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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/19090?offset=430</link>
	<atom:link href="https://bioinformaticsonline.com/related/19090?offset=430" rel="self" type="application/rss+xml" />
	<description><![CDATA[]]></description>
	
	
<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/7813/research-associate-indian-institute-of-spices-research</guid>
  <pubDate>Wed, 08 Jan 2014 10:10:03 -0600</pubDate>
  <link></link>
  <title><![CDATA[Research Associate @ INDIAN INSTITUTE OF SPICES RESEARCH]]></title>
  <description><![CDATA[
<p>INDIAN INSTITUTE OF SPICES RESEARCH<br />(Indian Council of Agricultural Research)<br />Marikunnu P.O., Kozhikode – 673 012, Kerala</p>

<p>Phone No. 0495 2731 410</p>

<p>WALK -IN- TEST CUM INTERVIEW<br />Walk- in- Test cum Interview (based on test) for the selection of Research Associate (Bioinformatics) &amp; Bioinformatic Trainees under the scheme ‘Distributed Information Sub Centre- DISC’ will be held at this Institute as per details indicated below.</p>

<p>Research Associate<br />Date of Interview : 21 -01-2014 at 10.00 A.M<br />Qualifications :<br />a) Essential: Doctorate degree in Bioinformatics or Biotechnology/Life Sciences/Biochemistry with expertise in  Bioinformatics as evidenced by publications.<br />OR Three years research experience after  MVSc/MPharm/ME/MTech with Bioinformatics Specialization.<br />b Desirable: Experience in handling NGS data  Programming skills in Python/Bioperl<br />Emoluments : Rs:22000/- per month + HRA  (higher pay upto Rs.24000/- can be paid depending on the qualifications and experience.<br />Upper age limit : 40 years for Men &amp; 45 years for Women as on date of Interview (Upper Age limits are relaxable for SC, ST and OBC candidates as per Govt. of India norms (at present 5 years for SC/ST and 3 years for OBC)<br />Duration of Project : Till the closure of the project.</p>

<p>General Terms and conditions<br />1. The above positions are purely on temporary basis and is co-terminus with the closure of the project. There is no provision of re-employment after termination of project.<br />The selected candidate will not have any right for claiming pay scale or absorption<br />against any regular post being vacant on a later date at this Institute.<br />2 . No TA/DA will be paid for attending the Interview.<br />3. Canvassing in any form will lead to cancellation of candidate.<br />4. The decision of Director, IISR would be final and binding in all aspects.<br />5. Candidates will not be permitted to enter the Examination Hall after 10.00 A.M.<br />6. Candidates who secure the minimum marks prescribed by the Institute in written test  only will be eligible for calling for the interview. The number of candidates to be  called for the interview will be decided by the Director of the Institute.<br />7 Those who do not possess original Degree/PG certificate or Provisional certificate will not be allowed to attend the Test/Interview.</p>

<p>Note: All relevant certificates (in original) and bio data, No objection certificate in case he/she is employed elsewhere and experience certificate in original (if any) need to be produced at the time of interview.<br />Advertisement: http://spicebioinfo.res.in/downloads/DISC-Website.pdf</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/5220/paolo-ruggerone-lab</guid>
  <pubDate>Tue, 01 Oct 2013 14:15:53 -0500</pubDate>
  <link></link>
  <title><![CDATA[Paolo Ruggerone Lab]]></title>
  <description><![CDATA[
<p>Efflux pumps (RND family)</p>

<p>Functioning of efflux systems in Gram-negative bacteria<br />Determinants of the compound-efflux system interactions<br />Action of inhibitors on efflux systems<br />Structural and dynamical features of the efflux systems</p>

<p>TatA<br />Assembly of the TatA system<br />Study of the dynamical features of the charge zipper</p>

<p>Methods<br />Setup of a kinetic Monte Carlo (KMC) scheme to study the flux of antibiotics through porins and efflux systems<br />Setup of protocol to integrate MD results in a ligand-based approach</p>

<p>Viral inhibitors<br />Interactions of selected compounds with RNA-dependent RNA polymerases (RdRps) of HCV and BVDV<br />Assessment of the role of mutations in RdRps<br />Antimicrobial peptides</p>

<p>Interactions of antimicrobial peptides with membranes: structure and dynamics<br />Interactions between antimicrobial peptides in the presence of different membranes<br />Protein-protein interactions<br />Effects of mutations</p>

