-
Notifications
You must be signed in to change notification settings - Fork 3
2. Methods
Depending on the data set type, different methods are used to explore the underlying information.
Archived data is uploaded as a BytesIO stream. That stream is then written to a file that represents a 1 to 1 copy of the originally uploaded data set. That archive is then extracted into the Data/uploaded/omic_name folder for further use. The next step that is shared among every archived data set is the addition of temporality. If file names are starting with D or W, then a user is confronted with an input box where a start date should be entered. After inputting the start date, every file name is converted to the ISO 8601 format (yyyy-mm-dd). If file names are already according to this format, the previous step is skipped. If there is an error during this process, an error message is shown to the user.
If an archived data set contains FASTA files (extensions .fa or .faa), the user is presented with an option for creating an additional data set with physico-chemical properties. These properties are different for genomics and proteomics data, as can be seen below:
- Genomics: Hydrogen bond, Stacking energy, Solvation (see paper Physico-chemical fingerprinting of RNA genes)
- Proteomics: Molecular weight, Gravy, Aromaticity, Instability index, Isoelectric point, Secondary structure fraction in helix, Secondary structure fraction in turn, Secondary structure fraction in sheet, Electricity, Fraction aliphatic, Fraction uncharged polar, Fraction polar, Fraction hydrophobic, Fraction positive, Fraction sulfur, Fraction negative, Fraction amide, Fraction alcohol
Since each FASTA file can contain multiple sequences, these physico-chemical values are averaged so that each file has a fixed set of physico-chemical values. When these values are calculated for all files, temporality is added to the newly-created data set using file names.
The next step of preprocessing FASTA files is embedding those files (and sequences within) into 100-dimensional vectors. The length of those vectors was chosen empirically. Word2Vec model is used to embed varying-length sequences into fixed-size vectors. A vector corresponding to one FASTA file represents an average vector of all sequences contained in that FASTA file. That means MOVIS is robust to the number of sequences in each FASTA file. When the whole archived data set (holding N FASTA files) is processed, the end result is a Nx101-dimensional matrix containing one 100-dimensional vector per FASTA file plus an additional dimension holding the temporal information of that file.