index.xml

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  <channel>
    <title>zqzneptune</title>
    <link>https://zqzneptune.github.io/</link>
      <atom:link href="https://zqzneptune.github.io/index.xml" rel="self" type="application/rss+xml" />
    <description>zqzneptune</description>
    <generator>Source Themes Academic (https://sourcethemes.com/academic/)</generator><language>en-us</language><copyright>©2014-2024 Qingzhou Zhang (CC) BY-NC-SA 4.0. All thoughts and opinions here are my own.</copyright><lastBuildDate>Sat, 01 Jun 2030 13:00:00 +0000</lastBuildDate>
    <image>
      <url>https://zqzneptune.github.io/images/icon_huf504e8a2f7c5cf9e43cec507892894a0_49847_512x512_fill_lanczos_center_2.png</url>
      <title>zqzneptune</title>
      <link>https://zqzneptune.github.io/</link>
    </image>
    
    <item>
      <title>Example Talk</title>
      <link>https://zqzneptune.github.io/talk/example/</link>
      <pubDate>Sat, 01 Jun 2030 13:00:00 +0000</pubDate>
      <guid>https://zqzneptune.github.io/talk/example/</guid>
      <description>&lt;div class=&#34;alert alert-note&#34;&gt;
  &lt;div&gt;
    Click on the &lt;strong&gt;Slides&lt;/strong&gt; button above to view the built-in slides feature.
  &lt;/div&gt;
&lt;/div&gt;
&lt;p&gt;Slides can be added in a few ways:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Create&lt;/strong&gt; slides using Academic&amp;rsquo;s &lt;a href=&#34;https://sourcethemes.com/academic/docs/managing-content/#create-slides&#34;&gt;&lt;em&gt;Slides&lt;/em&gt;&lt;/a&gt; feature and link using &lt;code&gt;slides&lt;/code&gt; parameter in the front matter of the talk file&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Upload&lt;/strong&gt; an existing slide deck to &lt;code&gt;static/&lt;/code&gt; and link using &lt;code&gt;url_slides&lt;/code&gt; parameter in the front matter of the talk file&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Embed&lt;/strong&gt; your slides (e.g. Google Slides) or presentation video on this page using &lt;a href=&#34;https://sourcethemes.com/academic/docs/writing-markdown-latex/&#34;&gt;shortcodes&lt;/a&gt;.&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;Further talk details can easily be added to this page using &lt;em&gt;Markdown&lt;/em&gt; and $\rm \LaTeX$ math code.&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Dropout or not?</title>
      <link>https://zqzneptune.github.io/post/2020-01-20-zerodrop/</link>
      <pubDate>Mon, 20 Jan 2020 11:26:14 -0600</pubDate>
      <guid>https://zqzneptune.github.io/post/2020-01-20-zerodrop/</guid>
      <description>&lt;p&gt;The &amp;ldquo;dropout event&amp;rdquo; was termed from the observations  of an abundance of zero expression values from the scRNA-seq data. The recent publishsed study demonstrated that genes with zero counts may not have to be dropout anymore, due to their potential biological variations.&lt;/p&gt;
&lt;p&gt;&lt;a href=&#34;https://doi.org/10.1038/s41587-019-0379-5&#34;&gt;&lt;strong&gt;Droplet scRNA-seq is not zero-inflated&lt;/strong&gt;&lt;/a&gt; Nature Biotechnology (2020), by Valentine Svensson&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Run EPIC</title>
      <link>https://zqzneptune.github.io/post/2019-12-12-runepic/</link>
      <pubDate>Thu, 12 Dec 2019 18:13:14 -0600</pubDate>
      <guid>https://zqzneptune.github.io/post/2019-12-12-runepic/</guid>
      <description>&lt;p&gt;The &lt;strong&gt;EPIC&lt;/strong&gt; toolkit was initially published here: Hu, L. Z., et al. &amp;ldquo;EPIC: software toolkit for elution profile-based inference of protein complexes.&amp;rdquo; Nature methods 16.8 (2019): 737-742. &lt;a href=&#34;https://www.nature.com/articles/s41592-019-0461-4&#34;&gt;Link to the publication&lt;/a&gt;&lt;/p&gt;
&lt;p&gt;Forked from the orignial &lt;a href=&#34;https://github.com/BaderLab/EPIC&#34;&gt;repository&lt;/a&gt;, I have created &lt;strong&gt;RunEPIC&lt;/strong&gt;  to provide the code to run &lt;strong&gt;EPIC&lt;/strong&gt; locally.&lt;/p&gt;
&lt;h2 id=&#34;1-environment&#34;&gt;1. Environment&lt;/h2&gt;
