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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/5388?</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/9627/brainspan-atlas-of-the-developing-human-brain</guid>
	<pubDate>Sun, 06 Apr 2014 18:43:00 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/9627/brainspan-atlas-of-the-developing-human-brain</link>
	<title><![CDATA[BrainSpan Atlas of the Developing Human Brain]]></title>
	<description><![CDATA[<p><span><strong>The BrainSpan atlas</strong>- web source for revealing transcriptional mechanisms involved in human brain development.</span></p>
<p><span><strong>Article</strong>:</span></p>
<p><span>http://www.nbcnews.com/science/science-news/maps-unborn-human-brains-point-medical-frontiers-n71336</span></p>
<p><span><strong>Paper</strong>:</span></p>
<p><span>http://www.nature.com/nature/journal/vaop/ncurrent/full/nature13185.html</span></p><p>Address of the bookmark: <a href="http://brainspan.org/static/home" rel="nofollow">http://brainspan.org/static/home</a></p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35420/telomerehunter</guid>
	<pubDate>Fri, 02 Feb 2018 04:23:59 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35420/telomerehunter</link>
	<title><![CDATA[TelomereHunter]]></title>
	<description><![CDATA[<p><span>TelomereHunter is a tool for estimating telomere content from human whole-genome sequencing data. It is designed to take BAM files from a tumor and a matching control sample as input. However, it is also possible to run TelomereHunter with one input file. TelomereHunter extracts and sorts telomeric reads from the input sample(s). For the estimation of telomere content, GC biases are taken into account. Finally, the results of TelomereHunter are visualized in several diagrams.</span><br><br><span>TelomereHunter is available for download at the following address:&nbsp;</span><a href="https://pypi.python.org/pypi/telomerehunter/" target="_blank">https://pypi.python.org/pypi/telomerehunter/</a></p><p>Address of the bookmark: <a href="http://www.dkfz.de/en/applied-bioinformatics/telomerehunter/telomerehunter.html" rel="nofollow">http://www.dkfz.de/en/applied-bioinformatics/telomerehunter/telomerehunter.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/10379/your-stressdepression-came-from-ancestor</guid>
	<pubDate>Sun, 04 May 2014 18:46:20 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/10379/your-stressdepression-came-from-ancestor</link>
	<title><![CDATA[Your stress/depression came from ancestor]]></title>
	<description><![CDATA[<p>"A study published in&nbsp;<em>Nature Neuroscience</em>&nbsp;finds that stress in early life alters the production of small RNAs, called microRNAs, in the sperm of mice. The mice show depressive behaviours that persist in their progeny."</p><p>Source:</p><p>http://www.nature.com/news/sperm-rna-carries-marks-of-trauma-1.15049</p>]]></description>
	<dc:creator>Rahul Agarwal</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34470/simngs-and-simlibrary-%E2%80%93-software-for-simulating-next-gen-sequencing-data</guid>
	<pubDate>Tue, 28 Nov 2017 06:49:11 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34470/simngs-and-simlibrary-%E2%80%93-software-for-simulating-next-gen-sequencing-data</link>
	<title><![CDATA[simNGS and simLibrary – Software for Simulating Next-Gen Sequencing Data]]></title>
	<description><![CDATA[<p>simNGS is software for simulating observations from Illumina sequencing machines using the statistical models behind the AYB base-calling software. By default, observations only incorporate noise due to sequencing and do not incorporate effects from more esoteric sources of noise that may be present in real data ("dust", bubbles, merged clusters, sequence-heterogeneous clusters, etc). Many of these additional sources may optionally applied.</p>
