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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/36267?offset=120</link>
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	<description><![CDATA[]]></description>
	
	
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/44400/pevzner-lab</guid>
  <pubDate>Thu, 02 Nov 2023 05:39:26 -0500</pubDate>
  <link></link>
  <title><![CDATA[Pevzner Lab !]]></title>
  <description><![CDATA[
<p>The laboratory works on genome sequencing, immunoproteogenomics, antibiotics sequencing, and comparative genomics - computational technologies that enabled new applications and allowed scientists to attack biological problems that remained beyond the reach of previous techniques.</p>

<p>https://bioalgorithms.ucsd.edu/research4.html</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37574/simlord-a-read-simulator-for-third-generation-sequencing-reads</guid>
	<pubDate>Wed, 22 Aug 2018 10:40:27 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37574/simlord-a-read-simulator-for-third-generation-sequencing-reads</link>
	<title><![CDATA[SimLoRD: A read simulator for third generation sequencing reads]]></title>
	<description><![CDATA[<p>SimLoRD is a read simulator for third generation sequencing reads and is currently focused on the Pacific Biosciences SMRT error model.</p>
<p>Reads are simulated from both strands of a provided or randomly generated reference sequence.</p>
<div id="rst-header-features">
<ul>
<li>The reference can be read from a FASTA file or randomly generated with a given GC content. It can consist of several chromosomes, whose structure is respected when drawing reads. (Simulation of genome rearrangements may be incorporated at a later stage.)</li>
<li>The read lengths can be determined in four ways: drawing from a log-normal distribution (typical for genomic DNA), sampling from an existing FASTQ file (typical for RNA), sampling from a a text file with integers (RNA), or using a fixed length</li>
<li>Quality values and number of passes depend on fragment length.</li>
<li>Provided subread error probabilities are modified according to number of passes</li>
<li>Outputs reads in FASTQ format and alignments in SAM format</li>
</ul>
</div><p>Address of the bookmark: <a href="https://bitbucket.org/genomeinformatics/simlord/" rel="nofollow">https://bitbucket.org/genomeinformatics/simlord/</a></p>]]></description>
	<dc:creator>Aaryan Lokwani</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>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/10659/gps-dna-tracking-university-of-sheffield</guid>
	<pubDate>Sat, 10 May 2014 04:33:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/10659/gps-dna-tracking-university-of-sheffield</link>
	<title><![CDATA[GPS DNA tracking - University of Sheffield]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/Aap-s1kle4Q" frameborder="0" allowfullscreen></iframe>University of Sheffield geneticist and bioinformatics expert Dr Eran Elhaik demonstrates the power of his new DNA research, which allows people to discover their genetic homeland from 1000 years ago. Find out more about our biological research here http://www.sheffield.ac.uk/aps]]></description>
	
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/27250/lawley-lab</guid>
  <pubDate>Mon, 09 May 2016 03:29:51 -0500</pubDate>
  <link></link>
  <title><![CDATA[Lawley Lab]]></title>
  <description><![CDATA[
<p>Lawley Lab are covered with a complex microbial community, known as our microbiota, which plays important roles in our physiology, immunity, metabolism and sustenance. Within the human gastrointestinal tract alone there are over 1,000 bacterial species, which amounts to approximately 10 times more cells than we harbor in our entire body and 200 times more genes than are found within our genome. Lawley Lab are really a 'supraorganism' consisting of our 'human' and 'microbial' selves.</p>

<p>More at http://www.sanger.ac.uk/science/groups/lawley-lab</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/42804/one-year-position-for-a-bioinformatician-computational-biologist-in-population-genetics</guid>
  <pubDate>Fri, 05 Feb 2021 11:19:02 -0600</pubDate>
  <link></link>
  <title><![CDATA[One-year position for a bioinformatician / computational biologist in population genetics]]></title>
  <description><![CDATA[
<p>The successful candidate will work as support staff mainly in the development, application and maintenance of pipelines for handling large omics datasets (including whole-genome sequences, high-density genotypes and mRNA sequences). These pipelines cover pre-processing of data, statistical analyses and genome bioinformatics. The postholder will also provide support in producing high-level graphic representations of<br />these data and of results from their analysis.  Our team is part of UMR<br />7268 ADES research unit (Anthropologie bio-culturelle, Droit, Ethique<br />et Sant=E9), located at the Timone Faculty of Medicine (Aix-Marseille<br />University, 13005 Marseille). </p>

