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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/26925?offset=1000</link>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/42664/common-bioinformatics-interview-questions</guid>
	<pubDate>Sat, 23 Jan 2021 06:07:50 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/42664/common-bioinformatics-interview-questions</link>
	<title><![CDATA[Common Bioinformatics Interview Questions !]]></title>
	<description><![CDATA[<p>The possibility of an interview for a bioinformatics position in the life sciences may be very disquieting, but the same concerns emerge time and again in my experience. So, it is exceedingly worthwhile to plan for future bioinformatics interview questions. Doing this will really give you the advantage in obtaining the position.</p><p>The following 5 questions are those that I have heard many times during the job-search process. There is no reason for not planning responses in such situations.</p><p><strong>1. Tell Us About Yourself</strong><br />This is a very typical opener in interviews. It's a perfect question to ask, and getting something planned will really help you concentrate and ease in the conversation. However, you need to make sure that your response is applicable to the job you're interviewing.<br />It's probably better to keep your answer professional. Try to include these in the answer as well: where did your love of science and bioinformatics come from? How the heck did you end up in this field? Why programming and scripting ?</p><p><strong>2. What is your plan for your bioinformatics career? / How do you look at yourself in five years? / How are your personal objectives to accomplish these goals / What are the plan for your research fundings ?</strong></p><p>Your CV/resume has already impressed the selection panel if you have been invited for an interview. The questions from the bioinformatics interview team provide an incentive for you to market yourself and illustrate the work in question with the most appropriate knowledge.</p><p><strong>3. What do you understand about the job description/What would your suggested research path be if you were a successful candidate?</strong><br />Summarize the specifics of the advertised bioinformatics position in your own words. Follow on with some suggestions of how you want to extend your research and create your own projects within the community.</p><p><strong>4. Will you work as a group or do you want to work on your own?</strong><br />This requirement can vary from jobs to job, so when addressing, be alert. A company/research PI may need a bioinformatician that is able to work on a single project autonomously, or they may need a person who can help direct and organize a team. In your response, refer to the job description.</p><p><strong>5. What particular methods have you used to date with your experiments?</strong><br />You might have experience with all the laboratory techniques described in the job description, but stress the ones you highly experienced with. Highlight your professional abilities and stress that you are extremely capable of mastering new techniques with others ...</p><p>At the end of the day, remember that you're questioning the jury as well as they're interviewing you. You will ought to think of any questions you would like the interview panel to pose. This indicates that you have done your homework and serious about the position.</p><p>All the best for your future job interview.</p>]]></description>
	<dc:creator>Jit</dc:creator>
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  <guid isPermaLink='true'>https://bioinformaticsonline.com/researchlabs/view/43044/kanthida-lab</guid>
  <pubDate>Wed, 28 Apr 2021 02:27:22 -0500</pubDate>
  <link></link>
  <title><![CDATA[Kanthida Lab !]]></title>
  <description><![CDATA[
<p>Research Interest: </p>

<p>Bioinformatics </p>

<p>High-throughput and high-dimensional data analysis</p>

<p>Microbiome data analysis (Main focus)</p>

<p>Next-generation and third-generation sequencing data analysis for genomics</p>

