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<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/26319?offset=830</link>
	<atom:link href="https://bioinformaticsonline.com/related/26319?offset=830" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43828/understanding-hifi-reads</guid>
	<pubDate>Thu, 24 Mar 2022 19:48:11 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43828/understanding-hifi-reads</link>
	<title><![CDATA[Understanding HiFi Reads !]]></title>
	<description><![CDATA[<p><span>While little public data is available for either of the new synthetic long read approaches, Illumina showed an example comparison earlier this year at the&nbsp;</span><a href="https://www.festivalofgenomics.com/rami-mehio" target="_blank">Festival of Genomics &amp; Biodata conference</a><span>&nbsp;(FoG 2022). In the IGV screenshot presented (below), synthetic Infinity reads &ndash; labeled &ldquo;Longas&rdquo; &ndash; are at the top, followed by standard Illumina short reads, and PacBio HiFi reads labeled &ldquo;CCS&rdquo; depicted at the bottom:</span></p><p>Address of the bookmark: <a href="http://pacb.com/blog/the-hifi-difference-true-long-reads-vs-synthetic-long-reads/" rel="nofollow">http://pacb.com/blog/the-hifi-difference-true-long-reads-vs-synthetic-long-reads/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/42972/list-of-bioinformatics-workflow-management-tools</guid>
	<pubDate>Sat, 20 Mar 2021 00:15:25 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/42972/list-of-bioinformatics-workflow-management-tools</link>
	<title><![CDATA[List of bioinformatics workflow management tools !]]></title>
	<description><![CDATA[<h3>Here are list of&nbsp;Workflow Managers</h3><ul>
<li><span><a href="https://github.com/pcingola/BigDataScript">BigDataScript</a></span>&nbsp;&ndash; A cross-system scripting language for working with big data pipelines in computer systems of different sizes and capabilities. [&nbsp;<a href="https://pubmed.ncbi.nlm.nih.gov/25189778">paper-2014</a>&nbsp;|&nbsp;<a href="https://pcingola.github.io/BigDataScript">web</a>&nbsp;]</li>
<li><span><a href="https://github.com/ssadedin/bpipe">Bpipe</a></span>&nbsp;&ndash; A small language for defining pipeline stages and linking them together to make pipelines. [&nbsp;<a href="http://docs.bpipe.org/">web</a>&nbsp;]</li>
<li><span><a href="https://github.com/common-workflow-language/common-workflow-language">Common Workflow Language</a></span>&nbsp;&ndash; a specification for describing analysis workflows and tools that are portable and scalable across a variety of software and hardware environments, from workstations to cluster, cloud, and high performance computing (HPC) environments. [&nbsp;<a href="http://www.commonwl.org/">web</a>&nbsp;]</li>
<li><span><a href="https://github.com/broadinstitute/cromwell">Cromwell</a></span>&nbsp;&ndash; A Workflow Management System geared towards scientific workflows. [&nbsp;<a href="https://cromwell.readthedocs.io/">web</a>&nbsp;]</li>
<li><span><a href="https://github.com/galaxyproject">Galaxy</a></span>&nbsp;&ndash; a popular open-source, web-based platform for data intensive biomedical research. Has several features, from data analysis to workflow management to visualization tools. [&nbsp;<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6030816">paper-2018</a>&nbsp;|&nbsp;<a href="https://galaxyproject.org/">web</a>&nbsp;]</li>
<li><span><a href="https://github.com/nextflow-io/nextflow">Nextflow</a>&nbsp;(recommended)</span>&nbsp;&ndash; A fluent DSL modelled around the UNIX pipe concept, that simplifies writing parallel and scalable pipelines in a portable manner. [&nbsp;<a href="https://pubmed.ncbi.nlm.nih.gov/29412134">paper-2018</a>&nbsp;|&nbsp;<a href="http://nextflow.io/">web</a>&nbsp;]</li>
<li><span><a href="https://github.com/cgat-developers/ruffus">Ruffus</a></span>&nbsp;&ndash; Computation Pipeline library for python widely used in science and bioinformatics. [&nbsp;<a href="https://pubmed.ncbi.nlm.nih.gov/20847218">paper-2010</a>&nbsp;|&nbsp;<a href="http://www.ruffus.org.uk/">web</a>&nbsp;]</li>
