adding mmseqs2

Author: linsalrob

MMSeqs2/README.md

@@ -0,0 +1,114 @@
+# MMSeqs2
+
+[MMSeqs2](https://github.com/soedinglab/MMseqs2) is a fast sequence searching algorithm that we use in replace of [blast](https://blast.ncbi.nlm.nih.gov/Blast.cgi) or [diamond](https://github.com/bbuchfink/diamond)
+
+MMSeqs2 has a lot of different options, and we have not yet included them all here, but you can find full details about MMSeqs2 in their [detailed manual](https://mmseqs.com/latest/userguide.pdf).
+
+# MMSeqs2 databases
+
+Like many tools, MMSeqs2 has precomputed databases that you can download. 
+
+There is a [complete list on their website](https://github.com/soedinglab/MMseqs2/wiki#downloading-databases)
+
+You can download a database with the `databases` command. For example, to download the [UniRef50](https://www.uniprot.org/help/uniref) database:
+
+```bash
+mkdir -p UniRef50
+mmseqs databases --threads 8 UniRef50 UniRef50/UniRef50 /tmp
+```
+
+Some of the databases have taxonomy included with them, and that enables you to use `mmseqs easy-taxonomy` to explore the metagenome.
+
+# Easy Taxonomy
+
+We use the MMSeqs2 easy taxonomy a _lot_ for analysing metagenomes, especially by comparing to the [UniRef50](https://www.uniprot.org/help/uniref) database.
+
+First, `mmseqs easy-taxonomy` _requires_ `fasta` files and does not work with `fastq` files. We have a [fast way to convert fastq to fasta](https://edwards.flinders.edu.au/fastq-to-fasta/) or you can find some tools online.
+
+We also take advantage of `mmseqs` [sensitivity sweep](https://github.com/soedinglab/MMseqs2/wiki#set-sensitivity--s-parameter) but you should consider comparing [sensitivity and resources](https://github.com/soedinglab/MMseqs2/wiki#optimizing-sensitivity-and-consumption-of-resources). There is a lot of discussion on the [MMSeqs2 wiki](https://github.com/soedinglab/MMseqs2/wiki) about setting sensitivity.
+
+
+We typically use this command to run the easy taxonomy:
+
+
+```bash
+mkdir easy-taxonomy
+mmseqs easy-taxonomy sequence.fasta UniRef50/UniRef50 easy-taxonomy/sequence_taxonomy /tmp --start-sens 1 --sens-steps 3 -s 7 --threads 32
+```
+
+The results will be in a series of files in the `easy-taxonomy` directory, whose names start with `sequence_taxonomy`:
+
+SAGCFN_22_00809_S34_lca.tsv.gz  SAGCFN_22_00809_S34_report.gz  SAGCFN_22_00809_S34_tophit_aln.gz  SAGCFN_22_00809_S34_tophit_report.gz
+
+- `sequence_taxonomy_lca.tsv.gz`: The lowest common ancestor of the sequences in tab separated text.
+
+Example output:
+
+```
+R100400180029:20220829140225:V350082744:2:1145432:5:58/2/2      310915  species Pangasianodon hypophthalmus     2       2       1       0.540
+```
+
+Columns are:
+1. the sequencing read
+2. the taxonomy ID from [NCBI taxonomy](https://www.ncbi.nlm.nih.gov/datasets/taxonomy/tree). For example, this is [310915](https://www.ncbi.nlm.nih.gov/datasets/taxonomy/310915/)
+3. the taxonomic clade. `Species` in this example
+4. The organism name. `Pangasianodon hypophthalmus`
+5. 
+
+
+- `sequence_taxonomy_report.gz` a Kraken2 style output report
+
+Example output:
+
+```
+0.8561  9653    9653    species 310915                                                          Pangasianodon hypophthalmus
+```
+
+- `sequence_taxonomy_tophit_aln.gz` the `blast m8` format 
+
+Example output:
+
+```
+R100400180029:20220829140225:V350082744:2:1145432:5:58/2/2      UniRef50_UPI00147C5152  0.382   163     30      0       0       50      0       163     1.796E-26       108
+```
+
+The columns are:
+
+1. Sequence Read
+2. Match database ID. In this case from the [UniRef50](https://www.uniprot.org/) we have sequence [UniRef50_UPI00147C5152](https://www.uniprot.org/uniref/UniRef50_UPI00147C5152)
+3. Similarity (38.2% identity)
+4. Alignment length (163 bases) 
+5. Gaps (30 bases)
+6. Mismatches (0 bases)
+7. Start on the sequence read (0)
+8. End on the sequence read (50)
+9. Start on the database sequence (0)
+10. End on the database sequence (163)
+11. E-value (1.796E-26)
+12. Bit score
+
+
+- `sequence_taxonomy_tophit_report.gz` the taxonomy and matches to all of the proteins
+
+Example output
+
+```
+UniRef50_UPI00147C5152  6970    0.312   1849.374        0.367   310915  species Pangasianodon hypophthalmus
+```
+
+The columns are:
+
+1. Database ID. In this case from the [UniRef50](https://www.uniprot.org/) we have sequence [UniRef50_UPI00147C5152](https://www.uniprot.org/uniref/UniRef50_UPI00147C5152)
+2. Number of sequences aligning to target
+3. Unique coverage of target uniqueAlignedResidues / targetLength
+4. Target coverage alignedResidues / targetLength
+5. Average sequence identity
+6. [NCBI taxonomy](https://www.ncbi.nlm.nih.gov/datasets/taxonomy/tree) identifier
+7. Taxonomic level `species`
+8. Taxonomic name, in this case `Pangasianodon hypophthalmus`
+
+
+
+
+
+

Workshops/INRB2023.md

@@ -181,7 +181,26 @@ Then, we can assemble the filtered bacterial sequences using spades:
 spades.py --meta -1 not_human/788707_20180129_S_R1.fastq.gz -2 not_human/788707_20180129_S_R2.fastq.gz -o not_human_assembly
 ```
 
-Once you have assembled the sequences, the first step is to visualise the assembly with [bandage](https://rrwick.github.io/Bandage/).
+Once you have assembled the sequences, the first step is to visualise the assembly with [bandage](https://rrwick.github.io/Bandage/), and hopefully you will find an image like this
+
+![Bandage plot](images/bandage.png)
+
+
+# Hecatomb
+
+You can [find the Hecatomb tutorial on the readthedocs website](https://hecatomb.readthedocs.io/)
+
+# Kraken annotations
+
+You can download the [kraken2 databases](https://benlangmead.github.io/aws-indexes/k2) and [run Kraken2](../Kraken2/) on your samples.
+
+# Functional annotations
+
+Just as with taxonomy, there are two broad approaches to figuring out the functions that are present. 
+
+There are a suite of tools that use [heuristics](https://en.wikipedia.org/wiki/Heuristic) (en Français: heuristique). For example, [superfocus](../SUPER-FOCUS) uses _k_-mers, short sequences, to find the functions that are present. 
+
+Another way to find the functions, is to use [MMSeqs2](https://github.com/soedinglab/MMseqs2) easy-taxonomy. You can find more details about the easy-taxonomy in the [MMSeqs2 manual](https://mmseqs.com/latest/userguide.pdf)