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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/41188?offset=30</link>
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	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/43550/basic-structure-of-snakemake-pipeline-run</guid>
	<pubDate>Thu, 14 Oct 2021 07:01:38 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/43550/basic-structure-of-snakemake-pipeline-run</link>
	<title><![CDATA[Basic Structure of Snakemake Pipeline Run !]]></title>
	<description><![CDATA[<div>/user/snakemake-demo$ ls</div><div>config.json data envs scripts slurm-240702.out Snakefile</div><ul>
<li>data = mock data for the snakefile to use</li>
<li>Snakefile = name of the snakemake &ldquo;formula&rdquo; file
<ul>
<li>Note: The default file that snakemake looks for in the current working directory is the&nbsp;<code>Snakefile</code>. If you would like to override that you can specify it following the&nbsp;<code>-s</code>
<ul>
<li><code>snakemake -s snakefile.py</code></li>
</ul>
</li>
</ul>
</li>
<li>envs = directory for storing the conda environments that the workflow will use.</li>
<li>scripts = directory for storing python scripts called by the snakemake formula.</li>
<li>config.json = json format file with extra parameters for our snakemake file to use.</li>
<li>cluster.json = json format file with specification for running on the HPC</li>
<li>samples.txt = file we will use later relating to the config.json file.</li>
</ul><p><span>Run the snakemake file as a dry run (the example workflow shown above).</span></p><ul>
<li>This will build a DAG of the jobs to be run without actually executing them.</li>
<li><code>snakemake --dry-run</code></li>
</ul><p>User can e<span>xecute rules of interest.</span></p><ul>
<li><code>snakemake --dry-run all</code>&nbsp;VS.&nbsp;<code>snakemake --dry-run call</code>&nbsp;VS.&nbsp;<code>snakemake --dry-run bwa</code></li>
</ul><p><span>Run the snakemake file in order to produce an image of the DAG of jobs to be run.</span></p><ul>
<li><code>snakemake --dag | dot -Tsvg &gt; dag.svg</code>&nbsp;OR&nbsp;<code>snakemake --dag | dot -Tsvg &gt; dag.svg</code></li>
</ul><p>Run the snakemake (this time not as a dry run)</p><ol>
<li><code>snakemake --use-conda</code></li>
</ol>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/44741/bioinformatician-in-pipeline-development</guid>
  <pubDate>Tue, 17 Dec 2024 23:43:54 -0600</pubDate>
  <link></link>
  <title><![CDATA[Bioinformatician in pipeline development]]></title>
  <description><![CDATA[
<p>Are you interested in working with pipeline development in bioinformatics, with the support of competent and friendly colleagues in an international environment? Are you looking for an employer that invests in sustainable employeeship and offers safe, favourable working conditions? We welcome you to apply for a position as Bioinformatician in pipeline development at Uppsala University.</p>

<p>National Bioinformatics Infrastructure Sweden (NBIS) (nbis.se) plays an important role in advancing life science research in Sweden by providing expert support and developing cutting-edge bioinformatics infrastructure. Operating as a truly national initiative, NBIS employs more than 120 bioinformaticians, system developers, and data stewards across multiple locations in Sweden. It serves as the bioinformatics platform at SciLifeLab, a national resource that facilitates research in molecular biosciences by offering access to state-of-the-art technologies and technical expertise. With strong ties to data-producing facilities and ongoing collaborations with leading research groups, NBIS is ideally positioned to support world-class bioinformatics analyses. Furthermore, NBIS is the Swedish node in ELIXIR, the European infrastructure for biological information.</p>

<p>NBIS is seeking an experienced bioinformatician to support both Swedish and international projects. As part of our dynamic team, you will work closely with researchers to process large-scale biological data and contribute to advancing our data analysis infrastructure. Strong problem-solving skills, attention to detail, and the ability to troubleshoot complex bioinformatics pipelines are essential for success in this role. Flexibility and a willingness to learn are also important, as NBIS continually adapts to meet the evolving needs of the Swedish research community.</p>

<p>More at https://www.uu.se/en/about-uu/join-us/jobs-and-vacancies/job-details?query=778701</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38487/betsy-a-new-backward-chaining-expert-system-for-automated-development-of-pipelines-in-bioinformatics</guid>
	<pubDate>Mon, 17 Dec 2018 18:46:51 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38487/betsy-a-new-backward-chaining-expert-system-for-automated-development-of-pipelines-in-bioinformatics</link>
	<title><![CDATA[BETSY: A new backward-chaining expert system for automated development of pipelines in Bioinformatics]]></title>
	<description><![CDATA[<p>The BETSY provides a command-line interface and available at&nbsp;<a href="https://github.com/jefftc/changlab">https://github.com/jefftc/changlab</a>. A user first searches in the knowledge base for desired output and then BETSY develops an initial workflow to produce that data which is later examined by the user. The user can optimize the parameters, the algorithm to preprocess the data, and normalize it depending on the task.</p>
<p>Currently, BETSY consists of modules required for the microarray and next-generation sequencing data [4] such as expression analysis, classification, peak calling, and visualization.</p><p>Address of the bookmark: <a href="https://github.com/jefftc/changlab" rel="nofollow">https://github.com/jefftc/changlab</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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