diff --git a/16S_2023.ipynb b/16S_2023.ipynb
index f83a88b..4bf49cb 100644
--- a/16S_2023.ipynb
+++ b/16S_2023.ipynb
@@ -18,7 +18,7 @@
"source": [
"# 🦠Amplicon Sequencing Data Analysis with Qiime 2\n",
"\n",
- "This notebook will accompany the first session of the 2022 ISB Virtual Microbiome Series. The presentation slides can be [found here](https://gibbons-lab.github.io/isb_course_2022/16S). \n",
+ "This notebook will accompany the first session of the 2023 ISB Virtual Microbiome Series. The presentation slides can be [found here](https://gibbons-lab.github.io/isb_course_2023/16S). \n",
"\n",
"Save your own local copy of this notebook by using `File > Save a copy in Drive`. At some point you may be prompted to trust the notebook. We promise that it is safe 🤞\n",
"\n",
@@ -28,7 +28,7 @@
"\n",
"## Setup\n",
"\n",
- "QIIME 2 is usually installed by following the [official installation instructions](https://docs.qiime2.org/2022.8/install/). However, because we are using Google Colab and there are some caveats to using conda here, we will have to hack around the installation a little bit. But no worries, we provide a setup script below which does all this work for us. 😌 \n",
+ "QIIME 2 is usually installed by following the [official installation instructions](https://docs.qiime2.org/2023.7/install/). However, because we are using Google Colab and there are some caveats to using conda here, we will have to hack around the installation a little bit. But no worries, we provide a setup script below which does all this work for us. 😌 \n",
"\n",
"So...let's start by pulling a local copy of the project repository down from GitHub."
]
@@ -41,7 +41,7 @@
},
"outputs": [],
"source": [
- "!git clone https://github.com/gibbons-lab/isb_course_2022 materials"
+ "!git clone https://github.com/gibbons-lab/isb_course_2023 materials"
]
},
{
@@ -104,7 +104,7 @@
"\n",
"Let's remember our workflow for today.\n",
"\n",
- "\n",
+ "\n",
"\n",
"The first thing we have to do is to get the data into an 'artifact'.\n",
"We can import the data with the `import` action from the tools. For that we have to give\n",
@@ -257,7 +257,7 @@
"\n",
"**Large fraction of reads is lost during merging (only paired-end)**\n",
"\n",
- "\n",
+ "\n",
"\n",
"In order to merge ASVs DADA2 uses an overlap of 12 bases between forward and reverse reads by default. Thus, your reads must allow for sufficient overlap *after* trimming. So if your amplified region is 450bp long and you have 2x250bp reads and you trim the last 30 bases of each read, truncating the length to 220bp, the total length of covered sequence is 2x220 = 440 which is shorter than 450bp so there will be no overlap. To solve this issue trim less of the reads or adjust the `--p-min-overlap` parameters to something lower (but not too low).\n",
"\n",
@@ -265,7 +265,7 @@
"\n",
"**Most of the reads are lost as chimeric**\n",
"\n",
- "\n",
+ "\n",
"\n",
"This is usually an experimental issue as chimeras are introduced during amplification. If you can adjust your PCR, try to run fewer cycles. Chimeras can also be introduced by incorrect merging. If your minimum overlap is too small ASVs may be merged randomly. Possible fixes are to increase the `--p-min-overlap` parameter or run the analysis on the forward reads only (in our empirical observations, chimeras are more likely to be introduced in the joined reads). *However, losing between 5-25% of your reads to chimeras is normal and does not require any adjustments.*\n",
"\n",
@@ -367,7 +367,7 @@
"\n",
"## Alpha and Beta Diversity\n",
"\n",
- "\n",
+ "\n",
"\n",
"One of our main goals will be to compare the microbial composition across different populations. QIIME 2 has \"run-all\" command for diversity analyses. This will\n",
"\n",
@@ -467,7 +467,7 @@
"source": [
"# Taxonomy\n",
"\n",
- "We will use a Bayes classifier trained on the NCBI Refseq database version 212. However, there are many more databases which can be downloaded from https://docs.qiime2.org/2022.8/data-resources/. For instance, the SILVA database is very popular and probably the largest 16S database available right now."
+ "We will use a Bayes classifier trained on the NCBI Refseq database version 212. However, there are many more databases which can be downloaded from https://docs.qiime2.org/2023.8/data-resources/. For instance, the SILVA database is very popular and probably the largest 16S database available right now."
]
},
{
diff --git a/16S_2023_solutions.ipynb b/16S_2023_solutions.ipynb
index 913843a..45a17dc 100644
--- a/16S_2023_solutions.ipynb
+++ b/16S_2023_solutions.ipynb
@@ -18,7 +18,7 @@
"source": [
"# 🦠Amplicon Sequencing Data Analysis with Qiime 2\n",
"\n",
- "This notebook will accompany the first session of the 2022 ISB Virtual Microbiome Series. The presentation slides can be [found here](https://gibbons-lab.github.io/isb_course_2022/16S). \n",
+ "This notebook will accompany the first session of the 2023 ISB Virtual Microbiome Series. The presentation slides can be [found here](https://gibbons-lab.github.io/isb_course_2023/16S). \n",
"\n",
"Save your own local copy of this notebook by using `File > Save a copy in Drive`. At some point you may be prompted to trust the notebook. We promise that it is safe 🤞\n",
"\n",
@@ -28,7 +28,7 @@
"\n",
"## Setup\n",
"\n",
- "QIIME 2 is usually installed by following the [official installation instructions](https://docs.qiime2.org/2022.8/install/). However, because we are using Google Colab and there are some caveats to using conda here, we will have to hack around the installation a little bit. But no worries, we provide a setup script below which does all this work for us. 😌 \n",
+ "QIIME 2 is usually installed by following the [official installation instructions](https://docs.qiime2.org/2023.7/install/). However, because we are using Google Colab and there are some caveats to using conda here, we will have to hack around the installation a little bit. But no worries, we provide a setup script below which does all this work for us. 😌 \n",
"\n",
"So...let's start by pulling a local copy of the project repository down from GitHub."