<p>Gene expression data analysis</p>

<p>Machine learning for biological data</p>

<p>Biomarkers identification </p>

<p>Database and web-application for biological data</p>

<p>More at <br />https://sites.google.com/mail.kmutt.ac.th/kanthida-k/home?authuser=0</p>
]]></description>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/43323/biostarhandbook</guid>
	<pubDate>Fri, 27 Aug 2021 01:31:01 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/43323/biostarhandbook</link>
	<title><![CDATA[biostarhandbook]]></title>
	<description><![CDATA[<p>Nice book collection for bioinformatician ... highly recommended.</p><p>Address of the bookmark: <a href="https://www.biostarhandbook.com/" rel="nofollow">https://www.biostarhandbook.com/</a></p>]]></description>
	<dc:creator>Neel</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/44472/pipesnake-bioinformatics-best-practice-analysis-pipeline-for-phylogenomic-reconstruction</guid>
	<pubDate>Wed, 21 Feb 2024 06:19:41 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/44472/pipesnake-bioinformatics-best-practice-analysis-pipeline-for-phylogenomic-reconstruction</link>
	<title><![CDATA[pipesnake: bioinformatics best-practice analysis pipeline for phylogenomic reconstruction]]></title>
	<description><![CDATA[<p dir="auto"><span>ausarg/pipesnake</span>&nbsp;is a bioinformatics best-practice analysis pipeline for phylogenomic reconstruction starting from short-read 'second-generation' sequencing data.</p>
<p dir="auto">The pipeline is built using&nbsp;<a href="https://www.nextflow.io/">Nextflow</a>, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The&nbsp;<a href="https://www.nextflow.io/docs/latest/dsl2.html">Nextflow DSL2</a>&nbsp;implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies.</p><p>Address of the bookmark: <a href="https://github.com/AusARG/pipesnake" rel="nofollow">https://github.com/AusARG/pipesnake</a></p>]]></description>
	<dc:creator>LEGE</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/blog/view/44930/bioinformatics-the-bridge-between-curiosity-and-discovery</guid>
	<pubDate>Mon, 24 Nov 2025 05:16:49 -0600</pubDate>
	<link>https://bioinformaticsonline.com/blog/view/44930/bioinformatics-the-bridge-between-curiosity-and-discovery</link>
	<title><![CDATA[Bioinformatics: The Bridge Between Curiosity and Discovery]]></title>
	<description><![CDATA[<p>In the sprawling universe of modern science, bioinformatics stands as one of the most transformative and empowering fields of our time. It is where biology meets computation, where data becomes meaning, and where curiosity becomes discovery. If you&rsquo;ve stepped into this world&mdash;or are considering it&mdash;here&rsquo;s your reminder: you&rsquo;re part of a revolution.</p><p><strong>Why Bioinformatics Matters More Than Ever</strong></p><p>Every day, our world generates massive amounts of biological data&mdash;from genome sequences to microbiome profiles to real-time pathogen surveillance. Hidden within these datasets are the answers to some of the greatest challenges humanity faces: emerging diseases, antimicrobial resistance, environmental stress, genetic disorders, sustainable agriculture, and more.</p><p>Bioinformatics isn&rsquo;t just a skill.<br />It&rsquo;s the language of the future of biology.</p><p>By mastering it, you give yourself the power to:</p><p>Decode genomes and understand life at its most fundamental level</p><p>Identify patterns no microscope could ever reveal</p><p>Predict disease outbreaks before they occur</p><p>Accelerate drug discovery with computational precision</p><p>Contribute to open-source tools that empower scientists worldwide</p><p>You don&rsquo;t just follow science&mdash;you drive it.</p><p><strong>Every Expert Was Once a Beginner</strong></p><p>Many newcomers feel intimidated. Command-line interfaces. R scripts. Python packages. Next-generation sequencing data. Complex machine learning models.</p><p>But here&rsquo;s the truth: every bioinformatician started exactly where you are now&mdash;curious, unsure, but excited.</p><p>No one writes perfect code on day one.</p><p>No one understands genomics pipelines immediately.</p><p>What makes you a bioinformatician is not perfection, but perseverance.</p><p>When your script throws a cryptic error&hellip;<br />When your data refuses to format&hellip;<br />When your pipeline runs for 6 hours only to crash&hellip;</p><p>Remember: this is part of the journey.<br />Every error teaches you. Every retry strengthens you. Every breakthrough energizes you.