (Editors), 1992, Oxford University Press</li>
<li>Mathematical Methods of Analysis of Biopolymer Sequences (Dimacs Series in Discrete Mathematics and Theoretical Computer Science ; Volume 8), S. G. Gindikin, 1992, American Mathematical Society</li>
<li>Mathematical Methods for DNA Sequences, Michael S. Waterman (Editor), 1989, CRC Press</li>
</ul><p>Programming Books for Bioinformatics</p><ul>
<li>Mastering Perl for Bioinformatics, James D. Tisdall, 2003, O'Reilly</li>
<li>Genomic Perl: From Bioinformatics Basics to Working Code, Rex A. Dwyer, 2002, Cambridge University Press</li>
<li>Beginning Perl for Bioinformatics, James Tisdall, 2001, O'Reilly</li>
<li>Developing Bioinformatics Computer Skills, Cynthia Gibas, Per Jambeck, 2001, O'Reilly</li>
</ul><p>General Genomics</p><ul>
<li>Functional Microbial Genomics (Volume 33), Brendan Wren, Nick Dorrell, 2003, Academic Press</li>
<li>Discovering Genomics, Proteomics, and Bioinformatics, A. Malcolm Campbell, Laurie J. Heyer, 2002, Benjamin/Cummings</li>
<li>Genomes, Terence A. Brown, 2002, John Wiley &amp; Sons</li>
<li>Essentials of Medical Genomics, Stuart M. Brown , 2002, John Wiley &amp; Sons</li>
<li>A Primer of Genome Science, Greg Gibson, Spencer V. Muse, 2002, Sinauer Associates</li>
<li>Pathogen Genomics: Impact on Human Health, Karen Joy, Phd Shaw (Editors), 2002, Humana Press</li>
<li>Genomics, John E. Antonopoulos, 2000, Xlibris Corporation</li>
<li>Genomics and Proteomics: Functional and Computational Aspects, Sandor Suhai (Editor), 2000, Plenum Pub Corp</li>
<li>Functional Genomics: A Practical Approach (The Practical Approach Series, 235), S. Hunt and F. Livesey (Editors), 2000, Oxford Univ Press</li>
<li>Human Molecular Genetics, Andrew P. Read, Tom Strachan 1999, BIOS Scientific Publishers Ltd.</li>
<li>Genomics: The Science and Technology Behind the Human Genome Project, Charles R. Cantor and Cassandra L. Smith, 1999, John Wiley &amp; Sons</li>
<li>Cells: A Laboratory Manual, 3 volumes, David L. Spector, Robert D. Goldman, Leslie A. Leinwand, 1998, Cold Spring Harbor Laboratory Press</li>
<li>Genome Analysis: A Laboratory Manual, 4 volumes, Bruce Birren, et al. (Editors), 1997, Cold Spring Harbor Laboratory Press</li>
<li>The Human Genome Project, N. G. Cooper (Editor), 1994, University Science Books</li>
</ul><p>Comparative Genomics</p><ul>
<li>Handbook of Comparative Genomics: Principles and Methodology, Cecilia Saccone, Graziano Pesole, 2003, Wiley-Liss</li>
<li>Sequence - Evolution - Function: Computational Approaches in Comparative Genomics, Eugene V. Koonin, Michael Y. Galperin, 2002, Kluwer Academic Publishers</li>
<li>Comparative Genomics - Empirical and Analytical Approaches to Gene Order Dynamics, Map Alignment and the Evolution of Gene Families, David Sankoff and Joseph H. Nadeau, 2000, Kluwer Academic Pub</li>
<li>Comparative Genomics, Melody Clark (Editor), 2000, Kluwer Academic Pub</li>
</ul><p>Proteomics</p><ul>
<li>Proteins and Proteomics: A Laboratory Manual, Richard J. Simpson (Editor), Cold Spring Harbor Laboratory</li>
<li>Proteomics in Practice: A Laboratory Manual of Proteome Analysis , Reiner Westermeier, Tom Naven, 2002, John Wiley &amp; Sons</li>
<li>Posttranslational Modifications of Proteins: Tools for Functional Proteomics (Methods in Molecular Biology, Vol 194) , Christoph Kannicht (Editor), 2002, Humana Press</li>
<li>Peptide Arrays on Membrane Supports: Synthesis and Applications (Springer Lab Manual), Joachim Koch, Michael Mahler (Editors), 2002, Springer Verlag</li>
<li>Proteomics , Timothy Palzkill, 2002, Kluwer Academic Publishers</li>
<li>Introduction to Proteomics: Tools for the New Biology , Daniel C. Liebler (Editor), 2001, Humana Press</li>
<li>Proteome Research: Mass Spectrometry (Principles and Practice) , P. James (Editor), 2001, Springer Verlag</li>