]
@@ -60,7 +60,7 @@
}
],
"source": [
- "!git clone https://github.com/gibbons-lab/isb_course_2022 materials"
+ "!git clone https://github.com/gibbons-lab/isb_course_2023 materials"
]
},
{
@@ -325,7 +325,7 @@
"\n",
"Let's remember our workflow for today.\n",
"\n",
- "\n",
+ "\n",
"\n",
"The first thing we have to do is to get the data into an 'artifact'.\n",
"We can import the data with the `import` action from the tools. For that we have to give\n",
@@ -536,7 +536,7 @@
"\n",
"**Large fraction of reads is lost during merging (only paired-end)**\n",
"\n",
- "\n",
+ "\n",
"\n",
"In order to merge ASVs DADA2 uses an overlap of 12 bases between forward and reverse reads by default. Thus, your reads must allow for sufficient overlap *after* trimming. So if your amplified region is 450bp long and you have 2x250bp reads and you trim the last 30 bases of each read, truncating the length to 220bp, the total length of covered sequence is 2x220 = 440 which is shorter than 450bp so there will be no overlap. To solve this issue trim less of the reads or adjust the `--p-min-overlap` parameters to something lower (but not too low).\n",
"\n",
@@ -544,7 +544,7 @@
"\n",
"**Most of the reads are lost as chimeric**\n",
"\n",
- "\n",
+ "\n",
"\n",
"This is usually an experimental issue as chimeras are introduced during amplification. If you can adjust your PCR, try to run fewer cycles. Chimeras can also be introduced by incorrect merging. If your minimum overlap is too small ASVs may be merged randomly. Possible fixes are to increase the `--p-min-overlap` parameter or run the analysis on the forward reads only (in our empirical observations, chimeras are more likely to be introduced in the joined reads). *However, losing between 5-25% of your reads to chimeras is normal and does not require any adjustments.*\n",
"\n",
@@ -688,7 +688,7 @@
"\n",
"## Alpha and Beta Diversity\n",
"\n",
- "\n",
+ "\n",
"\n",
"One of our main goals will be to compare the microbial composition across different populations. QIIME 2 has \"run-all\" command for diversity analyses. This will\n",
"\n",
@@ -843,7 +843,7 @@
"source": [
"# Taxonomy\n",
"\n",
- "We will use a Bayes classifier trained on the NCBI Refseq database version 212. However, there are many more databases which can be downloaded from https://docs.qiime2.org/2022.8/data-resources/. For instance, the SILVA database is very popular and probably the largest 16S database available right now."
+ "We will use a Bayes classifier trained on the NCBI Refseq database version 212. However, there are many more databases which can be downloaded from https://docs.qiime2.org/2023.8/data-resources/. For instance, the SILVA database is very popular and probably the largest 16S database available right now."
]
},
{
diff --git a/docs/micom/index.html b/docs/micom/index.html
index a7a7c88..036e335 100755
--- a/docs/micom/index.html
+++ b/docs/micom/index.html
@@ -5,7 +5,7 @@
- 2022 ISB Virtual Microbiome Series - day 2
+ 2023 ISB Virtual Microbiome Series - day 2
diff --git a/micom_2023.ipynb b/micom_2023.ipynb
index fde77ed..e7eff77 100644
--- a/micom_2023.ipynb
+++ b/micom_2023.ipynb
@@ -18,7 +18,7 @@
"source": [
"# 🧫🦠Modeling microbiota-wide metabolism with MICOM\n",
"\n",
- "This notebook will accompany the second session of the 2022 ISB Microbiome Course. The presentation slides can be [found here](https://gibbons-lab.github.io/isb_course_2022/micom). \n",
+ "This notebook will accompany the second session of the 2023 ISB Microbiome Course. The presentation slides can be [found here](https://gibbons-lab.github.io/isb_course_2023/micom). \n",
"\n",
"You can save your own local copy of this notebook by using `File > Save a copy in Drive`. You may be promted to cetify the notebook is safe. We promise that it is 🤞\n",
"\n",
@@ -48,7 +48,7 @@
},
"outputs": [],
"source": [
- "!git clone https://github.com/gibbons-lab/isb_course_2022 materials\n",
+ "!git clone https://github.com/gibbons-lab/isb_course_2023 materials\n",
"%cd materials"
]
},
@@ -95,8 +95,7 @@
},
"outputs": [],
"source": [
- "!pip install -q numpy Cython\n",
- "!pip install -q biom-format\n",
+ "!pip install -q numpy Cython biom-format\n",
"\n",
"print(\"Done! 🎉 \")"
]