</p><p>Bioinformatics Is Not Just a Career&mdash;It&rsquo;s a Mindset</p><p>It&rsquo;s the mindset of:</p><p>Problem-solving.</p><p>Continuous learning.</p><p>Turning chaos into clarity.</p><p>Seeing what others can&rsquo;t.</p><p>Bioinformaticians are detectives of biological complexity. You sit at the intersection of innovation, using tools that can shape public health, medicine, agriculture, and ecology. Few fields give you such direct impact on the world.</p><p><strong>Your Contribution Matters</strong></p><p>As you work on your script, pipeline, genome, or model, remember:</p><p>Somewhere, your analysis might contribute to:</p><p>A new therapy</p><p>A faster diagnostic test</p><p>A better understanding of a pathogen</p><p>A more resilient crop</p><p>An open-source dataset that helps thousands</p><p>A discovery that rewrites textbooks</p><p>Your code may be small, but its ripple effect is powerful.</p><p>The Future Is Bioinformatics&mdash;And You Are Part of It</p><p>The world is shifting. Wet labs are integrating AI. Hospitals rely on genomic insights. Farmers use gene-level predictions. Governments monitor disease in real time. Students launch pipelines that become global tools.</p><p>This is a golden era&mdash;and you are not late.<br />You are exactly where you need to be.</p><p>Keep Pushing. Keep Learning. Keep Discovering.</p><p>Bioinformatics is a journey filled with challenges, but also with unmatched rewards.</p><p>So the next time you feel stuck, frustrated, or overwhelmed, remember:<br />You&rsquo;re building the science of tomorrow.</p><p>Be proud. Stay curious. Keep going.<br />Your work matters more than you think.</p>]]></description>
	<dc:creator>BioStar</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38420/regioner-an-r-package-for-the-management-and-comparison-of-genomic-regions</guid>
	<pubDate>Tue, 11 Dec 2018 08:43:51 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38420/regioner-an-r-package-for-the-management-and-comparison-of-genomic-regions</link>
	<title><![CDATA[regioneR: an R package for the management and comparison of genomic regions]]></title>
	<description><![CDATA[<p><span>Regioner is an R package for the management and comparison of genomic regions. It offers a set of function for basic manipulation of region sets extending the functionality of GenomicRanges and a powerful and customizable permutation test framework. With it, it's possible to study the association of a set of regions with other sets of regions, functions defined over the genome or essentially any user defined function.</span></p>
<p><span>http://gattaca.imppc.org/regioner/</span></p><p>Address of the bookmark: <a href="http://gattaca.imppc.org/regioner/" rel="nofollow">http://gattaca.imppc.org/regioner/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/2422/bioinformatics-codes-search</guid>
	<pubDate>Thu, 15 Aug 2013 11:08:52 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/2422/bioinformatics-codes-search</link>
	<title><![CDATA[Bioinformatics Codes Search]]></title>
	<description><![CDATA[<p>I bet, this website will be your best friend in near future. This helps us to explore the existing open source codes and learn from it.</p>
<p>You can find some useful open source bioinformatics codes for your analysis work. You can use the left bar options to filtere out or narrow down your search result. This webpage can be an useful resource for a beginners bioinformatician as it contain several bioinformatics basics script that are commonly used by biological programmers and biologist.</p>
<p>Stand on the slumped, dandruff-covered shoulders of millions of computer nerds. _/\_</p>
<p>Enjoy the code and research work.</p>
<p>http://code.ohloh.net/search?s=bioinformatics</p><p>Address of the bookmark: <a href="http://code.ohloh.net/search?s=bioinformatics" rel="nofollow">http://code.ohloh.net/search?s=bioinformatics</a></p>]]></description>
	<dc:creator>Jitendra Narayan</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/7216/free-math-books</guid>
	<pubDate>Thu, 12 Dec 2013 19:38:34 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/7216/free-math-books</link>
	<title><![CDATA[Free math books]]></title>
	<description><![CDATA[<p>Bioinformatics require some match skills, therefore I decided to provide this wonderful math eBooks links to the BOL community.</p>
<p>Please add ur links/bookmarks in comment section.</p><p>Address of the bookmark: <a href="http://physicsdatabase.com/free-math-books/" rel="nofollow">http://physicsdatabase.com/free-math-books/</a></p>]]></description>