&lt;p&gt;The main function in &lt;strong&gt;EPIC&lt;/strong&gt; was implemented in Python, given the headache caused by various libraries, the Anaconda enrionment was used. Simply install Anaconda Python 2.7 version for your convience, and we will start from there.&lt;/p&gt;
&lt;h2 id=&#34;2-prerequisite&#34;&gt;2. Prerequisite&lt;/h2&gt;
&lt;p&gt;Let&amp;rsquo;s create the virtual enrionment:&lt;/p&gt;
&lt;pre&gt;&lt;code&gt;path to the anaconda directory/bin/conda create -n EPIC python=2.7 anaconda
&lt;/code&gt;&lt;/pre&gt;
&lt;p&gt;then we need step up some chanels:&lt;/p&gt;
&lt;pre&gt;&lt;code&gt;(EPIC)conda config --add channels defaults
(EPIC)conda config --add channels bioconda
(EPIC)conda config --add channels conda-forge
&lt;/code&gt;&lt;/pre&gt;
&lt;p&gt;get R installed:&lt;/p&gt;
&lt;pre&gt;&lt;code&gt;(EPIC)conda install r
&lt;/code&gt;&lt;/pre&gt;
&lt;p&gt;start R:&lt;/p&gt;
&lt;pre&gt;&lt;code&gt;(EPIC)R
&lt;/code&gt;&lt;/pre&gt;
&lt;p&gt;install wccsom for computing WCC scores:&lt;/p&gt;
&lt;pre&gt;&lt;code&gt;&amp;gt; install.packages(&amp;quot;kohonen&amp;quot;)
&amp;gt; install.packages(&amp;quot;https://cran.r-project.org/src/contrib/Archive/wccsom/wccsom_1.2.11.tar.gz&amp;quot;,
                 type = &amp;quot;source&amp;quot;)
&amp;gt; q()
&lt;/code&gt;&lt;/pre&gt;
&lt;p&gt;install conda packages:&lt;/p&gt;
&lt;pre&gt;&lt;code&gt;conda install requests scikit-learn beautifulsoup4 mock numpy rpy2
python -mpip install -U matplotlib
&lt;/code&gt;&lt;/pre&gt;
&lt;p&gt;lastly, make sure Java is installed, so that ClusterOne.jar could be used.&lt;/p&gt;
&lt;h2 id=&#34;3-run-epic&#34;&gt;3. Run EPIC&lt;/h2&gt;
&lt;pre&gt;&lt;code&gt;git clone https://github.com/zqzneptune/RunEPIC.git
&lt;/code&gt;&lt;/pre&gt;
&lt;p&gt;The main.py in &lt;strong&gt;EPIC&lt;/strong&gt; implemented all the functions:&lt;/p&gt;
&lt;pre&gt;&lt;code&gt;python directory to RunEPIC/src/main.py \
       -s 11101001 \
       [Directory to Input Folder/] \
       -c [path to the gold standard file ] \
       [Directory to Output Folder/] \
       -o PrefixName \
       -M RF \
       -n 6 \
       -m COMB \
       -f STRING
&lt;/code&gt;&lt;/pre&gt;
</description>
    </item>
    
    <item>
      <title>Rewiring of the Human Mitochondrial Interactome during Neuronal Reprogramming Reveals Regulators of the Respirasome and Neurogenesis</title>
      <link>https://zqzneptune.github.io/publication/j.isci.2019.08.057/</link>
      <pubDate>Sat, 21 Sep 2019 00:00:00 +0000</pubDate>
      <guid>https://zqzneptune.github.io/publication/j.isci.2019.08.057/</guid>
      <description></description>
    </item>
    
    <item>
      <title>A Tag-Based Affinity Purification Mass Spectrometry Workflow for Systematic Isolation of the Human Mitochondrial Protein Complexes</title>
      <link>https://zqzneptune.github.io/publication/978-981-13-8367-0_6/</link>
      <pubDate>Tue, 27 Aug 2019 00:00:00 +0000</pubDate>
      <guid>https://zqzneptune.github.io/publication/978-981-13-8367-0_6/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Statistical Modelling of AP-MS Data (SMAD)</title>
      <link>https://zqzneptune.github.io/post/2019-05-22-smad/</link>
      <pubDate>Wed, 22 May 2019 09:13:14 -0600</pubDate>
      <guid>https://zqzneptune.github.io/post/2019-05-22-smad/</guid>
      <description>&lt;p&gt;This R package implements statistical modelling of affinity purification–mass spectrometry (AP-MS) data to compute confidence scores to identify &lt;em&gt;bona fide&lt;/em&gt; protein-protein interactions (PPI).&lt;/p&gt;
&lt;h2 id=&#34;installation&#34;&gt;Installation&lt;/h2&gt;
&lt;p&gt;The development version can be installed through github:&lt;/p&gt;
&lt;pre&gt;&lt;code class=&#34;language-{r}&#34;&gt; devtools::install_github(repo=&amp;quot;zqzneptune/SMAD&amp;quot;)
 library(SMAD)
&lt;/code&gt;&lt;/pre&gt;
&lt;h2 id=&#34;input-data&#34;&gt;Input Data&lt;/h2&gt;
&lt;p&gt;A demo data.frame was provided as a hint how the input data should strcutured in order to run the scoring functions:&lt;/p&gt;
&lt;pre&gt;&lt;code class=&#34;language-{r}&#34;&gt;data(TestDatInput)
colnames(TestDataInput)

[1] &amp;quot;idRun&amp;quot; &amp;quot;idBait&amp;quot; &amp;quot;idPrey&amp;quot; &amp;quot;countPrey&amp;quot; &amp;quot;lenPrey&amp;quot; 
