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- W2801676502 abstract "EMBO Reports (2018) e46262Sometimes, it is not until after you have started a new job that you get a sense of the skills your employer was looking for when hiring you. I had been working as an assistant professor of biology for over 6 months when I finally figured out what kind of scientist the department was actually hoping to get: a bioinformatics fix‐it‐all, a quick cure for their expanding genomic woes, an in‐house computational genius willing to lend his powers to every graduate thesis containing a next‐generation sequencing (NGS) dataset. Sadly, I may have disappointed some of my colleagues.Like many evolutionary biologists, my research relies heavily on molecular sequence data, and because of this I am sometimes categorized as a bioinformatician—admittedly, I occasionally market myself as one—when, in fact, I am merely an end user of sophisticated software, pipelines, and programs that genuine bioinformaticians have designed. Of course, I have picked up some computational skills along the way, enough to assemble and analyze the mitochondrial and chloroplast genomes that encompass my research life. But I am a far cry from being able to perform, for example, the in‐depth analyses needed for high‐quality metagenomics work. Consequently, when a graduate student or colleague knocks on my office door and says, “Hey, Dave. We just did a ton of next‐gen on … and we were hoping that you could help analyze the data,” I feign a smile and resist the urge to crawl under my desk.When I started my job and word got around that a “sequencing” person had arrived, my inbox became bloated with emails from students and coworkers asking for help. In most cases, I tried my best to offer sound advice: “Have you tried this software?” “How about applying this measure to your data?” “Maybe you …" @default.
- W2801676502 created "2018-05-17" @default.
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- W2801676502 date "2018-05-03" @default.
- W2801676502 modified "2023-10-12" @default.
- W2801676502 title "Bringing bioinformatics to the scientific masses" @default.
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- W2801676502 doi "https://doi.org/10.15252/embr.201846262" @default.
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