	<dc:creator>Manisha Mishra</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/5191/programming-language-to-build-synthetic-dna</guid>
	<pubDate>Mon, 30 Sep 2013 16:37:24 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/5191/programming-language-to-build-synthetic-dna</link>
	<title><![CDATA[Programming language to build synthetic DNA]]></title>
	<description><![CDATA[<p style="color: #333333; font-size: 13px; font-style: normal; font-weight: normal; text-align: start;">A team led by <a href="http://homes.cs.washington.edu/~seelig/index.html">Georg Seelig</a>&nbsp;(<a href="http://homes.cs.washington.edu/~seelig/index.html">http://homes.cs.washington.edu/~seelig/index.html</a>) at&nbsp;University of Washington has developed a programming language for chemistry that it hopes will streamline efforts to design a network that can guide the behavior of chemical-reaction mixtures in the same way that embedded electronic controllers guide cars, robots and other devices. In medicine, such networks could serve as &ldquo;smart&rdquo; drug deliverers or disease detectors at the cellular level.</p><p style="color: #333333; font-size: 13px; font-style: normal; font-weight: normal; text-align: start;">Reference &amp; More @</p><p style="color: #333333; font-size: 13px; font-style: normal; font-weight: normal; text-align: start;"><a href="http://www.nature.com/nnano/journal/vaop/ncurrent/full/nnano.2013.189.html">http://www.nature.com/nnano/journal/vaop/ncurrent/full/nnano.2013.189.html</a></p><p style="color: #333333; font-size: 13px; font-style: normal; font-weight: normal; text-align: start;"><a href="http://www.washington.edu/news/2013/09/30/uw-engineers-invent-programming-language-to-build-synthetic-dna/">http://www.washington.edu/news/2013/09/30/uw-engineers-invent-programming-language-to-build-synthetic-dna/</a></p><p style="color: #333333; font-size: 13px; font-style: normal; font-weight: normal; text-align: start;">Image source:&nbsp;washington.edu</p><p style="color: #333333; font-size: 13px; font-style: normal; font-weight: normal; text-align: start;"><img src="http://www.washington.edu/news/files/2013/09/Programmable-chemistry-2.jpg" alt="image" style="border: 0px; border: 0px;"></p>]]></description>
	<dc:creator>Jit</dc:creator>
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	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/7387/bioinformatics-software-for-biologists-in-the-genomics-era</guid>
	<pubDate>Sun, 22 Dec 2013 17:31:05 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/7387/bioinformatics-software-for-biologists-in-the-genomics-era</link>
	<title><![CDATA[Bioinformatics software for biologists in the genomics era]]></title>
	<description><![CDATA[<p>The genome sequencing revolution is approaching a landmark figure of 1000 completely sequenced genomes. Coupled with fast-declining, per-base sequencing costs, this influx of DNA sequence data has encouraged laboratory scientists to engage large datasets in comparative sequence analyses for making evolutionary, functional and translational inferences. However, the majority of the scientists at the forefront of experimental research are not bioinformaticians, so a gap exists between the user-friendly software needed and the scripting/programming infrastructure often employed for the analysis of large numbers of genes, long genomic segments and groups of sequences. We see an urgent need for the expansion of the fundamental paradigms under which biologist-friendly software tools are designed and developed to fulfill the needs of biologists to analyze large datasets by using sophisticated computational methods. We argue that the design principles need to be sensitive to the reality that comparatively small teams of biologists have historically developed some of the most popular biological software packages in molecular evolutionary analysis. Furthermore, biological intuitiveness and investigator empowerment need to take precedence over the current supposition that biologists should re-tool and become programmers when analyzing genome scale datasets.</p><p>Address of the bookmark: <a href="http://bioinformatics.oxfordjournals.org/content/23/14/1713.full" rel="nofollow">http://bioinformatics.oxfordjournals.org/content/23/14/1713.full</a></p>]]></description>
	<dc:creator>Poonam Mahapatra</dc:creator>
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