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Here are aggregated notes forming a part of the developer documentation on the vidjil-algo.
These notes are a work-in-progress, they are not as polished as the user documentation.
Developers should also have a look at the documentation for bioinformaticians and server administrators, at the issues, at the commit messages, and at the source code.

Development notes -- Vidjil-algo⚓︎

Code organization⚓︎

The algorithm follows roughly those steps:

  1. The germlines are read. Germlines are in the fasta format and are read by the Fasta class (core/fasta.h). Germlines are built using the Germline (or MultiGermline) class (core/germline.h)
  2. The input sequence file (.fasta, .fastq, .gz) is read by an OnlineFasta (core/fasta.h). The difference with the Fasta class being that all the data is not stored in memory but the file is read online, storing only the current entry.
  3. Windows must be extracted from the read, which is done by the WindowExtractor class (core/windowExtractor.h). This class has an extract method which returns a WindowsStorage object (core/windows.h) in which windows are stored.
  4. To save space consumption, all the reads linked to a given window are not stored. Only the longer ones are kept. The BinReadStorage class is used for that purpose (core/read_storage.h).
  5. In the WindowStorage, we now have the information on the clusters and on the abundance of each cluster. However we lack a sequence representative of the cluster. For that purpose the class provides a getRepresentativeComputer method that provides a KmerRepresentativeComputer (core/representative.h). This class can compute a representative sequence using the (long) reads that were stored for a given window.
  6. The representative can then be segmented to determine what V, D and J genes are at play. This is done by the FineSegmenter (core/segment.h).

The xxx germline⚓︎

  • All germlines are inserted in one index using build_with_one_index() and the segmentation method is set to SEG_METHOD_MAX12 to tell that the segmentation must somehow differ.
  • So that the FineSegmenter correctly segments the sequence, the rep_5 and rep_3 members (class Fasta) of the xxx germline are modified by the FineSegmenter. The override_rep5_rep3_from_labels() method from the Germline is the one that overwrites those members with the Fasta corresponding to the affectation found by the KmerSegmenter.



Unit tests are managed using an internal lightweight poorly-designed library that outputs a TAP file. They are organized in the directory algo/tests.

All the tests are defined in the tests.cpp file. But, for the sake of clarity, this file includes other cpp files that incorporate all the tests. A call to make compiles and launches the tests.cpp file, which outputs a TAP file (in case of total success) and creates a tests.cpp.tap file (in every case).

  1. Tap test library

The library is defined in the testing.h file.

Tests must be declared in the tests.h file: 1. Define a new macro (in the enum) corresponding to the test name 2. In declare_tests() use RECORD_TAP_TEST to associate the macro with a description (that will be displayed in the TAP output file).

Then testing can be done using the `TAP_TEST` macro. The macro takes three
arguments. The first one is a boolean that is supposed to be true, the
second is the test name (using the macro defined in `tests.h`) and the
third one (which can be an empty string) is something which is displayed
when the test fails.