From 5150c3c1471d23c4fa0d73a3ead8f9b937ce06ea Mon Sep 17 00:00:00 2001 From: Rick Masonbrink Date: Tue, 15 Oct 2024 14:53:15 -0500 Subject: [PATCH] typos --- .../Secreted_Protein_Prediction_with_SignalP_and_TMHMM.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/dataAnalysis/GenomeAnnotation/Secreted_Protein_Prediction_with_SignalP_and_TMHMM.md b/dataAnalysis/GenomeAnnotation/Secreted_Protein_Prediction_with_SignalP_and_TMHMM.md index 7510198..69d16ae 100644 --- a/dataAnalysis/GenomeAnnotation/Secreted_Protein_Prediction_with_SignalP_and_TMHMM.md +++ b/dataAnalysis/GenomeAnnotation/Secreted_Protein_Prediction_with_SignalP_and_TMHMM.md @@ -8,7 +8,7 @@ header: overlay_image: /assets/images/dna.jpg --- -Here we will be using a set of predicted proteins from a plant parasitic nematode genome to predict secretion, transmembrane domains, and subcellular localization. +Here we will be using a set of predicted proteins from a plant parasitic nematode genome to predict protein secretion, transmembrane domains, and subcellular localization. **Software used in this tutorial** - SignalP 6.0 [Teufel et al., 2022](https://www.nature.com/articles/s41587-021-01156-3) @@ -131,7 +131,7 @@ TGEQQLKLLTF. ``` # Transmembrane domains ### TMHMM 2.0 -TMHMM 2.0 uses a hidden Markov model (HMM) to predict transmembrane helices in protein sequences. \ +TMHMM 2.0 uses a hidden Markov model (HMM) to predict transmembrane helices in protein sequences. **Install and run TMHMM** ```