<li><span><a href="https://github.com/SeqWare/seqware">SeqWare</a></span>&nbsp;&ndash; Hadoop Oozie-based workflow system focused on genomics data analysis in cloud environments. [&nbsp;<a href="https://pubmed.ncbi.nlm.nih.gov/21210981">paper-2010</a>&nbsp;|&nbsp;<a href="https://seqware.github.io/">web</a>&nbsp;]</li>
<li><span><a href="https://bitbucket.org/snakemake">Snakemake</a></span>&nbsp;&ndash; A workflow management system in Python that aims to reduce the complexity of creating workflows by providing a fast and comfortable execution environment. [&nbsp;<a href="https://pubmed.ncbi.nlm.nih.gov/29788404">paper-2018</a>&nbsp;|&nbsp;<a href="https://snakemake.readthedocs.io/">web</a>&nbsp;]</li>
<li><span><a href="https://github.com/broadinstitute/wdl">Workflow Descriptor Language</a></span>&nbsp;&ndash; Workflow standard developed by the Broad. [&nbsp;<a href="https://software.broadinstitute.org/wdl">web</a>&nbsp;]</li>
</ul>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38199/pacasus-correction-of-palindromes-in-long-reads-from-pacbio-and-nanopore</guid>
	<pubDate>Mon, 12 Nov 2018 05:26:48 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38199/pacasus-correction-of-palindromes-in-long-reads-from-pacbio-and-nanopore</link>
	<title><![CDATA[Pacasus: Correction of palindromes in long reads from PacBio and Nanopore]]></title>
	<description><![CDATA[<p><br>Tool for detecting and cleaning PacBio / Nanopore long reads after whole genome amplification. Check the poster from the Revolutionizing Next-Generation Sequencing (2nd edition) conference in the source folder:&nbsp;<a href="https://github.com/swarris/Pacasus/blob/master/vib2017.pdf">https://github.com/swarris/Pacasus/blob/master/vib2017.pdf</a>.</p>
<p>The prepint version is found on&nbsp;<a href="http://www.biorxiv.org/content/early/2017/08/09/173872">http://www.biorxiv.org/content/early/2017/08/09/173872</a></p>
<p>It uses the pyPaSWAS framework for sequence alignment (<a href="https://github.com/swarris/pyPaSWAS">https://github.com/swarris/pyPaSWAS</a>)</p><p>Address of the bookmark: <a href="https://github.com/swarris/Pacasus" rel="nofollow">https://github.com/swarris/Pacasus</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<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>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40715/mutatrix-a-population-genome-simulator-which-generates-simulated-genomes</guid>
	<pubDate>Tue, 28 Jan 2020 04:06:58 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40715/mutatrix-a-population-genome-simulator-which-generates-simulated-genomes</link>
	<title><![CDATA[mutatrix: a population genome simulator which generates simulated genomes.]]></title>
	<description><![CDATA[<p><span>genome simulation across a population with zeta-distributed allele frequency, snps, insertions, deletions, and multi-nucleotide polymorphisms</span></p>
<p><span>More at&nbsp;<a href="https://github.com/ekg/mutatrix">https://github.com/ekg/mutatrix</a></span></p>
<pre>./mutatrix -S sample -P test/ -p 2 -n 10 reference.fasta</pre><p>Address of the bookmark: <a href="https://github.com/ekg/mutatrix" rel="nofollow">https://github.com/ekg/mutatrix</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41571/wego-simple-but-useful-tool-for-visualizing-comparing-and-plotting-go-gene-ontology-annotation-results</guid>
	<pubDate>Sun, 12 Apr 2020 10:02:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41571/wego-simple-but-useful-tool-for-visualizing-comparing-and-plotting-go-gene-ontology-annotation-results</link>
	<title><![CDATA[WEGO : simple but useful tool for visualizing, comparing and plotting GO (Gene Ontology) annotation results]]></title>