<li>Interpreting Protein Mass Spectra: A Comprehensive Resource , A. Peter Snyder, 2000, American Chemical Society</li>
<li>Protein Sequencing and Identification Using Tandem Mass Spectrometry , Michael Kinter, Nicholas E. Sherman, 2000, Wiley-Interscience</li>
<li>From Genome to Proteome: Advances in the Practice and Application of Proteomics, Michael J. Dunn (Editor), 2000, Vch Verlagsgesellschaft Mbh</li>
<li>Proteomics: From Protein Sequence to Function, S. Pennington (Editor), M. Dunn (Editor), 2000, Springer Verlag</li>
<li>Proteome Research: Two-Dimensional Gel Electrophoresis and Detection Methods (Principles and Practice), T. Rabilloud (Editor), 2000, Springer Verlag</li>
<li>Proteome and Protein Analysis, R. M. Kamp, D. Kyriakidis, th Choli-Papadopoulou (Editor), 1999, Springer Verlag</li>
<li>Proteome Research: New Frontiers in Functional Genomics, M. R. Wilkins, et al. (Editors), 1997, Springer Verlag</li>
</ul><p>Protein Structure</p><ul>
<li>Structural Bioinformatics, Philip E. Bourne, Helge Weissig (Editors), 2003, John Wiley &amp; Sons</li>
<li>Protein Structure Prediction: Bioinfomatic Approach, I.F. Tsigelny, 2002, International University Line</li>
<li>Introduction to Protein Architecture: The Structural Biology of Proteins, Arthur M. Lesk, 2001, Oxford University Press</li>
<li>Protein Structure Prediction: Methods and Protocols, David M. Webster (Editor), 2000, Humana Press</li>
<li>Introduction to Protein Structure, Carl-Ivar Branden, John Tooze, 1999, Garland Publishing</li>
<li>Structure and Mechanism in Protein Science: A Guide to Enzyme Catalysis and Protein Folding, Alan Fersht, 1999, Freeman</li>
</ul><p>Pharmacogenomics</p><ul>
<li>Pharmacogenomics: Social, Ethical, and Clinical Dimensions, Mark A. Rothstein (Editor), 2003, Wiley-Liss</li>
<li>Pharmacogenomics: The Search for Individualized Therapies, Julio Licinio, Ma-Li Wong (Editors), 2002, John Wiley &amp; Sons</li>
<li>Pharmacogenomics, Werner Kalow, Urs A. Meyer, Rachel Tyndale (Editors), 2001, Marcel Dekker</li>
<li>Pharmacogenetics and Pharmcogenomics: Recent Conceptual and Technical Advances (Pharmacology, Volume 61, Number 3, 2000), Elliot S. Vesell (Editor), 2000, S. Karger Publishing</li>
<li>Pharmacogenetics, Wendell Weber, 1997, Oxford University Press</li>
</ul><p>DNA Microarrays</p><ul>
<li>Statistical Analysis of Gene Expression Microarray Data, T. P. Speed (Editor), 2003, CRC Press</li>
<li>Microarray Gene Expression Data Analysis: A Beginner's Guide, Helen C. Causton, John Quackenbush, Alvis Brazma, 2003, Blackwell Publishers</li>
<li>The Analysis of Gene Expression Data (Statistics for Biology and Health), G. Parmigiani, E. S. Garrett, R. A. Irizarry, S. Zeger , Graeme Clark (Editors), 2003, Springer Verlag</li>
<li>A Practical Approach to Microarray Data Analysis, Daniel P. Berrar, Werner Dubitzky, Martin Granzow (Editors), 2002, Kluwer Academic Publishers</li>
<li>DNA Microarrays and Gene Expression: From Experiments to Data Analysis and Modeling, Pierre Baldi, G. Wesley Hatfield, 2002, Cambridge University Press</li>
<li>DNA Microarrays: A Molecular Cloning Manual, David Bowtell, Joseph Sambrook (Editors), 2002, Cold Spring Harbor Laboratory</li>
<li>DNA Array Image Analysis: Nuts &amp; Bolts, Gerda Kamberova, Shishir Shah, 2002, DNA Press</li>
<li>Microarray Analysis, Mark Schena, 2002, John Wiley &amp; Sons</li>
<li>A Biologist's Guide to Analysis of DNA Microarray Data, Steen Knudsen, 2002, John Wiley &amp; Sons</li>
<li>Microarrays for an Integrative Genomics (Computational Molecular Biology), Isaac S. Kohane, Alvin Kho, Atul J. Butte, 2002, MIT Press</li>
<li>Microarrays for the Neurosciences: An Essential Guide (Cellular and Molecular Neuroscience), Daniel H. Geschwind, Jeffrey P. Gregg (Editors), 2002, MIT Press</li>
