comment out statistics and genomics

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@@ -87,23 +87,23 @@
    + GNU/Linux (Debian, Ubuntu, Red Hat)
    + Windows (5.0-10)
    + MacOS (10-10.13)
-** Statistics
- + Statistical modeling in very large datasets
- + Addressing confounders and batch effects
-** Genomics and Epigenomics
-+ NGS and array-based Genomics and Epigenomics of complex human
-  diseases using RNA-seq, targeted DNA sequencing, RRBS, Illumina
-  bead arrays, and Affymetrix microarrays from sample collection to
-  publication.
-+ Reproducible, scalable bioinformatics analysis using make,
-  nextflow, and cwl based workflows on cloud- and cluster-based
-  systems on terabyte-scale datasets
-+ Alignment, annotation, and variant calling using existing and custom
-  software, including GATK, bwa, STAR, and kallisto.
-+ Correcting for and experimental design to overcome multiple
-  testing, confounders, and batch effects using Bayesian and
-  frequentist methods approaches
-+ Using evolutionary genomics to identify causal human variants
+# ** Statistics
+#  + Statistical modeling in very large datasets
+#  + Addressing confounders and batch effects
+# ** Genomics and Epigenomics
+# + NGS and array-based Genomics and Epigenomics of complex human
+#   diseases using RNA-seq, targeted DNA sequencing, RRBS, Illumina
+#   bead arrays, and Affymetrix microarrays from sample collection to
+#   publication.
+# + Reproducible, scalable bioinformatics analysis using make,
+#   nextflow, and cwl based workflows on cloud- and cluster-based
+#   systems on terabyte-scale datasets
+# + Alignment, annotation, and variant calling using existing and custom
+#   software, including GATK, bwa, STAR, and kallisto.
+# + Correcting for and experimental design to overcome multiple
+#   testing, confounders, and batch effects using Bayesian and
+#   frequentist methods approaches
+# + Using evolutionary genomics to identify causal human variants
 ** Big Data
 + Parallel and Cloud Computing (slurm, torque, AWS, OpenStack, Azure)
 + Inter-process communication: MPI, OpenMP