&lt;/code&gt;&lt;/pre&gt;
&lt;table&gt;
&lt;thead&gt;
&lt;tr&gt;
&lt;th&gt;idRun&lt;/th&gt;
&lt;th align=&#34;center&#34;&gt;idBait&lt;/th&gt;
&lt;th align=&#34;center&#34;&gt;idPrey&lt;/th&gt;
&lt;th align=&#34;center&#34;&gt;countPrey&lt;/th&gt;
&lt;th&gt;lenPrey&lt;/th&gt;
&lt;/tr&gt;
&lt;/thead&gt;
&lt;tbody&gt;
&lt;tr&gt;
&lt;td&gt;Unique ID of one AP-MS run&lt;/td&gt;
&lt;td align=&#34;center&#34;&gt;Bait ID&lt;/td&gt;
&lt;td align=&#34;center&#34;&gt;Prey ID&lt;/td&gt;
&lt;td align=&#34;center&#34;&gt;Prey peptide count&lt;/td&gt;
&lt;td&gt;Protein sequence length of the prey&lt;/td&gt;
&lt;/tr&gt;
&lt;/tbody&gt;
&lt;/table&gt;
&lt;p&gt;In case of duplcates, a suffix or prefix of e.g. &amp;ldquo;A&amp;rdquo;, &amp;ldquo;B&amp;rdquo; could be added to &lt;strong&gt;idRun&lt;/strong&gt; in order to make &lt;strong&gt;&amp;ldquo;idRun-idBait&amp;rdquo;&lt;/strong&gt; combination unique to each replicate.&lt;/p&gt;
&lt;h2 id=&#34;run-scoring&#34;&gt;Run scoring&lt;/h2&gt;
&lt;h3 id=&#34;1-comppass&#34;&gt;1. CompPASS&lt;/h3&gt;
&lt;p&gt;Comparative Proteomic Analysis Software Suite (CompPASS) is based on spoke model. This algorithm was developed by Dr. Mathew Sowa for defining the human deubiquitinating enzyme interaction landscape &lt;a href=&#34;https://doi.org/10.1016/j.cell.2009.04.042&#34;&gt;(Sowa, Mathew E., et al., 2009)&lt;/a&gt;. The implementation of this algorithm was inspired by Dr. Sowa&amp;rsquo;s &lt;a href=&#34;http://besra.hms.harvard.edu/ipmsmsdbs/cgi-bin/tutorial.cgi&#34;&gt;online tutorial&lt;/a&gt;. The output includes Z-score, S-score, D-score and WD-score. In its implementation in BioPlex 1.0 &lt;a href=&#34;https://doi.org/10.1016/j.cell.2015.06.043&#34;&gt;(Huttlin, Edward L., et al., 2015)&lt;/a&gt; and
BioPlex 2.0 &lt;a href=&#34;https://www.nature.com/articles/nature22366&#34;&gt;(Huttlin, Edward L., et al., 2017)&lt;/a&gt;, a naive
Bayes classifier that learns to distinguish true interacting proteins from
non-specific background and false positive identifications was included in the
compPASS pipline. This function was optimized from the &lt;a href=&#34;https://github.com/dnusinow/cRomppass&#34;&gt;source code&lt;/a&gt;.&lt;/p&gt;
&lt;p&gt;The input data.frame, &lt;em&gt;datInput&lt;/em&gt;, should include:&lt;strong&gt;idRun&lt;/strong&gt;, &lt;strong&gt;idBait&lt;/strong&gt;, &lt;strong&gt;idPrey&lt;/strong&gt; and &lt;strong&gt;countPrey&lt;/strong&gt;.&lt;/p&gt;
&lt;pre&gt;&lt;code class=&#34;language-{r}&#34;&gt;datScore &amp;lt;- CompPASS(datInput)
&lt;/code&gt;&lt;/pre&gt;
&lt;h3 id=&#34;2-dice&#34;&gt;2. DICE&lt;/h3&gt;
&lt;p&gt;The Dice coefficient is used to score the interaction scores across prey pair-wise combinations, which was proposed by &lt;a href=&#34;https://doi.org/10.1093/bioinformatics/btn036&#34;&gt;(Bing Zhang et al., 2008)&lt;/a&gt;&lt;/p&gt;
&lt;p&gt;The input data.frame, &lt;em&gt;datInput&lt;/em&gt;, should include:&lt;strong&gt;idRun&lt;/strong&gt; and &lt;strong&gt;idPrey&lt;/strong&gt;.&lt;/p&gt;
&lt;pre&gt;&lt;code class=&#34;language-{r}&#34;&gt;datScore &amp;lt;- DICE(datInput)
&lt;/code&gt;&lt;/pre&gt;
&lt;h3 id=&#34;3-hart&#34;&gt;3. Hart&lt;/h3&gt;
&lt;p&gt;Hart scoring algorithm is based on a hypergeometric distribution error model &lt;a href=&#34;https://doi.org/10.1186/1471-2105-8-236&#34;&gt;(Hart et al., 2007)&lt;/a&gt;.&lt;/p&gt;
&lt;p&gt;The input data.frame, &lt;em&gt;datInput&lt;/em&gt;, should include:&lt;strong&gt;idRun&lt;/strong&gt; and &lt;strong&gt;idPrey&lt;/strong&gt;.&lt;/p&gt;
&lt;pre&gt;&lt;code class=&#34;language-{r}&#34;&gt;datScore &amp;lt;- Hart(datInput)
&lt;/code&gt;&lt;/pre&gt;
&lt;h3 id=&#34;4-hgscore&#34;&gt;4. HGScore&lt;/h3&gt;
&lt;p&gt;HGScore algorithm is based on a hypergeometric distribution error model &lt;a href=&#34;https://doi.org/10.1186/1471-2105-8-236&#34;&gt;(Hart et al., 2007)&lt;/a&gt; with incorporation of NSAF &lt;a href=&#34;https://doi.org/10.1021/pr060161n&#34;&gt;(Zybailov, Boris, et al., 2006)&lt;/a&gt;. This algorithm was first introduced to predict the protein complex network of Drosophila melanogaster &lt;a href=&#34;https://doi.org/10.1016/j.cell.2011.08.047&#34;&gt;(Guruharsha, K. G., et al., 2011)&lt;/a&gt;. This scoring algorithm was based on matrix model.&lt;/p&gt;
