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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/41991?offset=440</link>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/35563/bioinformatics-postdoctoral-position-at-instem</guid>
  <pubDate>Tue, 13 Feb 2018 03:18:54 -0600</pubDate>
  <link></link>
  <title><![CDATA[Bioinformatics postdoctoral position at inStem]]></title>
  <description><![CDATA[
<p>One postdoctoral position is available in the area of Bioinformatics. This position is available through a highly collaborative project involving multiple labs. </p>

<p>The primary focus here would be to analyse and integrate high throughput data from various aspects of translation regulation including non-coding RNAs, mRNAs and modification of ribosomal RNA. We request the interested candidates to approach either</p>

<p>Dasaradhi Palakodeti (dasaradhip@instem.res.in)<br />Or<br />Ravi Muddashetty (ravism@instem.res.in)</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38378/gwaspro-a-high-performance-genome-wide-association-analysis-server</guid>
	<pubDate>Fri, 07 Dec 2018 08:04:57 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38378/gwaspro-a-high-performance-genome-wide-association-analysis-server</link>
	<title><![CDATA[GWASpro: A High-Performance Genome-Wide Association Analysis Server]]></title>
	<description><![CDATA[<p>GWASpro supports building complex design matrices, by which complex experimental designs that may include replications, treatments, locations and times, can be accounted for in the linear mixed model (LMM). GWASpro is optimized to handle GWAS data that may consist of up to 10 million markers and 10,000 samples from replicable lines or hybrids. GWASpro provides an interface that significantly reduces the learning curve for new GWAS investigators.</p>
<p>&nbsp;</p><p>Address of the bookmark: <a href="https://bioinfo.noble.org/GWASPRO/" rel="nofollow">https://bioinfo.noble.org/GWASPRO/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/36100/bioinformatics-postdoctoral-position-at-csir-institute-of-himalayan-bioresource-technology-palampur-hp</guid>
  <pubDate>Thu, 05 Apr 2018 06:57:17 -0500</pubDate>
  <link></link>
  <title><![CDATA[Bioinformatics postdoctoral position at CSIR - Institute of Himalayan Bioresource Technology, Palampur (H.P.)]]></title>
  <description><![CDATA[
<p>Walk-in-Interview is scheduled to be held on the date as mentioned below for selection of Suitable candidates in the following areas under the  DBT sponsored projecT on purely temporary basis for the duration of the project(s) or till completion of projects whichever is earlier:</p>

<p>Project Title:<br />"Exploration of RBP-RNA interactions to reveal the post-transcriptional regulatory impact, and development of related tools and resource server".</p>

<p>Position: Research Associate (1 position)<br />Age : 35 years as on 02.05.2018<br />Salary : Rs.36,000/- P.M. + H.R.A.</p>

<p>as per the funds provisions in the respective projects.</p>

<p>Eligibility Criteria : <br />Essential Qualifications: Ph.D. in any area of Bioinformatics/ Computational Biology/ Life Sciences.<br />Desirable: Candidate having experience in Bioinformatics will be preferred.</p>

<p>Or</p>

<p>Essential Qualifications: M.Tech. in Bioinformatics/ Computation Biology/ Computer Science or any related area with 3 years of research experience.</p>

<p>Selection Procedure : Walk In Interview</p>

<p>Date :	02 May, 2018<br />Time :	9:30 A.M.<br />Venue : CSIR-IHBT Palampur (H.P.)</p>

