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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/30168?offset=250</link>
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	<description><![CDATA[]]></description>
	
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/37317/interview-puzzles-for-bioinformatician</guid>
	<pubDate>Tue, 17 Jul 2018 05:26:18 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/37317/interview-puzzles-for-bioinformatician</link>
	<title><![CDATA[Interview Puzzles for Bioinformatician !]]></title>
	<description><![CDATA[<p>These are some of the most famous Interview Puzzles being asked in top tech companies.<br /><br />Here is a list of Top 25 puzzles which have been asked in top Tech Interview.</p><ol>
<li><span><a href="http://puzzlefry.com/puzzles/2-eggs-and-100-floor-google-classic-question/" target="_blank">2 Eggs and 100 Floor Classic Puzzle</a></span></li>
<li><span><a href="http://puzzlefry.com/puzzles/gold-coins-puzzle/" target="_blank">Five pirates and gold coin Puzzle</a></span></li>
<li><span><a href="http://puzzlefry.com/puzzles/gold-puzzle/" target="_blank">Six pirates and Gold Coin puzzle</a></span></li>
<li><span><a href="http://puzzlefry.com/puzzles/probability-of-having-boy/" target="_blank">Probability of having boy</a></span></li>
<li><span><a href="http://puzzlefry.com/puzzles/random-airplane-seats/" target="_blank">Random Airplane Seats</a></span></li>
<li><span><a href="http://puzzlefry.com/puzzles/inverted-cards-puzzle/" target="_blank">Inverted playing card puzzle</a></span></li>
<li><span><a href="http://puzzlefry.com/puzzles/flipping-coins/" target="_blank">Flipping Coins Puzzle</a></span></li>
<li><span><a href="http://puzzlefry.com/puzzles/three-hat-colors/" target="_blank">Three hat colors Microsoft Puzzle</a></span></li>
<li><span><a href="http://puzzlefry.com/puzzles/25-horses-5-tracks-puzzle/" target="_blank">25 horses 5 tracks Puzzle</a></span></li>
<li><span><a href="http://puzzlefry.com/puzzles/gold-bar-puzzle-2/" target="_blank">Gold Bar Puzzle</a></span></li>
<li><span><a href="http://puzzlefry.com/puzzles/crossing-the-bridge-puzzle/" target="_blank">Crossing the Bridge Puzzle</a></span></li>
<li><span><a href="http://puzzlefry.com/puzzles/interview-questions/" target="_blank">Will you accept the bet?</a></span></li>
<li><span><a href="http://puzzlefry.com/puzzles/the-line-of-persons-with-hats/" target="_blank">The Puzzle of 100 Hats</a></span></li>
<li><span><a href="http://puzzlefry.com/puzzles/how-many-days/" target="_blank">Man fell in Well Puzzle</a></span></li>
<li><span><a href="http://puzzlefry.com/puzzles/minimum-number-of-weigths/" target="_blank">Minimum Number of Weigths</a></span></li>
<li><span><a href="http://puzzlefry.com/puzzles/one-bulb-with-3-switches/" target="_blank">One Bulb with 3 Switches</a></span></li>
<li><span><a href="http://puzzlefry.com/puzzles/find-the-minimum-number-of-aircraft/" target="_blank">Find the minimum number of aircraft</a></span></li>
<li><span><a href="http://puzzlefry.com/puzzles/burning-ropes-to-measure-time/" target="_blank">Burning ropes to measure time</a></span></li>
<li><span><a href="http://puzzlefry.com/puzzles/connect-3-houses-with-3-wells/" target="_blank">Connect 3 houses with 3 wells</a></span></li>
<li><span><a href="http://puzzlefry.com/puzzles/measure-9-minutes-from-2-hourglasses-puzzle/" target="_blank">Measure 9 minutes from 2 hourglasses puzzle</a></span></li>
<li><span><a href="http://puzzlefry.com/puzzles/ant-and-triangle-problem/" target="_blank">Ant and Triangle Problem</a></span></li>
<li><span><a href="http://puzzlefry.com/puzzles/the-man-in-the-elevator/" target="_blank">The Man in the Elevator</a></span></li>
<li><span><a href="http://puzzlefry.com/puzzles/find-the-survivor/" target="_blank">Find the survivor</a></span></li>
<li><span><a href="http://puzzlefry.com/puzzles/free-the-prisoners-puzzle/" target="_blank">Free the prisoners puzzle</a></span></li>
<li><span><a href="http://puzzlefry.com/puzzles/great-strategy-can-only-save-life/" target="_blank">GREAT STRATEGY CAN ONLY SAVE LIFE</a></span></li>
</ol><p><br /><span>Specially for Microsoft Interview Puzzles, you may refer,</span><br /><span><a href="http://puzzlefry.com/2015/08/top-15-famous-microsoft-interview-puzzles/" target="_blank">Top 15 Microsoft Interview Puzzles</a></span><br /><span><a href="http://puzzlefry.com/qa-tag/microsoft-interview-puzzles/" target="_blank">Microsoft Interview Puzzles</a></span><br /><br /><span>Other MOST COMMON Interview Puzzles-</span><br /><span><a href="http://puzzlefry.com/2015/08/top-25-tech-interview-puzzles-with-answers/" target="_blank">Top 25 Tech Interview&nbsp;</a></span><span><a href="http://puzzlefry.com/2015/08/top-25-tech-interview-puzzles-with-answers/" target="_blank">Logical Puzzles</a></span><br /><br /><span>Each of the puzzles got repeated a number of times in interviews&nbsp;</span><span>even for top tech companies&nbsp;</span></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
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	<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>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/38257/bioinformatics-programme-officer-international-centre-for-genetic-icgeb-engineering-and-biotechnology</guid>
  <pubDate>Fri, 23 Nov 2018 03:50:16 -0600</pubDate>
  <link></link>
  <title><![CDATA[Bioinformatics Programme Officer @ International Centre for Genetic ICGEB Engineering and Biotechnology]]></title>
  <description><![CDATA[
<p>The following vacancies are available in the DBT Apex Biotechnology Information project at ICGEB, New Delhi, India. These positions are available for a period of approx. two years, however, initial appointment offer will be for 6 months, which will be extended based on performance of work. Salaries will be offered as per DBT, educational qualification and experience. Depending on requirements, selected candidates may be required to work on location from the Department of Biotechnology, New Delhi. Shortlisted candidates will be invited for an interview at ICGEB. Only the selected candidates will be informed individually. No TA/DA or accommodation will be offered to the candidates attending the interview. </p>

