From: Don Armstrong <don@donarmstrong.com>
Date: Thu, 5 Apr 2018 23:16:36 +0000 (-0700)
Subject: comment out genomics and epigenomics
X-Git-Url: https://git.donarmstrong.com/?a=commitdiff_plain;h=c12cc31a822938bb47987f16900ab3eaa93cc4ab;p=resume.git

comment out genomics and epigenomics
---

diff --git a/don_armstrong_resume.org b/don_armstrong_resume.org
index 13b25de..1444ef3 100644
--- a/don_armstrong_resume.org
+++ b/don_armstrong_resume.org
@@ -86,20 +86,20 @@
 + Databases: Postgresql (PL/SQL), SQLite, Mysql, NoSQL
 + Office Software: Gnumeric, Libreoffice, \LaTeX, Word, Excel,
   Powerpoint
-** 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
+# ** 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 (regression, inference, prediction, and
   learning in very large (> 1TB) datasets)