From 6f18ba18aad9345a6a501c72781ee0c4e0e273c7 Mon Sep 17 00:00:00 2001 From: ravel-lab Date: Mon, 9 Sep 2024 11:28:57 -0400 Subject: [PATCH] updated Readme --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index b9c3e82..2d08526 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ SpeciateIT is an algorithm capable of fast, accurate individual sequence taxonomic classification. vSpeciateDB are models built from custom sets of reference sequences for classifying vaginal microbiota. Using a model guide tree and 7th order Markov Chain models to represent bacterial species trained on taxonomy-adjusted amplicon specific regions sequences, speciateIT requires little computational resources, and can quickly process large sequence datasets. SpeciateIT models for the vaginal microbiota include training sets for 16S rRNA gene V1-V3, V3-V4, and V4 regions sequences. "Cat Maps" are provided for each region to indicate which species are indistinguishable at the targeted variable regions. ### vSpeciateDB: -Holm, Johanna (2024). speciateIT: vSpeciateDB Models. figshare. Dataset. https://doi.org/10.6084/m9.figshare.25254229.v3 +Holm, Johanna (2024). speciateIT: vSpeciateDB Models. figshare. Dataset. https://doi.org/10.6084/m9.figshare.25254229 ### Requirements: - **RAM**: Minimum 1 GB for classification. @@ -14,7 +14,7 @@ Holm, Johanna (2024). speciateIT: vSpeciateDB Models. figshare. Dataset. https:/ This software runs on **Linux** or **MacOSX**. See specific executables within bin. 1. Clone repository. -2. Download vSpeciateDB (https://doi.org/10.6084/m9.figshare.25254229.v3) and place into "vSpeciateDB_models". +2. Download vSpeciateDB (https://doi.org/10.6084/m9.figshare.25254229) and place into "vSpeciateDB_models". 3. Unzip vSpeciateDB directories. cd /path/to/speciateIT/vSpeciateDB_models