Merge branch 'main' of https://github.com/WillNickols/assembly_workflow

README.rst

@@ -1,6 +1,19 @@
 **Assembly workflow**
 ========================
-This repository contains code for Will's assembly workflow based on Aaron Walsh's pets assembly workflow and Segata et al. 2019. For queries, contact Will Nickols (email: <[email protected]>).
+This assembly workflow produces quality-controlled metagenomic assembled genomes (MAGs) and profiles the composition of microbial communities from metagenomic shotgun sequencing. The pipeline is based on Aaron Walsh's pets assembly workflow and `Segata et al. 2019 <https://doi.org/10.1016/j.cell.2019.01.001>`_. For queries, contact Will Nickols ([email protected]).
+
+**Overview**
+................
+Often, a metagenomic sample contains new microbes not present in reference taxonomy databases. To characterize these new microbes, it is often helpful to determine their genomes in order to assess their functional abilities and taxonomy relative to known microbes. This workflow provides a method of building such genomes from cleaned metagenomic sequencing files by doing the following:
+
+#. Assemble cleaned reads into contigs with MEGAHIT.
+#. Align the reads to these contigs with Bowtie2 to determine contig coverage.
+#. Bin the contigs into MAGs with MetaBAT 2.
+#. Calculate the per-sample MAG abundance from the contig coverage.
+#. Quality control the MAGs with CheckM2.
+#. Taxonomically assign MAGs if possible with PhyloPhlAn 3.
+#. Recluster unassigned high- and medium-quality MAGs into SGBs with Mash.
+#. Create an abundance table from the taxonomically assigned MAGs and unassigned SGBs.
 
 -------
 
@@ -57,8 +70,8 @@ The following command runs the workflow on a set of paired-end fastq files that
       --input-extension fastq.gz \
       --paired paired \
       --pair-identifier _R1 \
-      --cores 8 \
-      --local-jobs 12 \
+      --cores 4 \
+      --local-jobs 8 \
       --remove-intermediate-output
 
 **Abundance output**
@@ -71,11 +84,11 @@ The output, ``example/output/sample_1/final_profile_by_sample.tsv`` is a MetaPhl
 
 **Phylogenetic output**
 ^^^^^^^^^^^^^^^^^^^^
-The output shows that *Pseudoalteromonas marina* is present in the sample along with the assembled genome ``sgb_01`` representing a new species genome bin (SGB). We can check the PhyloPhlAn placement of this new SGB by examining ``example/output/sample_1/main/phylophlan/phlophlan_relab.tsv``:
+The output shows that *Pseudoalteromonas marina* is present in the sample along with the assembled genome ``sgb_01`` representing a new species genome bin (SGB). We can check the PhyloPhlAn placement of this new SGB by examining ``example/output/sample_1/main/phylophlan/phylophlan_relab.tsv``:
 
 ::
 
-    head example/output/sample_1/main/phylophlan/phlophlan_relab.tsv
+    head example/output/sample_1/main/phylophlan/phylophlan_relab.tsv
 
 This confirms that the closest SGB, GGB, and FGB have Mash distances of more than 0.05, 0.15, and 0.3 respectively. 
 
@@ -106,8 +119,8 @@ We might want to create genome bins after running a standard biobakery workflow.
       --input-extension fastq.gz \
       --paired concatenated \
       --skip-contigs \
-      --cores 8 \
-      --local-jobs 12 \
+      --cores 4 \
+      --local-jobs 8 \
       --remove-intermediate-output
 
 **Example 3**
@@ -138,10 +151,11 @@ Samples 2 and 4 had MAGs that were close enough that they were merged into the s
 Finally, we can visualize how much of each sample's abundance is made of known microbes, new SGBs, and unknown microbes. The following script will produce a ``figures`` folder in the ``tutorial`` folder, from which you can examine the unknown abundance.
 
 ::
-                            
+
+    cd tutorial/
     Rscript abundance_script.R
 
-We can see that the vast majority of most samples consists of unknown genetic material. Patially, this is due to the fact that wild animal guts are not very well characterized, but it is also due to the fact that assembly methods tend to have low recall. 
+We can see that the vast majority of most samples consists of unknown genetic material. Partially, this is due to the fact that wild animal guts are not very well characterized, but it is also due to the fact that assembly methods tend to have low recall. 
 
 **Output file tree**
 ................