&lt;p&gt;The input data.frame, &lt;em&gt;datInput&lt;/em&gt;, should include:&lt;strong&gt;idRun&lt;/strong&gt;, &lt;strong&gt;idPrey&lt;/strong&gt;, &lt;strong&gt;countPrey&lt;/strong&gt; and &lt;strong&gt;lenPrey&lt;/strong&gt;.&lt;/p&gt;
&lt;pre&gt;&lt;code class=&#34;language-{r}&#34;&gt;datScore &amp;lt;- HG(datInput)
&lt;/code&gt;&lt;/pre&gt;
&lt;h3 id=&#34;5-pe&#34;&gt;5. PE&lt;/h3&gt;
&lt;p&gt;PE incorporated both spoke and matrix model as repored in &lt;a href=&#34;https://doi.org/10.1074/mcp.M600381-MCP200&#34;&gt;(Sean R. Collins, et al., 2007)&lt;/a&gt;.&lt;/p&gt;
&lt;p&gt;The input data.frame, &lt;em&gt;datInput&lt;/em&gt;, should include:&lt;strong&gt;idRun&lt;/strong&gt;, &lt;strong&gt;idBait&lt;/strong&gt; and &lt;strong&gt;idPrey&lt;/strong&gt;.&lt;/p&gt;
&lt;pre&gt;&lt;code class=&#34;language-{r}&#34;&gt;datScore &amp;lt;- PE(datInput)
&lt;/code&gt;&lt;/pre&gt;
&lt;h2 id=&#34;license&#34;&gt;License&lt;/h2&gt;
&lt;p&gt;MIT @ Qingzhou Zhang&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Slides</title>
      <link>https://zqzneptune.github.io/slides/example/</link>
      <pubDate>Tue, 05 Feb 2019 00:00:00 +0000</pubDate>
      <guid>https://zqzneptune.github.io/slides/example/</guid>
      <description>&lt;h1 id=&#34;create-slides-in-markdown-with-academic&#34;&gt;Create slides in Markdown with Academic&lt;/h1&gt;
&lt;p&gt;&lt;a href=&#34;https://sourcethemes.com/academic/&#34;&gt;Academic&lt;/a&gt; | &lt;a href=&#34;https://sourcethemes.com/academic/docs/managing-content/#create-slides&#34;&gt;Documentation&lt;/a&gt;&lt;/p&gt;
&lt;hr&gt;
&lt;h2 id=&#34;features&#34;&gt;Features&lt;/h2&gt;
&lt;ul&gt;
&lt;li&gt;Efficiently write slides in Markdown&lt;/li&gt;
&lt;li&gt;3-in-1: Create, Present, and Publish your slides&lt;/li&gt;
&lt;li&gt;Supports speaker notes&lt;/li&gt;
&lt;li&gt;Mobile friendly slides&lt;/li&gt;
&lt;/ul&gt;
&lt;hr&gt;
&lt;h2 id=&#34;controls&#34;&gt;Controls&lt;/h2&gt;
&lt;ul&gt;
&lt;li&gt;Next: &lt;code&gt;Right Arrow&lt;/code&gt; or &lt;code&gt;Space&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;Previous: &lt;code&gt;Left Arrow&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;Start: &lt;code&gt;Home&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;Finish: &lt;code&gt;End&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;Overview: &lt;code&gt;Esc&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;Speaker notes: &lt;code&gt;S&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;Fullscreen: &lt;code&gt;F&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;Zoom: &lt;code&gt;Alt + Click&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://github.com/hakimel/reveal.js#pdf-export&#34;&gt;PDF Export&lt;/a&gt;: &lt;code&gt;E&lt;/code&gt;&lt;/li&gt;
&lt;/ul&gt;
&lt;hr&gt;
&lt;h2 id=&#34;code-highlighting&#34;&gt;Code Highlighting&lt;/h2&gt;
&lt;p&gt;Inline code: &lt;code&gt;variable&lt;/code&gt;&lt;/p&gt;
&lt;p&gt;Code block:&lt;/p&gt;
&lt;pre&gt;&lt;code class=&#34;language-python&#34;&gt;porridge = &amp;quot;blueberry&amp;quot;
if porridge == &amp;quot;blueberry&amp;quot;:
    print(&amp;quot;Eating...&amp;quot;)
&lt;/code&gt;&lt;/pre&gt;
&lt;hr&gt;
&lt;h2 id=&#34;math&#34;&gt;Math&lt;/h2&gt;
&lt;p&gt;In-line math: $x + y = z$&lt;/p&gt;
&lt;p&gt;Block math:&lt;/p&gt;
&lt;p&gt;$$
f\left( x \right) = ;\frac{{2\left( {x + 4} \right)\left( {x - 4} \right)}}{{\left( {x + 4} \right)\left( {x + 1} \right)}}
$$&lt;/p&gt;
&lt;hr&gt;
&lt;h2 id=&#34;fragments&#34;&gt;Fragments&lt;/h2&gt;
&lt;p&gt;Make content appear incrementally&lt;/p&gt;
&lt;pre&gt;&lt;code&gt;{{% fragment %}} One {{% /fragment %}}
{{% fragment %}} **Two** {{% /fragment %}}
{{% fragment %}} Three {{% /fragment %}}
&lt;/code&gt;&lt;/pre&gt;
&lt;p&gt;Press &lt;code&gt;Space&lt;/code&gt; to play!&lt;/p&gt;
&lt;p&gt;&lt;span class=&#34;fragment &#34; &gt;
One
&lt;/span&gt;
&lt;span class=&#34;fragment &#34; &gt;
&lt;strong&gt;Two&lt;/strong&gt;
&lt;/span&gt;
&lt;span class=&#34;fragment &#34; &gt;
Three
&lt;/span&gt;&lt;/p&gt;
&lt;hr&gt;