<p>For more info refer to following doc:</p>

<p>http://ihbt.res.in/components/com_chronoforms5/chronoforms/uploads/Recruitment/20180404125233_AdvtNo11_2018.pdf</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/42419/biojupies-automatically-generates-rna-seq-data-analysis-notebooks</guid>
	<pubDate>Sun, 20 Dec 2020 11:43:45 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/42419/biojupies-automatically-generates-rna-seq-data-analysis-notebooks</link>
	<title><![CDATA[BioJupies: Automatically Generates RNA-seq Data Analysis Notebooks]]></title>
	<description><![CDATA[<p>With BioJupies you can produce in seconds a customized, reusable, and interactive report from your own raw or processed RNA-seq data through a simple user interface</p>
<p>BioJupies now supports user accounts! Sign in from the top right corner of the page for access to unlimited private notebooks, RNA-seq datasets and alignment jobs.</p><p>Address of the bookmark: <a href="https://amp.pharm.mssm.edu/biojupies/" rel="nofollow">https://amp.pharm.mssm.edu/biojupies/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/36674/bioinformatics-project-assistant-level-ii-position-at-csir-institute-of-himalayan-bioresource-technology-palampur-hp</guid>
  <pubDate>Thu, 17 May 2018 01:53:17 -0500</pubDate>
  <link></link>
  <title><![CDATA[Bioinformatics Project Assistant Level II position at CSIR - Institute of Himalayan Bioresource Technology, Palampur (H.P.)]]></title>
  <description><![CDATA[
<p>Walk-in-Interview is scheduled to be held on the date as mentioned below for selection of Suitable candidates in the following areas under the DBT sponsored project on purely temporary basis for the duration of the project(s) or till completion of projects whichever is earlier:</p>

<p>Project Title:<br />"Exploration of RBP-RNA interactions to reveal the post-transcriptional regulatory impact, and development of related tools and resource server".</p>

<p>Position: Project Assistant Level II (1 position)<br />Age : 28 years as on 14.06.2018<br />Salary : Rs.25,000/- P.M.</p>

<p>as per the funds provisions in the respective projects.</p>

<p>Eligibility Criteria : <br />Essential Qualifications: M.Sc. in Bioinformatics / Computational Biology or any area of Bioinformatics with 55% marks.</p>

<p>OR</p>

<p>Essential Qualifications: M.Sc. in any area of Life Sciences with 55% marks with Diploma in any area of Bloinformatics.</p>

<p>OR</p>

<p>Essential Qualifications: B.Tech. / M.Tech. in Bioinformatics / Computer Science with 55% marks.</p>

<p>Selection Procedure : Walk In Interview</p>

<p>Date :	14 June, 2018<br />Time :	9:30 A.M.<br />Venue : CSIR-IHBT Palampur (H.P.)</p>

