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All that information is available through various databases and APIs, but the exact pipeline you are looking for might not be easily accessible via a nice user

All that information is available through various databases and APIs, but the exact pipeline you are looking for might not be easily accessible via a nice user interface. It requires a lot of "glue" code to to connect the data from one resource to another.

malux85 5y ago
Can you please list them? Then I can write all the glue code and pull them together.
joshuamcginnis 5y ago
My idea is to essentially be the glue and build the interface.
- eweitz 5y ago
  Some notes towards those ends: WikiPathways supports advanced queries via their SPARQL API and UI. See [1] and [2]. I find WikiPathways nice because it lets logged-in users create and edit pathways, with a low barrier to entry. I've been building a way to find related genes using biochemical pathways [3]. The source code linked there includes practical examples for fetching information on genes in those pathways, which you rightly note is needed for something compelling. That and other code there might help spark ideas for you on how to glue together various biochemistry and molecular biology APIs to achieve your vision. I'm currently working on a way to drastically expand the set of organisms and pathways covered by WikiPathways. Yeast has 66 pathways there, compared to 1319 for human. By doing fast ortholog detection at runtime (using another SPARQL API, provided by OrthoDB [4]) I'm hoping to be able to convert relevant annotated pathways across organisms, e.g. human to yeast, mouse to rat, Arabidopsis to rice -- and vice versa. [1] http://sparql.wikipathways.org http://sparql.wikipathways.org [2] https://www.wikipathways.org/index.php/Help:WikiPathways_Sparql_queries https://www.wikipathways.org/index.php/Help:WikiPathways_Spa... [3] https://eweitz.github.io/ideogram/related-genes?q=RAD51&org=homo-sapiens https://eweitz.github.io/ideogram/related-genes?q=RAD51&org=... [4] https://sparql.orthodb.org https://sparql.orthodb.org
ravila4 5y ago
As far as I know genetic construct design remains a mostly manual process because the decision tree that goes into creating a viable clone is complicated to say the least. There are a lot of variables to optimize, from choosing the right expression vector, to cutting and gluing the right genes at the right spot. I think that in many cases, the science behind genetic engineering is mostly empirical. Biologists will often spend a long time to figure out how to express one particular product, and then apply that knowledge to similar products, but if they move to something vastly different, there is no guarantee of success. Automating the process is a valuable effort nevertheless. I think because of it's complexity, it would require a combination ofknowledge-graph based reasoing, with AI. There are databases such as Addgene: https://www.addgene.org/search/catalog/plasmids/?q=Cas9 https://www.addgene.org/search/catalog/plasmids/?q=Cas9, in which you can search existing plasmids created by the scientific community. I think this could be a valuable resource for a machine learning approach.

malux85 5y ago

Can you please list them? Then I can write all the glue code and pull them together.

joshuamcginnis 5y ago

My idea is to essentially be the glue and build the interface.

eweitz 5y ago
Some notes towards those ends: WikiPathways supports advanced queries via their SPARQL API and UI. See [1] and [2]. I find WikiPathways nice because it lets logged-in users create and edit pathways, with a low barrier to entry. I've been building a way to find related genes using biochemical pathways [3]. The source code linked there includes practical examples for fetching information on genes in those pathways, which you rightly note is needed for something compelling. That and other code there might help spark ideas for you on how to glue together various biochemistry and molecular biology APIs to achieve your vision. I'm currently working on a way to drastically expand the set of organisms and pathways covered by WikiPathways. Yeast has 66 pathways there, compared to 1319 for human. By doing fast ortholog detection at runtime (using another SPARQL API, provided by OrthoDB [4]) I'm hoping to be able to convert relevant annotated pathways across organisms, e.g. human to yeast, mouse to rat, Arabidopsis to rice -- and vice versa. [1] http://sparql.wikipathways.org http://sparql.wikipathways.org [2] https://www.wikipathways.org/index.php/Help:WikiPathways_Sparql_queries https://www.wikipathways.org/index.php/Help:WikiPathways_Spa... [3] https://eweitz.github.io/ideogram/related-genes?q=RAD51&org=homo-sapiens https://eweitz.github.io/ideogram/related-genes?q=RAD51&org=... [4] https://sparql.orthodb.org https://sparql.orthodb.org

ravila4 5y ago

As far as I know genetic construct design remains a mostly manual process because the decision tree that goes into creating a viable clone is complicated to say the least. There are a lot of variables to optimize, from choosing the right expression vector, to cutting and gluing the right genes at the right spot. I think that in many cases, the science behind genetic engineering is mostly empirical. Biologists will often spend a long time to figure out how to express one particular product, and then apply that knowledge to similar products, but if they move to something vastly different, there is no guarantee of success. Automating the process is a valuable effort nevertheless. I think because of it's complexity, it would require a combination ofknowledge-graph based reasoing, with AI. There are databases such as Addgene: https://www.addgene.org/search/catalog/plasmids/?q=Cas9 https://www.addgene.org/search/catalog/plasmids/?q=Cas9, in which you can search existing plasmids created by the scientific community. I think this could be a valuable resource for a machine learning approach.