<p>JOB QUALIFICATION<br />- PhD/Engineer/MSc in bioinformatics, biostatistics, genetics/genomics<br />  or any related field.<br />- Advanced knowledge of Bash/Perl scripting and job management on a Unix<br />  HPC and in at least one basic language for data<br />  manipulation/statistics (such as R/Python/Matlab) are required.<br />- Knowledge of at least one programming language (e.g. C), experience<br />  processing -omics data or skills in advanced graphical representation<br />  of data would be a plus.</p>

<p>DURATION<br />1 year, not extensible</p>

<p>SALARY<br />Gross salary is commensurate with experience and grade (MSc from<br />1,882=80/month and PhD/equivalent from 2,099=80/month).</p>

<p>APPLICATIONS/OPENING<br />Please send a motivation letter, a CV and the names of two referees to<br />pierre.faux@univ-amu.fr. The expected starting date is April 1st, 2021;<br />the job offer will however remain opened until the position is filled.</p>

<p>Pierre Faux</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/860/the-centre-for-bioinformatics-mcb-lab</guid>
  <pubDate>Sun, 14 Jul 2013 12:41:20 -0500</pubDate>
  <link></link>
  <title><![CDATA[The Centre for Bioinformatics (MCB) Lab]]></title>
  <description><![CDATA[
<p>The Centre for Bioinformatics (MCB) is a diverse collection of professors, postdoctoral fellows, and students, who share a common interest in Bioinformatics.</p>

<p>Research Area</p>

<p>We are interested in the development of the statistics and computational methods for the analysis of this data in breast cancer.<br />We have worked on probabilistic models for subcellular localization, protein-protein interactions, and problems related to chemical genomics.<br />We are interested in the development of bioinformatics/biostatistical methodology in the analysis of epigenetic/epigenomic data.<br />We are interested in integrative bioinformatics approaches to learn the gene, gene products, interactions, and regulatory mechanisms involved in mental retardation.</p>

<p>Link @ http://www.mcgill.ca/mcb/</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/872/jayaram-lab</guid>
  <pubDate>Sun, 14 Jul 2013 14:04:37 -0500</pubDate>
  <link></link>
  <title><![CDATA[Jayaram Lab]]></title>
  <description><![CDATA[
<p>Responsible (a) for developing Chemgenome, Bhageerath &amp; Sanjeevini methods &amp; softwares for genome annotation, protein tertiary structure prediction &amp; computer aided drug design respectively, (b) for setting up a multi-teraflop supercomputing facility for Bioinformatics &amp; Computational Biology at IIT Delhi, and (c) for making the hardware and software freely accessible at (www.scfbio-iitd.res.in) to the global scientific user community.</p>

<p>Faculty facilitator/Founder Director for two start-up companies (Leadinvent incubated at IIT, Delhi from 2006-2009 &amp; Novoinformatics, under incubation at IIT Delhi since 2011).</p>

<p>Research Interest <br />Genome Analysis, Protein Structure Prediction and Drug Design.</p>

