update

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

@@ -12,7 +12,7 @@ Each dimension is organized in a tree, for example the time performance metric i
 
 ![](../../../img/Snmekobrazovky20141204v12.56.36.png)
 
-_Figure 1. Screenshot of CUBE displaying data from Scalasca._
+\*Figure 1. Screenshot of CUBE displaying data from Scalasca.\*
 
 Each node in the tree is colored by severity (the color scheme is displayed at the bottom of the window, ranging from the least severe blue to the most severe being red). For example in Figure 1, we can see that most of the point-to-point MPI communication happens in routine exch_qbc, colored red.
 

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

@@ -92,7 +92,7 @@ Example of PBS Pro resource name, based on APP and FEATURE name:
 | matlab-edu  | MATLAB_Distrib_Comp_Engine | feature_matlab-edu_MATLAB_Distrib_Comp_Engine   |
 | matlab-edu  | Image_Acquisition_Toolbox  | feature_matlab-edu_Image_Acquisition_Toolbox\\  |
 
-**Be aware, that the resource names in PBS Pro are CASE SENSITIVE!**
+#### Be aware, that the resource names in PBS Pro are CASE SENSITIVE!
 
 ### Example of qsub Statement
 

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

@@ -11,7 +11,7 @@ The pipeline inputs the raw data produced by the sequencing machines and undergo
 ![OMICS MASTER solution overview. Data is produced in the external labs and comes to IT4I (represented by the blue dashed line). The data pre-processor converts raw data into a list of variants and annotations for each sequenced patient. These lists files together with primary and secondary (alignment) data files are stored in IT4I sequence DB and uploaded to the discovery (candidate priorization) or diagnostic component where they can be analysed directly by the user that produced
 them, depending of the experimental design carried out.](../../../img/fig1.png)
 
-** Figure 1. ** OMICS MASTER solution overview. Data is produced in the external labs and comes to IT4I (represented by the blue dashed line). The data pre-processor converts raw data into a list of variants and annotations for each sequenced patient. These lists files together with primary and secondary (alignment) data files are stored in IT4I sequence DB and uploaded to the discovery (candidate prioritization) or diagnostic component where they can be analyzed directly by the user that produced them, depending of the experimental design carried out.
+**Figure 1.** OMICS MASTER solution overview. Data is produced in the external labs and comes to IT4I (represented by the blue dashed line). The data pre-processor converts raw data into a list of variants and annotations for each sequenced patient. These lists files together with primary and secondary (alignment) data files are stored in IT4I sequence DB and uploaded to the discovery (candidate prioritization) or diagnostic component where they can be analyzed directly by the user that produced them, depending of the experimental design carried out.
 
 Typical genomics pipelines are composed by several components that need to be launched manually. The advantage of OMICS MASTER pipeline is that all these components are invoked sequentially in an automated way.
 
@@ -35,26 +35,26 @@ FastQC& FastQC.
 
 These steps are carried out over the original FASTQ file with optimized scripts and includes the following steps: sequence cleansing, estimation of base quality scores, elimination of duplicates and statistics.
 
-Input: ** FASTQ file **.
+Input: **FASTQ file**.
 
-Output: ** FASTQ file plus an HTML file containing statistics on the data **.
+Output: **FASTQ file plus an HTML file containing statistics on the data**.
 
 FASTQ format It represents the nucleotide sequence and its corresponding quality scores.
 
 ![FASTQ file.](../../../img/fig2.png "fig2.png")
-** Figure 2 **.FASTQ file.
+**Figure 2**.FASTQ file.
 
 #### Mapping
 
-Component: ** Hpg-aligner **.
+Component: **Hpg-aligner**.
 
 Sequence reads are mapped over the human reference genome. SOLiD reads are not covered by this solution; they should be mapped with specific software (among the few available options, SHRiMP seems to be the best one). For the rest of NGS machine outputs we use HPG Aligner. HPG-Aligner is an innovative solution, based on a combination of mapping with BWT and local alignment with Smith-Waterman (SW), that drastically increases mapping accuracy (97% versus 62-70% by current mappers, in the most common scenarios). This proposal provides a simple and fast solution that maps almost all the reads, even those containing a high number of mismatches or indels.
 
-Input: ** FASTQ file **.
+Input: **FASTQ file**.
 
-Output: ** Aligned file in BAM format **.
+Output: **Aligned file in BAM format**.
 
-** Sequence Alignment/Map (SAM) **
+**Sequence Alignment/Map (SAM)**
 
 It is a human readable tab-delimited format in which each read and its alignment is represented on a single line. The format can represent unmapped reads, reads that are mapped to unique locations, and reads that are mapped to multiple locations.
 
@@ -348,7 +348,7 @@ Once the file has been uploaded, a panel must be chosen from the Panel list. The
 
 For variant discovering/filtering we should upload the VCF file into BierApp by using the following form:
 
-_![BierApp VCF upload panel. It is recommended to choose a name for the job as well as a description.](../../../img/fig8.png)_
+\*![BierApp VCF upload panel. It is recommended to choose a name for the job as well as a description.](../../../img/fig8.png)\*
 
 ** Figure 8 **. \*BierApp VCF upload panel. It is recommended to choose a name for the job as well as a description \*\*.