<p>For more info refer to following doc:</p>

<p>http://ihbt.res.in/components/com_chronoforms5/chronoforms/uploads/Recruitment/20180516114701_Advt15_2018.pdf</p>
]]></description>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44470/phyloherb-phylogenomic-analysis-pipeline-for-herbarium-specimens</guid>
	<pubDate>Wed, 21 Feb 2024 06:15:13 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44470/phyloherb-phylogenomic-analysis-pipeline-for-herbarium-specimens</link>
	<title><![CDATA[PhyloHerb: Phylogenomic Analysis Pipeline for Herbarium Specimens]]></title>
	<description><![CDATA[<p><span>What is PhyloHerb</span><span>: PhyloHerb is a wrapper program to process&nbsp;</span><span>genome skimming</span><span>&nbsp;data collected from plant materials. The outcomes include the plastid genome (plastome) assemblies, mitochondrial genome assemblies, nuclear ribosomal DNAs (NTS+ETS+18S+ITS1+5.8S+ITS2+28S), alignments of gene and intergenic regions, and a species tree. It is designed to be a high throughput program dealing with lower quality data. Examples include&nbsp;</span><span>low-coverage (5x cpDNA) plastome phylogeny, recycling plastid genes from target enrichment data, retrieving low-copy nuclear genes from medium coverage (5x nucDNA) genome skimming</span><span>.</span></p><p>Address of the bookmark: <a href="https://github.com/lmcai/PhyloHerb/" rel="nofollow">https://github.com/lmcai/PhyloHerb/</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/file/view/37610/applied-statistics-for-bioinformatics-using-r</guid>
	<pubDate>Thu, 30 Aug 2018 03:45:39 -0500</pubDate>
	<link>https://bioinformaticsonline.com/file/view/37610/applied-statistics-for-bioinformatics-using-r</link>
	<title><![CDATA[Applied Statistics for Bioinformatics using R]]></title>
	<description><![CDATA[<p>The purpose of this book is to give an introduction into statistics in order to solve some problems of bioinformatics. Statistics provides procedures to explore and visualize data as well as to test biological hypotheses. The book intends to be introductory in explaining and programming elementary statistical concepts, thereby bridging the gap between high school levels and the specialized statistical literature</p>]]></description>
	<dc:creator>Neel</dc:creator>
	<enclosure url="https://bioinformaticsonline.com/file/download/37610" length="1368378" type="application/pdf" />
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/44724/step-by-step-guide-to-detect-pirnas-using-bioinformatics</guid>
	<pubDate>Fri, 13 Dec 2024 11:41:46 -0600</pubDate>
	<link>https://bioinformaticsonline.com/news/view/44724/step-by-step-guide-to-detect-pirnas-using-bioinformatics</link>
	<title><![CDATA[Step-by-Step Guide to Detect piRNAs Using Bioinformatics]]></title>
	<description><![CDATA[<p>Piwi-interacting RNAs (piRNAs) are a class of small non-coding RNAs that play crucial roles in silencing transposable elements and regulating gene expression, particularly in germline cells. Detecting piRNAs involves identifying their unique characteristics, such as size, sequence motifs, and association with Piwi proteins, from high-throughput RNA sequencing data.</p><p>This blog provides a comprehensive step-by-step guide to detect piRNAs using bioinformatics tools and workflows.</p><h4><strong>Step 1: Prepare Your Data</strong></h4><ol>
<li>
<p><strong>Obtain RNA Sequencing Data</strong><br />Acquire raw small RNA-seq data in FASTQ format. Datasets can be sourced from repositories like <strong>NCBI SRA</strong>, <strong>EMBL-EBI</strong>, or specific small RNA sequencing projects.</p>
</li>
<li>
<p><strong>Quality Control (QC)</strong><br />Use <strong>FastQC</strong> to assess the quality of raw reads:</p>
<div>
<div dir="ltr"><code>fastqc reads.fastq </code></div>
</div>
<p>Evaluate the per-base quality, adapter content, and overrepresented sequences.</p>
</li>
<li>
<p><strong>Trimming and Adapter Removal</strong><br />Use tools like <strong>Cutadapt</strong> or <strong>Trim Galore!</strong> to remove adapters and low-quality bases:</p>
<div>
<div dir="ltr"><code>cutadapt -a TGGAATTCTCGGGTGCCAAGG -o trimmed_reads.fastq reads.fastq </code></div>
</div>
<p>Ensure the remaining reads are of high quality for downstream analysis.</p>
</li>
</ol><h4><strong>Step 2: Map Reads to the Genome</strong></h4><p>Mapping reads to the reference genome is crucial for identifying piRNA loci.</p><ol>
<li>
<p><strong>Reference Genome Preparation</strong><br />Download the genome assembly of your organism from databases like <strong>Ensembl</strong>, <strong>UCSC Genome Browser</strong>, or <strong>NCBI</strong>.</p>
</li>
<li>
<p><strong>Align Reads</strong><br />Use <strong>Bowtie</strong> or <strong>STAR</strong> for small RNA alignment:</p>
<div>
<div dir="ltr"><code>bowtie -v 1 -k 1 --best genome_index trimmed_reads.fastq -S aligned_reads.sam </code></div>
</div>
<ul>
<li><code>-v 1</code>: Allows one mismatch.</li>
<li><code>-k 1</code>: Reports the best alignment.</li>
</ul>
</li>
<li>
<p><strong>Convert SAM to BAM</strong><br />Convert and sort alignments using <strong>SAMtools</strong>:</p>
<div>
<div dir="ltr"><code>samtools view -Sb aligned_reads.sam | samtools sort -o sorted_reads.bam </code></div>
</div>
</li>
</ol><h4><strong>Step 3: Identify Small RNAs</strong></h4><p>piRNAs are characterized by their size (24&ndash;32 nt) and strand bias.</p><ol>
<li>
<p><strong>Extract Reads by Size</strong><br />Use tools like <strong>BEDtools</strong> or custom scripts to filter reads between 24 and 32 nt:</p>
<div>
<div dir="ltr"><code>bedtools bamtofastq -i sorted_reads.bam -fq all_reads.fastq seqkit seq -m 24 -M 32 all_reads.fastq &gt; piRNA_size_reads.fastq </code></div>
</div>
</li>
<li>
