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Basil

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  1. Basil

    Bioinformatics vs Data Science

    The worlds of Bioinformatics and Data Science share a lot of commonalities. Although one focuses more on biological sciences than the other (Bioinformatics), they still use a lot of the same programming languages, software, and general principles. In this article, we go over exactly the differences and similarities between Bioinformatics vs Data Science and show you which path is right for you! What is Bioinformatics? What is Data Science? In a broad sense, Bioinformatics is the field involving the use of tools, software, and programming languages to understand and interpret biological data. Data Science is the field involving the use of similar tools and programs, but to understand data in general. In terms of programming languages, some examples of what Data Scientists and Bioinformaticians use could include Python, PERL, or Java. For software and tools, some examples are R, SAS, Pandas, Apache spark, and Tableau. A generalized image to give an overview of Data Science vs Bioinformatics At the End of the Day, It's Not Your Degree; It's Your Skills The biggest takeaway message we have is that it ultimately doesn’t matter what degree you chose, but the skill sets you gain from these majors. Are there jobs that have the requirement of a particular type of degree? Absolutely. These jobs are typically the exception. Instead, most jobs want a set of skills, which anybody can develop regardless if your major is in Data Science or Bioinformatics. Learn as much as you can and hone your skills, and you’ll find that you can make it in all sorts of data-oriented jobs. About the Author Basil Khuder is the director and founder of YDSOA. He started YDSOA in 2015, hoping to create an online community for those new to the fields of Data Science and Informatics. When he's not running the organization, he's busy with his research and studies as a Doctoral Bioinformatics student at Iowa State University. You can follow Basil through any of his social media accounts.
  2. Solution can be found here: https://stackoverflow.com/questions/52487581/converting-string-date-to-sql-date-format-in-java
  3. Bioinformatics and Computational Biology are two extremely related fields, and as such, many people in the scientific and academic worlds will refer to these two areas interchangeably. But is this truly accurate? Are these fields so close together, that we can lump them into one big category, or are there significant differences that need to be discussed and understood? In this article, we'll go over the commonalities and differences that these two fields face, and which path would be most suitable for you, depending on your research interests. Difference Between Computational Biology and Bioinformatics: What's the Major Difference? Like we mentioned above, to some people, Computational Biology and Bioinformatics hold no difference. To other, such as Dr. Russ Altman of Standford University, there is a very concrete difference between the two. He believes that Bioinformatics is where you create the tools, software, and algorithms that can be used to handle and work with large biological data systems. Likewise, in his mind, Computational Biology is all about learning and study biology, by using the computational tools and software made by Bioinformaticians. So, according to Dr. Altman's definition, if you're somebody who primarily enjoys being on the creation sides of things, and wanting to add to the available tools and resources for people to analyze their biological data with, Bioinformatics would be the path for you. But, if you'd rather use existing computational tools to study and understand biology better, then you'd probably want to go towards Computational Biology. Computational Biology vs Bioinformatics Academic Programs Luckily for many of you who go the Computational Biology and Bioinformatics academic route, most graduate programs combine these two fields into a one-degree program, which allows you more flexibility in figuring out exactly which niche fits you best. But what happens if you're interested in programs that are either Bioinformatics or Computational Biology instead of both? Which one should you choose? If this is your situation, you shouldn't make a decision based upon the name. Instead, look at the faculty members of each program, and see what kind of research they are doing. You might find that a program that is Bioinformatics-based has a lot of Computational Biology research, and vice versa. By researching faculty, and the type of research that is being conducted at prospective universities, you can get an idea of what kind of research you might be involved in if you choose to go to that specific school. Which One Should You Pursue? Choosing whether you want to become a Bioinformatician or a Computational Biologist comes down to figuring out whether you want to be at the forefront of creating computational software for biology or if you'd rather be using these tools to conduct your research. But just remember, things aren't as cut and dry as we've made it out to be, and you still may see job positions or academic programs use these two terms interchangeably. Instead of relying on the title to help decide where you wnat to go, figure out exactly what your research interests are and which program or position does the best job at utilizing them. About the Author Basil Khuder is the director and founder of YDSOA. He started YDSOA in 2015, hoping to create an online community for those new to the fields of Data Science and Informatics. When he's not running the organization, he's busy with his research and studies as a Doctoral Bioinformatics student at Iowa State University. You can follow Basil through any of his social media accounts.
  4. Hello everyone, it's been a while since we've posted any new announcements with regard to the site! 2017 was an exciting year for YDSOA! We moved away from our old blog format and shifted into the current community setting. Additionally, we have seen heavy increases in traffic the site has received, and our search engine rankings. Although our community forums haven't been as active as we'd like, we were fortunate enough to gain over 100 new members since our launch, to bring the total amount of members of YDSOA up to 186! We also had a lot of great discussions going on as well! We have many goals for the new year that we hope can take YDSOA to the next level and improve the user experience. They include the following: We're going to be condensing the amount of forum categories we have, especially the ones that saw the least amount of activity. Currently, our most active social media account is Facebook, and we will continue to use it as our main social account. We will soon phase out our Twitter and Instagram accounts. My personal Twitter account will be used to post updates about our site, and about topics regarding Data Science and Informatics. We will be updating our blog structure and adding more content. This will also include editing older posts that may have outdated information. We will be discontinuing our Graduate School Database and putting more of an emphasis on general graduate school discussions. We will be releasing our new site feature called YDSOA Jobs sometime in mid March. We are looking to implement a brand new site design and homepage. That's the gist of things that are in store. We thank everybody who joined YDSOA in 2017 and look forward to all the new members and great discussions in store!
  5. Answer the latest Data Science Poll!
  6. Our YDSOA Poll for 2/26 is different from some of the other questions we've asked. Here, we are interested in the type of career you are planning to pursue in the fields of Informatics or Data Science. Feel free to expand on your response below!
  7. Basil

