<?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/31566?offset=1470</link>
	<atom:link href="https://bioinformaticsonline.com/related/31566?offset=1470" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39671/flye-fast-and-accurate-de-novo-assembler-for-single-molecule-sequencing-reads</guid>
	<pubDate>Sat, 06 Jul 2019 03:48:22 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39671/flye-fast-and-accurate-de-novo-assembler-for-single-molecule-sequencing-reads</link>
	<title><![CDATA[Flye: Fast and accurate de novo assembler for single molecule sequencing reads]]></title>
	<description><![CDATA[<p><span>Flye is a de novo assembler for single molecule sequencing reads, such as those produced by PacBio and Oxford Nanopore Technologies. It is designed for a wide range of datasets, from small bacterial projects to large mammalian-scale assemblies. The package represents a complete pipeline: it takes raw PB / ONT reads as input and outputs polished contigs. Flye also includes a special mode for metagenome assembly.</span></p><p>Address of the bookmark: <a href="https://github.com/fenderglass/Flye" rel="nofollow">https://github.com/fenderglass/Flye</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/26391/radka-reifova-lab</guid>
  <pubDate>Mon, 15 Feb 2016 06:00:48 -0600</pubDate>
  <link></link>
  <title><![CDATA[Radka Reifová Lab]]></title>
  <description><![CDATA[
<p>We are generally interested in the mechanisms of species origin from a molecular and ecological perspective. Particularly, we are interested in the role of sex chromosomes in speciation. Most of our research is done on birds and mammals. Currently, we focus our research on two hybridizing song birds, the Common nightingale (Luscinia megarhynchos) and the Thrush Nightingale (L. luscinia). Combining population genomic and ecological approaches we try to elucidate the genetic architecture of reproductive isolation and understand the role of interspecific competition and song convergence in the evolution of reproductive isolation between the species. </p>

