https://github.com/stracquadaniolab/cubseq-nf
CUBseq analyses codon usage bias from RNA-seq data, producing robust CUB estimates that account for variants transcriptome-wide and in highly expressed genes.
https://github.com/stracquadaniolab/cubseq-nf
codon-bias codon-optimization codon-usage nextflow rnaseq transcriptomics
Last synced: 24 days ago
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CUBseq analyses codon usage bias from RNA-seq data, producing robust CUB estimates that account for variants transcriptome-wide and in highly expressed genes.
- Host: GitHub
- URL: https://github.com/stracquadaniolab/cubseq-nf
- Owner: stracquadaniolab
- License: mit
- Created: 2023-08-30T11:02:49.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-03-29T16:49:41.000Z (over 1 year ago)
- Last Synced: 2024-11-13T02:09:06.902Z (11 months ago)
- Topics: codon-bias, codon-optimization, codon-usage, nextflow, rnaseq, transcriptomics
- Language: R
- Homepage:
- Size: 187 MB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
- License: license.md
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README
# cubseq-nf
## What is CUBseq?
Codon Usage Bias from RNA-sequencing data (CUBseq) is a fully automatic
pipeline that produces robust estimates of codon usage frequencies at
the transcriptome level. CUBseq can be used for any organism with an
NCBI taxonomy ID, available RNA-sequencing data and a reference
genome/annotation. The end result is a dataset of transcriptome-wide
sequences with variants built in, allowing CUBseq to provide codon
relative frequencies as well as raw counts at codon and amino acid
resolution for custom downstream codon usage analysis.
### What can CUBseq be used for?
- Large-scale transcriptome-wide codon usage analysis.
- Generation of transcriptome-derived codon usage tables (expressed as
relative frequency and frequency per thousand).
- Quantification of transcriptome-wide genes.
- Robust identification of high expression genes.
- Reconstruction of transcriptomes per sample using variant calls.
- Analysis of mutation frequency per sample across the transcriptome
and at gene level.
- Comparison of codon frequency with tRNA abundance.
## Running CUBseq: quick-guide
> [!NOTE]
> Before running the workflow, you will need to have Nextflow
installed. See instructions on how to [here](https://www.nextflow.io/docs/latest/getstarted.html#installation).
### Install or update the workflow
```bash
nextflow pull stracquadaniolab/cubseq-nf -r main
```
### Define the configuration file
A `nextflow.config` configuration file will need to be created where parameters are defined, as specified below in `Configuring CUBseq`. This configuration file will need to be created in the same directory where the pipeline will be run. An example configuration file is provided in [example-nextflow.config](templatefiles/example-nextflow.config).
### Run the analysis
Assuming the configuration file is set, to run CUBseq, the bare minimum command required is:
```bash
nextflow run stracquadaniolab/cubseq-nf -r main -profile singularity -c conf/nextflow.config
```
Alternatively, you can define parameters and call custom profiles (examples available on [example-nextflow.config](templatefiles/example-nextflow.config)) directly in the `nextflow run` command:
```bash
nextflow run stracquadaniolab/cubseq-nf -r main -profile singularity,cell -c conf/nextflow.config --resultsDir ./results/test-run
```
For example, here we call a profile, `cell`, which we defined in our config file (which we used to specify the executor, RAM/CPU requirements and error strategy for each process). We also specify a custom results directory path to save output files to.
## Configuring CUBseq
To run CUBseq you will need to specify a number of paths for storing
results, and provide appropriate parameter options based on the
organism being analysed. These parameters need to be defined in a
configuration file called `nextflow.config`. Required parameters are
indicated with an asterisk, the rest of the parameters are optional.