<li>DNA Microarrays: Gene Expression Applications, Bertrand Jordan (Editor), 2001, Springer Verlag</li>
<li>DNA Arrays: Methods and Protocols (Methods in Molecular Biology, Volume 170), Jang B. Rampal (Editor), 2001, Humana Press</li>
<li>DNA Arrays: Technologies and Experimental Strategies, Elena V. Grigorenko (Editor), 2001, CRC Press</li>
<li>Microarray Biochip Technology, Mark Schena (Editor), 2000, Eaton Pub</li>
<li>Expression Genetics: Accelerated and High-Throughput Methods (Biotechniques Update Series), Michael McClelland (Editor), Arthur B. Pardee (Editor), 1999, Eaton Pub</li>
<li>DNA Microarrays: A Practical Approach (Practical Approach Series 205), Mark Schena (Editor), 1999, Oxford Univ Press</li>
<li>cDNA Preparation and Characterization (Methods in Enzymology Volume 303), S.M. Weissman (Editor), 1999, Academic Press</li>
</ul><p>Systems Biology, Genetic and Biochemical Network</p><ul>
<li>Handbook of Graphs and Networks : From the Genome to the Internet, Stefan Bornholdt, Heinz Georg Schuster (Editors), 2003, Vch Verlagsgesellschaft Mbh</li>
<li>Computational Cell Biology, Christopher Fall, Eric Marland, John Wagner, John Tyson (Editors), 2002, Springer Verlag</li>
<li>Gene Regulation and Metabolism: Post-Genomic Computational Approaches (Computational Molecular Biology), Julio Collado-Vides, Ralf Hofestadt (Editors), 2002, MIT Press</li>
<li>Foundations of Systems Biology, Hiroaki Kitano (Editor), 2001, MIT Press</li>
<li>Genomic Regulatory Systems: Development and Evolution, Eric H. Davidson , 2001, Academic Press</li>
<li>Genes &amp; Signals, Mark Ptashne, Alexander Gann, 2001, Cold Spring Harbor Laboratory</li>
<li>Computational Modeling of Genetic and Biochemical Networks (Computational Molecular Biology), James M. Bower and Hamid Bolouri (Editors), 2001, MIT Press</li>
<li>Protein-Protein Interactions: A Molecular Cloning Manual, Erica Golemis (Editor), 2001, Cold Spring Harbor Laboratory</li>
<li>Computational Analysis of Biochemical Systems: A Practical Guide for Biochemists and Molecular Biologists, Eberhard O. Voit, 2000, Cambridge University Press</li>
<li>Mathematical Physiology, James P. Keener, James Sneyd, 1998, Springer Verlag</li>
</ul><p>&nbsp;</p><p>DNA Sequencing</p><ul>
<li>DNA Sequencing: From Experimental Methods to Bioinformatics (Introduction to Biotechniques Series), Luke Alphey, 1997, Springer Verlag</li>
<li>Automated DNA sequencing and analysis, Adams M.D., Fields C., Venter J.C. (Editors), 1994, Academic Press</li>
</ul><p>&nbsp;</p><p>Apart from above mentioned books, you can also find some useful books links at following mentioned URLs:</p><p>&nbsp;</p><p><a href="http://www.amazon.com/Biological-Sequence-Analysis-Probabilistic-Proteins/dp/0521629713">http://www.amazon.com/Biological-Sequence-Analysis-Probabilistic-Proteins/dp/0521629713</a></p><p><a href="http://www.amazon.com/Bioinformatics-Genes-Proteins-Computers-Advanced/dp/1859960545">http://www.amazon.com/Bioinformatics-Genes-Proteins-Computers-Advanced/dp/1859960545</a></p><p><a href="http://www.amazon.com/Introduction-Bioinformatics-Algorithms-Computational-Molecular/dp/0262101068">http://www.amazon.com/Introduction-Bioinformatics-Algorithms-Computational-Molecular/dp/0262101068</a></p><p><a href="http://books.google.no/books?id=pxSM7R1sdeQC&amp;dq=Pierre+baldi+%2B+bioinformatics&amp;printsec=frontcover&amp;source=bn&amp;hl=en&amp;ei=IoGRS6uCIJT-NYLA8Z0N&amp;sa=X&amp;oi=book_result&amp;ct=result&amp;redir_esc=y#v=onepage&amp;q&amp;f=false">http://books.google.no/books?id=pxSM7R1sdeQC&amp;dq=Pierre+baldi+%2B+bioinformatics&amp;printsec=frontcover&amp;source=bn&amp;hl=en&amp;ei=IoGRS6uCIJT-NYLA8Z0N&amp;sa=X&amp;oi=book_result&amp;ct=result&amp;redir_esc=y#v=onepage&amp;q&amp;f=false</a></p><p><a href="http://www.amazon.com/Statistical-Methods-Bioinformatics-Introduction-Statistics/dp/0387400826">http://www.amazon.com/Statistical-Methods-Bioinformatics-Introduction-Statistics/dp/0387400826</a></p><p>&nbsp;</p><p>If you think your favourite books are not listed then please write it down in comment section for the benefits of other users.