3. -> 3.0

docs.it4i/anselm-cluster-documentation/capacity-computing.md

@@ -15,8 +15,8 @@ However, executing huge number of jobs via the PBS queue may strain the system.
 
 ## Policy
 
-1.  A user is allowed to submit at most 100 jobs. Each job may be [a job array](capacity-computing/#job-arrays).
-2.  The array size is at most 1000 subjobs.
+1. A user is allowed to submit at most 100 jobs. Each job may be [a job array](capacity-computing/#job-arrays).
+1. The array size is at most 1000 subjobs.
 
 ## Job Arrays
 
@@ -286,9 +286,9 @@ In this example, the jobscript executes in multiple instances in parallel, on al
 
 When deciding this values, think about following guiding rules:
 
-1.  Let n=N/16.  Inequality (n+1) \* T < W should hold. The N is number of tasks per subjob, T is expected single task walltime and W is subjob walltime. Short subjob walltime improves scheduling and job throughput.
-2.  Number of tasks should be modulo 16.
-3.  These rules are valid only when all tasks have similar task walltimes T.
+1. Let n=N/16.  Inequality (n+1) \* T < W should hold. The N is number of tasks per subjob, T is expected single task walltime and W is subjob walltime. Short subjob walltime improves scheduling and job throughput.
+1. Number of tasks should be modulo 16.
+1. These rules are valid only when all tasks have similar task walltimes T.
 
 ### Submit the Job Array
 

docs.it4i/anselm-cluster-documentation/job-priority.md

@@ -6,9 +6,9 @@ Scheduler gives each job an execution priority and then uses this job execution
 
 Job execution priority on Anselm is determined by these job properties (in order of importance):
 
-1.  queue priority
-2.  fair-share priority
-3.  eligible time
+1. queue priority
+1. fair-share priority
+1. eligible time
 
 ### Queue Priority
 

docs.it4i/anselm-cluster-documentation/job-submission-and-execution.md

@@ -4,12 +4,12 @@
 
 When allocating computational resources for the job, please specify
 
-1.  suitable queue for your job (default is qprod)
-2.  number of computational nodes required
-3.  number of cores per node required
-4.  maximum wall time allocated to your calculation, note that jobs exceeding maximum wall time will be killed
-5.  Project ID
-6.  Jobscript or interactive switch
+1. suitable queue for your job (default is qprod)
+1. number of computational nodes required
+1. number of cores per node required
+1. maximum wall time allocated to your calculation, note that jobs exceeding maximum wall time will be killed
+1. Project ID
+1. Jobscript or interactive switch
 
 !!! note
     Use the **qsub** command to submit your job to a queue for allocation of the computational resources.

docs.it4i/anselm-cluster-documentation/software/debuggers/intel-performance-counter-monitor.md

@@ -276,6 +276,6 @@ Sample output:
 
 ## References
 
-1.  <https://software.intel.com/en-us/articles/intel-performance-counter-monitor-a-better-way-to-measure-cpu-utilization>
-2.  <https://software.intel.com/sites/default/files/m/3/2/2/xeon-e5-2600-uncore-guide.pdf> Intel® Xeon® Processor E5-2600 Product Family Uncore Performance Monitoring Guide.
-3.  <http://intel-pcm-api-documentation.github.io/classPCM.html> API Documentation
+1. <https://software.intel.com/en-us/articles/intel-performance-counter-monitor-a-better-way-to-measure-cpu-utilization>
+1. <https://software.intel.com/sites/default/files/m/3/2/2/xeon-e5-2600-uncore-guide.pdf> Intel® Xeon® Processor E5-2600 Product Family Uncore Performance Monitoring Guide.
+1. <http://intel-pcm-api-documentation.github.io/classPCM.html> API Documentation

docs.it4i/anselm-cluster-documentation/software/debuggers/intel-vtune-amplifier.md

@@ -70,4 +70,4 @@ You may also use remote analysis to collect data from the MIC and then analyze i
 
 ## References
 
-1.  <https://www.rcac.purdue.edu/tutorials/phi/PerformanceTuningXeonPhi-Tullos.pdf> Performance Tuning for Intel® Xeon Phi™ Coprocessors
+1. <https://www.rcac.purdue.edu/tutorials/phi/PerformanceTuningXeonPhi-Tullos.pdf> Performance Tuning for Intel® Xeon Phi™ Coprocessors

docs.it4i/anselm-cluster-documentation/software/debuggers/papi.md

@@ -232,6 +232,6 @@ To use PAPI in offload mode, you need to provide both host and MIC versions of P
 
