I am finding it difficult to think of anything that has been truly impactful from this year. It feels like we have reached some form of a plateau in bioinformatics whereby we now have the tools that we can utilise to really understand better the data that we produce, and thus begin to make differences. However, in my interactions with some research groups, it seems as if they are searching for anything novel to do (for publications), as opposed to doing something that can really make a difference. Many researchers lack that ability to be able to see how their data and results can be used to, e.g., improve healthcare - well, either that or they are just not trained to think that way, or actually just not interested.

On that final point, and I may be incorrect, but, it seems like some journals are sacrificing quality for sensationalism. Are scientific journals transforming themselves into general media content?

Apart from anything else, this one was quite cool:

• Read trimming is not required for mapping and quantification of RNA-seq reads

Kevin

Edit: in the cancer field right now, for example, a lot of tools have already been developed and we are now really beginning to probe the inner workings of tumours, with very promising results coming out. In this particular field, therefore, it's now more about utilising the tools that we've already produced in order to bring about good, as to which I alluded (above)

Not sure if I am picking a correct paper because the important part of the work was done by Google's team alone rather than the group of authors listed, but here goes:

https://www.ncbi.nlm.nih.gov/pubmed/31602685

It is about this work:

https://deepmind.com/blog/article/alphafold

I believe Deep Learning continued to lead the way in many subfields of biology, One obvious winner was Alphafold that Mensur mentioned. I would like to offer three more notable contributions -

Deep Learning to identify kinase inhibitors - https://www.nature.com/articles/s41587-019-0224-x

Deep Learning to quantify cancer metastases - https://www.cell.com/cell/fulltext/S0092-8674(19)31269-3

Deep Learning to infer gene expression levels using only DNA sequence - https://www.biorxiv.org/content/10.1101/416685v2.full.pdf+html