<p>4 Programme Officer 1 <br />5 Technical Research Assistant 1 </p>

<p>Minimum Educational Qualification, desirable experience and expected duties: </p>

<p>4: The applicants should be Postgraduates with experience in Data collection and Statistics, especially in Biotechnology-related data. </p>

<p>Expected duties: Collection of Biotechnology related information from India, to facilitate the Apex BTIC experts committee review of programmes at centres and R&amp;D programs funded by DBT. </p>

<p>5: The applicants should be Postgraduates in Science with experience in Bioinformatics-related projects. <br />Expected duties: The candidates will assist the senior staff of the centre in daily activities and help in the preparation of the Annual Training Calendar, seminar and training podcasts/videos, repository of training material and Apex BTIC Newsletter. </p>

<p>Interested candidates should submit their full, updated Curriculum Vitae with a detailed description of relevant experience, along with two references by December 14th, 2018, addressed to, The Chairperson, DBT- Apex BTIC, ICGEB, Aruna Asaf Ali Marg, New Delhi 110067, Email: abtic@icgeb.res.in, kindly write “Application for DBT Apex BTIC vacancy” in the subject of the email or envelope, if sending by post.</p>

<p>Advertisement: http://www.icgeb.org/tl_files/Vacancies/dbt-abtic-vac-annmntrevsk.pdf</p>
]]></description>
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	<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>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/38642/thank-you-email-after-bioinformatics-interview</guid>
	<pubDate>Tue, 08 Jan 2019 15:37:33 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/38642/thank-you-email-after-bioinformatics-interview</link>
	<title><![CDATA[Thank You Email After Bioinformatics Interview !]]></title>
	<description><![CDATA[<p>A good interview thank you email or note should contain three essential pieces:</p><p>a) Show appreciation for their time and thank them</p><p>b) Mention something specific you talked about in the interview, so they know it&rsquo;s not a cut &amp; paste email</p><p>c) Express interest in the position and tell them you&rsquo;re excited to learn more</p><p>d)&nbsp;Invite them to contact you if they have any questions/concerns, or need clarification on anything discussed</p><p>First sample:</p><blockquote><p>Dear Dr XYZ<br />I enjoyed speaking with you today about the XXX position&nbsp;at the X Lab, Uni. The job seems to be an excellent match for my&nbsp;skills and interests.<br /><br />The lab loaded with new updated technology and international experts,&nbsp;that you informed while interviewing confirmed my desire to work with&nbsp;X lab.<br /><br />In addition to my enthusiasm, I will bring to the position strong&nbsp;writing skills, assertiveness, and the ability to encourage others to&nbsp;work cooperatively with the group<br /><br />I appreciate the time you took to interview me. I am very interested&nbsp;in working with you and look forward to hearing from you regarding&nbsp;this position.<br /><br />Sincerely,<br />XXX</p></blockquote><p>Second sample:</p><p>&nbsp;</p><blockquote><p>Dear Dr XXX,</p><p>I wanted to take a second to thank you for your time . I enjoyed our conversation about and enjoyed learning about the position overall.</p><p>It sounds like an exciting opportunity, and an opportunity I could succeed and excel in! I&rsquo;m looking forward to hearing any updates you can share, and don&rsquo;t hesitate to contact me if you have any questions or concerns in the meantime.</p><p>Thanks again for the great conversation .</p><p>Best Regards,<br />XXX</p></blockquote>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/34221/alignment-free-sequence-comparison-tools-available-for-next-generation-sequencing-data-analysis</guid>
