Release notes

Release in future

Big new feature brie-count now supports cell barcodes based platforms, including 10x Genomics
provide small test files in the tests folder
Update brie-count manual

Support to use minimum minor isoform frequency to fileter genes (default=0.001)
Add pseudo count (default=0.01) for none-empty element in both unique counts for more robust results
Reduce the sample size for Monte Carlo Expectation (10 to 3) for computational efficiency
Restructure the arguments in brie-quant
Initialise the example notebook on multiple sclerosis data

Change the whole BRIE model from MCMC sampler (v1) to variational inference (v2)
Change the usage of each read to a summarised read counts for speedup
Support splicing quantification without any feature or cell features or gene features or both types of features.
Support detection of variable splicing event associated with cell features
Support acceleration with Graphic card (Nvidia GPU)
Compatible with Scanpy analysis suite with a variety of plotting functions
Restructure the whole package
BRIE earlier version is still avaible with brie1

move __version__ into a separate file, so import brie is not required before installation.
support cram file as input aligned reads

split the pre-processing functions in BRIE to another separate package BRIE-kit, as some functions in the pre-processing require Python2 environment. So, BRIE will keep two functions brie and brie-diff, while BRIE-kit will have briekit-event, briekit-event-filter, and briekit-factor. See BRIE-KIT: https://github.com/huangyh09/briekit/wiki

brie-diff takes multiple cells, which could handle pair-wise comparisons for 100 cells in ~10min with 30 CPUs; and 1000 cells within a few hours.
Simulation wraps on Spanki are provided for simulating splicing events at different coverages or drop-out probability and drop-out rate for single cells: https://github.com/huangyh09/brie/tree/master/simulator