<p>Lab Page<br />http://www.dsf.unica.it/~paolo/Site/Home.html</p>
]]></description>
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<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/pages/view/44618/important-bioinformatics-tools</guid>
	<pubDate>Tue, 30 Jul 2024 05:03:29 -0500</pubDate>
	<link>https://bioinformaticsonline.com/pages/view/44618/important-bioinformatics-tools</link>
	<title><![CDATA[Important Bioinformatics Tools !]]></title>
	<description><![CDATA[<p><span>1. Ktrim: An extra-fast, accurate adapter trimmer for sequencing data. It processes FASTQ files from multiple lanes with minimal mismatching and over-trimming of adapters.</span><span><br /></span><span><br /></span><span>2. BWA MEM: A reliable alignment tool (particularly for mapping ALT contigs and HLA genes, which are not fully addressed in BWA-MEM2).</span><span><br /></span><span><br /></span><span>3. Sambamba markdup: Quickly marks or removes duplicate reads using Picard's criteria.</span><span><br /></span><span><br /></span><span>4. ichorCNA: Estimates the tumor DNA fraction in cell-free DNA from ultra-low-pass whole genome sequencing (0.1x coverage) based on copy number alterations (CNA).</span><span><br /></span><span><br /></span><span>5. Fragle: A deep learning method for quantifying ctDNA levels from cell-free DNA fragmentomic profiles. It detects TF as low as ~1% ctDNA and works with targeted genomic panel sequencing data.</span><span><br /></span><span><br /></span><span>6. AlfredQC: A quality control tool for high-throughput sequencing data. It assesses metrics like read quality scores, GC content, and duplication rates, visualized through detailed plots and summary statistics.</span><span><br /></span><span><br /></span><span>7. Mosdepth: A fast tool for calculating sequencing coverage depth, offering a quicker alternative to samtools/sambamba depth by processing BAM and CRAM files.</span><span><br /></span><span><br /></span><span>8. Bedtools: A versatile toolkit for genomics, enabling operations like intersect, merge, count, and shuffle on genomic intervals across formats such as BAM, BED, GFF/GTF, and VCF.</span><span><br /></span><span><br /></span><span>9. Datamash: A command-line tool for basic numeric, textual, and statistical operations on input data streams. It supports operations such as grouping, sorting, transposing, and performing arithmetic calculations on tabular data.</span><span><br /></span><span><br /></span><span>10.</span><span> </span><a href="http://gwf.app/" target="_self">gwf.app</a><span>: A pragmatic alternative to Snakemake. Developed at</span><span> </span><a href="https://www.linkedin.com/company/aarhus-university-denmark-/" target="_self"><span>Aarhus University</span></a><span>, this flexible, generic workflow tool builds and runs large scientific workflows.</span></p>]]></description>
	<dc:creator>BioStar</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/5310/bergman-lab</guid>
  <pubDate>Thu, 03 Oct 2013 17:20:09 -0500</pubDate>
  <link></link>
  <title><![CDATA[Bergman Lab]]></title>
  <description><![CDATA[
<p>Broad area of research:</p>

<p>Genome Annotation and Functional Genomics</p>

<p>Bergman Lab is actively engaged in the development and application of computational methods to improve the annotation of functional biological features in genome sequences.  Bergman Lab work focuses on improving annotation of non-protein-coding regions of the genome including conserved noncoding sequences (CNSs), cis-regulatory modules (CRMs), transcription factor binding sites (TFBSs), transposable elements (TEs) and noncoding RNA (ncRNA) genes. Current projects include improving the (i) annotation of TEs in the fly and yeast genomes, (ii) annotation of CRMs and TFBSs in the fly genome, and (iii) analysis of transposon knockout collections in flies. Research in this area is supported by the EC FP7 programme.</p>