    Transcriptomics

    Introduction to Transcriptomics Transcriptomics is the study of all of the transcripts produced by a single cell, individual or population. It has gained much traction since the creation of RNA-Seq, a Next-Generation sequencing method that allows for high-throughput analysis of transcripts. But the question remains: how can we benefit as researchers from studying RNA and transcripts that we couldn't from looking at the DNA level? Why Transcripts? There was a time when scientists believed that anything that didn't code for a protein was junk. This misbelief meant that we only cared about transcripts that were being translated into proteins. Over time, researchers began to realize that non-coding regions of the genome were not junk, and held significant and biologically functional roles. For example, we now know introns play vital roles in gene regulation, so if we disregard all of the non-protein coding regions, we are missing out on a lot of relevant information. Because of this newfound belief, science has a seen a substantial increase in many researchers harnessing the powers of Next-Generation Sequencing, especially RNA-Seq. Transcriptomic Software and Tools So we already mentioned that RNA-Seq is one of the primary methods to finding out all of the RNA that a particular cell or tissue. But, you'll need some downstream pipeline or software tool ready to be able to process all the information produced by it. We've compiled a list of software that can be used when studying transcriptomics. RNA-Seq by Expectation Maximization: RSEM is a software package that allows the users to find expression level information about transcripts, present within their genomic data. If you're using RNA-Seq data, there's a pipeline available that allows for simultaneous genomic alignment of your data, and expression information. Once the pipeline is run, RSEM will output how much transcriptional expression each transcript has, and gives you valuable visualization tools based on your data as well. Trinity RNA-Seq: Trinity is a transcriptome assembly and annotation software package. It allows for de novo transcriptome assembly based on RNA-Seq data. Some of the downstream analysis that it provides include: Quantifying the abundance of genes and transcripts Checking the quality of samples and replicates Conducting differential gene expression analysis. VennBLAST: VennBLAST is a transcriptome tool that allows for transcriptome visualization comparison across samples. The researchers who created VennBLAST refer to it as a downstream transcriptome tool. Specifically, they state the following:
  8. Basil

    Do Ph.D Programs accept MCAT scores

    Just saw that Wake Forest has a bunch of masters programs that will accept the MCAT instead of the GRE. Check it out below: http://graduate.wfu.edu/admissions-required-tests/ Also went ahead and edited Eli's post.
  9. I posted this article over on my personal site (https://basilkh.com). It's an article describing how you can use social media to help you in your professional field! Check it out and let me know what you guys think: https://basilkh.com/use-social-media-stay-ahead-field/
  10. I know of two people in my current Ph.D. Program who attended another program for a year, realized it wasn't a good fit, and decided to leave. Both of them were still able to receive multiple admissions from other universities and are now very happy with their current situation. Obviously, the biggest con is you're potentially going to have to sit out some time from school while you apply elsewhere. However, some programs admit students in the Fall and Spring, which means less time away.
  11. Those are some excellent points! That was one of the hardest things for me as well, trying to figure out what subjects I should prioritize first. Should it be computer programming/science, biological sciences or mathematics? The lab I ended up joining for my masters focused on computer science, so I spent most of my time there. Now at the Ph.D. level, a lot of the labs focus on tool development rather than implementation, and it's made me go back to try to become an even better programmer. It definitely is a never-ending cycle of reevaluating where you are at, and what you need to do to make it to the next step. However, this challenge is also what has made Bioinformatics so rewarding!
  12. As a Bioinformaticians or Computational Biologist, what are some issues that you have faced early on in your career? This could range from issues with programming, issues with software, issues with algorithms, and even issues with biological questions/ideas.
  13. Basil