 ## References
 
-1.  <http://icl.cs.utk.edu/papi/> Main project page
-2.  <http://icl.cs.utk.edu/projects/papi/wiki/Main_Page> Wiki
-3.  <http://icl.cs.utk.edu/papi/docs/> API Documentation
+1. <http://icl.cs.utk.edu/papi/> Main project page
+1. <http://icl.cs.utk.edu/projects/papi/wiki/Main_Page> Wiki
+1. <http://icl.cs.utk.edu/papi/docs/> API Documentation

docs.it4i/anselm-cluster-documentation/software/debuggers/scalasca.md

@@ -17,9 +17,9 @@ There are currently two versions of Scalasca 2.0 [modules](../../environment-and
 
 Profiling a parallel application with Scalasca consists of three steps:
 
-1.  Instrumentation, compiling the application such way, that the profiling data can be generated.
-2.  Runtime measurement, running the application with the Scalasca profiler to collect performance data.
-3.  Analysis of reports
+1. Instrumentation, compiling the application such way, that the profiling data can be generated.
+1. Runtime measurement, running the application with the Scalasca profiler to collect performance data.
+1. Analysis of reports
 
 ### Instrumentation
 
@@ -67,4 +67,4 @@ Refer to [CUBE documentation](cube/) on usage of the GUI viewer.
 
 ## References
 
-1.  <http://www.scalasca.org/>
+1. <http://www.scalasca.org/>

docs.it4i/anselm-cluster-documentation/software/debuggers/score-p.md

@@ -17,9 +17,9 @@ There are currently two versions of Score-P version 1.2.6 [modules](../../enviro
 
 There are three ways to instrument your parallel applications in order to enable performance data collection:
 
-1.  Automated instrumentation using compiler
-2.  Manual instrumentation using API calls
-3.  Manual instrumentation using directives
+1. Automated instrumentation using compiler
+1. Manual instrumentation using API calls
+1. Manual instrumentation using directives
 
 ### Automated Instrumentation
 

docs.it4i/anselm-cluster-documentation/software/kvirtualization.md

@@ -53,11 +53,11 @@ Our recommended solution is that job script creates distinct shared job director
 
 ### Procedure
 
-1.  Prepare image of your virtual machine
-2.  Optimize image of your virtual machine for Anselm's virtualization
-3.  Modify your image for running jobs
-4.  Create job script for executing virtual machine
-5.  Run jobs
+1. Prepare image of your virtual machine
+1. Optimize image of your virtual machine for Anselm's virtualization
+1. Modify your image for running jobs
+1. Create job script for executing virtual machine
+1. Run jobs
 
 ### Prepare Image of Your Virtual Machine
 

docs.it4i/anselm-cluster-documentation/software/omics-master/overview.md

@@ -360,32 +360,32 @@ Each prioritization (‘job’) has three associated screens that facilitate the
 