<p>simNGS takes fasta format sequences and a file describing the covariance of noise between bases and cycles observed in an actual run of the machine, randomly generates noisy intensities representing the signals for the sequence at each cycle and calculates likelihoods for all possible base calls.</p><p>Address of the bookmark: <a href="https://www.ebi.ac.uk/goldman-srv/simNGS/" rel="nofollow">https://www.ebi.ac.uk/goldman-srv/simNGS/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38205/sim3c-read-pair-simulation-of-3c-based-sequencing-methodologies-hic-meta3c-dnase-hic</guid>
	<pubDate>Tue, 13 Nov 2018 07:25:38 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38205/sim3c-read-pair-simulation-of-3c-based-sequencing-methodologies-hic-meta3c-dnase-hic</link>
	<title><![CDATA[sim3C: Read-pair simulation of 3C-based sequencing methodologies (HiC, Meta3C, DNase-HiC)]]></title>
	<description><![CDATA[<p><strong>Required python modules</strong></p>
<ul>
<li>biopython</li>
<li>intervaltree</li>
<li>numpy</li>
<li>scipy</li>
<li>tqdm</li>
<li>PyYAML</li>
</ul><p>Address of the bookmark: <a href="https://github.com/cerebis/sim3C" rel="nofollow">https://github.com/cerebis/sim3C</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44503/entire-human-genome-sequencing</guid>
	<pubDate>Tue, 02 Apr 2024 01:19:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44503/entire-human-genome-sequencing</link>
	<title><![CDATA[Entire Human Genome Sequencing !]]></title>
	<description><![CDATA[<p>Cost-effective whole human genome sequencing has revolutionized the landscape of genetic research and personalized medicine by making comprehensive genetic analysis accessible to a wider population. Through advancements in sequencing technologies, such as next-generation sequencing (NGS), costs have significantly decreased, enabling researchers and healthcare providers to analyze an individual's complete genetic makeup with greater efficiency and affordability. This has profound implications for disease diagnosis, prognosis, and treatment, as it allows for the identification of genetic predispositions and the customization of healthcare interventions based on an individual's unique genetic profile. Moreover, as the cost continues to decline, the potential for population-scale genomic studies and large-scale screening programs becomes increasingly feasible, promising to further enhance our understanding of human genetics and improve healthcare outcomes on a global scale.</p><p>Here are few companies:</p><p>https://mynucleus.com/</p><p>https://myome.com/</p><p>https://nebula.org/whole-genome-sequencing-dna-test/</p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/36833/bfc-a-standalone-high-performance-tool-for-correcting-sequencing-errors-from-illumina-sequencing-data</guid>
	<pubDate>Thu, 31 May 2018 09:35:23 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/36833/bfc-a-standalone-high-performance-tool-for-correcting-sequencing-errors-from-illumina-sequencing-data</link>
	<title><![CDATA[BFC: a standalone high-performance tool for correcting sequencing errors from Illumina sequencing data]]></title>
	<description><![CDATA[BFC is a standalone high-performance tool for correcting sequencing errors from Illumina sequencing data. It is specifically designed for high-coverage whole-genome human data, though also performs well for small genomes.

The BFC algorithm is a variant of the classical spectrum alignment algorithm introduced by Pevzner et al (2001). It uses an exhaustive search to find a k-mer path through a read that minimizes a heuristic objective function jointly considering penalties on correction, quality and k-mer support. This algorithm was first implemented in my fermi assembler and then refined a few times in fermi, fermi2 and now in BFC. In the k-mer counting phase, BFC uses a blocked bloom filter to filter out most singleton k-mers and keeps the rest in a hash table (Melsted and Pritchard, 2011). The use of bloom filter is how BFC is named, though other correctors such as Lighter and Bless actually rely more on bloom filter than BFC.