	<description><![CDATA[<p><span>WEGO (Web Gene Ontology Annotation Plot) is a simple but useful tool for visualizing, comparing and plotting GO (Gene Ontology) annotation results. As the GO vocabulary became more and more popular, WEGO was widely adopted and used in many researches. Therefore we have updated WEGO 2.0 in 2018. Here are some changes we&rsquo;ve made:</span><br><span>1. The limit of input file numbers was cancelled. Now the users could upload as many files as they want with one operation.</span><br><span>2. We have added the reference data of 9 species for users selection.</span><br><span>3. Besides the traditional WEGO histogram, WEGO 2.0 outputs an additional type of bar graph showing GO terms with significant gene number differences.</span></p><p>Address of the bookmark: <a href="http://wego.genomics.org.cn/" rel="nofollow">http://wego.genomics.org.cn/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39039/dotplotly-generate-an-interactive-dot-plot-from-mummer-or-minimap-alignments</guid>
	<pubDate>Thu, 21 Feb 2019 10:22:17 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39039/dotplotly-generate-an-interactive-dot-plot-from-mummer-or-minimap-alignments</link>
	<title><![CDATA[dotPlotly: Generate an interactive dot plot from mummer or minimap alignments]]></title>
	<description><![CDATA[<p>Create an interactive dot plot from mummer output OR PAF format</p>
<p>R script that makes a plotly interactive and/or static (png/pdf) dot plot.</p>
<p><a href="https://tom-poorten.shinyapps.io/dotplotly_shiny/">Shiny app available for testing</a></p><p>Address of the bookmark: <a href="https://github.com/tpoorten/dotPlotly" rel="nofollow">https://github.com/tpoorten/dotPlotly</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43319/k-mers-tutorial-classification-and-taxonomy</guid>
	<pubDate>Thu, 26 Aug 2021 10:28:43 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43319/k-mers-tutorial-classification-and-taxonomy</link>
	<title><![CDATA[k-mers tutorial - classification and taxonomy]]></title>
	<description><![CDATA[<p>DNA k-mers underlie much of our assembly work, and we (along with many others!) have spent a lot of time thinking about how to&nbsp;<a href="http://www.pnas.org/content/109/33/13272">store k-mer graphs efficiently</a>,&nbsp;<a href="http://ivory.idyll.org/blog/what-is-diginorm.html">discard redundant data</a>, and&nbsp;<a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0101271">count them efficiently</a>.</p>
<p>More recently, we've been enthused about&nbsp;<a href="http://joss.theoj.org/papers/3d793c6e7db683bee7c03377a4a7f3c9">using k-mer based similarity measures</a>&nbsp;and&nbsp;<a href="http://ivory.idyll.org/blog/2016-sourmash-sbt.html">computing and searching k-mer-based sketch search databases for all the things</a>.</p>
<p>But I haven't spent too much talking about using k-mers for taxonomy, although that has become an&nbsp;<em>ahem</em>&nbsp;area of interest recently,&nbsp;<a href="http://www.biorxiv.org/content/early/2017/07/03/155358">if you read into our papers a bit</a>.</p>
<p>In this blog post I'm going to fix this by doing a little bit of a literature review and waxing enthusiastic about other people's work. Then in a future blog post I'll talk about how we're building off of this work in fun! and interesting? ways!</p><p>Address of the bookmark: <a href="http://ivory.idyll.org/blog/2017-something-about-kmers.html" rel="nofollow">http://ivory.idyll.org/blog/2017-something-about-kmers.html</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/44284/tools-for-geospatial-data-analysis</guid>
	<pubDate>Wed, 22 Mar 2023 02:10:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/44284/tools-for-geospatial-data-analysis</link>
	<title><![CDATA[Tools for Geospatial data analysis !]]></title>
	<description><![CDATA[<div><div><div><div><div><div><div><div><div><div><p>Geospatial data is becoming increasingly important in many fields, including urban planning, environmental science, public health, and more. These tools can help you work with data from a variety of sources, including satellite imagery, GPS data, and other forms of spatial data. They can help you visualize data, perform complex analysis, and even create maps and other visualizations.</p><p>The list includes some of the most popular and widely used geospatial tools available in Python. These tools can help you work with data from a variety of sources and in a variety of formats. Some of the tools are focused on visualization, such as Cartopy, Folium, and Contextily, which allow you to create interactive maps and other visualizations. Other tools are more focused on data manipulation and analysis, such as Fiona, GeoPandas, and Rasterio, which allow you to manipulate and analyze spatial data in a variety of ways.