 ## References
 
-1.  Heng Li, Bob Handsaker, Alec Wysoker, Tim Fennell, Jue Ruan, Nils Homer, Gabor Marth5, Goncalo Abecasis6, Richard Durbin and 1000 Genome Project Data Processing Subgroup: The Sequence Alignment/Map format and SAMtools. Bioinformatics 2009, 25: 2078-2079.
-2.  McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA: The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. _Genome Res_ >2010, 20:1297-1303.
-3.  Petr Danecek, Adam Auton, Goncalo Abecasis, Cornelis A. Albers, Eric Banks, Mark A. DePristo, Robert E. Handsaker, Gerton Lunter, Gabor T. Marth, Stephen T. Sherry, Gilean McVean, Richard Durbin, and 1000 Genomes Project Analysis Group. The variant call format and VCFtools. Bioinformatics 2011, 27: 2156-2158.
-4.  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.
-5.  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.
-6.  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.
-7.  UniProt Consortium. (2012) Reorganizing the protein space at the Universal Protein Resource (UniProt). Nucleic   Acids Res., 40, D71–D75.
-8.  Kozomara,A. and Griffiths-Jones,S. (2011) miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res., 39, D152–D157.
-9.  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.
-10. 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.
-11. Friedman,R.C., Farh,K.K., Burge,C.B. and Bartel,D.P. (2009) Most mammalian mRNAs are conserved targets of microRNAs. Genome Res., 19, 92–105. 12. Betel,D., Wilson,M., Gabow,A., Marks,D.S. and Sander,C. (2008) The microRNA.org resource: targets and expression. Nucleic Acids Res., 36, D149–D153.
-12. Dreszer,T.R., Karolchik,D., Zweig,A.S., Hinrichs,A.S., Raney,B.J., Kuhn,R.M., Meyer,L.R., Wong,M., Sloan,C.A., Rosenbloom,K.R. et al. (2012) The UCSC genome browser database: extensions and updates 2011. Nucleic Acids Res.,40, D918–D923.
-13. 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.
-14. 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.
-15. 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.
-16. 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.
-17. 1000 Genomes Project Consortium. (2010) A map of human genome variation from population-scale sequencing. Nature, 467, 1061–1073.
-18. 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.
-19. 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.
-20. Johnson,A.D. and O’Donnell,C.J. (2009) An open access database of genome-wide association results. BMC Med. Genet, 10, 6.
-21. McKusick,V. (1998) A Catalog of Human Genes and Genetic Disorders, 12th edn. John Hopkins University Press,Baltimore, MD.
-22. 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.
-23. 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.
-24. 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.
-25. 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.
-26. 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.
-27. [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.
-28. Landrum,M.J., Lee,J.M., Riley,G.R., Jang,W., Rubinstein,W.S., Church,D.M. and Maglott,D.R. (2014) ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res., 42, D980–D985.
-29. Medina I, Salavert F, Sanchez R, de Maria A, Alonso R, Escobar P, Bleda M, Dopazo J: Genome Maps, a new generation genome browser. Nucleic Acids Res 2013, 41:W41-46.
+1. Heng Li, Bob Handsaker, Alec Wysoker, Tim Fennell, Jue Ruan, Nils Homer, Gabor Marth5, Goncalo Abecasis6, Richard Durbin and 1000 Genome Project Data Processing Subgroup: The Sequence Alignment/Map format and SAMtools. Bioinformatics 2009, 25: 2078-2079.
+1. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA: The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. _Genome Res_ >2010, 20:1297-1303.
+1. Petr Danecek, Adam Auton, Goncalo Abecasis, Cornelis A. Albers, Eric Banks, Mark A. DePristo, Robert E. Handsaker, Gerton Lunter, Gabor T. Marth, Stephen T. Sherry, Gilean McVean, Richard Durbin, and 1000 Genomes Project Analysis Group. The variant call format and VCFtools. Bioinformatics 2011, 27: 2156-2158.
+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. 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.
+1. Friedman,R.C., Farh,K.K., Burge,C.B. and Bartel,D.P. (2009) Most mammalian mRNAs are conserved targets of microRNAs. Genome Res., 19, 92–105. 12. Betel,D., Wilson,M., Gabow,A., Marks,D.S. and Sander,C. (2008) The microRNA.org resource: targets and expression. Nucleic Acids Res., 36, D149–D153.
+1. Dreszer,T.R., Karolchik,D., Zweig,A.S., Hinrichs,A.S., Raney,B.J., Kuhn,R.M., Meyer,L.R., Wong,M., Sloan,C.A., Rosenbloom,K.R. et al. (2012) The UCSC genome browser database: extensions and updates 2011. Nucleic Acids Res.,40, D918–D923.
+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. 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. 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. 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.
+1. Landrum,M.J., Lee,J.M., Riley,G.R., Jang,W., Rubinstein,W.S., Church,D.M. and Maglott,D.R. (2014) ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res., 42, D980–D985.
+1. Medina I, Salavert F, Sanchez R, de Maria A, Alonso R, Escobar P, Bleda M, Dopazo J: Genome Maps, a new generation genome browser. Nucleic Acids Res 2013, 41:W41-46.

docs.it4i/anselm-cluster-documentation/storage.md

@@ -22,9 +22,9 @@ If multiple clients try to read and write the same part of a file at the same ti
 
