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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/40937?offset=60</link>
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	<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/bookmarks/view/40703/%CF%80-cyc-a-reference-free-snp-discovery-application-using-parallel-graph-search</guid>
	<pubDate>Tue, 28 Jan 2020 03:34:23 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40703/%CF%80-cyc-a-reference-free-snp-discovery-application-using-parallel-graph-search</link>
	<title><![CDATA[Π-cyc: A Reference-free SNP Discovery Application using Parallel Graph Search]]></title>
	<description><![CDATA[<p>Reference free SNP search for comparative population genomics: multiple samples run simultanously. **experimental phase, compiles and runs with OpenMPI-1.8.8 with Intel Compiler only</p>
<p><span>Cycles enumeration (aka Bubbles) as part of de novo de bruijn graphs assembly using colours can be unpractical for large error prone genomes which makes the assembly process produce an excessive number of false positive cycles.&nbsp; Our solution is to search the graph in multicores shared memory parallel mode using graph decomposition then use filtering method to generate good quality SNPs.</span></p>
<p><a href="https://arxiv.org/abs/1809.06700">https://arxiv.org/abs/1809.06700</a></p>
<p><a href="https://github.com/redayounsi/2KP2P">https://github.com/redayounsi/2KP2P</a></p>
<blockquote>
<p>/2kp2omp/bin/main_2kp2_K63_C2 -i fastq_files.txt -o fungus_bub.fasta -r stat_fungus.txt -c cov_fungus_hash.txt -k 63 -h 20 -b 100 -g 600 -l 100 -f 16 -t 5.0 -x 1 -v 0 -p 1 -y 1 -u 1</p>
<p>&nbsp;</p>
</blockquote><p>Address of the bookmark: <a href="https://github.com/redayounsi/2KP2P" rel="nofollow">https://github.com/redayounsi/2KP2P</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35619/tallymer-method-to-compute-k-mer-frequencies-and-its-application-to-annotate-large-repetitive-plant-genomes</guid>
	<pubDate>Thu, 15 Feb 2018 10:21:02 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35619/tallymer-method-to-compute-k-mer-frequencies-and-its-application-to-annotate-large-repetitive-plant-genomes</link>
	<title><![CDATA[Tallymer: method to compute K-mer frequencies and its application to annotate large repetitive plant genomes]]></title>
	<description><![CDATA[<p>Tallymer is based on enhanced suffix arrays. This gives a much larger flexibility concerning the choice of the&nbsp;<span>k</span>-mer size. Tallymer can process large data sizes of several billion bases. We used it in a variety of applications to study the genomes of maize and other plant species. In particular, Tallymer was used to index a set whole genome shotgun sequences from maize (B73) (total size 10<sup>9</sup>&nbsp;bp).&nbsp;<br>Tallymer was effective in a variety of applications to aid genome annotation in maize, despite limitations imposed by the relatively low coverage of sequence available.</p>
<p>A manual can be found&nbsp;<a href="https://www.zbh.uni-hamburg.de/fileadmin/gi/tallymer/tallymer.pdf" target="_blank" title="tallymer.pdf (111 KB)">here</a>.</p><p>Address of the bookmark: <a href="https://www.zbh.uni-hamburg.de/forschung/arbeitsgruppe-genominformatik/software/tallymer.html" rel="nofollow">https://www.zbh.uni-hamburg.de/forschung/arbeitsgruppe-genominformatik/software/tallymer.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44630/genofig-a-user-friendly-application-for-the-visualization-and-comparison-of-genomic-regions</guid>
	<pubDate>Mon, 05 Aug 2024 23:06:58 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44630/genofig-a-user-friendly-application-for-the-visualization-and-comparison-of-genomic-regions</link>
	<title><![CDATA[GenoFig: a user-friendly application for the visualization and comparison of genomic regions]]></title>
	<description><![CDATA[<p>Tool for graphical vizualisation of annotated genetic regions, and homologous regions comparison. It is an independent recoding of Easyfig 2 initially developped by at the S. Beatson Lab [<a href="https://mjsull.github.io/Easyfig/" target="_blank">https://mjsull.github.io/Easyfig/</a>]</p>
