Fix LD_LIBRARY_PATH and documentation images with transparency

BUILD_NOTES_tch.txt

@@ -9,7 +9,7 @@ Then set the following environment variables:
 
 export LIBTORCH={path/to/}libtorch
 export DYLD_LIBRARY_PATH=${LIBTORCH}/lib
-export LD_LIBRARY_PATH=${DYLD_LIBRARY_PATH}
+export LD_LIBRARY_PATH="${LD_LIBRARY_PATH:+${LD_LIBRARY_PATH}:}${DYLD_LIBRARY_PATH}"
 
 You can check that everything is working by running `modkit open-chromatin predict <args> --dryrun`.
 

book/src/intro_entropy.md

@@ -9,7 +9,7 @@ Probably the simplest visual description of methylation entropy is the following
 
 <div style="text-align: center;">
 
-![methylation_entropy_background](./images/me.png)
+![methylation_entropy_background](./images/me_no_alpha.png)
 
 </div>
 

book/src/intro_open_chromatin.md

@@ -3,7 +3,7 @@
 Nanopore sequencing can detect multiple base modifications simultaneously and we can leverage this capability by introducing exogenous base modifications at specific functional regions.
 One such method uses a 6mA methyltransferase such as EcoGII or Hia5 to label accessible regions of chromatinized DNA, usually by treatment of cell nuclei with the enzyme.
 
-![Chromatin Accessibility treatment](./images/chromatin_stenciling_3.png "Cartoon schematic showing exogenous 6mA introduced to regions of accessible chromatin")
+![Chromatin Accessibility treatment](./images/chromatin_stenciling_3_no_alpha.png "Cartoon schematic showing exogenous 6mA introduced to regions of accessible chromatin")
 
 ## Predict regions of open chromatin
 Modkit comes with a machine learning model that has been trained to identify regions of open chromatin based on 6mA signal.

docs/intro_entropy.html

@@ -185,7 +185,7 @@ <h1 id="calculating-methylation-entropy"><a class="header" href="#calculating-me
 To quote <a href="https://doi.org/10.1371/journal.pcbi.1010946">Lee et al.</a>, “This information is important because such ‘phased’ methylation states can inform us about the epigenetic diversity of cell populations as well as the local regulation states of the epigenome”.
 Probably the simplest visual description of methylation entropy is the following, a version of which appears in many of the methods papers:</p>
 <div style="text-align: center;">
-<p><img src="./images/me.png" alt="methylation_entropy_background"></p>
+<p><img src="./images/me_no_alpha.png" alt="methylation_entropy_background"></p>
 </div>
 
 <p>Citation: <a href="https://github.com/dohlee/metheor/blob/master/img/me.png">Lee et al.</a></p>

docs/intro_open_chromatin.html

@@ -180,7 +180,7 @@ <h1 class="menu-title">Modkit</h1>
                         <h1 id="find-regions-of-accessible-chromatin"><a class="header" href="#find-regions-of-accessible-chromatin">Find regions of accessible chromatin</a></h1>
 <p>Nanopore sequencing can detect multiple base modifications simultaneously and we can leverage this capability by introducing exogenous base modifications at specific functional regions.
 One such method uses a 6mA methyltransferase such as EcoGII or Hia5 to label accessible regions of chromatinized DNA, usually by treatment of cell nuclei with the enzyme.</p>
-<p><img src="./images/chromatin_stenciling_3.png" title="Cartoon schematic showing exogenous 6mA introduced to regions of accessible chromatin" alt="Chromatin Accessibility treatment"></p>
+<p><img src="./images/chromatin_stenciling_3_no_alpha.png" title="Cartoon schematic showing exogenous 6mA introduced to regions of accessible chromatin" alt="Chromatin Accessibility treatment"></p>
 <h2 id="predict-regions-of-open-chromatin"><a class="header" href="#predict-regions-of-open-chromatin">Predict regions of open chromatin</a></h2>
 <p>Modkit comes with a machine learning model that has been trained to identify regions of open chromatin based on 6mA signal.
 You can invoke this model with the following command:</p>