</p><p>The list also includes some tools for working with specific types of geospatial data. For example, the H3 library is designed specifically for working with hexagonal grids, while PySAL is focused on spatial econometrics and spatial analysis. Whether you are a data scientist, GIS specialist, or geospatial enthusiast, these tools are sure to enhance your work and help you achieve your goals.</p><p>In summary, this list is an excellent resource for anyone working with geospatial data in Python. It contains a wide range of tools for working with different types of data, and can help you visualize data, perform complex analysis, and create maps and other visualizations. If you're looking to enhance your skills in geospatial analysis, this list is definitely worth checking out.</p></div></div></div><div><p>These tools are:</p><ul>
<li>ArcGIS - <a href="https://lnkd.in/dgC6sKJH" target="_new">https://lnkd.in/dgC6sKJH</a></li>
<li>Cartopy - <a href="https://lnkd.in/dc8ijXRg" target="_new">https://lnkd.in/dc8ijXRg</a></li>
<li>Contextily - <a href="https://lnkd.in/dTdQsmKX" target="_new">https://lnkd.in/dTdQsmKX</a></li>
<li>Descartes - <a href="https://lnkd.in/dCJykxwW" target="_new">https://lnkd.in/dCJykxwW</a></li>
<li>Fiona - <a href="https://lnkd.in/d8sJ3Q5a" target="_new">https://lnkd.in/d8sJ3Q5a</a></li>
<li>Folium - <a href="https://lnkd.in/dfSsE-MB" target="_new">https://lnkd.in/dfSsE-MB</a></li>
<li>GDAL - <a href="https://lnkd.in/dYBJBaAY" target="_new">https://lnkd.in/dYBJBaAY</a></li>
<li>Geohash - <a href="https://lnkd.in/d_NxJ4_M" target="_new">https://lnkd.in/d_NxJ4_M</a></li>
<li>GeoJSON - <a href="https://lnkd.in/daGs2WYq" target="_new">https://lnkd.in/daGs2WYq</a></li>
<li>GeoPandas - <a href="https://lnkd.in/dBTFKKV3" target="_new">https://lnkd.in/dBTFKKV3</a></li>
<li>Geopy - <a href="https://lnkd.in/dfAzR8Xa" target="_new">https://lnkd.in/dfAzR8Xa</a></li>
<li>Gevent - <a href="http://www.gevent.org/" target="_new">http://www.gevent.org</a></li>
<li>H3 - <a href="https://h3geo.org/docs/" target="_new">https://h3geo.org/docs/</a></li>
<li>OSMnx - <a href="https://lnkd.in/dm3pHgUS" target="_new">https://lnkd.in/dm3pHgUS</a></li>
<li>PyQGIS - <a href="https://lnkd.in/dShWyWVr" target="_new">https://lnkd.in/dShWyWVr</a></li>
<li>PySAL - <a href="https://pysal.org/" target="_new">https://pysal.org</a></li>
<li>Pydeck - <a href="https://lnkd.in/dGBFu-iw" target="_new">https://lnkd.in/dGBFu-iw</a></li>
<li>Pyproj - <a href="https://lnkd.in/dNG9fdkm" target="_new">https://lnkd.in/dNG9fdkm</a></li>
<li>RTree - <a href="https://lnkd.in/dURMiYpU" target="_new">https://lnkd.in/dURMiYpU</a></li>
<li>Rasterio - <a href="https://lnkd.in/dEMC6ve6" target="_new">https://lnkd.in/dEMC6ve6</a></li>
<li>Scikit-mobility - <a href="https://lnkd.in/dpHhaX2J" target="_new">https://lnkd.in/dpHhaX2J</a></li>
<li>Shapely - <a href="https://lnkd.in/d568datK" target="_new">https://lnkd.in/d568datK</a></li>
</ul><p>These tools offer a wide range of capabilities for working with geospatial data, from visualizing and manipulating data to performing complex analysis and modeling. Whether you are a data scientist, GIS specialist, or geospatial enthusiast, these tools are sure to enhance your work and help you achieve your goals.</p></div></div></div></div></div></div></div></div>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/18380/jrfsrf-at-university-of-hyderabad</guid>
  <pubDate>Fri, 17 Oct 2014 01:55:44 -0500</pubDate>
  <link></link>
  <title><![CDATA[JRF/SRF at University of Hyderabad]]></title>
  <description><![CDATA[
<p>Applications are invited for the following post of Junior Research Fellow (temporary position coterminous with the project) under DBT funded research project on ““Understanding the functions of α1β1γ1/α2β1γ1 selective AMPK Modulators in dissecting the pharmacological role of these isozymes in metabolic diseases”</p>

