From 033f2b38bce916f9613230e78315e63c1e716a13 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?David=20Hrb=C3=A1=C4=8D?= <david@hrbac.cz>
Date: Fri, 27 Jan 2017 14:19:39 +0100
Subject: [PATCH] Corrected double spaces

---
 .../software/omics-master/overview.md         | 22 +++++++++----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/docs.it4i/anselm-cluster-documentation/software/omics-master/overview.md b/docs.it4i/anselm-cluster-documentation/software/omics-master/overview.md
index bc366f79e..7c3876e23 100644
--- a/docs.it4i/anselm-cluster-documentation/software/omics-master/overview.md
+++ b/docs.it4i/anselm-cluster-documentation/software/omics-master/overview.md
@@ -119,7 +119,7 @@ Output:VCF
 
 Variant Call Format (VCF)
 
-VCF (3) is a standardized format for storing the most prevalent types of sequence variation, including SNPs, indels and larger structural variants, together with rich annotations. The format was developed with the primary intention to represent human genetic variation, but its use is not restricted >to diploid genomes and can be used in different contexts as well. Its flexibility and user extensibility allows representation of a wide variety of genomic variation with respect to a single reference sequence.
+VCF (3) is a standardized format for storing the most prevalent types of sequence variation, including SNPs, indels and larger structural variants, together with rich annotations. The format was developed with the primary intention to represent human genetic variation, but its use is not restricted to diploid genomes and can be used in different contexts as well. Its flexibility and user extensibility allows representation of a wide variety of genomic variation with respect to a single reference sequence.
 
 A VCF file consists of a header section and a data section. The header contains an arbitrary number of metainformation lines, each starting with characters ‘##’, and a TAB delimited field definition line, starting with a single ‘#’ character. The meta-information header lines provide a standardized description of tags and annotations used in the data section. The use of meta-information allows the information stored within a VCF file to be tailored to the dataset in question. It can be also used to provide information about the means of file creation, date of creation, version of the reference sequence, software used and any other information relevant to the history of the file. The field definition line names eight mandatory columns, corresponding to data columns representing the chromosome (CHROM), a 1-based position of the start of the variant (POS), unique identifiers of the variant (ID), the reference allele (REF), a comma separated list of alternate non-reference alleles (ALT), a phred-scaled quality score (QUAL), site filtering information (FILTER) and a semicolon separated list of additional, user extensible annotation (INFO). In addition, if samples are present in the file, the mandatory header columns are followed by a FORMAT column and an arbitrary number of sample IDs that define the samples included in the VCF file. The FORMAT column is used to define the information contained within each subsequent genotype column, which consists of a colon separated list of fields. For example, the FORMAT field GT:GQ:DP in the fourth data entry of Figure 1a indicates that the subsequent entries contain information regarding the genotype, genotype quality and read depth for each sample. All data lines are TAB delimited and the number of fields in each data line must match the number of fields in the header line. It is strongly recommended that all annotation tags used are declared in the VCF header section.
 
@@ -131,15 +131,15 @@ two bases by another base (SAMPLE2); the second line shows a SNP and an insertio
 
 ### Annotating
 
-Component:  HPG-Variant
+Component: HPG-Variant
 
 The functional consequences of every variant found are then annotated using the HPG-Variant software, which extracts from CellBase, the Knowledge database, all the information relevant on the predicted pathologic effect of the variants.
 
 VARIANT (VARIant Analysis Tool) (4) reports information on the variants found that include consequence type and annotations taken from different databases and repositories (SNPs and variants from dbSNP and 1000 genomes, and disease-related variants from the Genome-Wide Association Study (GWAS) catalog, Online Mendelian Inheritance in Man (OMIM), Catalog of Somatic Mutations in Cancer (COSMIC) mutations, etc. VARIANT also produces a rich variety of annotations that include information on the regulatory (transcription factor or miRNAbinding sites, etc.) or structural roles, or on the selective pressures on the sites affected by the variation. This information allows extending the conventional reports beyond the coding regions and expands the knowledge on the contribution of non-coding or synonymous variants to the phenotype studied.
 