&lt;p&gt;A fragment can accept two optional parameters:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;&lt;code&gt;class&lt;/code&gt;: use a custom style (requires definition in custom CSS)&lt;/li&gt;
&lt;li&gt;&lt;code&gt;weight&lt;/code&gt;: sets the order in which a fragment appears&lt;/li&gt;
&lt;/ul&gt;
&lt;hr&gt;
&lt;h2 id=&#34;speaker-notes&#34;&gt;Speaker Notes&lt;/h2&gt;
&lt;p&gt;Add speaker notes to your presentation&lt;/p&gt;
&lt;pre&gt;&lt;code class=&#34;language-markdown&#34;&gt;{{% speaker_note %}}
- Only the speaker can read these notes
- Press `S` key to view
{{% /speaker_note %}}
&lt;/code&gt;&lt;/pre&gt;
&lt;p&gt;Press the &lt;code&gt;S&lt;/code&gt; key to view the speaker notes!&lt;/p&gt;
&lt;aside class=&#34;notes&#34;&gt;
  &lt;ul&gt;
&lt;li&gt;Only the speaker can read these notes&lt;/li&gt;
&lt;li&gt;Press &lt;code&gt;S&lt;/code&gt; key to view&lt;/li&gt;
&lt;/ul&gt;

&lt;/aside&gt;
&lt;hr&gt;
&lt;h2 id=&#34;themes&#34;&gt;Themes&lt;/h2&gt;
&lt;ul&gt;
&lt;li&gt;black: Black background, white text, blue links (default)&lt;/li&gt;
&lt;li&gt;white: White background, black text, blue links&lt;/li&gt;
&lt;li&gt;league: Gray background, white text, blue links&lt;/li&gt;
&lt;li&gt;beige: Beige background, dark text, brown links&lt;/li&gt;
&lt;li&gt;sky: Blue background, thin dark text, blue links&lt;/li&gt;
&lt;/ul&gt;
&lt;hr&gt;
&lt;ul&gt;
&lt;li&gt;night: Black background, thick white text, orange links&lt;/li&gt;
&lt;li&gt;serif: Cappuccino background, gray text, brown links&lt;/li&gt;
&lt;li&gt;simple: White background, black text, blue links&lt;/li&gt;
&lt;li&gt;solarized: Cream-colored background, dark green text, blue links&lt;/li&gt;
&lt;/ul&gt;
&lt;hr&gt;

&lt;section data-noprocess data-shortcode-slide
  
      
      data-background-image=&#34;/img/boards.jpg&#34;
  &gt;

&lt;h2 id=&#34;custom-slide&#34;&gt;Custom Slide&lt;/h2&gt;
&lt;p&gt;Customize the slide style and background&lt;/p&gt;
&lt;pre&gt;&lt;code class=&#34;language-markdown&#34;&gt;{{&amp;lt; slide background-image=&amp;quot;/img/boards.jpg&amp;quot; &amp;gt;}}
{{&amp;lt; slide background-color=&amp;quot;#0000FF&amp;quot; &amp;gt;}}
{{&amp;lt; slide class=&amp;quot;my-style&amp;quot; &amp;gt;}}
&lt;/code&gt;&lt;/pre&gt;
&lt;hr&gt;
&lt;h2 id=&#34;custom-css-example&#34;&gt;Custom CSS Example&lt;/h2&gt;
&lt;p&gt;Let&amp;rsquo;s make headers navy colored.&lt;/p&gt;
&lt;p&gt;Create &lt;code&gt;assets/css/reveal_custom.css&lt;/code&gt; with:&lt;/p&gt;
&lt;pre&gt;&lt;code class=&#34;language-css&#34;&gt;.reveal section h1,
.reveal section h2,
.reveal section h3 {
  color: navy;
}
&lt;/code&gt;&lt;/pre&gt;
&lt;hr&gt;
&lt;h1 id=&#34;questions&#34;&gt;Questions?&lt;/h1&gt;
&lt;p&gt;&lt;a href=&#34;https://spectrum.chat/academic&#34;&gt;Ask&lt;/a&gt;&lt;/p&gt;
&lt;p&gt;&lt;a href=&#34;https://sourcethemes.com/academic/docs/managing-content/#create-slides&#34;&gt;Documentation&lt;/a&gt;&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Awsome affinity purification mass spectrometry (AP-MS)</title>
      <link>https://zqzneptune.github.io/post/2018-07-08-apms/</link>
      <pubDate>Sun, 08 Jul 2018 09:23:14 -0600</pubDate>
      <guid>https://zqzneptune.github.io/post/2018-07-08-apms/</guid>
      <description>&lt;p&gt;A collection of resources regarding affinity purification mass spectrometry proteomics for the identification of protein-protein interactions.&lt;/p&gt;
&lt;h2 id=&#34;algorithms&#34;&gt;Algorithms&lt;/h2&gt;
&lt;table&gt;
&lt;thead&gt;
&lt;tr&gt;
&lt;th&gt;Year&lt;/th&gt;
&lt;th&gt;Algorithm&lt;/th&gt;
&lt;th&gt;Publication&lt;/th&gt;
&lt;th&gt;Implementation&lt;/th&gt;
&lt;/tr&gt;
&lt;/thead&gt;
&lt;tbody&gt;
&lt;tr&gt;
&lt;td&gt;2006&lt;/td&gt;
&lt;td&gt;SAI (socio-affinity index)&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://www.nature.com/articles/nature04532&#34;&gt;Anne-Claude Gavin et al., Nature&lt;/a&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;2007&lt;/td&gt;
&lt;td&gt;partial least squares based regression model&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://www.embopress.org/doi/10.1038/msb4100134&#34;&gt;Rob M Ewing et al., Mol Syst Biol&lt;/a&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;2007&lt;/td&gt;