<p>More at http://web.natur.cuni.cz/~radkas/index.php?page=research</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40972/deepbinner-a-signal-level-demultiplexer-for-oxford-nanopore-reads</guid>
	<pubDate>Mon, 10 Feb 2020 02:45:53 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40972/deepbinner-a-signal-level-demultiplexer-for-oxford-nanopore-reads</link>
	<title><![CDATA[Deepbinner: a signal-level demultiplexer for Oxford Nanopore reads]]></title>
	<description><![CDATA[<p>Deepbinner is a tool for demultiplexing barcoded <a href="https://nanoporetech.com/">Oxford Nanopore</a> sequencing reads. It does this with a deep <a href="https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/">convolutional neural network</a> classifier, using many of the <a href="https://towardsdatascience.com/neural-network-architectures-156e5bad51ba">architectural advances</a> that have proven successful in image classification. Unlike other demultiplexers (e.g. Albacore and <a href="https://github.com/rrwick/Porechop">Porechop</a>), Deepbinner identifies barcodes from the raw signal (a.k.a. squiggle) which gives it greater sensitivity and fewer unclassified reads.</p><p>Address of the bookmark: <a href="https://github.com/rrwick/Deepbinner" rel="nofollow">https://github.com/rrwick/Deepbinner</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42139/mixtures-a-novel-tool-for-bacterial-strain-reconstruction-from-reads</guid>
	<pubDate>Fri, 21 Aug 2020 08:23:19 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42139/mixtures-a-novel-tool-for-bacterial-strain-reconstruction-from-reads</link>
	<title><![CDATA[mixtureS: a novel tool for bacterial strain reconstruction from reads]]></title>
	<description><![CDATA[<div>
<p>mixtureS that can de novo identify bacterial strains from shotgun reads of a clonal or metagenomic sample, without prior knowledge about the strains and their variations. Tested on 243 simulated datasets and 195 experimental datasets, mixtureS reliably identified the strains, their numbers and their abundance. Compared with three tools, mixtureS showed better performance in almost all simulated datasets and the vast majority of experimental datasets.</p>
</div>
<div>
<div>Availability</div>
<p>The source code and tool mixtureS is available at&nbsp;<a href="http://www.cs.ucf.edu/~xiaoman/mixtureS/" target="_blank">http://www.cs.ucf.edu/&tilde;xiaoman/mixtureS/</a>.</p>
</div><p>Address of the bookmark: <a href="http://www.cs.ucf.edu/~xiaoman/mixtureS/" rel="nofollow">http://www.cs.ucf.edu/~xiaoman/mixtureS/</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/26539/scikit-learn</guid>
	<pubDate>Mon, 29 Feb 2016 17:39:24 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/26539/scikit-learn</link>
	<title><![CDATA[scikit-learn]]></title>
	<description><![CDATA[<p>Machine Learning in Python</p>
<p>Simple and efficient tools for data mining and data analysis<br> Accessible to everybody, and reusable in various contexts<br> Built on NumPy, SciPy, and matplotlib<br> Open source, commercially usable - BSD license</p>
<p>More at&nbsp;http://scikit-learn.org/stable/index.html</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="http://scikit-learn.org/stable/auto_examples/index.html" rel="nofollow">http://scikit-learn.org/stable/auto_examples/index.html</a></p>]]></description>
	<dc:creator>Jitendra Prajapati</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38792/nxrepair-error-correction-in-de-novo-assemblies-using-nextera-mate-pair-reads</guid>
	<pubDate>Thu, 24 Jan 2019 10:35:12 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38792/nxrepair-error-correction-in-de-novo-assemblies-using-nextera-mate-pair-reads</link>
	<title><![CDATA[NxRepair: error correction in de novo assemblies using Nextera Mate Pair Reads]]></title>
	<description><![CDATA[<p>NxRepair is a python module that automatically detects large structural errors in de novo assemblies using Nextera mate pair reads. The decector will break a contig at the site of an identified misassembly and will generate a new fasta file containing both the corrected contigs and the correct, unaffected contigs.</p>
<p>https://nxrepair.readthedocs.io/en/latest/tutorial.html</p>
<div>
<div>
<div id="js-repo-pjax-container">
<div>
<div>
<div id="readme">
<div>
<div>
<pre>nxrepair aligned_matepairs.bam assemblyfasta.fasta error_locations.csv new_fasta.fasta</pre>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
<div>&nbsp;</div><p>Address of the bookmark: <a href="https://github.com/rebeccaroisin/nxrepair" rel="nofollow">https://github.com/rebeccaroisin/nxrepair</a></p>]]></description>
	<dc:creator>BioStar</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/26554/ra-at-north-eastern-hill-university</guid>
  <pubDate>Wed, 02 Mar 2016 08:27:32 -0600</pubDate>
  <link></link>
  <title><![CDATA[RA at North-Eastern Hill University]]></title>
  <description><![CDATA[
<p>North-Eastern Hill University</p>

<p>Umshing, Shillong- 793 022</p>

<p>Applications are invited for the following positions (purely temporary posts) in an UGC-ISF funded Indo-Israel Joint Research Project entitled “Interactions of mRNA export factors and nuclear pores characterized and quantified by biochemistry, biophysics and high-resolution imaging” sanctioned to Dr. Timir Tripathi, Molecular and Structural Biophysics Laboratory, Department of Biochemistry, NEHU, Shillong for 3 years (2016-19).</p>

<p>Details of positions:</p>

<p>1. Research Associate (two): bioinformatics/computational biology (One) and wet-lab biophysics (one).</p>

<p>2. Junior Research Fellow, JRF (One).</p>

<p>3. Project Assistant (One).</p>

<p>Fellowship: As per GOI rules.</p>

<p>Essential Qualifications:</p>

<p>1. Research Associate: Ph.D. in the above-mentioned fields, should be evident through quality publications. Those who have submitted Ph.D. thesis can also apply.</p>

<p>2. Junior Research Fellow: M.Sc. or equivalent in any branch of life sciences with a good academic record. Prior research experience is desirable.</p>