&nbsp;Feel free to add many more books in comment section.&nbsp;</p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43676/vcf2poptree-a-client-side-software-to-construct-population-phylogeny-from-genome-wide-snps</guid>
	<pubDate>Sat, 25 Dec 2021 00:13:31 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43676/vcf2poptree-a-client-side-software-to-construct-population-phylogeny-from-genome-wide-snps</link>
	<title><![CDATA[VCF2PopTree: a client-side software to construct population phylogeny from genome-wide SNPs]]></title>
	<description><![CDATA[<p>VCF2PopTree is a client-side software written in Javascript and it runs purely within the user&rsquo;s computer/browser.&nbsp; VCF2PopTree is compatible with all population browsers including Chrome, Opera, Edge and Firefox and works equally efficient in Mac, Windows and Linux (Ubuntu).&nbsp;</p>
<p>Furthermore, it displays the tree in a mobile phone (iPhone and Android) if the input file size is small.&nbsp; CITATION: Subramanian, S., Ramasamy, U. and Chen, D. (2019).&nbsp; VCF2PopTree: a client-side software to construct population phylogeny from genome-wide SNPs.&nbsp; Peer J. x:yy.</p><p>Address of the bookmark: <a href="https://github.com/sansubs/vcf2pop" rel="nofollow">https://github.com/sansubs/vcf2pop</a></p>]]></description>
	<dc:creator>Surabhi Chaudhary</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40792/haslr-a-tool-for-rapid-genome-assembly-of-long-sequencing-reads</guid>
	<pubDate>Fri, 31 Jan 2020 05:50:15 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40792/haslr-a-tool-for-rapid-genome-assembly-of-long-sequencing-reads</link>
	<title><![CDATA[HASLR: a tool for rapid genome assembly of long sequencing reads]]></title>
	<description><![CDATA[<p><span>HASLR is a tool for rapid genome assembly of long sequencing reads. HASLR is a hybrid tool which means it requires long reads generated by Third Generation Sequencing technologies (such as PacBio or Oxford Nanopore) together with Next Generation Sequencing reads (such as Illumina) from the same sample.&nbsp;</span></p><p>Address of the bookmark: <a href="https://github.com/vpc-ccg/haslr" rel="nofollow">https://github.com/vpc-ccg/haslr</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37820/s-plot2-rapid-visual-and-statistical-analysis-of-genomic-sequences</guid>
	<pubDate>Tue, 02 Oct 2018 17:57:27 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37820/s-plot2-rapid-visual-and-statistical-analysis-of-genomic-sequences</link>
	<title><![CDATA[S-plot2: Rapid Visual and Statistical Analysis of Genomic Sequences]]></title>
	<description><![CDATA[<p><span>S-plot2 creates an interactive, two-dimensional heatmap capturing the similarities and dissimilarities in nucleotide usage between genomic sequences (partial or complete). In S-plot2, whole eukaryotic chromosomes and smaller prokaryotic genomes can be efficiently compared. The tool includes functionality to extract, analyze, and automate BLAST queries of regions of interest within the heatmap. This facilitates the investigation of quickly evolving coding regions, novel coding regions, and laterally transferred elements.</span></p><p>Address of the bookmark: <a href="https://bitbucket.org/lkalesinskas/splot" rel="nofollow">https://bitbucket.org/lkalesinskas/splot</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>

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