	<pubDate>Tue, 07 Nov 2017 05:33:33 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/34221/alignment-free-sequence-comparison-tools-available-for-next-generation-sequencing-data-analysis</link>
	<title><![CDATA[Alignment-free sequence comparison tools available for next-generation sequencing data analysis]]></title>
	<description><![CDATA[<div><p><span>kallisto</span></p></div><div><p>Transcript abundance quantification from RNA-seq data (uses pseudoalignment for rapid determination of read compatibility with targets)</p><p>Software (C++)</p><p><a href="https://pachterlab.github.io/kallisto/">https://pachterlab.github.io/kallisto/</a></p><p>Sailfish</p><p>Estimation of isoform abundances from reference sequences and RNA-seq data (<em>k</em>-mer based)</p><p>Software (C++)</p><p><a href="http://www.cs.cmu.edu/~ckingsf/software/sailfish/">http://www.cs.cmu.edu/~ckingsf/software/sailfish/</a></p><p>Salmon</p><p>Quantification of the expression of transcripts using RNA-seq data (uses&nbsp;<em>k</em>-mers)</p><p><a href="https://combine-lab.github.io/salmon/">https://combine-lab.github.io/salmon/</a></p><p>RNA-Skim</p><p>RNA-seq quantification at transcript-level (partitions the transcriptome into disjoint transcript clusters; uses&nbsp;<em>sig</em>-mers, a special type of&nbsp;<em>k</em>-mers)</p><p>Software (C++)</p><p><a href="http://www.csbio.unc.edu/rs/">http://www.csbio.unc.edu/rs/</a></p><p>Variant calling</p><p>ChimeRScope</p><p>Fusion transcript prediction using gene&nbsp;<em>k</em>-mers profiles of the RNA-seq paired-end reads</p><p>Software (Java)</p><p><a href="https://github.com/ChimeRScope/ChimeRScope/wiki">https://github.com/ChimeRScope/ChimeRScope/wiki</a></p><p>FastGT</p><p>Genotyping of known SNV/SNP variants directly from raw NGS sequence reads by counting unique&nbsp;<em>k</em>-mers</p><p>Software (C)</p><p><a href="https://github.com/bioinfo-ut/GenomeTester4/">https://github.com/bioinfo-ut/GenomeTester4/</a></p><p>Phy-Mer</p><p>Reference-independent mitochondrial haplogroup classifier from NGS data (<em>k</em>-mer based)</p><p>Software (Python)</p><p><a href="https://github.com/danielnavarrogomez/phy-mer">https://github.com/danielnavarrogomez/phy-mer</a></p><p>LAVA</p><p>Genotyping of known SNPs (dbSNP and Affymetrix's Genome-Wide Human SNP Array) from raw NGS reads (<em>k</em>-mer based)</p><p>Software (C)</p><p><a href="http://lava.csail.mit.edu/">http://lava.csail.mit.edu/</a></p><p>MICADo</p><p>Detection of mutations in targeted third-generation NGS data (can distinguish patients&rsquo; specific mutations; algorithm uses&nbsp;<em>k</em>-mers and is based on colored de Bruijn graphs)</p><p>Software (Python)</p><p><a href="http://github.com/cbib/MICADo">http://github.com/cbib/MICADo</a></p><p>General mapper</p><p>Minimap</p><p>Lightweight and fast read mapper and read overlap detector (uses the concept of &ldquo;minimazers&rdquo;, a special type of&nbsp;<em>k</em>-mers)</p><p>Software (C)</p><p><a href="https://github.com/lh3/minimap">https://github.com/lh3/minimap</a></p><p>Assembly</p><p>De novo genome assembly</p><p>MHAP</p><p>Produces highly continuous assembly (fully resolved chromosome arms) from third-generation long and noisy reads (10 kbp) using a dimensionality reduction technique MinHash</p><p>Software (Java)</p><p><a href="https://github.com/marbl/MHAP">https://github.com/marbl/MHAP</a></p><p>Miniasm</p><p>Assembler of long noisy reads (SMRT, ONT) using the Overlap-Layout Consensus (OLC) approach without the necessity of an error correction stage (uses minimap)</p><p>Software (C)</p><p><a href="https://github.com/lh3/miniasm">https://github.com/lh3/miniasm</a></p><p>LINKS</p><p>Scaffolding genome