<p>Link @ http://www.scfbio-iitd.res.in/</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/1161/genomics-for-bioinformatician</guid>
	<pubDate>Sat, 20 Jul 2013 07:03:00 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/1161/genomics-for-bioinformatician</link>
	<title><![CDATA[Genomics for Bioinformatician]]></title>
	<description><![CDATA[<p>Genomics is the study of the genomes of organisms. The field includes intensive efforts to determine the entire DNA sequence of organisms and fine-scale genetic mapping efforts. The field also includes studies of intragenomic phenomena such as heterosis, epistasis, pleiotropy and other interactions between loci and alleles within the genome. In contrast, the investigation of the roles and functions of single genes is a primary focus of molecular biology or genetics and is a common topic of modern medical and biological research. Research of single genes does not fall into the definition of genomics unless the aim of this genetic, pathway, and functional information analysis is to elucidate its effect on, place in, and response to the entire genome's networks.<br /><br />Genomics was established by Fred Sanger when he first sequenced the complete genomes of a virus and a mitochondrion. His group established techniques of sequencing, genome mapping, data storage, and bioinformatic analyses in the 1970-1980s. A major branch of genomics is still concerned with sequencing the genomes of various organisms, but the knowledge of full genomes has created the possibility for the field of functional genomics, mainly concerned with patterns of gene expression during various conditions. The most important tools here are microarrays and bioinformatics. Study of the full set of proteins in a cell type or tissue, and the changes during various conditions, is called proteomics. A related concept is materiomics, which is defined as the study of the material properties of biological materials (e.g. hierarchical protein structures and materials, mineralized biological tissues, etc.) and their effect on the macroscopic function and failure in their biological context, linking processes, structure and properties at multiple scales through a materials science approach. The actual term 'genomics' is thought to have been coined by Dr. Tom Roderick, a geneticist at the Jackson Laboratory (Bar Harbor, ME) over beer at a meeting held in Maryland on the mapping of the human genome in 1986.<br /><br />The outcome of almost two years of intense discussions with literally hundreds of scientists and members of the public, has three major areas of focus: Genomics to Biology, Genomics to Health, and Genomics to Society.<br /><br /><strong><em>Genomics to Biology:</em></strong>&nbsp;<br />The human genome sequence provides foundational information that now will allow development of a comprehensive catalog of all of the genome's components, determination of the function of all human genes, and deciphering of how genes and proteins work together in pathways and networks.<br /><br /><strong><em>Genomics to Health:<br /></em></strong>Completion of the human genome sequence offers a unique opportunity to understand the role of genetic factors in health and disease, and to apply that understanding rapidly to prevention, diagnosis, and treatment. This opportunity will be realized through such genomics-based approaches as identification of genes and pathways and determining how they interact with environmental factors in health and disease, more precise prediction of disease susceptibility and drug response, early detection of illness, and development of entirely new therapeutic approaches.<br /><br /><strong><em>Genomics to Society:</em>&nbsp;<br /></strong>Just as the HGP has spawned new areas of research in basic biology and in health, it has created new opportunities in exploring the ethical, legal, and social implications (ELSI) of such work. These include defining policy options regarding the use of genomic information in both medical and non-medical settings and analysis of the impact of genomics on such concepts as race, ethnicity, kinship, individual and group identity, health, disease, and "normality" for traits and behaviors.<br /><br />This vision for the future of genomics is not just about the NHGRI. It encompasses the whole field of genomics, including the work of all the other Institutes and Centers at the NIH and of a number of other federal agencies. All of the NIH Institutes are already taking full advantage of the sequence and will apply its data to the better understanding of both rare and common diseases, almost all of which have a genetic component. A recent example of the way that the HGP and the knowledge and new technologies it has spawned are already facilitating science is the extremely rapid sequencing by groups in Canada and at the Centers for Disease Control and Prevention (CDC) in Atlanta of the genome of the virus that causes Severe Acute Respiratory Syndrome (SARS). The sequencing of the SARS virus genome provides insight into this new and deadly disease at a speed never before possible in science. In turn, this should lead to the rapid development of diagnostic tests and, in time, vaccines and effective treatments.