    More Informatics blog posts

    Let us know if you need any help getting this up and running!
  14. Hi there, Sorry nobody has replied to this! I would reach out the program coordinator of the program and ask them about this. I am sure you are not the only person who has lots of research experience, but no publications. Don't let this be a deterrent for you applying just yet.
  15. Thought this was interesting and relevant to this section. A platform that is used by called Splunk many tech companies is now going to start incorporating artificial intelligence. The people behind Splunk state that by incorporating artificial intelligence, they are able to improve operations by: Check out the full article below! https://www.forbes.com/sites/bernardmarr/2017/09/26/new-tool-uses-machine-learning-and-artificial-intelligence-to-improve-it-operations/#6c7f1ba63789
  16. Basil

    Do Ph.D Programs accept MCAT scores

    Went ahead and updated Eli's list with your suggestion Stephanie.
  17. Can't answer this question directly, but I will say, I personally did my undergraduate and masters degree at the same university, and am doing my Ph.D somewhere else. It's been great having the opportunity to explore another college and break out of my comfort zone.
  18. Which Software Do You Use for Data Mining?
  19. This goes to out to everyone, especially our new members! If you haven't realized already, one of the features of becoming a member of YDSOA is that you are able to create your own blog, and post articles related to Informatics, Data Science, or any other topic you'd like. To do this, head over to this link and select "Create New Blog." From there you can give a name to blog, add a description, and then eventually you can post your own topics. Let us know if you have any questions!
  20. Basil

    Introduction to SQL

    When you think about handling and processing the huge amount of data, what comes to mind? For many, thoughts of utilizing Python coupled with machine learning algorithms arise. What may not initially come to thought is the notion of using SQL instead. You might be scratching your head at this prospect of using SQL in Data Science or Bioinformatics when there are other alternatives. Or, perhaps, you’re not familiar enough with this particular language to jump to a conclusion. The fact of the matter is that SQL is a programming language you should familiarize yourself with if you’re looking to jump into the world of big data. So why would somebody use SQL instead of the many alternatives? Simply put, SQL provides simplicity and robustness that you can seldom find anywhere else. Add to the equation that Data Science careers sometimes require more than handling big data. A big skill set that one can have is the ability to conduct database management on web applications; a feat for SQL and an RDMS (we’ll discuss this a little bit later.) Database Introduction (Introduction to SQL) Since we aren’t going to go in-depth with the DBMS, you’re probably wondering how you’ll be playing around with SQL code. There are some programs you can download to do so, or you can utilize this free online SQL interpreter. This website allows you to test out some basic SQL code without having to download more complicated DBMS’. The Almighty Select Statement In any SQL or SQL in Data Science course, the first statement you’ll learn is select. It’s quite simple as to why this is the first statement you hear. In SQL, the objective is to alter and configure the database to suit your needs. To do this, you must select certain attributes and do something with them. However, just selecting something won’t do much. You’ll need to pick something, tell the system where you are selecting it from and then what you want to do with it. This concept of actually doing something with the data leads us to the from statement. Whenever you utilize select, you’ll almost always use from as well. Take a look at the following: Say, for example; you want to choose all of the client’s addresses from the table example from above. You would utilize the following SQL statement (given that the table is named table1): Select address From table1; The results would yield you all of the addresses for every single row. You could use the same structure if you wanted to pull all of the ClientID’s or any of the other columns from the table. Don’t Forget The Semi-Colon The semi-colon in SQL denotes termination, so you’ll need to place it at the end of your SQL statement. The Where Clause Makes Things Happen While the select and from statements allow you to pick which specific data you want to handle, the where statement allows you to conduct the altering itself. There are numerous operators that the where clause can use to help you make things happen. Using the where clause for equality, non-equality, and showing greater than or less than. (=, <>, >, <.) Using the where clause to join other clauses. Using the where clause for mathematical calculations. Using the where clause to group data. The list goes on and on for how you can incorporate where into SQL, but just remember that it allows you to make things happen within SQL. Taking the Basics and Applying it to Data Our introduction to SQL was overly simplified and was aimed at providing a very brief introduction to those who have never used SQL. If you’re eager to learn more, we recommend a free MOOC provided by Stanford Lagunita. I’ve personally taken this course, and it’s an excellent introduction to SQL! We touched at the beginning of this article about some of the ways you might be using SQL in Data Science or Bioinformatics careers. Ultimately, it does depend on the particular job. You may never touch SQL once you are in your future career. Or, on the contrary, you may find that your position uses SQL extensively alongside other programming languages and software. Due to the unexpected nature of whether or not you’re going to need SQL, it’s worth a shot to know it at some level. At the minimum, I would recommend you at least have a basic understanding of SQL and how to do simple database analysis and alterations. Again, you never know if this can come in handy. Some careers may also not care about the specific programming languages, as long as you can conduct the data analysis that they need. Whether you’re using Python, Perl or SQL doesn’t matter nearly as much as whether or not you can perform the tasks. Final Thoughts If you have used any other SQL MOOC’s or have any useful materials for beginners to grasp this programming language, feel free to post the resources below! Be sure also to join us over at the YDSOA Community Forums as we discuss a wide variety of related topics!
  21. Basil