<p dir="auto">Download the GenoFig source code using the 'Download' button on top of this page. Cloning is currently not available for people not member of the INRAE French Institution. After decompression, open a terminal in the folder containing the decompressed files and run:</p>
<div>
<pre id="code-47"><code><span>conda env create -f extras/requirements.yml</span>
<span>extras/SETUP.sh</span></code></pre>
</div><p>Address of the bookmark: <a href="https://forgemia.inra.fr/public-pgba/genofig" rel="nofollow">https://forgemia.inra.fr/public-pgba/genofig</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/20439/interactive-market-intelligence</guid>
	<pubDate>Mon, 19 Jan 2015 08:20:38 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/20439/interactive-market-intelligence</link>
	<title><![CDATA[Interactive Market Intelligence]]></title>
	<description><![CDATA[<p>BioInformatics LLC, a premier research and advisory firm serving the life science industry, has launched groundbreaking, dynamic-data presentation platform, Interactive Market Intelligence&mdash; the only cloud-based market research analytics tool for the life science tools industry.<br /><br />Superior to traditional PDF and PowerPoint reports, Interactive Market Intelligence allows end-users to filter, create and export literally thousands of views of data &mdash; all easily obtainable from a set of core metrics that include market, brand, customer and workflow analytics in well-defined segments of the life science market.<br /><br />The Market for Real-Time PCR is the first in a series of topics to be explored using the Interactive Market Intelligence platform. The primary research analysis is based on a survey of 900+ international scientists performing qPCR in their laboratories.<br /><br />Key data findings from "The Market for Real-Time PCR": Global market for qPCR in 2015 is estimated to be $3.6B; The average growth in qPCR throughput is expected to be at 9.8% in 2015; 22% of respondents are highly likely to switch primary suppliers of qPCR products; 50% of respondents use pre-designed primer/probe sets.</p>]]></description>
	<dc:creator>Pranjali Yadav</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36012/gmol-an-interactive-tool-for-3d-genome-structure-visualization</guid>
	<pubDate>Wed, 21 Mar 2018 12:25:20 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36012/gmol-an-interactive-tool-for-3d-genome-structure-visualization</link>
	<title><![CDATA[GMOL: An Interactive Tool for 3D Genome Structure Visualization]]></title>
	<description><![CDATA[<p><span>GMOL was developed based upon our multi-scale approach that allows a user to scale between six separate levels within the genome. With GMOL, a user can choose any unit at any scale and scale it up or down to visualize its structure and retrieve corresponding genome sequences.</span></p><p>Address of the bookmark: <a href="https://www.nature.com/articles/srep20802" rel="nofollow">https://www.nature.com/articles/srep20802</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39917/chromomap-an-r-package-for-interactive-visualization-and-annotation-of-chromosomes</guid>
	<pubDate>Sat, 07 Sep 2019 10:45:31 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39917/chromomap-an-r-package-for-interactive-visualization-and-annotation-of-chromosomes</link>
	<title><![CDATA[chromoMap-An R package for Interactive Visualization and Annotation of Chromosomes]]></title>
	<description><![CDATA[<p><code>chromoMap</code>&nbsp;provides interactive, configurable and elegant graphics visualization of chromosomes or chromosomal regions allowing users to map chromosome elements (like genes,SNPs etc.) on the chromosome plot.Each chromosome is composed of loci(representing a specific range determined based on chromosome length) that, on hover, shows details about the annotations in that locus range. The plots can be saved as HTML documents that can be shared easily. In addition, you can include them in R Markdown or in R Shiny applications.</p>
<p>Some of the prominent features of the package are:</p>
<ul>
<li>visualizing polyploidy simultaneously on the same plot.</li>
<li>annotating groups of elements as distinct colors.</li>
<li>creating chromosome heatmaps.</li>
<li>adjusting chromosome range or visualizing chromosome regions such as genes</li>
<li>adding labels to the plot</li>
