From c3b666bc12750a3e48de242ab6cc231f62675a39 Mon Sep 17 00:00:00 2001 From: cailigd Date: Wed, 7 Dec 2022 23:24:19 +0800 Subject: [PATCH] update docs --- README.md | 2 +- docs/usage.rst | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index eb14af3..0da3f05 100644 --- a/README.md +++ b/README.md @@ -9,7 +9,7 @@ ## 1. Introduction -Germline mutation rates are crucial parameters in genetics, genomics and evolutionary biology. It is long known that mutation rates vary substantially across the genome, but existing methods can only obtain very rough estimates of local mutation rates and are difficult to be applied to non-model species. +Germline mutation rates are important in genetics, genomics and evolutionary biology. It is long known that mutation rates vary substantially across the genome, but existing methods can only obtain very rough estimates of local mutation rates and are difficult to be applied to non-model species. **MuRaL**, short for **Mu**tation **Ra**te **L**earner, is a generalizable framework to estimate single-nucleotide mutation rates based on deep learning. MuRaL has better predictive performance at different scales than current state-of-the-art methods. Moreover, it can generate genome-wide mutation rate maps with rare variants from a moderate number of sequenced individuals (e.g. ~100 individuals), and can leverage transfer learning to further reduce data and time requirements. It can be applied to many sequenced species with population polymorphism data. diff --git a/docs/usage.rst b/docs/usage.rst index f29c144..67ed9ec 100644 --- a/docs/usage.rst +++ b/docs/usage.rst @@ -1,7 +1,7 @@ Overview -------- -Germline mutation rates are crucial parameters in genetics, genomics and +Germline mutation rates are important in genetics, genomics and evolutionary biology. It is long known that mutation rates vary substantially across the genome, but existing methods can only obtain very rough estimates of local mutation rates and are difficult to be @@ -565,8 +565,8 @@ available at `ScienceDB `__. Citation -------- -Fang Y, Deng S, Li C. 2022. A generalizable deep learning framework for -inferring fine-scale germline mutation rate maps. *Nature Machine Intelligence* (2022) +Fang Y, Deng S, Li C. A generalizable deep learning framework for inferring +fine-scale germline mutation rate maps. *Nature Machine Intelligence* (2022) `doi:10.1038/s42256-022-00574-5 `__ Contact