assembly with error-containing long sequence (e.g., ONT or PacBio reads, draft genomes)</p><p>Software (Perl)</p><p><a href="https://github.com/warrenlr/LINKS/">https://github.com/warrenlr/LINKS/</a></p><p>Read clustering</p><p>afcluster</p><p>Clustering of reads from different genes and different species based on&nbsp;<em>k</em>-mer counts</p><p>Software (C++)</p><p><a href="https://github.com/luscinius/afcluster">https://github.com/luscinius/afcluster</a></p><p>QCluster</p><p>Clustering of reads with alignment-free measures (<em>k</em>-mer based) and quality values</p><p>Software (C++)</p><p><a href="http://www.dei.unipd.it/~ciompin/main/qcluster.html">http://www.dei.unipd.it/~ciompin/main/qcluster.html</a></p><p>Reads error correction</p><p>Lighter</p><p>Correction of sequencing errors in raw, whole genome sequencing reads (<em>k</em>-mer based)</p><p>Software (C++)</p><p><a href="https://github.com/mourisl/Lighter">https://github.com/mourisl/Lighter</a></p><p>QuorUM</p><p>Error corrector for Illumina reads using k-mers</p><p>Software (C++)</p><p><a href="https://github.com/gmarcais/Quorum">https://github.com/gmarcais/Quorum</a></p><p>Trowel</p><p>Software (C++)</p><p><a href="https://sourceforge.net/projects/trowel-ec/">https://sourceforge.net/projects/trowel-ec/</a></p><p>Metagenomics</p><p>Assembly-free phylogenomics</p><p>AAF</p><p>Phylogeny reconstruction directly from unassembled raw sequence data from whole genome sequencing projects; provides bootstrap support to assess uncertainty in the tree topology (<em>k</em>-mer based)</p><p>Software (Python)</p><p><a href="https://github.com/fanhuan/AAF">https://github.com/fanhuan/AAF</a></p><p>kSNP v3</p><p>Reference-free SNP identification and estimation of phylogenetic trees using SNPs (based on&nbsp;<em>k</em>-mer analysis)</p><p>Software (C)</p><p><a href="https://sourceforge.net/projects/ksnp/files/">https://sourceforge.net/projects/ksnp/files/</a></p><p>NGS-MC</p><p>Phylogeny of species based on NGS reads using alignment-free sequence dissimilarity measures d2* and d2&nbsp;S&nbsp;under different Markov chain models (using&nbsp;<em>k</em>-words)</p><p>R package</p><p><a href="http://www-rcf.usc.edu/~fsun/Programs/NGS-MC/NGS-MC.html">http://www-rcf.usc.edu/~fsun/Programs/NGS-MC/NGS-MC.html</a></p><p>Species identification/taxonomic profiling</p><p>CLARK</p><p>Taxonomic classification of metagenomic reads to known bacterial genomes using&nbsp;<em>k</em>-mer search and LCA assignment</p><p>Software (C++)</p><p><a href="http://clark.cs.ucr.edu/">http://clark.cs.ucr.edu/</a></p><p>FOCUS</p><p>Reports organisms present in metagenomic samples and profiles their abundances (uses composition-based approach and non-negative least squares for prediction)</p><p>Web service Software (Python)</p><p><a href="http://edwards.sdsu.edu/FOCUS/">http://edwards.sdsu.edu/FOCUS/</a></p><p>GSM</p><p>Estimation of abundances of microbial genomes in metagenomic samples (<em>k</em>-mer based)</p><p>Software (Go)</p><p><a href="https://github.com/pdtrang/GSM">https://github.com/pdtrang/GSM</a></p><p>Mash</p><p>Species identification using assembled or unassembled Illumina, PacBio, and ONT data (based on MinHash dimensionality-reduction technique)</p><p>Software (C++)</p><p><a href="https://github.com/marbl/mash">https://github.com/marbl/mash</a></p><p>Kraken</p><p>Taxonomic assignment in metagenome analysis by exact&nbsp;<em>k</em>-mer search; LCA assignment of short reads based on a comprehensive sequence database</p><p>Software (C++)</p><p><a href="https://ccb.jhu.edu/software/kraken/">https://ccb.jhu.edu/software/kraken/</a></p><p>LMAT</p><p>Assignment of taxonomic labels to reads by&nbsp;<em>k</em>-mers searches in precomputed database</p><p>Software (C++/Python)</p><p><a