# Tag Info

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JVCF is a JSON schema for describing genetic variants. BioJSON is a JSON schema for multiple sequence alignments. The output of mygene.info's gene annotation web service is JSON-formatted. You might build one of your own schemas from any or all of these, as inspiration for structure and content. Consider that most genomics formats are historically ...

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"Feature Barcoding" refers to using antibodies linked to specific oligos. The antibodies can then bind to cell surface markers of interest and the oligos they're conjugated with turned into normal scRNA-seq reads. This effectively means you can get a FACs profile for each cell without needing to run it through a FACs sorter and individually label it. The "...

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Protein autocorrelation is similar to usual autocorrelation of time series (that is, function $r(k)$ measuring correlation between values lagged by $k$), except that instead of time dimension we slide along the backbone of amino acid chain. The "dependent variable" can be any quantifiable property of the amino acids - then the autocorrelation measures ...

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If you want to do this with biopython, the SeqUtils package could be a solution.

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Just a recommendation: Be careful when making your data sets. P31327_57, P31327_119, P31327_157 are all annotated as "By similarity" to the mouse entry in Swiss-Prot https://www.uniprot.org/uniprot/P31327#ptm_processing, which means there is no direct experimental evidence in the human entry. If you do have publications that report experimental ...

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This answer is no longer on topic. The question is about what is the best machine learning algorithm to use to analyse atomic coordinates from an MD trajectory in order to infer a novel properties.   Also, it should be preface for any confused reader that unfortunately "feature" is a technical term in both genetics and statistics. For the former a ...

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I've written a Python library called PyReference which converts a GTF / GFF (RefSeq and Ensembl) into a gzipped JSON file. You can just use this if you like but there's also a Python wrapper around the JSON, which allows you to write genomics code more naturally. Python isn't known for being fast but the library function for reading a JSON file is highly ...

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BioJS has capacity to read and write GFF as streams, it might have something of interest such as this: https://github.com/biojs-io/biojs-io-gff

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If you are training a machine learning algorithm you probably want to train against things you know to be true, rather than making predictions from the sequence and training against the predictions. In this case you will want to use protein structures with the relevant ligands bound as your data. If you go to the RCSB PDB advanced search you can search for ...

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