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        <title>Nova Reader - Subject</title>
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        <copyright>Newgen KnowledgeWorks</copyright>
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            <title><![CDATA[dadi.CUDA: Accelerating Population Genetics Inference with Graphics Processing Units]]></title>
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            <link>https://www.novareader.co/book/isbn/10.1093/molbev/msaa305</link>
            <description><![CDATA[<p class="para" id="N65541">dadi is a popular but computationally intensive program for inferring models of demographic history and natural selection from population genetic data. I show that running dadi on a Graphics Processing Unit can dramatically speed computation compared with the CPU implementation, with minimal user burden. Motivated by this speed increase, I also extended dadi to four- and five-population models. This functionality is available in dadi version 2.1.0, https://bitbucket.org/gutenkunstlab/dadi/.</p>]]></description>
            <pubDate><![CDATA[2021-01-22T00:00]]></pubDate>
        </item><item>
            <title><![CDATA[dbCNS: A New Database for Conserved Noncoding Sequences]]></title>
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            <link>https://www.novareader.co/book/isbn/10.1093/molbev/msaa296</link>
            <description><![CDATA[<p class="para" id="N65541">We developed dbCNS (http://yamasati.nig.ac.jp/dbcns), a new database for conserved noncoding sequences (CNSs). CNSs exist in many eukaryotes and are assumed to be involved in protein expression control. Version 1 of dbCNS, introduced here, includes a powerful and precise CNS identification pipeline for multiple vertebrate genomes. Mutations in CNSs may induce morphological changes and cause genetic diseases. For this reason, many vertebrate CNSs have been identified, with special reference to primate genomes. We integrated ∼6.9 million CNSs from many vertebrate genomes into dbCNS, which allows users to extract CNSs near genes of interest using keyword searches. In addition to CNSs, dbCNS contains published genome sequences of 161 species. With purposeful taxonomic sampling of genomes, users can employ CNSs as queries to reconstruct CNS alignments and phylogenetic trees, to evaluate CNS modifications, acquisitions, and losses, and to roughly identify species with CNSs having accelerated substitution rates. dbCNS also produces links to dbSNP for searching pathogenic single-nucleotide polymorphisms in human CNSs. Thus, dbCNS connects morphological changes with genetic diseases. A test analysis using 38 gnathostome genomes was accomplished within 30 s. dbCNS results can evaluate CNSs identified by other stand-alone programs using genome-scale data.</p>]]></description>
            <pubDate><![CDATA[2020-11-16T00:00]]></pubDate>
        </item><item>
            <title><![CDATA[
<i>CoreCruncher</i>: Fast and Robust Construction of Core Genomes in Large Prokaryotic Data Sets]]></title>
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            <link>https://www.novareader.co/book/isbn/10.1093/molbev/msaa224</link>
            <description><![CDATA[<p class="para" id="N65541">The core genome represents the set of genes shared by all, or nearly all, strains of a given population or species of prokaryotes. Inferring the core genome is integral to many genomic analyses, however, most methods rely on the comparison of all the pairs of genomes; a step that is becoming increasingly difficult given the massive accumulation of genomic data. Here, we present <i>CoreCruncher</i>; a program that robustly and rapidly constructs core genomes across hundreds or thousands of genomes. <i>CoreCruncher</i> does not compute all pairwise genome comparisons and uses a heuristic based on the distributions of identity scores to classify sequences as orthologs or paralogs/xenologs. Although it is much faster than current methods, our results indicate that our approach is more conservative than other tools and less sensitive to the presence of paralogs and xenologs. <i>CoreCruncher</i> is freely available from: https://github.com/lbobay/CoreCruncher. <i>CoreCruncher</i> is written in Python 3.7 and can also run on Python 2.7 without modification. It requires the python library Numpy and either <i>Usearch</i> or <i>Blast</i>. Certain options require the programs <i>muscle</i> or <i>mafft</i>.</p>]]></description>
            <pubDate><![CDATA[2020-09-04T00:00]]></pubDate>
        </item><item>
            <title><![CDATA[Multiplexed CRISPR/CAS9‐mediated engineering of pre‐clinical mouse models bearing native human B cell receptors]]></title>
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            <link>https://www.novareader.co/book/isbn/10.15252/embj.2020105926</link>
            <description><![CDATA[<p class="para" id="N65542">B‐cell receptor (BCR) knock‐in (KI) mouse models play an important role in vaccine development and fundamental immunological studies. However, the time required to generate them poses a bottleneck. Here we report a one‐step CRISPR/Cas9 KI methodology to combine the insertion of human germline immunoglobulin heavy and light chains at their endogenous loci in mice. We validate this technology with the rapid generation of three BCR KI lines expressing native human precursors, instead of computationally inferred germline sequences, to HIV broadly neutralizing antibodies. We demonstrate that B cells from these mice are fully functional: upon transfer to congenic, wild type mice at controlled frequencies, such B cells can be primed by eOD‐GT8 60mer, a germline‐targeting immunogen currently in clinical trials, recruited to germinal centers, secrete class‐switched antibodies, undergo somatic hypermutation, and differentiate into memory B cells. KI mice expressing functional human BCRs promise to accelerate the development of vaccines for HIV and other infectious diseases.</p><p class="para" id="N65541">Application of a new one‐step genome editing protocol enables fast and efficient insertion of both immunoglobulin heavy and light chains from the human germline at their respective endogenous loci in mice.<div class="section"><div class="box" id="N65543"><div class="imageVideo"><img src="/dataresources/secured/content-1765838672637-2c8bf36d-5fa5-4bce-aacb-f943363468e7/assets/EMBJ-40-e105926-g009.jpg" alt=""/></div></div></div>
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            <pubDate><![CDATA[2020-12-01T00:00]]></pubDate>
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