&lt;td&gt;PE (purification enrichment)&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://www.mcponline.org/content/6/3/439&#34;&gt;Sean R. Collins et al., Molecular &amp;amp; Cellular Proteomics&lt;/a&gt;&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://github.com/zqzneptune/SMAD&#34;&gt;SMAD&lt;/a&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;2007&lt;/td&gt;
&lt;td&gt;Hart&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-8-236&#34;&gt;G Traver Hart et al., BMC Bioinformatics&lt;/a&gt;&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://github.com/zqzneptune/SMAD&#34;&gt;SMAD&lt;/a&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;2008&lt;/td&gt;
&lt;td&gt;Dice coefficient&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://doi.org/10.1093/bioinformatics/btn036&#34;&gt;Bing Zhang et al., Bioinformatics&lt;/a&gt;&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://github.com/zqzneptune/SMAD&#34;&gt;SMAD&lt;/a&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;2008&lt;/td&gt;
&lt;td&gt;PP-NSAF&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://www.pnas.org/content/105/5/1454.short&#34;&gt;Mihaela E. Sardiu et al., PNAS&lt;/a&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;2009&lt;/td&gt;
&lt;td&gt;CompPASS&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://doi.org/10.1016/j.cell.2009.04.042&#34;&gt;Mathew E.Sowa et al., Cell&lt;/a&gt;&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://github.com/zqzneptune/SMAD&#34;&gt;SMAD&lt;/a&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;2010&lt;/td&gt;
&lt;td&gt;Decontaminator&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://pubs.acs.org/doi/abs/10.1021/pr100795z&#34;&gt;MathieLavallée-Adam et al. J. Proteome Res. &lt;/a&gt;&lt;/td&gt;
&lt;td&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;2011&lt;/td&gt;
&lt;td&gt;MiST&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://www.nature.com/articles/nature10719&#34;&gt;Stefanie Jäger et al., Nature&lt;/a&gt;&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://github.com/kroganlab/mist&#34;&gt;mist&lt;/a&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;2011&lt;/td&gt;
&lt;td&gt;HGScore&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://doi.org/10.1016/j.cell.2011.08.047&#34;&gt;K.G.Guruharsha et al., Cell&lt;/a&gt;&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://github.com/zqzneptune/SMAD&#34;&gt;SMAD&lt;/a&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;2011&lt;/td&gt;
&lt;td&gt;SAINT&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://doi.org/10.1038/nmeth.1541&#34;&gt;Hyungwon Choi et al., Nature Methods&lt;/a&gt;&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;http://saint-apms.sourceforge.net/Main.html&#34;&gt;SAINT&lt;/a&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;2015&lt;/td&gt;
&lt;td&gt;SFINX&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://pubs.acs.org/doi/10.1021/acs.jproteome.5b00666&#34;&gt;Kevin Titeca et al. J. Proteome Res.&lt;/a&gt;&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://cran.r-project.org/web/packages/sfinx/index.html&#34;&gt;SFINX&lt;/a&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;/tbody&gt;
&lt;/table&gt;
&lt;h2 id=&#34;datasets&#34;&gt;Datasets&lt;/h2&gt;
&lt;table&gt;
&lt;thead&gt;
&lt;tr&gt;
&lt;th&gt;Year&lt;/th&gt;
&lt;th&gt;Species&lt;/th&gt;
&lt;th&gt;Abstract&lt;/th&gt;
&lt;th&gt;Publication&lt;/th&gt;
&lt;/tr&gt;
&lt;/thead&gt;
&lt;tbody&gt;
&lt;tr&gt;
&lt;td&gt;2002&lt;/td&gt;
&lt;td&gt;&lt;em&gt;S.cerevisiae&lt;/em&gt;&lt;/td&gt;
&lt;td&gt;725 bait proteins, one-step immuno-affinity purification based on the Flag epitope tag&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://www.nature.com/articles/415180a&#34;&gt;Yuen Ho et al., Nature&lt;/a&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;2002&lt;/td&gt;
&lt;td&gt;&lt;em&gt;S.cerevisiae&lt;/em&gt;&lt;/td&gt;
&lt;td&gt;tandem-affinity purification processed 1,739 genes&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://www.nature.com/articles/415141a&#34;&gt;Anne-Claude Gavin et al., Nature&lt;/a&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;2006&lt;/td&gt;
