![](http://www.phylo.org/images/interface/logo_cipres.gif)

# Bio::Phylo::CIPRES
Phylogenomic analysis on the CIPRES REST portal

## Prerequisites
Usage of CIPRES requires a [DEVELOPER account](https://www.phylo.org/restusers/register.action) 
(not a normal user account) for the CIPRES REST API (CRA), and a 
[registration](https://www.phylo.org/restusers/createApplication!input.action) for the app 
`corvid19_phylogeny`.With the account and app key, you can then populate a YAML file 
`cipres_appinfo.yml` thusly, substituting the fields with pointy brackets with the 
appropriate values:

```yaml
---
URL: https://cipresrest.sdsc.edu/cipresrest/v1
KEY: <app key>
CRA_USER: <user>
PASSWORD: <pass>
```

Additional prerequisites, which should be resolved automatically during your chosen installation
procedure (conda, cpanm) are listed under the `PREREQ_PM` field in the file [Makefile.PL](Makefile.PL).

## Installation

### CPANM

```bash
$ cpanm Bio::Phylo::CIPRES
```

## Example workflow

### 1. Aligning sequences

To align sequences in a FASTA file with [MAFFT](http://www.phylo.org/index.php/rest/mafft_xsede.html):

```bash
cipresrun \
     -t MAFFT_XSEDE \
     -p vparam.anysymbol_=1 \
     -i <infile> \
     -y cipres_appinfo.yml \
     -o output.mafft
```

- By adding the `-v` (or `--verbose`) flag, the XML returned by the server is shown. In the
  last status check, this will show additional values for `-o`, e.g. to retrieve STDERR and
  other outputs.
- Most other parameters shown on the REST documentation page can also be used.
- The output is written to a file with the same name is the output field (i.e. in this case
  a file called `output.mafft`), which optionally ends up in a `-wd` working directory.

### 2. Inferring trees

To infer trees from an aligned FASTA file using [IQTree](http://www.phylo.org/index.php/rest/iqtree_xsede.html):

```bash
cipresrun \
    -t IQTREE_XSEDE \
    -p vparam.specify_runtype_=2 \
    -p vparam.specify_dnamodel_=HKY \
    -p vparam.bootstrap_type_=bb \
    -p vparam.use_bnni_=1 \
    -p vparam.num_bootreps_=1000 \
    -p vparam.specify_numparts_=1 \
    -i output.mafft \
    -y cipres_appinfo.yml \    
    -o output.contree
```

<!--

## Orchestrating the workflow
Workflow steps will be orchestrated by wrapping scripts that are inside a
docker container (and which in turn are wrapping some executables and web service calls). The
outside wrapping will be [CWL](https://www.commonwl.org/user_guide/07-containers/index.html).
Steps to wrap are:

### 1. preprocessing

The first stage is preprocessing pipeline that does the following:

1. **seqfilter** - filter out short sequence records (default: <25k, change with `--length=20000`)
2. **sequniqid** - filter out duplicate accession numbers (e.g. when merging from multiple taxon levels)
3. **sequniqseq** - filter out duplicate sequence data (i.e. exact same genome in multiple samples)
4. **seqchunk** - split stream into files with 25 records (default for CIPRES, change with `--chunk=30`)

Example usage:

```bash
# each seq* util can be run in turn, reading/write to and from files,
# but a pipe is less polluting and easier to understand anyway.
gunzip -c /data/genomes/*.gz | seqfilter | sequniqid | sequniqseq | seqchunk -o /data/tmp
```

The end result is a folder (specified with `-o`) that contains files of the right 
dimensions to submit to the CIPRES web server. Across those files, there will be no
short sequences, no duplicate IDs and no duplicate sequences.

### 2. align the viral genomes

The next stage is to align the genomes. This follows a spread/gather model where the 
files are submitted to the CIPRES server in batches of 20 files, with a process thread
monitoring each of these. The files are returned with an *.aln suffix. Then, these 
aligned chunks are profile aligned relative to one another:

1. **alnspread** - scans `-i <indir>` for \*.fasta files, dispatches using `-y <YAML>`
2. **alngather** - scans `-i <indir>` for \*.aln files, produces `-o <outfile>`

Example usage:

```bash
# these steps cannot be piped
alnspread -i /data/tmp -y /data/cipres_appinfo.yml
alngather -i /data/tmp -o /data/alignments/profile.aln
```

This results in a large-ish, gapped FASTA file, e.g. ±13MB for the full NCBI genomes of
SARS-CoV-2 at time of writing. This can ostensibly be consumed directly by IQ-Tree.

-->

<!--
2. preprocess the reference genome using `script/refseqpp -v`, results ending up in `/data/genes/*`
3. makeblastdb on the concatenated genomes in `data/genomes/\*.fasta`, e.g. 
    `makeblastdb -in gisaid_cov2020_sequences.fasta -dbtype nucl`
-->
