Forum:What were the most impactful Bioinformatics papers published this year
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4.5 years ago
c_u ▴ 520

2019 is coming to a close, so I thought of asking this question - what were some of the Bioinformatics papers published this year that you thought were the most impactful. Preprints count.

I am not looking for papers that got the most number of citations (which would be a poor metric for papers published recently, and certainly bad for preprints). I think impactful could be thought of in terms of the importance of the problem it solved and the efficiency with which it did that (feel free to suggest changes).

papers publications • 1.9k views
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...it's already the end of the year? * awakes from a bioinformatics-induced trance-like state *

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Impactful is going to be relative so here goes:
Large scale long read sequencing at genomic level: https://www.biorxiv.org/content/10.1101/848366v1
Computational histopathology: https://www.biorxiv.org/content/10.1101/813543v1
Better CRISPR: https://www.nature.com/articles/s41586-019-1711-4

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4.5 years ago

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:

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)

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This summarises my feeling too I think. Nothing jumps out at me as having made a splash. Mensur's suggestion is one I remember thinking 'wow' about, but I don't think it fits the criteria of impactful while they keep it behind closed doors.

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4.5 years ago
Mensur Dlakic ★ 27k

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

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Unless they make the tool available to researchers (which I don't believe they have yet, or will) it remains more of a curiosity than an important or impactful paper at this stage IMHO.

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GPCRs are most interesting group of receptors, at least in terms of pharmacology:

https://www.ncbi.nlm.nih.gov/pubmed/31675498

Switchable protein systems de novo:

https://www.ncbi.nlm.nih.gov/pubmed/31341284

https://www.ncbi.nlm.nih.gov/pubmed/31341280

Computational design of peptides to modulate natural cytokines:

https://www.ncbi.nlm.nih.gov/pubmed/30626941

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4.5 years ago
c_u ▴ 520

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

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