<p><strong>Check for Sequence Bias</strong><br />piRNAs often have a strong bias for a uridine at the 5&rsquo; end (1U bias). Use tools like <strong>WebLogo</strong> to visualize sequence motifs.</p>
</li>
</ol><h4><strong>Step 4: Detect Ping-Pong Signature</strong></h4><p>The ping-pong amplification loop is a hallmark of piRNA biogenesis, characterized by a 10 nt overlap between piRNAs on opposite strands.</p><ol>
<li>
<p><strong>Generate Overlap Statistics</strong><br />Use the <strong>piPipes</strong> tool or custom scripts to calculate overlap:</p>
<div>
<div dir="ltr"><code>python ping_pong_overlap.py sorted_reads.bam </code></div>
</div>
</li>
<li>
<p><strong>Visualize Overlap Distribution</strong><br />Plot the distribution of overlaps to confirm the presence of the 10 nt ping-pong signature.</p>
</li>
</ol><h4><strong>Step 5: Annotate piRNA Clusters</strong></h4><p>piRNAs are often generated from genomic clusters.</p><ol>
<li>
<p><strong>Cluster Identification</strong><br />Use tools like <strong>proTRAC</strong> or <strong>PIRANHA</strong> to identify piRNA-producing clusters:</p>
<div>
<div dir="ltr"><code>proTRAC.pl -s sorted_reads.bam -g genome.fa -o clusters </code></div>
</div>
</li>
<li>
<p><strong>Annotate Genomic Regions</strong><br />Annotate the identified clusters using gene annotation files (GTF/GFF). Tools like <strong>BEDtools intersect</strong> can help associate piRNA clusters with genes or transposable elements:</p>
<div>
<div dir="ltr"><code>bedtools intersect -a clusters.bed -b genome_annotation.gtf &gt; annotated_clusters.bed </code></div>
</div>
</li>
</ol><h4><strong>Step 6: Functional Analysis</strong></h4><p>Functional analysis of piRNAs can uncover their targets and regulatory roles.</p><ol>
<li>
<p><strong>Predict piRNA Targets</strong><br />Use tools like <strong>IntaRNA</strong> or <strong>RNAhybrid</strong> to predict interactions between piRNAs and potential target mRNAs:</p>
<div>
<div dir="ltr"><code>RNAhybrid -t target_transcripts.fa -q piRNAs.fa &gt; piRNA_targets.txt </code></div>
</div>
</li>
<li>
<p><strong>Enrichment Analysis</strong><br />Perform GO or KEGG enrichment analysis of target genes using tools like <strong>g:Profiler</strong> or <strong>DAVID</strong>.</p>
</li>
</ol><h4><strong>Step 7: Validation and Visualization</strong></h4><ol>
<li>
<p><strong>Validate piRNA Candidates</strong><br />Cross-check the identified piRNAs against known piRNA databases, such as <strong>piRBase</strong> or <strong>piRNAdb</strong>.</p>
</li>
<li>
<p><strong>Visualize Results</strong></p>
<ul>
<li>Use <strong>IGV</strong> (Integrative Genomics Viewer) to visualize piRNA alignment and clusters on the genome.</li>
<li>Generate heatmaps or circos plots to present piRNA distributions.</li>
</ul>
</li>
</ol><h4><strong>Step 8: Share and Publish Findings</strong></h4><ol>
<li>
<p><strong>Archive Data</strong><br />Submit sequencing data to public repositories like <strong>SRA</strong> or <strong>GEO</strong> with metadata specifying piRNA-related experiments.</p>
</li>
<li>
<p><strong>Publish Results</strong><br />Share findings in journals or conferences, emphasizing novel piRNA candidates, target genes, or regulatory mechanisms.</p>
</li>
</ol><h4><strong>Conclusion</strong></h4><p>Detecting piRNAs involves a combination of computational and analytical methods to identify these unique small RNAs and their roles in gene regulation and transposable element suppression. By following this step-by-step guide, you can confidently navigate the complexities of piRNA detection and contribute to the growing understanding of their biological significance.</p>]]></description>
	<dc:creator>Abhi</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38487/betsy-a-new-backward-chaining-expert-system-for-automated-development-of-pipelines-in-bioinformatics</guid>
	<pubDate>Mon, 17 Dec 2018 18:46:51 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38487/betsy-a-new-backward-chaining-expert-system-for-automated-development-of-pipelines-in-bioinformatics</link>
	<title><![CDATA[BETSY: A new backward-chaining expert system for automated development of pipelines in Bioinformatics]]></title>
	<description><![CDATA[<p>The BETSY provides a command-line interface and available at&nbsp;<a href="https://github.com/jefftc/changlab">https://github.com/jefftc/changlab</a>. A user first searches in the knowledge base for desired output and then BETSY develops an initial workflow to produce that data which is later examined by the user. The user can optimize the parameters, the algorithm to preprocess the data, and normalize it depending on the task.</p>
<p>Currently, BETSY consists of modules required for the microarray and next-generation sequencing data [4] such as expression analysis, classification, peak calling, and visualization.</p><p>Address of the bookmark: <a href="https://github.com/jefftc/changlab" rel="nofollow">https://github.com/jefftc/changlab</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/39603/tenure-track-position-in-bioinformatics-at-institute-of-neurobiology-unam-queretaro-mexico</guid>
  <pubDate>Mon, 10 Jun 2019 00:48:54 -0500</pubDate>
  <link></link>
  <title><![CDATA[Tenure Track position in Bioinformatics at Institute of Neurobiology, UNAM, Querétaro, México]]></title>
  <description><![CDATA[
<p>The Institute of Neurobiology UNAM (www.inb.unam.mx) offers a tenure-track position at the level of Assistant Professor (Investigador Asociado C) to develop an original research program in Bioinformatics with applications to neuroscience and to establish multidisciplinary collaboration with other members of the Institute. Applicants are expected to have a doctorate degree, postdoctoral experience related to bioinformatics or genome biology, and a strong track record of peer-reviewed publications. No previous experience in neuroscience is required.</p>