https://github.com/lh3/bfc<p>Address of the bookmark: <a href="https://github.com/lh3/bfc" rel="nofollow">https://github.com/lh3/bfc</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/45235/the-sweet-side-of-human-evolution-did-sugar-help-build-the-human-brain</guid>
	<pubDate>Mon, 17 Aug 2026 01:47:36 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/45235/the-sweet-side-of-human-evolution-did-sugar-help-build-the-human-brain</link>
	<title><![CDATA[The Sweet Side of Human Evolution: Did Sugar Help Build the Human Brain?]]></title>
	<description><![CDATA[<p>For a long time, people have focused on meat when talking about human evolution. The common idea is that our ancestors ate more animal foods, which gave them lots of energy and nutrients, helping their brains grow bigger and more demanding than those of other primates.</p><p>A new study in Science offers a different and surprisingly sweet perspective. Researchers Jennie Brand-Miller, Karen Hardy, David Raubenheimer, and Les Copeland suggest that sugars from fruits and honey, and later starch from cooked plants, may have been key in helping the human brain evolve.</p><p><strong>The brain&rsquo;s carbohydrate problem</strong></p><p>The human brain uses a lot of energy. Even though it is only about 2% of an adult&rsquo;s body weight, it takes up a large share of the body&rsquo;s resting energy, mainly using glucose as fuel. Over millions of years, as our ancestors&rsquo; brains grew from about 300 grams in Australopithecus afarensis to around 1,500 grams in modern humans, the need for easy-to-access glucose would have gone up a lot.</p><p>The researchers, therefore, asked a simple question: where did all that glucose come from?</p><p>Their analysis suggests that early hominins may have gotten much of their glucose from naturally sweet foods. Fruit and honey could give them easy access to sugars before they learned to digest cooked starch well. The study&rsquo;s models show that sugars may have made up a big part of their diet.</p><p>From fruit and honey to cooked starch</p><p>The story did not end with fruit.</p><p>The researchers suggest that humans shifted from eating sweet foods to eating cooked starchy foods. Once people learned to control fire and process food, cooking made starchy plants much easier to digest and turned them into a key source of glucose.</p><p>From this perspective, new ways of preparing food, like pounding, processing, and cooking, were not just about making meals softer or tastier. These changes may have given our ancestors access to much more carbohydrate energy.</p><p>This gives us a more detailed view of how the human diet evolved. Animal foods were clearly important, but carbohydrates may have been just as important for meeting the high energy needs of a growing brain.</p><p>A different way to think about the human diet</p><p>The study does not claim that our ancestors ate sugar the way we do today. There is a big difference between eating whole fruits or natural honey and eating the highly processed, refined sugars common now.</p><p>Instead, the research points to something bigger. Human evolution may have depended on our ability to find, process, and get energy from many different foods.</p><p>Seasonal fruit, honey, underground plant foods, and later cooked starches could have been important sources of glucose. The researchers think that changes in how available these foods were may have shaped how our ancestors searched for food and even affected human evolution.</p><p>What does this mean today?</p><p>The findings should not be interpreted as a license to eat more refined sugar. Instead, they challenge the simple idea that carbohydrates were less important than meat in human evolution. Our ancestors survived and evolved by exploiting different nutritional opportunities&mdash;and by developing technologies that made previously difficult foods more useful.</p><p>So perhaps the history of the human brain was not written by meat alone. ISo maybe the story of the human brain is not just about meat. It could also be about fruit, honey, roots, grains, and our unique ability to turn plants into energy. sweeter than we once imagined.<br /><br />Read more about it at&nbsp;https://www.science.org/doi/epdf/10.1126/science.aed8437</p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/19556/genome-origami</guid>
