DRAM2 (Distilled and Refined Annotation of Metabolism Version 2) is a tool for annotating metagenomic assembled genomes. DRAM2 annotates MAGs using KEGG (if provided by the user), UniRef90, PFAM, dbCAN, RefSeq viral, VOGDB and the MEROPS peptidase database as well as custom user databases. DRAM is run in two stages. First an annotation step to assign database identifiers to gene, and then a distill step to curate these annotations into useful functional categories. DRAM2 was implemented in Nextflow due to its innate scalability on HPCs and containerization, ensuring rigorous reproducibility and version control, thus making it ideally suited for high-performance computing environments.
For more detail on DRAM and how DRAM2 works please see our paper.
The DRAM development team is actively working on DRAM2. We do not anticipate adding any additional functionality to DRAM, i.e. DRAM1.
- Future updates will include:
- Pre-formatted annotation and description databases avaiable via GLOBUS.
For further documentation, tutorials and background information, please visit the Read the Docs page.
- Installation
- Databases
- Example command-line usage
- All command-line options
- Software versions
- Cool Nextflow Tips and Tricks
- System Requirements
- Clone the DRAM2 GitHub Repository
- Download pre-formatted databases
- Install Anaconda/Miniconda
- Install Nextflow >= v23.04.2.5870
- Clone the DRAM2 GitHub Repository
- Install Singularity >= v3.7.0 (to pull Singualrity images from SyLabs).
- Download Singularity container
- Download pre-formatted databases
- Install Nextflow >= v23.04.2.5870
git clone https://github.com/WrightonLabCSU/DRAM2.git
cd DRAM2
./pull_singularity_containers.py
./pull_databases_full.py
DRAM2 utilizes either Conda or Singularity for dependency management and the user MUST choose one of the following options on execution of any DRAM2 command
The Nextflow profile option is used (-profile) - yes! a single hyphen!
-profile conda
This option relies on the local systems Conda. Nextflow will create its own Conda environments to run in.
-profile conda_slurm
This option will submit each individual DRAM2 process as its own SLURM job. (See Wiki Resource Management for details). This option relies on the local systems Conda. Nextflow will create its own Conda environments to run in.
-profile singularity
This option relies on the local systems Singularity. Nextflow will create its own Conda environments to run in.
-profile singularity_slurm
This option will submit each individual DRAM2 process as its own SLURM job. (See Wiki Resource Management for details) This option relies on the local systems Singularity to run the downloaded Singularity container.
- Beginner-Friendly: Easy to install and use, making it accessible for newcomers.
- Reproducibility: Efficient management of environments facilitates reproducibility.
- Generally slower than using Singularity containers (will have metrics in the future).
- Dependency Conflicts: Dependency resolution can be slow and may lead to conflicts.
- Limited Portability: System dependencies may introduce variability, affecting portability.
- System Variability: Reliance on the host system's architecture and libraries can cause variability between systems.
- Generally faster than using Conda environments (will have metrics in the future).
- Consistent Environments: Ensures consistent runtime environments, enhancing reproducibility.
- HPC Ideal: Perfect for high-performance computing (HPC) environments without the need for root access.
- Isolation: Offers isolation from the host system, minimizing conflicts.
- Wide Portability: Containers are portable across any Linux system with Singularity.
- Installation Complexity: Can be trickier to install compared to Conda environments.
- Storage Space: May consume more storage space.
Conda is recommended for its ease of use and versatility across different programming languages. Singularity excels in ensuring reproducibility and compatibility in high-performance computing environments.
DRAM2 databases, unlike DRAM1 databases, will be pre-formatted and hosted online. Users of DRAM2 will need to:
- decide which databases suits their needs
- download DRAM2 databases via the provided `pull_databases_*.py scripts.
However, these databases can be quite large and it is therefore important to look through the options below.
These databases rely on an SQL database of database descriptions which is provided in 3 different sizes based on ther user's needs.
- KEGG
- Kyoto Encyclopedia of Genes and Genomes.
- (140G)
- dbCAN
- A database for automated carbohydrate-active enzyme annotation.