- Input:  VCF
+ Input: VCF
 
- Output:  The output of this step is the Variant Calling Format (VCF) file, which contains changes with respect to the reference genome with the corresponding QC and functional annotations.
+ Output: The output of this step is the Variant Calling Format (VCF) file, which contains changes with respect to the reference genome with the corresponding QC and functional annotations.
 
 #### CellBase
 
@@ -225,7 +225,7 @@ second one.
 
       --prefix. Prefix for PBS Job names.
 
-      -s, --start & -e, --end.  Initial and final stage. If we want to launch the pipeline in a specific stage we must use -s. If we want to end the pipeline in a specific stage we must use -e.
+      -s, --start & -e, --end. Initial and final stage. If we want to launch the pipeline in a specific stage we must use -s. If we want to end the pipeline in a specific stage we must use -e.
 
       --log. Using log argument NGSpipeline will prompt all the logs to this file.
 
@@ -338,13 +338,13 @@ The output folder contains all the subfolders with the intermediate data. This f
 
 ![TEAM upload panel. Once the file has been uploaded, a panel must be chosen from the Panel list. Then, pressing the Run button the diagnostic process starts.]\((../../../img/fig7.png)
 
- Figure 7.  _TEAM upload panel._ _Once the file has been uploaded, a panel must be chosen from the Panel_ list. Then, pressing the Run button the diagnostic process starts.
+ Figure 7. _TEAM upload panel._ _Once the file has been uploaded, a panel must be chosen from the Panel_ list. Then, pressing the Run button the diagnostic process starts.
 
 Once the file has been uploaded, a panel must be chosen from the Panel list. Then, pressing the Run button the diagnostic process starts. TEAM searches first for known diagnostic mutation(s) taken from four databases: HGMD-public (20), [HUMSAVAR](http://www.uniprot.org/docs/humsavar), ClinVar (29) and COSMIC (23).
 
 ![The panel manager. The elements used to define a panel are (A) disease terms, (B) diagnostic mutations and (C) genes. Arrows represent actions that can be taken in the panel manager. Panels can be defined by using the known mutations and genes of a particular disease. This can be done by dragging them to the Primary Diagnostic box (action D). This action, in addition to defining the diseases in the Primary Diagnostic box, automatically adds the corresponding genes to the Genes box. The panels can be customized by adding new genes (action F) or removing undesired genes (action G). New disease mutations can be added independently or associated to an already existing disease term (action E). Disease terms can be removed by simply dragging themback (action H).](../../../img/fig7x.png)
 
- Figure 7.  The panel manager. The elements used to define a panel are ( A ) disease terms, ( B ) diagnostic mutations and ( C ) genes. Arrows represent actions that can be taken in the panel manager. Panels can be defined by using the known mutations and genes of a particular disease. This can be done by dragging them to the  Primary Diagnostic  box (action  D ). This action, in addition to defining the diseases in the  Primary Diagnostic  box, automatically adds the corresponding genes to the  Genes  box. The panels can be customized by adding new genes (action  F ) or removing undesired genes (action G). New disease mutations can be added independently or associated to an already existing disease term (action  E ). Disease terms can be removed by simply dragging them back (action  H ).
+ Figure 7. The panel manager. The elements used to define a panel are ( A ) disease terms, ( B ) diagnostic mutations and ( C ) genes. Arrows represent actions that can be taken in the panel manager. Panels can be defined by using the known mutations and genes of a particular disease. This can be done by dragging them to the Primary Diagnostic box (action D ). This action, in addition to defining the diseases in the Primary Diagnostic box, automatically adds the corresponding genes to the Genes box. The panels can be customized by adding new genes (action F ) or removing undesired genes (action G). New disease mutations can be added independently or associated to an already existing disease term (action E ). Disease terms can be removed by simply dragging them back (action H ).
 