 There is default stripe configuration for Anselm Lustre filesystems. However, users can set the following stripe parameters for their own directories or files to get optimum I/O performance:
 
-1.  stripe_size: the size of the chunk in bytes; specify with k, m, or g to use units of KB, MB, or GB, respectively; the size must be an even multiple of 65,536 bytes; default is 1MB for all Anselm Lustre filesystems
-2.  stripe_count the number of OSTs to stripe across; default is 1 for Anselm Lustre filesystems one can specify -1 to use all OSTs in the filesystem.
-3.  stripe_offset The index of the OST where the first stripe is to be placed; default is -1 which results in random selection; using a non-default value is NOT recommended.
+1. stripe_size: the size of the chunk in bytes; specify with k, m, or g to use units of KB, MB, or GB, respectively; the size must be an even multiple of 65,536 bytes; default is 1MB for all Anselm Lustre filesystems
+1. stripe_count the number of OSTs to stripe across; default is 1 for Anselm Lustre filesystems one can specify -1 to use all OSTs in the filesystem.
+1. stripe_offset The index of the OST where the first stripe is to be placed; default is -1 which results in random selection; using a non-default value is NOT recommended.
 
 !!! note
     Setting stripe size and stripe count correctly for your needs may significantly impact the I/O performance you experience.

docs.it4i/get-started-with-it4innovations/accessing-the-clusters/graphical-user-interface/x-window-system.md