<p>Interested applicants must submit CV and addresses of three references to ataulfo@unam.mx.</p>

<p>Tenure Track position in Genomic Sciences  </p>

<p>Laboratorio Internacional de Investigación sobre el Genoma Humano, UNAM Juriquilla, Querétaro, México </p>

<p>The International Laboratory for Human Genome Research, LIIGH-UNAM (www.liigh.unam.mx) offers a tenure-track position at the level of Assistant Professor (Investigador Asociado C) to perform research, teaching and formation of human resources in the area of: “Genomics of Mendelian Diseases” </p>

<p>Applicants are expected to have a doctorate degree, postdoctoral experience related to the above mentioned area and a strong track record of peer-reviewed publications. Interested applicants must submit CV, email addresses of three references, and a three-page project to Dr. Rafael Palacios, Coordinator of LIIGH-UNAM (palacios@liigh.unam.mx) before June 21, 2019 ………………………………………………………………</p>

<p>Tenure Track position in Genomic Sciences </p>

<p>Laboratorio Internacional de Investigación sobre el Genoma Humano, UNAM Juriquilla, Querétaro, México </p>

<p>The International Laboratory for Human Genome Research, LIIGH-UNAM (www.liigh.unam.mx) offers a tenure-track position at the level of Assistant Professor (Investigador Asociado C) to perform research, teaching and formation of human resources in the area of: “Statistic Population Genomics and its Impact in Complex Diseases” </p>

<p>Applicants are expected to have a doctorate degree, postdoctoral experience related to the above mentioned area and a strong track record of peer-reviewed publications. Interested applicants must submit CV, email addresses of three references, and a three-page statement of research interests to Dr. Rafael Palacios, Coordinator of LIIGH-UNAM (palacios@liigh.unam.mx) before June 21, 2019</p>
]]></description>
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