<p>Genome and Molecular Evolution<br />Text and Data Mining</p>

<p>More @ http://bergmanlab.smith.man.ac.uk/</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/5436/the-anatomy-of-successful-computational-biology-software</guid>
	<pubDate>Thu, 10 Oct 2013 11:53:08 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/5436/the-anatomy-of-successful-computational-biology-software</link>
	<title><![CDATA[The anatomy of successful computational biology software]]></title>
	<description><![CDATA[<p>Creators of software widely used in computational biology discuss the factors that contributed to their success</p><p><em>Nature Biotechnology</em><span>&nbsp;spoke with Altschul and several other originators of computational biology software programs widely used today (</span><a href="http://www.nature.com/nbt/journal/v31/n10/full/nbt.2721.html#t1">Table 1</a><span>). The conversations explored what makes certain software tools successful, the unique challenges of developing them for biological research and how the field of computational biology, as a whole, can move research agendas forward. What follows is an edited compilation of interviews.</span></p><p>Detail @&nbsp;<a href="http://www.nature.com/nbt/journal/v31/n10/full/nbt.2721.html">http://www.nature.com/nbt/journal/v31/n10/full/nbt.2721.html</a></p><p>News Source @ Nature</p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44707/rna-seq-analysis-a-guide-for-bioinformaticians</guid>
	<pubDate>Sat, 07 Dec 2024 22:22:24 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44707/rna-seq-analysis-a-guide-for-bioinformaticians</link>
	<title><![CDATA[RNA-Seq Analysis: A Guide for Bioinformaticians]]></title>
	<description><![CDATA[<p>RNA sequencing (RNA-Seq) has revolutionized transcriptomics, offering unprecedented insights into gene expression, splicing, and transcript diversity. For bioinformaticians, RNA-Seq analysis is a gateway to exploring the complexity of RNA biology and its implications in health and disease. This blog post provides an overview of RNA-Seq analysis, key computational steps, and tools for bioinformaticians eager to delve into this powerful technique.</p><h3>What is RNA-Seq?</h3><p>RNA-Seq is a next-generation sequencing (NGS) technology used to study the transcriptome&mdash;the complete set of RNA molecules in a cell. It quantifies gene expression, detects novel transcripts, and captures alternative splicing events with high sensitivity and resolution.</p><h3>Workflow for RNA-Seq Analysis</h3><p>RNA-Seq analysis involves several stages, each requiring computational tools and expertise.</p><h4>1. <strong>Experimental Design and Data Acquisition</strong></h4><p>Before diving into analysis, bioinformaticians should consider:</p><ul>
<li><strong>Biological Replicates</strong>: Ensure statistical power to detect meaningful differences.</li>
<li><strong>Sequencing Depth</strong>: Align sequencing depth to study objectives (e.g., higher depth for low-abundance transcripts).</li>
<li><strong>Paired-End vs. Single-End</strong>: Paired-end sequencing provides more detailed information on transcript structure.</li>
</ul><p>Once sequencing is complete, raw data is provided in FASTQ format, containing sequence reads and quality scores.</p><h4>2. <strong>Quality Control and Preprocessing</strong></h4><p>Quality control (QC) ensures data integrity. Tools such as <strong>FastQC</strong> evaluate metrics like base quality, GC content, and adapter contamination.</p><p><strong>Preprocessing Steps</strong>:</p><ul>
<li><strong>Trimming</strong>: Tools like <strong>Trimmomatic</strong> or <strong>Cutadapt</strong> remove low-quality bases and adapter sequences.</li>
<li><strong>Filtering</strong>: Discard reads below a certain quality threshold or length.</li>
</ul><h4>3. <strong>Read Alignment</strong></h4><p>Reads are mapped to a reference genome or transcriptome to determine their origin. Alignment tools include:</p><ul>
<li><strong>HISAT2</strong>: Handles large genomes efficiently and supports spliced alignments.</li>
<li><strong>STAR</strong>: High-speed aligner optimized for RNA-Seq.</li>
<li><strong>Bowtie2</strong>: Suitable for short-read alignment.</li>
</ul><p><strong>Output</strong>: A SAM/BAM file containing aligned reads.</p><h4>4. <strong>Transcript Assembly and Quantification</strong></h4><p>This step involves identifying transcripts and quantifying their expression levels. Tools used include:</p><ul>