 For variant discovering/filtering we should upload the VCF file into BierApp by using the following form:
 
@@ -366,7 +366,7 @@ Each prioritization (‘job’) has three associated screens that facilitate the
 1. Medina I, De Maria A, Bleda M, Salavert F, Alonso R, Gonzalez CY, Dopazo J: VARIANT: Command Line, Web service and Web interface for fast and accurate functional characterization of variants found by Next-Generation Sequencing. Nucleic Acids Res 2012, 40:W54-58.
 1. Bleda M, Tarraga J, de Maria A, Salavert F, Garcia-Alonso L, Celma M, Martin A, Dopazo J, Medina I: CellBase, a comprehensive collection of RESTful web services for retrieving relevant biological information from heterogeneous sources. Nucleic Acids Res 2012, 40:W609-614.
 1. Flicek,P., Amode,M.R., Barrell,D., Beal,K., Brent,S., Carvalho-Silva,D., Clapham,P., Coates,G., Fairley,S., Fitzgerald,S. et al. (2012) Ensembl 2012. Nucleic Acids Res., 40, D84–D90.
-1. UniProt Consortium. (2012) Reorganizing the protein space at the Universal Protein Resource (UniProt). Nucleic   Acids Res., 40, D71–D75.
+1. UniProt Consortium. (2012) Reorganizing the protein space at the Universal Protein Resource (UniProt). Nucleic Acids Res., 40, D71–D75.
 1. Kozomara,A. and Griffiths-Jones,S. (2011) miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res., 39, D152–D157.
 1. Xiao,F., Zuo,Z., Cai,G., Kang,S., Gao,X. and Li,T. (2009) miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res., 37, D105–D110.
 1. Hsu,S.D., Lin,F.M., Wu,W.Y., Liang,C., Huang,W.C., Chan,W.L., Tsai,W.T., Chen,G.Z., Lee,C.J., Chiu,C.M. et al. (2011) miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic Acids Res., 39, D163–D169.
@@ -375,15 +375,15 @@ Each prioritization (‘job’) has three associated screens that facilitate the
 1. Smith,B., Ashburner,M., Rosse,C., Bard,J., Bug,W., Ceusters,W., Goldberg,L.J., Eilbeck,K., Ireland,A., Mungall,C.J. et al. (2007) The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat. Biotechnol., 25, 1251–1255.
 1. Hunter,S., Jones,P., Mitchell,A., Apweiler,R., Attwood,T.K.,Bateman,A., Bernard,T., Binns,D., Bork,P., Burge,S. et al. (2012) InterPro in 2011: new developments in the family and domain prediction database. Nucleic Acids Res.,40, D306–D312.
 1. Sherry,S.T., Ward,M.H., Kholodov,M., Baker,J., Phan,L., Smigielski,E.M. and Sirotkin,K. (2001) dbSNP: the NCBI database of genetic variation. Nucleic Acids Res., 29, 308–311.
-1. Altshuler,D.M., Gibbs,R.A., Peltonen,L., Dermitzakis,E., Schaffner,S.F., Yu,F., Bonnen,P.E., de Bakker,P.I.,  Deloukas,P., Gabriel,S.B. et al. (2010) Integrating common and rare genetic variation in diverse human populations. Nature, 467, 52–58.
+1. Altshuler,D.M., Gibbs,R.A., Peltonen,L., Dermitzakis,E., Schaffner,S.F., Yu,F., Bonnen,P.E., de Bakker,P.I., Deloukas,P., Gabriel,S.B. et al. (2010) Integrating common and rare genetic variation in diverse human populations. Nature, 467, 52–58.
 1. 1000 Genomes Project Consortium. (2010) A map of human genome variation from population-scale sequencing. Nature, 467, 1061–1073.
