<?xml version='1.0'?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:georss="http://www.georss.org/georss" xmlns:atom="http://www.w3.org/2005/Atom" >
<channel>
	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/36918?offset=530</link>
	<atom:link href="https://bioinformaticsonline.com/related/36918?offset=530" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/32129/lordec-a-hybrid-error-correction-program-for-long-pacbio-reads</guid>
	<pubDate>Mon, 10 Apr 2017 04:16:09 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/32129/lordec-a-hybrid-error-correction-program-for-long-pacbio-reads</link>
	<title><![CDATA[LoRDEC: a hybrid error correction program for long, PacBio reads]]></title>
	<description><![CDATA[<p>LoRDEC is a program to correct sequencing errors in long reads from 3rd generation sequencing with high error rate, and is especially intended for PacBio reads. It uses a hybrid strategy, meaning that it uses two sets of reads: the reference read set, whose error rate is assumed to be small, and the PacBio read set, which is then corrected using the reference set. Typically, the reference set contains Illumina reads.</p>
<p><br> Usually, errors in PacBio reads include many insertions and deletions, and comparatively less substitutions. LoRDEC can correct errors of all these types.<br> After correction, a larger portion of the sequence of PacBio reads is usable for detection of region of similarity with other sequences, for aligning them to the contigs of an assembly, etc.</p>
<p>Why is LoRDEC different?</p>
<ul>
<li>It is efficient and can process large read data sets, included from eukaryotic or vertebrate species, on a usual computing server, and even works on desktop/laptop computers.</li>
<li>It adopts a novel graph based approach: it builds a succinct De Bruijn Graph (DBG) representing the short reads, and seeks a corrective sequence for each erroneous region of a long read by traversing chosen paths in the graph.</li>
</ul><p>Address of the bookmark: <a href="http://www.atgc-montpellier.fr/lordec/" rel="nofollow">http://www.atgc-montpellier.fr/lordec/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38563/hecil-a-hybrid-error-correction-algorithm-for-long-reads-with-iterative-learning</guid>
	<pubDate>Tue, 01 Jan 2019 12:01:00 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38563/hecil-a-hybrid-error-correction-algorithm-for-long-reads-with-iterative-learning</link>
	<title><![CDATA[HECIL: A Hybrid Error Correction Algorithm for Long Reads with Iterative Learning]]></title>
	<description><![CDATA[<p><span>HECIL&mdash;Hybrid Error Correction with Iterative Learning&mdash;a hybrid error correction framework that determines a correction policy for erroneous long reads, based on optimal combinations of decision weights obtained from short read alignments.&nbsp;</span></p>
<p><span><span>HECIL&rsquo;s core algorithm by introducing an iterative learning paradigm that enhances the correction policy at each iteration by incorporating knowledge gathered from previous iterations via data-driven confidence metrics assigned to prior corrections.</span></span></p><p>Address of the bookmark: <a href="https://github.com/NDBL/HECIL" rel="nofollow">https://github.com/NDBL/HECIL</a></p>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<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/44848/trust-but-verify-sequencing-your-cell-lines-might-reveal-an-uninvited-guest</guid>
	<pubDate>Wed, 04 Jun 2025 00:07:57 -0500</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44848/trust-but-verify-sequencing-your-cell-lines-might-reveal-an-uninvited-guest</link>
	<title><![CDATA[Trust But Verify: Sequencing Your Cell Lines Might Reveal an Uninvited Guest]]></title>
	<description><![CDATA[<p>High-throughput sequencing has become indispensable in cell biology, enabling detailed insights into chromatin structure, gene expression, and regulatory dynamics. Yet, when faced with unexpectedly low mapping rates to the human genome, researchers often rush to troubleshoot technical parameters&mdash;sequencer quality, adapter trimming, or aligner settings.</p><p>Before you go down that path, consider this critical biological question:<br /> <strong>Are you sequencing human cells&mdash;or bacterial contamination?</strong></p><h2>The Silent Saboteur: Mycoplasma in Cell Cultures</h2><p><em>Mycoplasma</em> contamination remains one of the most widespread and underdiagnosed issues in tissue culture work. Studies suggest that <strong>15&ndash;35% of cell lines in use may be contaminated</strong>, often without visible signs. Unlike other microbial infections, <em>Mycoplasma</em> does not produce cloudiness, odor, or a change in pH. Many researchers won&rsquo;t detect it unless they specifically test for it.</p><p>The consequences, however, are profound. <em>Mycoplasma</em> can significantly alter:</p><ul>
<li>
<p>Host gene expression patterns</p>
</li>
<li>
<p>Cell proliferation rates</p>
</li>
<li>
<p>Epigenetic profiles and chromatin accessibility</p>
</li>
<li>
<p>Cytokine signaling and immune responses</p>
</li>
</ul><p>In short, it can skew your results, compromise your biological conclusions, and invalidate weeks or months of research.</p><h2>A Simple Diagnostic Step: Map Against <em>Mycoplasma</em> Genomes</h2><p>If you encounter poor alignment rates to the human genome, consider mapping your reads to a <em>Mycoplasma</em> reference genome&mdash;or better yet, use a <strong>combined human + <em>Mycoplasma</em></strong> reference. There have been cases where over half of all reads, initially assumed to be from human cells, were in fact bacterial in origin. This check is fast, easy, and could save your project.</p><h2>How Contamination Happens&mdash;and Persists</h2><p><em>Mycoplasma</em> is small (0.1&ndash;0.3 &mu;m), lacks a cell wall, and can pass through standard filters undetected. Common sources include:</p><ul>
<li>
<p>Contaminated reagents (e.g., FBS)</p>
</li>
<li>
<p>Infected cell lines obtained from other labs</p>
</li>
<li>
<p>Poor aseptic technique or shared equipment</p>
</li>
</ul><p>Once present, it spreads quickly between cultures and can persist for months, silently affecting results.</p><h2>Why Treatment Is Difficult</h2><p>While antibiotics such as Plasmocin or BM-Cyclin are sometimes used, they often offer only partial resolution and may themselves alter cell behavior. In many cases, the best course of action is to <strong>discard the contaminated culture</strong> and start with a fresh, verified stock.</p><h2>Practical Recommendations for Researchers</h2><ul>
<li>
<p><strong>Routinely test for <em>Mycoplasma</em></strong> using PCR, qPCR, or fluorescence-based assays</p>
</li>
<li>
<p><strong>Incorporate contamination screens into your sequencing QC pipeline</strong></p>
</li>
<li>
<p><strong>Use combined reference genomes</strong> when mapping ambiguous reads</p>
</li>
<li>
<p><strong>Practice strict aseptic technique</strong> and monitor all incoming cell lines</p>
</li>
<li>
<p><strong>Don&rsquo;t ignore unexplained data anomalies</strong>&mdash;they might point to contamination</p>
</li>
</ul><h2>Closing Thought: Contamination Is a Biological Variable</h2><p>It&rsquo;s easy to view poor mapping as a technical issue, but sometimes the problem lies deeper&mdash;in the biology itself. <em>Mycoplasma</em> contamination doesn&rsquo;t just interfere with sequencing; it interferes with science. As a research community, we must treat contamination not as an afterthought, but as a key variable to control.</p><p>So next time your reads won&rsquo;t align, don&rsquo;t just tune the aligner. Ask if your cells are telling the truth&mdash;or if they're hiding something.</p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>

</channel>
</rss>