&lt;td&gt;&lt;em&gt;S.cerevisiae&lt;/em&gt;&lt;/td&gt;
&lt;td&gt;Genome-wide TAP–MS, first introduced&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://www.nature.com/articles/nature04532&#34;&gt;Anne-Claude Gavin et al., Nature&lt;/a&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;2009&lt;/td&gt;
&lt;td&gt;&lt;em&gt;H.sapiens&lt;/em&gt;&lt;/td&gt;
&lt;td&gt;Human Deubiquitinating Enzyme Interaction Landscape(DUB)&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://doi.org/10.1016/j.cell.2009.04.042&#34;&gt;Mathew E.Sowa et al., Cell&lt;/a&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;2010&lt;/td&gt;
&lt;td&gt;&lt;em&gt;S.cerevisiae&lt;/em&gt;&lt;/td&gt;
&lt;td&gt;kinase and phosphatase interaction (KPI) network&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://science.sciencemag.org/content/328/5981/1043&#34;&gt;Ashton Breitkreutz et al., Science&lt;/a&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;2009&lt;/td&gt;
&lt;td&gt;&lt;em&gt;H.sapiens&lt;/em&gt;&lt;/td&gt;
&lt;td&gt;autophagy interaction network (AIN)&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://www.nature.com/articles/nature09204&#34;&gt;Christian Behrends et al., Nature&lt;/a&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;2011&lt;/td&gt;
&lt;td&gt;&lt;em&gt;D.melanogaster&lt;/em&gt;&lt;/td&gt;
&lt;td&gt;a Drosophila protein interaction map (DPiM)&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://doi.org/10.1016/j.cell.2011.08.047&#34;&gt;K.G.Guruharsha et al., Cell&lt;/a&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;2012&lt;/td&gt;
&lt;td&gt;&lt;em&gt;S.cerevisiae&lt;/em&gt;&lt;/td&gt;
&lt;td&gt;membrane-protein complexes in yeast&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://www.nature.com/articles/nature11354&#34;&gt;Mohan Babu et al., Nature&lt;/a&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;2014&lt;/td&gt;
&lt;td&gt;&lt;em&gt;H.sapiens&lt;/em&gt;&lt;/td&gt;
&lt;td&gt;A comprehensive chromatin-related protein-protein interaction map&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://doi.org/10.1016/j.celrep.2014.05.050&#34;&gt;Edyta Marcon et al., Cell Reports&lt;/a&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;2015&lt;/td&gt;
&lt;td&gt;&lt;em&gt;H.sapiens&lt;/em&gt;&lt;/td&gt;
&lt;td&gt;BioPlex v1&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://doi.org/10.1016/j.cell.2015.06.043&#34;&gt;Edward L. Huttlin et al., Cell&lt;/a&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;2015&lt;/td&gt;
&lt;td&gt;&lt;em&gt;H.sapiens&lt;/em&gt;&lt;/td&gt;
&lt;td&gt;Autism Spectrum Disorders related&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://doi.org/10.1016/j.cels.2015.11.002&#34;&gt;Jingjing Li et al., Cell Systems&lt;/a&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;2016&lt;/td&gt;
&lt;td&gt;&lt;em&gt;H.sapiens&lt;/em&gt;&lt;/td&gt;
&lt;td&gt;PPI mapping of 50 unannotated mitochondrial proteins&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://doi.org/10.1016/j.molcel.2016.06.033&#34;&gt;Brendan J.Floyd et al., Molecular Cell&lt;/a&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;2016&lt;/td&gt;
&lt;td&gt;&lt;em&gt;H.sapiens&lt;/em&gt;&lt;/td&gt;
&lt;td&gt;Comprehensive mitochondrial sirtuin interactome&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://doi.org/10.1016/j.cell.2016.10.016&#34;&gt;Wen Yang et al., Cell&lt;/a&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;2017&lt;/td&gt;
&lt;td&gt;&lt;em&gt;H.sapiens&lt;/em&gt;&lt;/td&gt;
&lt;td&gt;BioPlex v2&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://www.nature.com/articles/nature22366&#34;&gt;Edward L. Huttlin et al., Cell&lt;/a&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;2017&lt;/td&gt;
&lt;td&gt;&lt;em&gt;E.coli&lt;/em&gt;&lt;/td&gt;
&lt;td&gt;Global landscape of cell envelope protein complexes&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://www.nature.com/articles/nbt.4024&#34;&gt;Mohan Babu et al., Nature Biotech&lt;/a&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;2017&lt;/td&gt;
&lt;td&gt;&lt;em&gt;H.sapiens&lt;/em&gt;&lt;/td&gt;
&lt;td&gt;A Map of Human Mitochondrial Protein Interactions related to Neurodegeneration&lt;/td&gt;