<li><strong>StringTie</strong>: Assembles and quantifies transcripts from aligned reads.</li>
<li><strong>Salmon/Kallisto</strong>: Perform pseudo-alignment for rapid and accurate quantification.</li>
</ul><p>Expression levels are typically measured as TPM (transcripts per million) or FPKM (fragments per kilobase of transcript per million mapped reads).</p><h4>5. <strong>Differential Expression Analysis</strong></h4><p>To identify genes with altered expression between conditions, bioinformaticians use tools such as:</p><ul>
<li><strong>DESeq2</strong>: Accounts for data normalization and variability.</li>
<li><strong>edgeR</strong>: Handles overdispersed count data efficiently.</li>
<li><strong>Limma-voom</strong>: Combines linear modeling with RNA-Seq count data.</li>
</ul><p>The output includes a list of differentially expressed genes (DEGs) with statistical significance and fold-change values.</p><h4>6. <strong>Functional Annotation and Pathway Analysis</strong></h4><p>Understanding the biological significance of DEGs involves:</p><ul>
<li><strong>Gene Ontology (GO) Analysis</strong>: Tools like <strong>DAVID</strong> or <strong>clusterProfiler</strong> categorize genes based on their biological functions.</li>
<li><strong>Pathway Enrichment Analysis</strong>: Identifies pathways enriched in DEGs using tools like <strong>KEGG</strong>, <strong>Reactome</strong>, or <strong>GSEA</strong>.</li>
</ul><h4>7. <strong>Visualization</strong></h4><p>Visualizing results enhances interpretability. Common visualizations include:</p><ul>
<li><strong>Heatmaps</strong>: Show expression patterns across samples (e.g., <strong>pheatmap</strong>).</li>
<li><strong>Volcano Plots</strong>: Highlight significant DEGs (e.g., <strong>ggplot2</strong>).</li>
<li><strong>PCA/UMAP</strong>: Assess sample clustering and variability (e.g., <strong>Seurat</strong>).</li>
</ul><h3>Challenges in RNA-Seq Analysis</h3><ol>
<li><strong>Batch Effects</strong>: Technical variability can confound biological signals. Combat this with normalization techniques or batch-correction tools like <strong>ComBat</strong>.</li>
<li><strong>Low-Quality Samples</strong>: Poor-quality RNA impacts downstream analyses.</li>
<li><strong>Computational Complexity</strong>: RNA-Seq generates massive datasets, requiring robust computing resources and optimized pipelines.</li>
</ol><h3>Key Tools and Resources</h3><ul>
<li><strong>Bioconductor</strong>: A treasure trove of R packages for RNA-Seq analysis.</li>
<li><strong>Galaxy</strong>: A web-based platform for running RNA-Seq workflows.</li>
<li><strong>Nextflow/Snakemake</strong>: Workflow management tools to streamline analyses.</li>
</ul><h3>Applications of RNA-Seq</h3><p>RNA-Seq is used in diverse research areas, including:</p><ul>
<li><strong>Cancer Transcriptomics</strong>: Identifying tumor-specific expression profiles.</li>
<li><strong>Developmental Biology</strong>: Studying dynamic transcriptome changes.</li>
<li><strong>Drug Discovery</strong>: Screening genes modulated by therapeutic compounds.</li>
</ul><h3>Conclusion</h3><p>RNA-Seq analysis is a cornerstone of modern transcriptomics, offering bioinformaticians a versatile toolkit for unraveling gene expression and regulation. Mastering RNA-Seq workflows and tools empowers researchers to transform raw sequencing data into biological discoveries.</p><p>Whether you&rsquo;re investigating disease mechanisms, exploring cellular pathways, or developing new therapeutics, RNA-Seq is a powerful ally in your bioinformatics arsenal.</p>]]></description>
	<dc:creator>LEGE</dc:creator>
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<item>
  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/5748/troyanskaya-lab</guid>
  <pubDate>Fri, 18 Oct 2013 10:57:40 -0500</pubDate>
  <link></link>
  <title><![CDATA[Troyanskaya  Lab]]></title>
  <description><![CDATA[
<p>In our research, we combine computational methods with an experimental component in a unified effort to develop comprehensive descriptions of genetic systems of cellular controls, including those whose malfunctioning becomes the basis of genetic disorders, such as cancer, and others whose failure might produce developmental defects in model systems.</p>