| Parameter | Description |
| :----------- | :------- |
| `resultsDir` | Directory where all results are stored [default: `"./results/"`]. |
| Paths to genome files |
| `genome.reference` * | Path to genome reference (fasta) file [example: `"data/genome/ecoli.fa"`]. |
| `genome.annotation` * | Path to genome annotation (GTF/GFF/GFF3) file [example: `"data/genome/ecoli.gff"`]. |
| ENA metadata retrieval parameters | |
| `taxonId` * | NCBI taxonomy ID of organism to be analysed [default: `"562"`]. |
| `limitSearch` | Limit number of records output from ENA search query [default: `0`]. |
| `removeRun` | Remove run by specifying its run accession [default: `"NULL"`, example: `"SRR13894889"`]. |
| `max_sra_bytes` | Specify runs to remove if they exceed size of sra_bytes [default: `"55000000000"`]. |
| `dateMin` | Set minimum date (YYYY/MM/DD) to filter runs by (inclusive) [default: `"1950-01-01"`]. |
| `dateMax` | Set maximum date (YYYY/MM/DD) to filter runs by (inclusive), uses current date by default [default: `"FALSE"`]. |
| STAR align parameters | |
| `star.sjdbOverhang` | The "--sjdbOverhang" option of STAR, specifies length of genomic sequence on each side of the junctions, refer to STAR documentation for more detail. Here, we use STAR's default option [default: `"100"`]. |
| `star.genomeSAindexNbases` * | The "--genomeSAindexNbases" option of STAR, specifying the length (bases) of SA pre-indexing string. This must be scaled down for small genomes, using formula: min(14, log2(GenomeLength)/2 - 1). [default: `"10"`]. |
| `star.alignIntronMax` | The "--alignIntronMax" option of STAR, specifying maximum intron size [default: `"1"`.] |
| `star.limitBAMsortRAM` | The "--limitBAMsortRAM" option of STAR, specifying maximum available RAM (bytes) [default: `"2342750981"`]. |
| `star.outBAMsortingBinsN` | The "--outBAMsortingBinsN" option of STAR, specifying the number of genome bins for coordinate-sorting [default: `"50"`]. |
| featureCounts parameters | |
| `featureCounts.type.feature` | The "-t" option of featureCounts, specifying feature type(s) in a GTF annotation to be used for read mapping. Multiple types should be separated by "," with no space in between [default: `"exon"`]. |
| `featureCounts.type.attribute` | The "-g" option of featureCounts, specifying attribute type in the GTF annotation [default" `"gene_id"`]. |
| Freebayes parameters | |
| `freebayes.ploidy` * | The "--ploidy" option of Freebayes, specifying the default ploidy for the organism used in the analysis. [default: `"1"`]. |
| `freebayes.args` | Additional Freebayes arguments, refer to their documentation [default: ""]. |
| bcftools parameters | |
| `bcftools.filter_vcf.args` | Additional bcftools filter arguments for filtering the VCF file, refer to their documentation [default: `'QUAL>20 && TYPE="snp"'`, note the use of quotation marks here]. |
| Salmon indexing parameters | |
| `salmon.index.args` | Additional arguments for salmon indexing, refer to their documentation [default: ""]. |
| Salmon quantification parameters | |
| `salmon.quant.libtype` | The "--libType" option of Salmon quant, specifying library type, CUBseq sets this to "Automatic" detection by default. Refer to their documentation for more information [default: `"A"`]. |
| `salmon.quant.args` | Additional arguments for salmon quant, refer to their documentation [example: `"--writeUnmappedNames"`]. |
| tximport parameters | |
| `summarize_to_gene. counts_from_abundance` | Generate counts from abundances in tximport [default: `"no"`]. |
## CUBseq results
CUBseq results are stored in the following directories:
- `results/metadata/metadata.csv`: file containing the ENA metadata of RNA sequencing runs.
- `results/bams/`: directory containing the bam files, as processed by STAR.
- `results/featureCounts/` : directory containing featureCounts gene quantification results per sample and summary statistics.
- `results/freebayes-vcf/` : directory containing vcf files, as processed by Freebayes.
- `results/vcf/` : directory containing filtered vcf files, as processed by bcftools norm and bcftools filter.
- `results/transcriptome-consensus/` : directory containing consensus transcriptomes in fasta format.
- `results/wt-transcriptome/` : directory containing the wild-type transcriptome, as generated by gffread.
- `results/mut-transcriptome/` : directory containing the reconstructed mutated transcriptomes per sequencing run, as processed by gffread.
- `results/salmon-quant/` : directory containing gene abundance results per sequencing run, as processed by salmon quantification.
- `results/dataset/` : directory containing the tximport RDS file that sumamrises salmon quantification results at the gene-level (expressed as TPM matrix).
- `results/gene-rank-analysis/` : directory containing results of CUBseq's gene rank analysis.
- `results/heg-mut-transcriptome/` : directory of fasta files per sequencing run, containing only highly expressed genes.
- `results/protein-mut-transcriptome/` : directory of fasta files per sequencing run, containing transcriptome-wide (i.e. all protein-coding) gemes.
- `results/cu-data/` : directory containing codon usage count data for highly expressed genes, protein coding genes, as well as from the Kazusa and CoCoPUTs databases (if available).
- `results/summarise-codon-counts/` : directory containing codon counts summarised at codon and amino acid resolution.
## Authors
- Anima Sutradhar (A.Sutradhar@sms.ed.ac.uk): developer and maintainer.
- Giovanni Stracquadanio (giovanni.stracquadanio@ed.ac.uk): principal investigator.
## Contact us about CUBseq
If you have any questions, issues or feature requests, please get in
touch using the emails above or posting an Issue.