&lt;td&gt;&lt;a href=&#34;https://doi.org/10.1016/j.cels.2017.10.010&#34;&gt;Ramy H.Malty et al., Cell Systems&lt;/a&gt;&lt;/td&gt;
&lt;/tr&gt;
&lt;/tbody&gt;
&lt;/table&gt;
</description>
    </item>
    
    <item>
      <title>Global landscape of cell envelope protein complexes in Escherichia coli</title>
      <link>https://zqzneptune.github.io/publication/nbt.4024/</link>
      <pubDate>Mon, 27 Nov 2017 00:00:00 +0000</pubDate>
      <guid>https://zqzneptune.github.io/publication/nbt.4024/</guid>
      <description></description>
    </item>
    
    <item>
      <title>A Map of Human Mitochondrial Protein Interactions Linked to Neurodegeneration Reveals New Mechanisms of Redox Homeostasis and NF-κB Signaling</title>
      <link>https://zqzneptune.github.io/publication/j.cels.2017.10.010/</link>
      <pubDate>Fri, 17 Nov 2017 00:00:00 +0000</pubDate>
      <guid>https://zqzneptune.github.io/publication/j.cels.2017.10.010/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Renal oncocytoma characterized by the defective complex I of the respiratory chain boosts the synthesis of the ROS scavenger glutathione</title>
      <link>https://zqzneptune.github.io/publication/oncotarget.22413/</link>
      <pubDate>Thu, 21 Sep 2017 00:00:00 +0000</pubDate>
      <guid>https://zqzneptune.github.io/publication/oncotarget.22413/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Renal Gene Expression Database (RGED) a relational database of gene expression profiles in kidney disease</title>
      <link>https://zqzneptune.github.io/publication/rged/</link>
      <pubDate>Tue, 07 Oct 2014 00:00:00 +0000</pubDate>
      <guid>https://zqzneptune.github.io/publication/rged/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Hello, RGED!</title>
      <link>https://zqzneptune.github.io/post/2014-09-23-rged/</link>
      <pubDate>Wed, 23 Jul 2014 01:13:14 +0800</pubDate>
      <guid>https://zqzneptune.github.io/post/2014-09-23-rged/</guid>
      <description>&lt;p&gt;The Renal Gene Expression Database (RGED) is online. Number of samples colleceted in the database reached around ~10,000, including DNA microarray and RNA-seq experiments.&lt;/p&gt;
&lt;p&gt;Just go ahead to this URL: &lt;a href=&#34;http://rged.wall-eva.net/&#34;&gt;http://rged.wall-eva.net/&lt;/a&gt;&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>1. QC and Preprocessing</title>
      <link>https://zqzneptune.github.io/project/01_qc_preprocess/</link>
      <pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate>
      <guid>https://zqzneptune.github.io/project/01_qc_preprocess/</guid>
      <description>


&lt;p&gt;&lt;img src=&#34;https://zqzneptune.github.io/project/01_QC_Preprocess_files/figure-html/Load%20Library-1.png&#34; width=&#34;672&#34; /&gt;&lt;/p&gt;
&lt;div id=&#34;sample-description&#34; class=&#34;section level2&#34;&gt;
&lt;h2&gt;Sample Description&lt;/h2&gt;
&lt;table&gt;
&lt;thead&gt;
&lt;tr class=&#34;header&#34;&gt;
&lt;th&gt;Sample Name&lt;/th&gt;
&lt;th&gt;Species&lt;/th&gt;
&lt;th&gt;Tissue&lt;/th&gt;
&lt;th&gt;Description&lt;/th&gt;
&lt;/tr&gt;
&lt;/thead&gt;
&lt;tbody&gt;
&lt;tr class=&#34;odd&#34;&gt;
&lt;td&gt;384-M&lt;/td&gt;
&lt;td&gt;Mus musculus&lt;/td&gt;
&lt;td&gt;Kidney&lt;/td&gt;
&lt;td&gt;Healthy control&lt;/td&gt;
&lt;/tr&gt;
&lt;tr class=&#34;even&#34;&gt;
&lt;td&gt;403-P&lt;/td&gt;
&lt;td&gt;Mus musculus&lt;/td&gt;
&lt;td&gt;Kidney&lt;/td&gt;
&lt;td&gt;PKD1 Knock out&lt;/td&gt;
&lt;/tr&gt;
&lt;/tbody&gt;
&lt;/table&gt;
&lt;/div&gt;
&lt;div id=&#34;cell-phases-assignment&#34; class=&#34;section level2&#34;&gt;
&lt;h2&gt;Cell phases assignment&lt;/h2&gt;
&lt;/div&gt;
&lt;div id=&#34;number-of-cells&#34; class=&#34;section level2&#34;&gt;
&lt;h2&gt;Number of cells&lt;/h2&gt;
&lt;/div&gt;
&lt;div id=&#34;distribution-of-total-umi-count&#34; class=&#34;section level2&#34;&gt;
&lt;h2&gt;Distribution of total UMI count&lt;/h2&gt;
&lt;/div&gt;
&lt;div id=&#34;distribution-of-reads-in-mitochondrial-genes&#34; class=&#34;section level2&#34;&gt;
&lt;h2&gt;Distribution of reads in mitochondrial genes&lt;/h2&gt;
&lt;/div&gt;
</description>
    </item>
    
  </channel>
</rss>