-1. Hindorff,L.A., Sethupathy,P., Junkins,H.A., Ramos,E.M., Mehta,J.P., Collins,F.S. and Manolio,T.A. (2009)   Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl Acad. Sci. USA, 106, 9362–9367.
+1. Hindorff,L.A., Sethupathy,P., Junkins,H.A., Ramos,E.M., Mehta,J.P., Collins,F.S. and Manolio,T.A. (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl Acad. Sci. USA, 106, 9362–9367.
 1. Stenson,P.D., Ball,E.V., Mort,M., Phillips,A.D., Shiel,J.A., Thomas,N.S., Abeysinghe,S., Krawczak,M. and Cooper,D.N. (2003) Human gene mutation database (HGMD): 2003 update. Hum. Mutat., 21, 577–581.
 1. Johnson,A.D. and O’Donnell,C.J. (2009) An open access database of genome-wide association results. BMC Med. Genet, 10, 6.
 1. McKusick,V. (1998) A Catalog of Human Genes and Genetic Disorders, 12th edn. John Hopkins University Press,Baltimore, MD.
 1. Forbes,S.A., Bindal,N., Bamford,S., Cole,C., Kok,C.Y., Beare,D., Jia,M., Shepherd,R., Leung,K., Menzies,A. et al. (2011) COSMIC: mining complete cancer genomes in the catalogue of somatic mutations in cancer. Nucleic Acids Res., 39, D945–D950.
 1. Kerrien,S., Aranda,B., Breuza,L., Bridge,A., Broackes-Carter,F., Chen,C., Duesbury,M., Dumousseau,M., Feuermann,M., Hinz,U. et al. (2012) The Intact molecular interaction database in 2012. Nucleic Acids Res., 40, D841–D846.
-1. Croft,D., O’Kelly,G., Wu,G., Haw,R., Gillespie,M., Matthews,L., Caudy,M., Garapati,P., Gopinath,G., Jassal,B. et al. (2011) Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res.,    39, D691–D697.
+1. Croft,D., O’Kelly,G., Wu,G., Haw,R., Gillespie,M., Matthews,L., Caudy,M., Garapati,P., Gopinath,G., Jassal,B. et al. (2011) Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res., 39, D691–D697.
 1. Demir,E., Cary,M.P., Paley,S., Fukuda,K., Lemer,C., Vastrik,I.,Wu,G., D’Eustachio,P., Schaefer,C., Luciano,J. et al. (2010) The BioPAX community standard for pathway data sharing. Nature Biotechnol., 28, 935–942.
 1. Alemán Z, García-García F, Medina I, Dopazo J (2014): A web tool for the design and management of panels of genes for targeted enrichment and massive sequencing for clinical applications. Nucleic Acids Res 42: W83-7.
 1. [Alemán A](http://www.ncbi.nlm.nih.gov/pubmed?term=Alem%C3%A1n%20A%5BAuthor%5D&cauthor=true&cauthor_uid=24803668)>, [Garcia-Garcia F](http://www.ncbi.nlm.nih.gov/pubmed?term=Garcia-Garcia%20F%5BAuthor%5D&cauthor=true&cauthor_uid=24803668)>, [Salavert F](http://www.ncbi.nlm.nih.gov/pubmed?term=Salavert%20F%5BAuthor%5D&cauthor=true&cauthor_uid=24803668)>, [Medina I](http://www.ncbi.nlm.nih.gov/pubmed?term=Medina%20I%5BAuthor%5D&cauthor=true&cauthor_uid=24803668)>, [Dopazo J](http://www.ncbi.nlm.nih.gov/pubmed?term=Dopazo%20J%5BAuthor%5D&cauthor=true&cauthor_uid=24803668)> (2014). A web-based interactive framework to assist in the prioritization of disease candidate genes in whole-exome sequencing studies. [Nucleic Acids Res.](http://www.ncbi.nlm.nih.gov/pubmed/?term=BiERapp "Nucleic acids research.")>42 :W88-93.
-- 
GitLab