	<pubDate>Fri, 12 Dec 2014 22:48:17 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/19556/genome-origami</link>
	<title><![CDATA[Genome Origami]]></title>
	<description><![CDATA[<p>There are several interesting factoid about our genomes, one of them is their folding. If we stretched out the DNA in a single cell, which is only a few millionths of an inch wide, it would span more than six feet. In other word, the size of six feet DNA fold themself to fit in a few millionths of an inch wide space. These DNA folding is a dynamic process that changes over time (!!). Researchers around the world have been trying to understand how DNA folds itself up so efficiently, and a recent post on the NIH Director&rsquo;s Blog highlights new research illustrating how the human genome folds inside the cell&rsquo;s nucleus, as well as how DNA folding affects gene regulation. The research team created this delightful video that demonstrates the principles involved using origami art.</p><p>http://bioinformaticsonline.com/videolist/watch/19555/a-3d-map-of-the-human-genome<br /><br />Researchers have been working to determine how cells regulate gene expression for nearly as long as we&rsquo;ve known about DNA. How, for example, do nerve cells know to turn off only nerve cell genes and turn off bone cell genes? DNA folding loops are part of the answer. This research team, which published their findings in a paper in Cell http://www.cell.com/cell/abstract/S0092-8674%2814%2901497-4 , found that the number of loops is much lower than expected. There are only 10,000 loops instead of the predicted millions, and they form on/off switches in DNA.<br /><br /></p><p>More at http://www.eurekalert.org/pub_releases/2014-12/ru-3mr121114.php</p><p>Reference http://www.cell.com/cell/abstract/S0092-8674%2814%2901497-4</p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43909/human-complete-genome</guid>
	<pubDate>Wed, 06 Jul 2022 06:42:55 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43909/human-complete-genome</link>
	<title><![CDATA[Human Complete Genome]]></title>
	<description><![CDATA[<h1 dir="auto">Telomere-to-telomere consortium</h1>
<p dir="auto">We have sequenced the CHM13hTERT human cell line with a number of technologies. Human genomic DNA was extracted from the cultured cell line. As the DNA is native, modified bases will be preserved. The data includes 30x&nbsp;<a href="https://www.pacb.com/">PacBio</a>&nbsp;<a href="https://www.ncbi.nlm.nih.gov/sra/?term=SRX789768*+CHM13">HiFi</a>, 120x coverage of&nbsp;<a href="https://nanoporetech.com/">Oxford Nanopore</a>, 70x&nbsp;<a href="https://www.pacb.com/">PacBio</a>&nbsp;CLR, 50x&nbsp;<a href="https://www.10xgenomics.com/">10X Genomics</a>, as well as&nbsp;<a href="https://bionanogenomics.com/technology/dls-technology/">BioNano DLS</a>&nbsp;and&nbsp;<a href="https://arimagenomics.com/kit/">Arima Genomics HiC</a>. Most raw data is available from this site, with the exception of the PacBio data which was generated by the University of Washington/PacBio and is available from&nbsp;<a href="https://www.ncbi.nlm.nih.gov/sra?linkname=bioproject_sra_all&amp;from_uid=269593">NCBI SRA</a>.</p>
<p dir="auto">A UCSC browser is available for&nbsp;<a href="https://genome.ucsc.edu/h/GCA_009914755.4">v2.0</a>&nbsp;(as well as legacy&nbsp;<a href="http://genome.ucsc.edu/cgi-bin/hgTracks?genome=t2t-chm13-v1.0&amp;hubUrl=http://t2t.gi.ucsc.edu/chm13/hub/hub.txt">v1.0</a>&nbsp;and&nbsp;<a href="http://genome.ucsc.edu/cgi-bin/hgTracks?genome=t2t-chm13-v1.1&amp;hubUrl=http://t2t.gi.ucsc.edu/chm13/hub/hub.txt">v1.1</a>&nbsp;versions). An interactive dotplot visualization of all genomic repeats is also available from&nbsp;<a href="https://resgen.io/paper-data/T2T-Nurk-et-al-2021/views/t2t-identity-v2">resgen.io</a>. Known issues identified in the assembly are tracked at&nbsp;<a href="https://github.com/marbl/CHM13-issues">CHM13 issues</a>.</p>
<p dir="auto">&nbsp;</p>
<p dir="auto">MORE at&nbsp;https://github.com/marbl/CHM13</p><p>Address of the bookmark: <a href="https://www.science.org/doi/10.1126/science.abj6987" rel="nofollow">https://www.science.org/doi/10.1126/science.abj6987</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
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