- (202M)
- Kofam
- Customized HMM database of KEGG Orthologs (KOs).
- (14G)
- MEROPS
- A database of proteolytic enzymes and their substrates.
- (3.6G)
- Viral
- RefSeq viral database.
- (1.6G)
- CAMPER
- Curated Annotations for Microbial (Poly)phenol Enzymes and Reactions.
- (846M)
- CANT-HYD
- Curated database of phylogeny-derived hidden markov models for annotation of marker genes involved in hydrocarbon degradation.
- (877M)
- FeGenie
- Placeholder description.
- (6.6M)
- Sulfur
- Custom Sulfur database.
- (1.7M)
- Methyl
- Hidden Markov models (HMMs) based on genes related to iron acquisition, storage, and reduction/oxidation in Bacteria and Archaea.
- (52K)
- UniRef
- A comprehensive and non-redundant database of protein sequences.
- (477G)
- Pfam
- A large collection of protein families, each represented by multiple sequence alignments and hidden Markov models (HMMs).
- (8.8G)
- VOGDB
- Placeholder description.
- (4.5G)
Big set
- Includes all medium databases + UniRef
- Excludes KEGG*
./pull_databases_full.py
OR
Follow these instructions to pull manually via GLOBUS.
Routine Set
- Includes: dbCAN, Kofam, MEROPS, Viral, CAMPER, CANT-HYD, FeGenie, Sulfur, Methyl, Pfam, VOGDB
- Excludes KEGG*
- Excludes UniRef
./pull_databases_routine.py
OR
Follow these instructions to pull manually via GLOBUS.
Minimal Set
- Includes:
./pull_databases_minimal.py
OR
Follow these instructions to pull manually via GLOBUS.
A subscription is required to download the kegg database.
If you have a subscription and would like to use KEGG you will need to 1) download the KEGG file, 2) run the format-KEGG-DRAM2.py script to format the KEGG database for DRAM2 and 3) place the formatted database files in the appropriate location.
Download the amino acid FASTA file (*.pep) downloaded from KEGG. This can be any of the gene fasta files that are provided by the KEGG FTP server or a concatenated version of them.
Run the format-KEGG-DRAM2.sh script:
./format-KEGG-DRAM2.py <path-to-downloaded-kegg.pep>
Big set
- Includes Uniref and KEGG
./pull_descriptions_full.py
OR
Follow these instructions to pull manually via GLOBUS.
Routine Set
- Excludes Uniref and KEGG
./pull_descriptions_routine.py
OR
Follow these instructions to pull manually via GLOBUS.
DRAM2 apps Call, Annotate and Distill can all be run at once or alternatively, each app can be run individualy (assuming you provide the required input data for each app).
Additionally, --merge-annotations and --rename can be run idenpendently of any other apps.
- Rename fasta headers based on input sample file names:
nextflow run DRAM2.nf --rename --input_fasta_dir <path/to/fasta/directory/>
- Call genes using input fastas (use --rename to rename FASTA headers):
nextflow run DRAM2.nf --call --rename --input_fasta_dir <path/to/fasta/directory/>
- Annotate called genes using input called genes and the KOFAM database:
nextflow run DRAM2.nf --annotate --input_genes <path/to/called/genes/directory> --use_kofam
- Annotate called genes using input fasta files and the KOFAM database:
nextflow run DRAM2.nf --annotate --input_fasta <path/to/called/genes/directory> --use_kofam
- Merge verious existing annotations files together (Must be generated using DRAM2.):
nextflow run DRAM2.nf --merge_annotations <path/to/directory/with/multiple/annotation/TSV/files>
- Distill using input annotations:
nextflow run DRAM2.nf --distill_<topic|ecosystem|custom> --annotations <path/to/annotations.tsv>
- (Combined): Call, annotate and distill input fasta files:
nextflow run DRAM2.nf --rename --call --annotate --use_<database(s) --distill_topic <distillate(s)>
- Call and Annotate genes using input fastas and KOFAM database. Distill using the default topic and the AG ecosystem:
nextflow run DRAM2.nf --input_fasta_dir <path/to/fasta/directory/> --outdir <path/to/output/directory/> --call --annotate --distill_topic default --distill_ecosystem ag --threads <threads> --use_kofam
- "Real-world" example using the test data provided in this repository:
(put on multiple lines for clarity)
nextflow run -bg
DRAM2.nf
--input_fasta ../test_data/DRAM2_test_data/
--outdir DRAM2-test-data-call-annotate-distill
--threads 8
--call --rename --annotate
--use_uniref --use_kegg --use_merops --use_viral --use_pfam --use_camper
--use_kofam --use_dbcan --use_methyl --use_canthyd --use_vog --use_fegenie --use_sulfur
--distill_topic default --distill_ecosystem 'eng_sys ag' --distill_custom test-data/custom-test-distilalte.tsv
-profile conda_slurm --slurm_node main -with-report -with-trace -with-timeline
Breakdown of example (9):
-bg: Nextflow option to push the run immediately into the background. (Thus, you can log out on an HPC and the run will continue).-profile: Nextflow option to select profile (Conda vs Singularity and SLURM vs no-SLURM).--slurm_node: DRAM2 option to select a specific node to compute on during the whole run.-with-trace: Nextflow option to output a process-by-process report of the run. (TEXT)-with-report: Nextflow option to output a process-by-process report of the run. (HTML)-with-timeline: Nextflow option to output a process-by-process HTML timeline report of the run. (HTML)
Description:
The purpose of DRAM2 is to provide FASTA annotation, across a vast array of databases, with expertly-currated distillation.
DRAM2 can be used to call, annotate and distill annotations from input FASTA files.
Call, annotate and distill can be run together or, each can be run idependently.
Bring up help menu:
nextflow run DRAM2.nf --help (--h)
Bring up versions menu:
nextflow run DRAM2.nf --version (--v)
Usage:
nextflow run DRAM2.nf --rename --call --annotate --use_<database(s) --distill_topic <distillate(s)>
Call genes using input fastas (use --rename to rename FASTA headers):
nextflow run DRAM2.nf --call --rename --input_fasta_dir <path/to/fasta/directory/>
Annotate called genes using input fastas:
nextflow run DRAM2.nf --annotate --input_genes <path/to/called/genes/directory>
Distill using input annotations:
nextflow run DRAM2.nf --distill_<topic|ecosystem|custom> --annotations <path/to/annotations.tsv>
(Combined): Call, annotate and distill input fasta files:
nextflow run DRAM2.nf --rename --call --annotate --use_<database(s) --distill_topic <distillate(s)>
(Real) example: (on multiple lines for clarity)
nextflow run DRAM2.nf --input_fasta ../test_data/
--outdir DRAM2-test-data-Feb012024/
--call --rename
--annotate --use_uniref --use_kegg --use_merops --use_viral --use_camper --use_kofam --use_dbcan --use_methyl --use_canthyd --use_vog --use_fegenie --use_sulfur
--add_annotations ../test-data/old-DRAM-annotations.tsv
--distill_topic 'carbon transport energy' --distill_ecosystem 'eng_sys ag'
--distill_custom assets/forms/distill_sheets/test.tsv -resume --slurm_node zenith
--trnas ../test-data/trnas.tsv
--rrnas ../test-data/rrnas.tsv
--bin_quality ../test-data/checkM1-test-data.tsv
--taxa ../test-data/gtdbtk.bac120.summary.tsv
--generate_gff
--generate_gbk
--threads 5
-with-report -with-trace -with-timeline
Main DRAM2 Operations:
--call : Call genes using prodigal
--annotate : Annotate called genes using downloaded databases
--distill : Distill the annotations into a multi-sheet distillate.xlsx
REQUIRED DRAM2 profile options:
-profile STRING <conda, conda_slurm, singularity, singularity_conda>
Runs DRAM2 either using Conda (must be installed) or Singularity (must be installed).
Runs DRAM2 with no scheduling or scheduling via SLURM.
See SLURM options in full help menu.
Call options:
--call OPTION Call genes on the input FASTA files using Prodigal.