<li>adding hyperlinks to each element</li>
</ul><p>Address of the bookmark: <a href="https://cran.r-project.org/web/packages/chromoMap/vignettes/chromoMap.html" rel="nofollow">https://cran.r-project.org/web/packages/chromoMap/vignettes/chromoMap.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35135/alitv%E2%80%94interactive-visualization-of-whole-genome-comparisons</guid>
	<pubDate>Wed, 10 Jan 2018 07:08:17 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35135/alitv%E2%80%94interactive-visualization-of-whole-genome-comparisons</link>
	<title><![CDATA[AliTV—interactive visualization of whole genome comparisons]]></title>
	<description><![CDATA[<p>AliTV, which provides interactive visualization of whole genome alignments. AliTV reads multiple whole genome alignments or automatically generates alignments from the provided data. Optional feature annotations and phylo- genetic information are supported. The user-friendly, web-browser based and highly customizable interface allows rapid exploration and manipulation of the visualized data as well as the export of publication-ready high-quality figures. AliTV is freely available at&nbsp;<a href="https://github.com/AliTVTeam/AliTV">https://github.com/AliTVTeam/AliTV</a></p>
<p>https://alitvteam.github.io/AliTV/</p><p>Address of the bookmark: <a href="https://github.com/AliTVTeam/AliTV" rel="nofollow">https://github.com/AliTVTeam/AliTV</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37259/epiviz-an-interactive-visualization-tool-for-functional-genomics-data</guid>
	<pubDate>Mon, 09 Jul 2018 05:27:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37259/epiviz-an-interactive-visualization-tool-for-functional-genomics-data</link>
	<title><![CDATA[Epiviz: an interactive visualization tool for functional genomics data.]]></title>
	<description><![CDATA[<p><span>Epiviz is an interactive visualization tool for functional genomics data. It supports genome navigation like other genome browsers, but allows multiple visualizations of data within genomic regions using scatterplots, heatmaps and other user-supplied visualizations. It also includes data from the&nbsp;</span><a href="http://barcode.luhs.org/" target="_blank">Gene Expression Barcode project</a><span>&nbsp;for transcriptome visualization. It has a flexible plugin framework so users can add</span><a href="http://d3js.org/" target="_blank">d3</a><span>&nbsp;visualizations. You can see a video tour&nbsp;</span><a href="http://youtu.be/099c4wUxozA" target="_blank">here</a><span>.</span></p>
<p><span>https://bioconductor.org/packages/release/bioc/html/epivizr.html</span></p>
<p><span>https://github.com/epiviz</span></p>
<p><span>https://github.com/epiviz/epiviz</span></p><p>Address of the bookmark: <a href="https://epiviz.github.io/" rel="nofollow">https://epiviz.github.io/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38561/hawkeye-an-interactive-visual-analytics-tool-for-genome-assemblies</guid>
	<pubDate>Tue, 01 Jan 2019 11:56:17 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38561/hawkeye-an-interactive-visual-analytics-tool-for-genome-assemblies</link>
	<title><![CDATA[Hawkeye: an interactive visual analytics tool for genome assemblies]]></title>
	<description><![CDATA[<p><span>Genome sequencing remains an inexact science, and genome sequences can contain significant errors if they are not carefully examined. Hawkeye is our new visual analytics tool for genome assemblies, designed to aid in identifying and correcting assembly errors. Users can analyze all levels of an assembly along with summary statistics and assembly metrics, and are guided by a ranking component towards likely mis-assemblies. Hawkeye is freely available and released as part of the open source AMOS project&nbsp;</span><span><a href="http://amos.sourceforge.net/hawkeye"><span>http://amos.sourceforge.net/hawkeye</span></a></span><span>.</span></p>
<p>https://genomebiology.biomedcentral.com/articles/10.1186/gb-2007-8-3-r34</p><p>Address of the bookmark: <a href="http://amos.sourceforge.net/wiki/index.php?title=Hawkeye" rel="nofollow">http://amos.sourceforge.net/wiki/index.php?title=Hawkeye</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>

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