From 71ab718c2186c1ca19da039cb0e3a0ba689e4487 Mon Sep 17 00:00:00 2001
From: Tham Cheng Yong <25361260+cytham@users.noreply.github.com>
Date: Wed, 4 Mar 2020 15:31:18 +0800
Subject: [PATCH] Update README.md

---
 README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 8cc0b1a..3d2b739 100644
--- a/README.md
+++ b/README.md
@@ -107,7 +107,7 @@ cp hs-blastn ~/bin
 ```
 
 ## Documentation
-See [Wiki](https://github.com/cytham/nanovar/wiki) for more information.
+See [wiki](https://github.com/cytham/nanovar/wiki) for more information.
 
 ## Versioning
 See [CHANGELOG](./CHANGELOG.txt)
@@ -115,7 +115,7 @@ See [CHANGELOG](./CHANGELOG.txt)
 ## Citation
 If you use NanoVar, please cite:
 
-Tham, C., Tirado-Magallanes, R., Goh, Y. et al. NanoVar: accurate characterization of patients’ genomic structural variants using low-depth nanopore sequencing. Genome Biol 21, 56 (2020). https://doi.org/10.1186/s13059-020-01968-7
+Tham, CY., Tirado-Magallanes, R., Goh, Y. et al. NanoVar: accurate characterization of patients’ genomic structural variants using low-depth nanopore sequencing. Genome Biol 21, 56 (2020). https://doi.org/10.1186/s13059-020-01968-7
 
 
 ## Authors
@@ -131,4 +131,4 @@ This project is licensed under GNU General Public License - see [LICENSE.txt](./
 ## Simulation datasets and scripts used in the manuscript
 SV simulation datasets used in the manuscript can be downloaded [here](https://doi.org/10.5281/zenodo.3569479 ). Scripts used for simulation dataset generation and tool performance comparison are available [here](./scripts).
 
-Although NanoVar is provided with a universal model and threshold score, instructions required for building a custom neural-network model is available [here](https://github.com/cytham/nanovar/wiki/Model-training).
\ No newline at end of file
+Although NanoVar is provided with a universal model and threshold score, instructions required for building a custom neural-network model is available [here](https://github.com/cytham/nanovar/wiki/Model-training).