docs: fixed typos & added note to hashdeep

bih-cluster/docs/storage/migration-faq.md

@@ -1,6 +1,6 @@
 # Data Migration Tips and tricks
 Please use `hpc-transfer-1` and `hpc-transfer-2` for moving large amounts of files.
-This not only leaves the compute notes available for actual computation, but also has now risk of your jobs being killed by Slurm.
+This not only leaves the compute notes available for actual computation, but also has no risk of your jobs being killed by Slurm.
 You should also use `tmux` to not risk connection loss during long running transfers.
 
 ## Useful commands
@@ -24,6 +24,9 @@ $ rm -r $SOURCE
     When defining your source location, do not use the `*` wildcard character.
     This will not match hidden (dot) files and leave them behind.
 
+!!! Note
+	Paranoid users may want to consider using `hashdeep` to ensure that all files were successfully copied.
+
 ## Conda environments
 Conda environment tend to not react well when the folder they are stored in is moved from its original location.
 There are numerous ways to move the state of your environments, which are described [here](https://www.anaconda.com/blog/moving-conda-environments).
@@ -49,4 +52,4 @@ $ conda env create -f environment.yml
 
     ```sh
     $ conda activate /fast/home/users/your-user/path/to/conda/envs/env-name-here
-    ```
+    ```

bih-cluster/docs/storage/storage-migration.md

@@ -117,7 +117,7 @@ A typical workflow would be:
 
 1. Copy your `fastq` files from Tier 2 to Tier 1.
 2. Perform raw data quality control, and store the outcome on Tier 2.
-3. Get expression levels, for example using `salmon` or `STAR`, and store the results on Tier 1.
+3. Get expression levels, for example using `salmon` or `STAR`, and store the results on Tier 2.
 4. Import the expression levels into `R`, using `tximport` and `DESeq2` or `featureCounts` & `edgeR`, for example.
 5. Save expression levels (`R` objects) and the output of `salmon`, `STAR`, or any mapper/aligner of your choice to Tier 2.
 6. **Remove raw data, bam & count files from Tier 1.**
@@ -134,7 +134,7 @@ Therefore, a typical workflow would be:
 
 1. Copy your `fastq` files from Tier 2 to Tier 1.
 2. Perform raw data QC, and store the results on Tier 2. 
-3. Get the count matrix, e.  g. using `Cell Ranger` or `alevin-fry`, perform count matrix QC and store the results on Tier 1.
+3. Get the count matrix, e. g. using `Cell Ranger` or `alevin-fry`, perform count matrix QC and store the results on Tier 2.
 4. **Remove raw data, bam & count files from Tier 1.**
 5. Downstream analysis with `seurat`, `scanpy`, or `Loupe Browser`.