href="https://sourceforge.net/projects/lmat/">https://sourceforge.net/projects/lmat/</a></p><p>stringMLST</p><p><em>k</em>-mer-based tool for MLST directly from the genome sequencing reads</p><p>Software (Python)</p><p><a href="http://jordan.biology.gatech.edu/page/software/stringMLST">http://jordan.biology.gatech.edu/page/software/stringMLST</a></p><p>Taxonomer</p><p><em>k</em>-mer-based ultrafast metagenomics tool for assigning taxonomy to sequencing reads from clinical and environmental samples</p><p>Web service</p><p><a href="http://taxonomer.iobio.io/">http://taxonomer.iobio.io/</a></p><p>Other</p><p>d2-tools</p><p>Word-based (<em>k</em>-tuple) comparison (pairwise dissimilarity matrix using d2S measure) of metatranscriptomic samples from NGS reads</p><p>Software (Python/R)</p><p><a href="https://code.google.com/p/d2-tools/">https://code.google.com/p/d2-tools/</a></p><p>VirHostMatcher</p><p>Prediction of hosts from metagenomic viral sequences based on ONF using various distance measures (e.g., d2)</p><p>Software (C++)</p><p><a href="https://github.com/jessieren/VirHostMatcher">https://github.com/jessieren/VirHostMatcher</a></p><p>MetaFast</p><p>Statistics calculation of metagenome sequences and the distances between them based on assembly using de Bruijn graphs and Bray&ndash;Curtis dissimilarity measure</p><p>Software (Java)</p><p><a href="https://github.com/ctlab/metafast">https://github.com/ctlab/metafast</a></p></div>]]></description>
	<dc:creator>Abhimanyu Singh</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/40228/bioinformatics-services-cro-services</guid>
	<pubDate>Wed, 06 Nov 2019 00:33:11 -0600</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/40228/bioinformatics-services-cro-services</link>
	<title><![CDATA[Bioinformatics Services / CRO Services]]></title>
	<description><![CDATA[<p>RASA is set to provide premium technical and scientific services in a form of solutions, product development and training. .We are also very proficient in providing the high quality Research &amp; Development services in life science informatics field like Next Generation Sequencing (NGS) Data Analysis,Computational Drug Discovery, Bioinformatics, Chemo-informatics and BIO-IT.</p><p>RASA offers faster, better and cost effective cutting edge technology solutions to chemical and life science research and industry. We provide our customers with A seamless model of wide expertise and comprehensive platforms. Our Value is to take our customers</p>]]></description>
	<dc:creator>RASA Life Sciences</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34488/scripts-for-the-analysis-of-hgt-in-genome-sequence-data</guid>
	<pubDate>Wed, 29 Nov 2017 16:44:10 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34488/scripts-for-the-analysis-of-hgt-in-genome-sequence-data</link>
	<title><![CDATA[Scripts for the analysis of HGT in genome sequence data.]]></title>
	<description><![CDATA[<p><span>Scripts for the analysis of HGT in genome sequence data</span></p><p>Address of the bookmark: <a href="https://github.com/reubwn/hgt" rel="nofollow">https://github.com/reubwn/hgt</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/40503/3-phd-positions-available-in-the-area-of-bioinformaticscomputational-biology-at-ulsteracuk</guid>
  <pubDate>Thu, 02 Jan 2020 12:41:10 -0600</pubDate>
  <link></link>
  <title><![CDATA[3 PhD positions available in the area of Bioinformatics/Computational Biology at ulster.ac.uk]]></title>
  <description><![CDATA[
<p>3 PhD positions available in the area of Bioinformatics/Computational Biology, Machine Learning (ML)/Artificial Intelligence (AI), Biomarker Discovery, Stratified/Personalized Medicine in Mental Health, Diabetes and Multimorbidity. Please see details (weblinks) below:</p>