    Introduction to SQL

    Hi George, There's a free online SQL course provided by Stanford Lagunita - I highly recommend it (you also get a certificate afterwards that you can add to your resume.) You can access the course here. As for books, one that I used when first learning SQL and like a lot is called "SQL in 10 Minutes, Sams Teach Yourself." The kindle version of the book costs around $14. Also.. implementing new abilities in projects is an excellent way to really get an in-depth understanding of the subject! Hope that helps! (P.S. Thanks for registering to YDSOA! Please feel free to use the YDSOA Forums if you have any questions, and join in on the discussion!)
  22. Data Science Poll 8/13/17 If you utilize cloud computing, which service provider do you currently use?
  23. When dealing with Next-Generation Sequencing data for the first time, you might be a little confused when seeing all the different types of sequencing files that are out there. Although it may seem intimidating at first, a little bit of time around these files and you'll become a sequencing pro in no time! FASTQ Format FASTQ files are sometimes referred to as the raw sequencing reads. They are usually the format file that you receive from whatever company you have chosen to conduct the Next-Generation Sequencing of your data (or the machine itself, if you performed the sequencing.) The reason we refer to them as raw reads is because the file has all of the reads from your data, without any additional processes conducted on them. The other format files that we talk about later will have had something done to them, as to change the way we can process the data. The image below shows an extremely simplified view of how the FASTQ file comes to be. For example, let's say you are interested in getting heart tissue sequenced for your research. You isolate the heart tissue sample and send it off to a company to get it sequenced. Due to how sequencing is currently conducted by the most popular companies, the file that you will end up getting will be chunks of your original DNA sequence in X amounts of base-pairs (anywhere between 75-200), with a quality score right below the nucleotides. The quality score will be a character that corresponds to a particular number. In our example, we have included the @ quality score, which has a value of 31. Aligned Format Files: BAM and SAM Raw sequencing files can give you an idea of the quality of the sequencing that was conducted and other general information about your data. But what if you wanted to find out how your heart tissue data was different than the tissue of other individuals? You would not be able to find this information out by just analyzing your raw FASTQ file. This is where genomic alignment comes into play. Genomic alignment is the process of taking your raw sequencing data and aligning it to a reference genome. (If you don't know what a reference genome, it's an assembled genome sequence that is representative of a particular species.) The SAM file, which stands for sequence aligned mapping file, will have all the reads of your data, just like the FASTQ file had, but it will also have what the reference genome at that particular nucleotide is, right below it. So, going back to our example data, if we had aligned it to a reference genome, we may see something like this: As you can see, all of our data matches the references, besides the bolded G. So what does this mean? It could be that at that position, our data has a single nucleotide polymorphism or it could be some sequencing error. Variant Call Format Files: VCF We just mentioned, that comparing our data to a reference genome is useful in finding how our data is different than what the consensus genomic sequence is. At this stage, you could use a genomic viewer, such as the Integrative Genomic Viewer and manually analyze these differences. Or, you could run something called variant-calling, and produce a list of all of the variants that are present, in a file format called a Variant Call Format File, or VCF. A VCF file will tell you the exact position of the variant present, what the allele should have been in comparison to the consensus genome (reference allele), and what the allele currently is for your individual (alternative allele.) An example VCF file is shown below: The first column of a VCF file is chromosomal location. Depending on what reference genome was used for alignment, you may get chromosome number listed similar to the image (with the chromosome abbreviation, chr, and the number of a chromosome), or you may only get the chromosome number. The second column has the actual location, within the specified chromosome. The third column in our example has a period, but VCF files typically will have a variant identification number, denoted as a SNP id, in this column, which means that this variant has been identified and is listed within various databases. The fourth column is the reference allele that we referred to above, while the fifth column is the alternative allele. The last two columns both contain tidbits of information that we will discuss in a later article. For now, just know that the sixth column refers to a variant quality score, while the seventh column refers to whether that variant passed or failed a statistical test to remove false-positives.
  24. Basil

    How Important is GRE for Ph.D Programs?

    Stephanie has the right idea. GRE scores are an important part of somebody's application. However, other factors are crucial as well (like she mentioned.) Every program is also different, and most programs will state on their homepage the average GRE percentile for those admitted into the program. This can help you gauge your competitiveness for a program, and also offer you a target score if you have not taken your test yet.
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