updating to v0.2.3

README.md

@@ -48,15 +48,7 @@ Virus genomes or segments are considered "detected" if:
 
 `EsViritu -h`
 
-3) (OPTIONAL BUT RECOMMENDED) Install the `R` package `dataui` manually in an R session. Without `dataui` reports won't show genome coverage sparklines.
-
-`R`
-
-then:
-
-`remotes::install_github("timelyportfolio/dataui")`
-
-4)  Download the database in the `Esviritu` directory (\~300 MB when decompressed).
+3)  Download the database (\~300 MB when decompressed).
 
 `cd EsViritu` *or `cd` where you want the database to reside*
 
@@ -72,10 +64,18 @@ should return `8e207e6a9465d7e40e948d7559b014c4`
 
 DB files should be in `DBs/v2.0.2`
 
-5)  Set the database path:
+4)  Set the database path:
 
 `conda env config vars set ESVIRITU_DB=/path/to/DBs/v2.0.2`
 
+5) (OPTIONAL BUT RECOMMENDED) Install the `R` package `dataui` manually in an R session. Without `dataui` reports won't show genome coverage sparklines.
+
+`R`
+
+then:
+
+`remotes::install_github("timelyportfolio/dataui")`
+
 ### Developmental verision
 
 1)  Clone repo
@@ -98,15 +98,8 @@ DB files should be in `DBs/v2.0.2`
 
 `pip install .`
 
-6) (OPTIONAL BUT RECOMMENDED) Install the `R` package `dataui` manually in an R session. Without `dataui` reports won't show genome coverage sparklines.
 
-`R`
-
-then:
-
-`remotes::install_github("timelyportfolio/dataui")`
-
-7)  Download the database in the `Esviritu` directory (\~300 MB when decompressed).
+6)  Download the database (\~300 MB when decompressed).
 
 `cd EsViritu` *or `cd` where you want the database to reside*
 
@@ -122,10 +115,18 @@ should return `8e207e6a9465d7e40e948d7559b014c4`
 
 DB files should be in `DBs/v2.0.2`
 
-8)  Set the database path:
+7)  Set the database path:
 
 `conda env config vars set ESVIRITU_DB=/path/to/DBs/v2.0.2`
 
+8) (OPTIONAL BUT RECOMMENDED) Install the `R` package `dataui` manually in an R session. Without `dataui` reports won't show genome coverage sparklines.
+
+`R`
+
+then:
+
+`remotes::install_github("timelyportfolio/dataui")`
+
 ## (OPTIONAL) Database for filtering out host reads and spike-ins
 
 You could filter unwanted sequences out upstream of this tool, but this will allow you to do it within `EsViritu` using `minimap2`. The pipeline script will look for a file at `filter_seqs/filter_seqs.fna` which could be any fasta-formatted sequence file you want to use to remove matching reads (e.g. from host or spike-in).

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "EsViritu"
-version = "0.2.2"
+version = "0.2.3"
 authors = [
   { name="Mike Tisza", email="[email protected]" },
 ]

src/EsViritu/EsViritu.py

@@ -18,7 +18,7 @@ def str2bool(v):
 esviritu_script_path = os.path.abspath(pathname)      
 print(esviritu_script_path) 
 
-__version__='0.2.2'
+__version__='0.2.3'
 
 def EsViritu():
     parser = argparse.ArgumentParser(description='EsViritu is a read mapping pipeline for detection and measurement of human and animal virus pathogens from short read metagenomic or clinical samples. Version ' + str(__version__))