<p>3. Project Assistant: Graduation in any subject.</p>

<p>Must be familiar with working on computer and MS-Office.</p>

<p>Interested students can apply for the positions online using the following link http://goo.gl/forms/FEa802lNGc , latest by 16.03.16. The hard copy of the application is not required. The date of interview will be informed after primary scrutiny of the applications.</p>

<p>No TA/DA will be paid if called for interview. For any other enquiry email at msb.biochem@gmail.com .</p>

<p>For details of the research work of the PI’s group please visit www.ttripathi.webs.com</p>
]]></description>
</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>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/26627/scientist-computational-genomics-two-positions</guid>
  <pubDate>Sat, 12 Mar 2016 18:07:56 -0600</pubDate>
  <link></link>
  <title><![CDATA[Scientist - Computational Genomics (Two Positions)]]></title>
  <description><![CDATA[
<p>ICRISAT is a non-profit, non-political organization that conducts agricultural research for development in Asia and sub-Saharan Africa with a wide array of partners throughout the world. Covering 6.5 million square kilometers of land in 55 countries, the semi-arid tropics is home to over 2 billion people, with 650 million of these being the poorest of the poor. ICRISAT and its partners help empower those living in the semi-arid tropics, especially smallholder farmers, to overcome poverty, hunger, malnutrition and a degraded environment through more efficient and profitable agriculture.</p>

<p>ICRISAT is headquartered in Patancheru near Hyderabad, India, with two regional hubs and five country offices in sub-Saharan Africa. ICRISAT, established in 1972, is a member of the CGIAR Consortium. For more details, see www.icrisat.org.</p>

<p>Responsibilities:Design efficient SQL queries for pulling large sequencing projects.<br />Serve as a technical adviser to the project leadership and provide computational perspective on product design and deliverability.<br />Develop and oversee a rapid and incremental software development and release schedule.<br />Design the software architecture, oversee the implementation and evolution of the design on appropriate hardware platforms.<br />Working collaboratively in a team environment to design, code, test, debug, and document programs for an integrated genomic analysis pipeline in a rapid and incremental software development and release schedule.<br />Supervise and review code development and ensure that software products meet project objectives in terms of functionality, scalability, robustness and user experience.<br />Implement and oversee the QA/QC practices to ensure the development team is adhering to quality standards.<br />Work closely with the application specialist to integrate feedbacks from teams in each CGIAR center into software customization and improvement.<br />Assist in training of breeders in the CGIAR centers to use software developed.<br /> Personal Profile:</p>

<p>The applicant should have:</p>

<p>Understanding of genomics data and advanced knowledge of Java, and C/C++ as the programming languages and any of the scripting language like perl and/or Python, SQL<br />High Performance Computing, data architecture, database platforms and QA/QC practices in software engineering.<br />She/he should have solid experience in software development projects, preferably as a senior programmer or in the software project management role, and in projects involving big data.<br />Excellent communication skills are needed to work in this multi-disciplinary, multi-location and multi-cultural team.<br />Ability to mentor colleagues in quality software development practices is desired.<br />Educational Qualification : Ph. D or Masters Degree in Computational Biology / Computational Genomics or Equivalent with Research Experience in Mentioned Areas.</p>

<p>More at http://www.icrisat.org/careers/</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/33789/i-pv-interactive-protein-sequence-visualization</guid>
	<pubDate>Mon, 03 Jul 2017 07:52:51 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/33789/i-pv-interactive-protein-sequence-visualization</link>
	<title><![CDATA[I-PV: Interactive Protein Sequence Visualization]]></title>
	<description><![CDATA[<p><span>I-PV is a interactive data visualization software designed for inspection of protein sequences and mutation information. It is mainly used for Genetics and Bioinformatics. So what exactly makes it standout?</span></p>
<p><span>http://i-pv.org/ipv_rec</span></p><p>Address of the bookmark: <a href="http://i-pv.org/" rel="nofollow">http://i-pv.org/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

</channel>
</rss>