<p>Qualified and interested candidates can send their curriculum vitae by e-mail to hr@drils.org on or before 27th October 2014 mention in the subject line of the mail the following code: AMPK-Biology.</p>

<p>Selected candidates will be called for a personal interview to Dr. Reddy’s Institute of Life Sciences, University of Hyderabad Campus, Gachibowli, Hyderabad. The selected candidate is expected to report within two weeks from the date of selection to start work on the project.</p>

<p>Junior Research Fellowship (Molecular Modeling/Biology) for two years and Senior Research fellowship for one year</p>

<p>Junior Research Fellowship: Rs. 15,600/- (consolidated) per month for first two years.<br />Senior Research Fellowship: Rs. 18,200/-(consolidated) per month for the 3rd year.</p>

<p>Duration: The duration of the fellowship is for three years. However, the performance of the candidate will be reviewed after the completion of every year and the fellowship will be renewed only upon satisfactory performance.</p>

<p>Responsibilities:</p>

<p>1) Literature search.<br />2) Design, plan and execute experiments under the supervision of the scientist.<br />3) Provide scientific support to the scientist in his/her research activities.<br />4) Book keeping and maintenance of stocks and consumables.</p>

<p>Essential Qualifications:</p>

<p>Required: M.Sc. in Microbiology/Biotechnology/Bioinformatics or any other related branch of basic Sciences from a recognized university/institute with a consistent academic record of minimum 60% aggregate in all qualifying examinations. The candidate should be NET qualified for lectureship. The candidate should be motivated to work with dedication.</p>

<p>Desirable: expertise/experience in both Molecular Modeling and Molecular Biology.</p>

<p>Experience: 0-2 years in the areas of Molecular Modeling and/or Molecular Biology and cell biology and Biochemistry.</p>

<p>Preferable: Relevant research experience as evident from thesis/dissertation/project work.</p>

<p>Advertisement: http://www.ilsresearch.org/userfiles/Junior%20REsearch%20Fellowship%20-%20AMPK(Biology).pdf</p>
]]></description>
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