<p>1. https://www.ulster.ac.uk/doctoralcollege/find-a-phd/510894<br />2. https://www.ulster.ac.uk/doctoralcollege/find-a-phd/511458<br />3. https://www.ulster.ac.uk/doctoralcollege/find-a-phd/512618</p>

<p>Looking for students with good computational/programming skills (preferable in Linux/Shell, Python and/or R) and knowledge in computational biology and statistics. However, students from more biology oriented background but strong interest to learn bioinformatics and programming are also encouraged to apply.</p>

<p>Informal inquiries are welcomed at: p.shukla@ulster.ac.uk</p>

<p>Dr Priyank Shukla PhD FHEA FCHERP<br />Lecturer (Asst Prof) in Stratified Medicine (Bioinformatics)</p>

<p>Northern Ireland Centre for Stratified Medicine<br />Biomedical Sciences Research Institute<br />University of Ulster (Magee Campus)<br />C-TRIC Building, Altnagelvin Area Hospital<br />Glenshane Road, Derry/Londonderry<br />BT47 6SB, Northern Ireland, United Kingdom</p>

<p>T: +44 28 7167 5690<br />E: p.shukla@ulster.ac.uk<br />W: https://www.ulster.ac.uk/staff/p-shukla</p>
]]></description>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/35131/giggle-a-search-engine-for-large-scale-integrated-genome-analysis</guid>
	<pubDate>Wed, 10 Jan 2018 03:10:45 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/35131/giggle-a-search-engine-for-large-scale-integrated-genome-analysis</link>
	<title><![CDATA[GIGGLE: a search engine for large-scale integrated genome analysis]]></title>
	<description><![CDATA[<p><span>GIGGLE is a genomics search engine that identifies and ranks the significance of genomic loci shared between query features and thousands of genome interval files. GIGGLE (</span><a href="https://github.com/ryanlayer/giggle">https://github.com/ryanlayer/giggle</a><span>) scales to billions of intervals and is over three orders of magnitude faster than existing methods. Its speed extends the accessibility and utility of resources such as ENCODE, Roadmap Epigenomics, and GTEx by facilitating data integration and hypothesis generation.</span></p>
<p>https://www.nature.com/articles/nmeth.4556</p><p>Address of the bookmark: <a href="https://github.com/ryanlayer/giggle" rel="nofollow">https://github.com/ryanlayer/giggle</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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