diff --git a/README.html b/README.html
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+
+  rCASC
+
+Since the end of the 90’s omics high-throughput technologies have
+generated an enormous amount of data, reaching today an exponential
+growth phase. Analysis of omics big data is a revolutionary means of
+understanding the molecular basis of disease regulation and
+susceptibility, and this resource is accessible to the
+biological/medical community via bioinformatics frameworks. However,
+because of the fast evolution of computation tools and omics methods,
+the /reproducibility crisis/
+<https://en.wikipedia.org/wiki/Replication_crisis> is becoming a very
+important issue [/Nature, 6 July 2018/
+<https://www.nature.com/collections/prbfkwmwvz>] and there is a
+mandatory need to to guarantee robust and reliable results to the
+research community [/Global Engage Blog/
+<http://www.global-engage.com/life-science/reproducibility-computational-biology/>].
+
+Our group is deeply involved in developing workflows that guarantee both
+*functional* (i.e. the information about data and the utilized tools are
+saved in terms of meta-data) and *computation* reproducibility (i.e. the
+real image of the computation environment used to generate the data is
+stored). For this reason we have founded a bioinformatics community
+called /reproducible-bioinformatics.org/
+<http://www.reproducible-bioinformatics.org/> /Kulkarni et al. BMC
+Bioinformatics/ <https://rdcu.be/9gMq> designed to provide to the
+biological community a reproducible bioinformatics ecosystem [/Beccuti
+et al. Bioinformatics 2018/
+<https://academic.oup.com/bioinformatics/article/34/5/871/4562334>].
+
+rCASC, Cluster Analysis of Single Cells, is part of the
+/reproducible-bioinformatics.org/
+<http://www.reproducible-bioinformatics.org/> project and provides
+single cell analysis functionalities within the reproducible rules
+described by Sandve et al. [/PLoS Comp Biol. 2013/
+<http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003285>].
+rCASC is designed to provide a complete workflow (Figure 1) for
+cell-subpopulation discovery.
+
+Fig. 1:rCASC workflow
+
+Fig. 1:rCASC workflow
+
+
+    Installation
+
+|install.packages("devtools")
+library(devtools)
+install_github("kendomaniac/rCASC", ref="master")|
+
+
+    Requirements
+
+You need to have docker installed on your linux machine, for more info
+see this document: https://docs.docker.com/engine/installation/.
+
+The functions in CASC package require that user is sudo or part of a
+docker group. See the following document for more info:
+https://docs.docker.com/engine/installation/linux/ubuntulinux/#/manage-docker-as-a-non-root-user
+
+IMPORTANT The first time casc is installed the downloadContainers needs
+to be executed to download to the local repository the containers that
+are needed for the use of docker4seq
+
+|library(rCASC)
+downloadContainers()|
+
+More info: CASC vignette <http://rpubs.com/rcaloger/285423>
+
diff --git a/man/SCannoByGtf.Rd b/man/SCannoByGtf.Rd
index 2a8e3a2..c997622 100644
--- a/man/SCannoByGtf.Rd
+++ b/man/SCannoByGtf.Rd
@@ -43,7 +43,7 @@ This function executes the docker container annotate.1, where refGenome is used
      system("wget ftp://ftp.ensembl.org/pub/release-92/gtf/mus_musculus/Mus_musculus.GRCm38.92.gtf.gz")
      system("gunzip Mus_musculus.GRCm38.92.gtf.gz")
      scannobyGtf(group="docker", file=paste(getwd(),"lorenz_testSCumi_mm10.csv",sep="/"),
-                   gtf.name="Mus_musculus.GRCm38.92.gtf",
+                   gtf.name="Homo_sapiens.GRCh38.94.gtf",
 biotype="protein_coding", mt=TRUE, ribo.proteins=TRUE,umiXgene=3)
 }