<br /><br /><strong>Links for the addition material available on Net</strong></p><p><a href="http://pevsnerlab.kennedykrieger.org/bioinformatics/bioinf10_genomes.htm">Genomes and genomics:</a></p><p><a href="http://www.123genomics.com/learning.html">Bioinformatics and Genomics:</a></p><p><a href="http://www.ebi.ac.uk/pdbe/docs/roadshow_tutorial/strgenomics/tutorial.html">Structural genomics tutorial:</a></p><p><a href="http://www.hgu.mrc.ac.uk/Users/Philippe.Gautier/tutorial/index.html">Comparative Genomics Tutorial:</a></p><p><a href="http://www.scfbio-iitd.res.in/tutorial/genomics.html">GENOME TUTORIAL:</a></p><p><a href="http://genomebiology.com/content/pdf/gb-2001-3-1-reviews2001.pdf">Tools and resources for identifying protein families, domains and motifs</a></p><p><a href="http://www.ornl.gov/sci/techresources/Human_Genome/posters/chromosome/tools.shtml">Bioinformatics Tools</a><a href="http://www.ornl.gov/sci/techresources/Human_Genome/posters/chromosome/tools.shtml">&nbsp;<br />Tips, Tutorials, and Terminology for Using Selected Resources in Genome Database Guide:</a></p><p><a href="http://www.doe-mbi.ucla.edu/Reprints/R31%20Strong%20A%20Web-based%20Comparative%20Genomics%20tutorial%20Microbiology%20Eduction%202004.pdf">A Web-Based Comparative Genomics Tutorial for Investigating Microbial Genomes:</a></p><p><a href="http://www.genome.gov/27530225">Free Online Tutorials Teach Anyone How to Use Genome Databases:</a></p><p><a href="http://mkweb.bcgsc.ca/circos/?tutorials">Circos to create concise, explanatory, unique and print-ready visualizations of your data:</a></p><p><a href="http://www.igd.cornell.edu/Comparative%20Genomics/Comparative%20Genomics%20Proj.html">Genomics and Comparative Genomics</a><a href="http://www.igd.cornell.edu/Comparative%20Genomics/Comparative%20Genomics%20Proj.html">&nbsp;Learning Module:</a></p><p><a href="http://psb.stanford.edu/psb10/conference-materials/tutorials/compgen-notes.pdf">Computational Challenges in Comparative Genomics</a></p><p><a href="http://psb.stanford.edu/psb10/conference-materials/tutorials/compgen-notes.pdf">A Tutorial:</a></p><p><a href="http://gramene.agrinome.org/tutorials/modules_tutorial.pdf">A Comparative Genomics Resource for Grains</a>:</p><p><a href="http://www.plantcell.org/cgi/content/full/21/12/3718">PLAZA: A Comparative Genomics Resource to Study Gene and Genome Evolution in Plants:</a></p><p><a href="http://en.wikipedia.org/wiki/VISTA_(comparative_genomics)">VISTA</a><a href="http://en.wikipedia.org/wiki/VISTA_(comparative_genomics)">:</a></p><p>Software for Genomics</p><ol>
<li><strong>Artemis</strong>&nbsp;Artemis is a free genome viewer and annotation tool that allows visualization of sequence features and the results of analyses within the context of the sequence, and its six-frame translation.</li>
<li><strong>Chromas&nbsp;</strong>It will display and prints chromatogram files from ABI automated DNA sequencers, and Staden SCF files which the analysis programs for ALF, Li-Cor and Visible Genetics OpenGene sequencers can create.</li>
<li><strong>Glimmer</strong>&nbsp;A system for finding genes in microbial DNA, especially the genomes of bacteria and archaea.Glimmer (Gene Locator and Interpolated Markov Modeler) uses interpolated Markov models (IMMs) to identify the coding regions and distinguish them from noncoding DN</li>
<li><strong>Glimmer</strong>&nbsp;HMM&nbsp;A fast and accurate gene finder based on a GHMM architecture, developed specifically for eukaryotes. It incorporates splice site models adapted from the GeneSplicer program and uses interpolated Markov models for evaluating the coding regions.</li>
<li><strong>Glimmer</strong>&nbsp;M&nbsp;A gene finder derived from Glimmer, but developed specifically for eukaryotes. It is based on a dynamic programming algorithm that considers all combinations of possible exons for inclusion in a gene model and chooses the best of these combinations. The d</li>
<li><strong>MUMmer</strong>&nbsp;MUMmer is a system for rapidly aligning entire genomes, whether in complete or draft form.</li>
<li><strong>pDRAW</strong>&nbsp;pDRAW32 is being developed as a free time hobby project. It is far from finished, but as it has reached a point where it could be helpful for many labs, it is now available to the scientific community.</li>
<li><strong>Sequin</strong>&nbsp;Sequin is a stand-alone software tool developed by the NCBI for submitting and updating entries to the GenBank, EMBL, or DDBJ sequence databases. It is capable of handling simple submissions that contain a single short mRNA sequence, and complex submissio</li>
<li><strong>Staden&nbsp;</strong>The Staden Package consists of a series of tools for DNA sequence preparation (pregap4), assembly (gap4), editing (gap4) and DNA/protein sequence analysis (spin).</li>
</ol><p>For more software @&nbsp;<a href="http://bioinformaticsonline.com/bookmarks/view/926/list-of-popular-bioinformatics-softwaretools">http://bioinformaticsonline.com/bookmarks/view/926/list-of-popular-bioinformatics-softwaretools</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/videolist/watch/4003/personalised-medicine-animation</guid>
	<pubDate>Tue, 27 Aug 2013 10:07:24 -0500</pubDate>
	<link>https://bioinformaticsonline.com/videolist/watch/4003/personalised-medicine-animation</link>
	<title><![CDATA[Personalised Medicine - Animation]]></title>
	<description><![CDATA[<iframe width="" height="" src="https://www.youtube-nocookie.com/embed/fEY3Khsmuak" frameborder="0" allowfullscreen></iframe>Two animated case scenarios set now and in the future. These highlight potential differences in the way patients are treated now, and how they might be treated as healthcare becomes more tailored.]]></description>
	
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