I'm a first year post doc in bioinformatics and am currently working on an application project based on other's method previously developed in the lab. The application would simply be applying the existing algorithm to a biological problem and validate with experiment data. Since I'm interested in a academia faculty position, would this diminish my chance (potentially due to novelty issues by simply cashing out others work), or should I suggest to my advisor that I would rather work on other project based on my own ideas and develop my own methods? Does application of an existing method counted as novel by the academia? This project is assigned by my current PI and I'm constantly worry that I will be viewed as unoriginal by working on others method. I had to ask because my PI keep me piling up more of these "case studies" recently. I just feel that my PI care more about successful applications of this existing algorithm. If I did a great job on these projects, would this be helpful to me academically? I just want to make sure before I accept more of these cases or maybe talk to my PI so I can work on a novel direction. Thanks for advices/suggestions.
As a general rule: publications tend to be the measure of whether something is novel. So, if you are publishing, I'd say you indeed are helping your academic career. If you aren't publishing: that is a major complaint you can bring up with your PI. Here you can use the outsize importance of publications in academia to your advantage. You can debate all day long whether your work is novel enough with your PI, but no one can justify leaving you without publications.
Also, it is important whether these applications are helping you grow as a researcher. Are these applications trivial, mindless work, or do you learn things while working on them? Are you getting faster/better at implementing them? Are you ever surprised at results, or can you predict what the results are going to be before the experiments are done?
Note that breakthroughs in science often rely on a strong grasp of the fundamentals and the context in which the work is done. Having strong experience in applying those algorithms could be essential in later designing them yourself.