Change to links

Author: aaronkw

docs/deepsea.rst

@@ -5,7 +5,7 @@ Sei / DeepSEA
 Introduction
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-Sei is a deep-learning-based framework for systematically predicting sequence regulatory activities and applying sequence information to understand human genetics data. Sei provides a global map from any sequence to regulatory activities, as represented by 40 sequence classes. Each sequence class integrates predictions for 21,907 chromatin profiles (transcription factor, histone marks, and chromatin accessibility profiles across a wide range of cell types) from the underlying Sei deep learning model. You can also find the Sei code repository here (https://github.com/FunctionLab/sei-framework) or read about our manuscript here (https://www.biorxiv.org/content/10.1101/2021.07.29.454384v1).
+Sei is a deep-learning-based framework for systematically predicting sequence regulatory activities and applying sequence information to understand human genetics data. Sei provides a global map from any sequence to regulatory activities, as represented by 40 sequence classes. Each sequence class integrates predictions for 21,907 chromatin profiles (transcription factor, histone marks, and chromatin accessibility profiles across a wide range of cell types) from the underlying Sei deep learning model. You can also find the Sei code repository `here <https://github.com/FunctionLab/sei-framework>`_ or read about our manuscript `here <https://www.biorxiv.org/content/10.1101/2021.07.29.454384v1>`_.
 
 Sequence class-level variant effects are computed by comparing the predictions for the reference and the alternative alleles. A positive score indicates an increase in sequence class activity by the alternative allele and vice versa. Sequence class-level scores are computed by projecting the 21,907 chromatin profile predictions for the sequence to the unit vector that represents each sequence class.