@@ -141,7 +141,7 @@ PuTTY X11 proxy: unable to connect to forwarded X server: Network error: Connect
   (gnome-session:23691): WARNING **: Cannot open display:**
 ```
 
-1.  Locate and modify Cygwin shortcut that uses [startxwin](http://x.cygwin.com/docs/man1/startxwin.1.html)
+1. Locate and modify Cygwin shortcut that uses [startxwin](http://x.cygwin.com/docs/man1/startxwin.1.html)
     locate
     C:cygwin64binXWin.exe
     change it
@@ -150,7 +150,7 @@ PuTTY X11 proxy: unable to connect to forwarded X server: Network error: Connect
 
 ![XWin-listen-tcp.png](../../../img/XWinlistentcp.png "XWin-listen-tcp.png")
 
-1.  Check Putty settings:
+1. Check Putty settings:
      Enable X11 forwarding
 
     ![](../../../img/cygwinX11forwarding.png)

docs.it4i/get-started-with-it4innovations/obtaining-login-credentials/obtaining-login-credentials.md

@@ -31,9 +31,9 @@ Log in to the [IT4I Extranet portal](https://extranet.it4i.cz) using IT4I creden
 
 In order to authorize a Collaborator to utilize the allocated resources, the PI should contact the [IT4I support](https://support.it4i.cz/rt/) (E-mail: [support\[at\]it4i.cz](mailto:support@it4i.cz)) and provide following information:
 
-1.  Identify your project by project ID
-2.  Provide list of people, including himself, who are authorized to use the resources allocated to the project. The list must include full name, e-mail and affiliation. Provide usernames as well, if collaborator login access already exists on the IT4I systems.
-3.  Include "Authorization to IT4Innovations" into the subject line.
+1. Identify your project by project ID
+1. Provide list of people, including himself, who are authorized to use the resources allocated to the project. The list must include full name, e-mail and affiliation. Provide usernames as well, if collaborator login access already exists on the IT4I systems.
+1. Include "Authorization to IT4Innovations" into the subject line.
 
 Example (except the subject line which must be in English, you may use Czech or Slovak language for communication with us):
 
@@ -59,12 +59,12 @@ Should the above information be provided by e-mail, the e-mail **must be** digit
 
 Once authorized by PI, every person (PI or Collaborator) wishing to access the clusters, should contact the [IT4I support](https://support.it4i.cz/rt/) (E-mail: [support\[at\]it4i.cz](mailto:support@it4i.cz)) providing following information:
 
-1.  Project ID
-2.  Full name and affiliation
-3.  Statement that you have read and accepted the [Acceptable use policy document](http://www.it4i.cz/acceptable-use-policy.pdf) (AUP).
-4.  Attach the AUP file.
-5.  Your preferred username, max 8 characters long. The preferred username must associate your surname and name or be otherwise derived from it. Only alphanumeric sequences, dash and underscore signs are allowed.
-6.  In case you choose [Alternative way to personal certificate](obtaining-login-credentials/#alternative-way-of-getting-personal-certificate), a **scan of photo ID** (personal ID or passport or driver license) is required
+1. Project ID
+1. Full name and affiliation
+1. Statement that you have read and accepted the [Acceptable use policy document](http://www.it4i.cz/acceptable-use-policy.pdf) (AUP).
+1. Attach the AUP file.
+1. Your preferred username, max 8 characters long. The preferred username must associate your surname and name or be otherwise derived from it. Only alphanumeric sequences, dash and underscore signs are allowed.
+1. In case you choose [Alternative way to personal certificate](obtaining-login-credentials/#alternative-way-of-getting-personal-certificate), a **scan of photo ID** (personal ID or passport or driver license) is required
 
 Example (except the subject line which must be in English, you may use Czech or Slovak language for communication with us):
 
@@ -94,9 +94,9 @@ For various reasons we do not accept PGP keys.** Please, use only X.509 PKI cert
 
 You will receive your personal login credentials by protected e-mail. The login credentials include:
 
-1.  username
-2.  ssh private key and private key passphrase
-3.  system password
+1. username
+1. ssh private key and private key passphrase
+1. system password
 
 The clusters are accessed by the [private key](../accessing-the-clusters/shell-access-and-data-transfer/ssh-keys/) and username. Username and password is used for login to the [information systems](http://support.it4i.cz/).
 

docs.it4i/index.md

@@ -4,9 +4,9 @@ Welcome to IT4Innovations documentation pages. The IT4Innovations national super
 
 ## How to Read the Documentation
 
-1.  Read the list in the left column. Select the subject of interest. Alternatively, use the Search in the upper right corner.
-2.  Scan for all the notes and reminders on the page.
-3.  Read the details if still more information is needed. **Look for examples** illustrating the concepts.
+1. Read the list in the left column. Select the subject of interest. Alternatively, use the Search in the upper right corner.
+1. Scan for all the notes and reminders on the page.
+1. Read the details if still more information is needed. **Look for examples** illustrating the concepts.
 
 ## Getting Help and Support
 

docs.it4i/salomon/capacity-computing.md

@@ -15,8 +15,8 @@ However, executing huge number of jobs via the PBS queue may strain the system.
 
 ## Policy
 
-1.  A user is allowed to submit at most 100 jobs. Each job may be [a job array](capacity-computing/#job-arrays).
-2.  The array size is at most 1000 subjobs.
+1. A user is allowed to submit at most 100 jobs. Each job may be [a job array](capacity-computing/#job-arrays).
+1. The array size is at most 1000 subjobs.
 
 ## Job Arrays
 
@@ -288,9 +288,9 @@ In this example, the jobscript executes in multiple instances in parallel, on al
 
 When deciding this values, think about following guiding rules :
 
-1.  Let n = N / 24.  Inequality (n + 1) x T < W should hold. The N is number of tasks per subjob, T is expected single task walltime and W is subjob walltime. Short subjob walltime improves scheduling and job throughput.
-2.  Number of tasks should be modulo 24.
-3.  These rules are valid only when all tasks have similar task walltimes T.
+1. Let n = N / 24.  Inequality (n + 1) x T < W should hold. The N is number of tasks per subjob, T is expected single task walltime and W is subjob walltime. Short subjob walltime improves scheduling and job throughput.
+1. Number of tasks should be modulo 24.
+1. These rules are valid only when all tasks have similar task walltimes T.
 
 ### Submit the Job Array
 

docs.it4i/salomon/job-priority.md

@@ -6,9 +6,9 @@ Scheduler gives each job an execution priority and then uses this job execution
 
 Job execution priority is determined by these job properties (in order of importance):
 
-1.  queue priority
-2.  fair-share priority
-3.  eligible time
+1. queue priority
+1. fair-share priority
+1. eligible time
 
 ### Queue Priority
 

docs.it4i/salomon/job-submission-and-execution.md

@@ -4,12 +4,12 @@
 
 When allocating computational resources for the job, please specify
 
-1.  suitable queue for your job (default is qprod)
-2.  number of computational nodes required
-3.  number of cores per node required
-4.  maximum wall time allocated to your calculation, note that jobs exceeding maximum wall time will be killed
-5.  Project ID
-6.  Jobscript or interactive switch
+1. suitable queue for your job (default is qprod)
+1. number of computational nodes required
+1. number of cores per node required
+1. maximum wall time allocated to your calculation, note that jobs exceeding maximum wall time will be killed
+1. Project ID
+1. Jobscript or interactive switch
 
 !!! note
     Use the **qsub** command to submit your job to a queue for allocation of the computational resources.

docs.it4i/salomon/software/ansys/ansys-fluent.md

@@ -3,7 +3,7 @@
 [ANSYS Fluent](http://www.ansys.com/products/fluids/ansys-fluent)
 software contains the broad physical modeling capabilities needed to model flow, turbulence, heat transfer, and reactions for industrial applications ranging from air flow over an aircraft wing to combustion in a furnace, from bubble columns to oil platforms, from blood flow to semiconductor manufacturing, and from clean room design to wastewater treatment plants. Special models that give the software the ability to model in-cylinder combustion, aeroacoustics, turbomachinery, and multiphase systems have served to broaden its reach.
 
-1.  Common way to run Fluent over pbs file
+1. Common way to run Fluent over pbs file
 
 To run ANSYS Fluent in batch mode you can utilize/modify the default fluent.pbs script and execute it via the qsub command.
 
@@ -56,7 +56,7 @@ Journal file with definition of the input geometry and boundary conditions and d
 
 The appropriate dimension of the problem has to be set by parameter (2d/3d).
 
-2.  Fast way to run Fluent from command line
+1. Fast way to run Fluent from command line
 