<p>Research Interest<br />Genomic Data Integration</p>

<p>Microarray Analysis</p>

<p>Gene and Protein Function Prediction</p>

<p>Detection and Analysis of Chromosomal Abnormalities and Functional Evolution</p>

<p>Integration of Computation and Experiments</p>

<p>Identification of Biological Networks and Pathways</p>

<p>Evaluation and Validation of Computational Predictions</p>

<p>Scalable Visualization-Based Data Analysis</p>

<p>More @ http://reducio.princeton.edu/cm/<br />PI page @ http://reducio.princeton.edu/cm/ogt</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/5957/assistant-professor-in-molecular-synthesis-for-drug-discovery-and-development-cbmr-lucknow</guid>
  <pubDate>Wed, 30 Oct 2013 06:42:27 -0500</pubDate>
  <link></link>
  <title><![CDATA[Assistant Professor in Molecular Synthesis for Drug Discovery and Development @ CBMR, Lucknow]]></title>
  <description><![CDATA[
<p>ADVERTISEMENT FOR FACULTY POSITIONS AT CENTRE OF BIOMEDICAL RESEARCH (CBMR), LUCKNOW</p>

<p>Details of the Positions and Pay Structure:</p>

<p>03 Posts for Assistant Professor in Molecular Synthesis for Drug Discovery and Development</p>

<p>Essential Qualifications and Requirements:</p>

<p>1. PhD in Synthetic Organic Chemistry/Medicinal Chemistry with research publications in high quality international journals and first class grade at the preceding degree from recognised University/Institute in India or abroad with consistently good academic record.<br />2. Three Yrs of Post-doctoral experience in relevant area.<br />3. Below 35 Yrs of age at the time of application</p>

<p>Desirable Experience: Candidates having strong research background in organic synthesis, total synthesis of structurally complex and medicinally important natural products/drugs related to cancer, neurodegenerative diseases (neurotropically active molecules for Alzheimer's, Parkinson’s, dementia etc) and infectious diseases such as malaria, TB etc. will be preferred.</p>

<p>Interested candidates may apply with:</p>

<p>1. Filled up Application Form (download from CBMR Website: http://www.cbmr.res.in) along with the Cover Letter, Curriculum Vitae including academic record (Bachelor degree onwards), awards, honours, list of Publications and reprints of 5 best publications.<br />2. Proposed research plan (max 3-4 pages).<br />3. Names and address (with valid e-mail and Phone number) of at least 3 academic referees.<br />4. Online Payment Receipt with transaction reference no. of Rs. 1000/- (USD 100 or equivalent foreign currency) on following details.<br />Account Number: 30054847814 Name: Director, Centre of Biomedical Research<br />Bank: STATE BANK OF INDIA, SGPGI Campus Branch, LUCKNOW</p>

<p>IFSC Code: SBIN0007789<br />MICR No: 22602034</p>

<p>Applications can be sent by registered/speed post or by e-mail to the following address:</p>

<p>The Director,<br />Centre of Biomedical Research (CBMR),<br />Sanjay Gandhi PGI Campus,<br />Raebareli Road, Lucknow-226014<br />e-mail: cbmr.admin@cbmr.res.in,<br />gp.pandey@cbmr.res.in</p>

<p>More Info:</p>

<p>http://www.cbmr.res.in/career/Advertisement%20for%20the%20post%20of%20Professors%20and%20Assistant%20Professors.pdf</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/24041/junior-bioinformatic-position</guid>
  <pubDate>Wed, 26 Aug 2015 05:35:28 -0500</pubDate>
  <link></link>
  <title><![CDATA[Junior Bioinformatic position]]></title>
  <description><![CDATA[
<p>Junior Bioinformatic position in the laboratory of Inflammation and immunology in cardiovascular pathologies at Humanitas:</p>

<p>We are seeking a highly motivated young PhD student with strong interest in high throughput data analysis.<br />Detailed descriptions of our recent research activities may be found here:<br />http://www.humanitas-research.org/condorelli-gianluigi-md-phd/</p>

<p>Position is available starting from October/November. A probationary period of one month will be required.<br /> <br />Please send a CV along with a cover letter stating the reasons for applying and contact details of one or more referees to Dr. Paolo Kunderfranco (paolo.kunderfranco@humanitasresearch.it).</p>
]]></description>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/opportunity/view/6268/project-fellow-national-institute-of-malaria-research</guid>
  <pubDate>Tue, 12 Nov 2013 07:40:51 -0600</pubDate>
  <link></link>
  <title><![CDATA[Project Fellow @ National Institute of Malaria Research]]></title>
  <description><![CDATA[
<p>National Institute of Malaria Research</p>

<p>Sector 8, Dwarka, Delhi -110077</p>

<p>WALK IN INTERVIEW</p>

<p>One position of project fellow is to be filled up in a DRL- funded research project on Molecular and morphological characterization of An. fluviatilis in North-eastern states and bordering areas. The position is purely temporary for one year and can be extended</p>

<p>Essential qualifications</p>

<p>Master’s degree in any branch of Life Sciences with hands on experience in molecular biology and/or bioinformatics.</p>

<p>Age limit: 28 years, (relaxation for SC/ST/OBC candidates as per government of India rules)</p>

<p>Stipend: Rs.12, 000.00 per month (fixed)</p>

<p>Eligible candidates may walk in for an interview on 15 November 2013 at 11 AM at the above mentioned address along with a copy of CV (with a passport size photograph affixed), photocopies of all mark sheets/certificates and originals (for verifications). No TA/DA will be paid for attending the interview .Registration of candidates will start at 10:00AM and end at 10:45 AM.</p>

<p>Advertisement: http://www.mrcindia.org/vacancy/add-4.doc</p>
]]></description>
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