 ```bash
 fluent solver_version [FLUENT_options] -i journal_file -pbs
@@ -64,7 +64,7 @@ fluent solver_version [FLUENT_options] -i journal_file -pbs
 
 This syntax will start the ANSYS FLUENT job under PBS Professional using the qsub command in a batch manner. When resources are available, PBS Professional will start the job and return a job ID, usually in the form of _job_ID.hostname_. This job ID can then be used to query, control, or stop the job using standard PBS Professional commands, such as qstat or qdel. The job will be run out of the current working directory, and all output will be written to the file fluent.o _job_ID_.
 
-3.  Running Fluent via user's config file
+1. Running Fluent via user's config file
 
 The sample script uses a configuration file called pbs_fluent.conf if no command line arguments are present. This configuration file should be present in the directory from which the jobs are submitted (which is also the directory in which the jobs are executed). The following is an example of what the content of pbs_fluent.conf can be:
 
@@ -141,7 +141,7 @@ To run ANSYS Fluent in batch mode with user's config file you can utilize/modify
 
 It runs the jobs out of the directory from which they are submitted (PBS_O_WORKDIR).
 
-4.  Running Fluent in parralel
+1. Running Fluent in parralel
 
 Fluent could be run in parallel only under Academic Research license. To do so this ANSYS Academic Research license must be placed before ANSYS CFD license in user preferences. To make this change anslic_admin utility should be run
 

docs.it4i/salomon/software/debuggers/vampir.md

@@ -19,4 +19,4 @@ You can find the detailed user manual in PDF format in $EBROOTVAMPIR/doc/vampir-
 
 ## References
 
-1.  <https://www.vampir.eu>
+1. <https://www.vampir.eu>

docs.it4i/salomon/software/intel-suite/intel-advisor.md

@@ -26,6 +26,6 @@ In the left pane, you can switch between Vectorization and Threading workflows.
 
 ## References
 
-1.  [Intel® Advisor 2015 Tutorial: Find Where to Add Parallelism - C++ Sample](https://software.intel.com/en-us/intel-advisor-tutorial-vectorization-windows-cplusplus)
-2.  [Product page](https://software.intel.com/en-us/intel-advisor-xe)
-3.  [Documentation](https://software.intel.com/en-us/intel-advisor-2016-user-guide-linux)
+1. [Intel® Advisor 2015 Tutorial: Find Where to Add Parallelism - C++ Sample](https://software.intel.com/en-us/intel-advisor-tutorial-vectorization-windows-cplusplus)
+1. [Product page](https://software.intel.com/en-us/intel-advisor-xe)
+1. [Documentation](https://software.intel.com/en-us/intel-advisor-2016-user-guide-linux)