--input_fasta PATH <path/to/fasta/directory/>
Directory containing input fasta files.
Default: <./input_fasta/*.fa*>
--rename OPTION Rename FASTA headers based on file name.
Example: sample1.fa --> (fasta header renamed to) > sample1......
Why? DRAM2 output is focused on scaffolds/contigs with respect to each provided input sample.
Thus, without renaming FASTA headers, the individual scaffolds/contigs will not be distinguashable.
*If you have already renamed your FASTA headers, do not include '--call'.
--prodigal_mode STRING <single|meta>
Default: 'single'
--prodigal_tras_table NUMBER (1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25)
Specify a translation table to use (default: '1').
--min_contig_len NUMBER <number in base pairs>
Default: '2500'
Annotate options:
--use_<db-name> STRING <camper|cant_hyd|dbcan|fegenie|kegg|kofam|merops|methyl|heme|pfam|sulfur|uniref]
Specify databases to use. Can use more than one. Can be used in combination with --use_dbset.
--use_dbset STRING <metabolism_kegg_set|metabolism_set|adjectives_kegg_set|adjectivs set>
metabolism_kegg_set = kegg, dbcan, merops, pfam, heme
metabolism_set = kofam, dbcan, merops, pfam, heme
adjectives_kegg_set = kegg, dbcan, merops, pfam, heme, sulfur, camper, methyl, fegenie
adjectives_set = kofam, dbcan, merops, pfam, heme, sulfur, camper, methyl, fegenie
*Only one set can be used. Can be used in combination with --use_[db-name]
--input_genes PATH <path/to/called/genes/directory/>
Directory containing called genes (.faa)
--add_annotations PATH <path/to/old-annoations.tsv>
--generate_gff OPTION Will generate an output GFF for each sample based on the raw-annotations.tsv.
--generate_gbk OPTION Will generate an output GBK for each sample based on the raw-annotations.tsv.
Used to add in old annotations to the current run. (See example for format.)
Distill options:
--annotations PATH <path/to/annotations.tsv>
Required if you are running distill without --call and --annotate.
--rrnas PATH <path/to/rRNA.tsv> (See example for format.)
rRNA information will be included in distill output.
--trnas PATH <path/to/tRNA.tsv> (See example for format.)
tRNA information will be included in distill output.
--bin_quality PATH <path/to/bin-quality.tsv> (See example for format.)
CheckM and CheckM2 compatible.
--taxa PATH <path/to/bin-taxonomy.tsv>
Compatible with GTDB. (See example for format.)
--distill_topic STRING <carbon|energy|misc|nitrogen|transport> OR <default = carbon, energy, misc, nitrogen, transport>
If more than one topic included, they must be enclosed in single quotes
--distill_ecosystem STRING <eng_sys|ag>
If more than one ecosystem included, they must be enclosed in single quotes
--distill_custom STRING <path/to/custom_distillate.tsv> (See example for format and options.)
As of now, only one custom distillate may be included.
General options:
--outdir PATH <path/to/output/directory>
Default: './DRAM2_output/'
--threads NUMBER Number of threads to use for processing.
Default: '10'
--slurm_node STRING <node_name>
Example --slurm_queue c001
--slurm_queue STRING <slurm partition name>
Example: --slurn_queue 'smith-hi,smith-low'
-with-trace OPTION Nextflow option to output a process-by-process report of the run. (TEXT)
-with-report OPTION Nextflow option to output a process-by-process report of the run. (HTML)
-with-timeline: OPTION Nextflow option to output a process-by-process HTML timeline report of the run. (HTML)
Call description: The purpose of DRAM2 --call is to call genes on input FASTA files.
Usage:
Call genes using input fastas:
nextflow run DRAM2.nf --call --input_fasta_dir <path/to/fasta/directory/> --outdir <path/to/output/directory/> --threads <threads>
REQUIRED DRAM2 profile options:
-profile STRING <conda, conda_slurm, singularity, singularity_conda>
Runs DRAM2 either using Conda (must be installed) or Singularity (must be installed).
Runs DRAM2 with no scheduling or scheduling via SLURM.
See SLURM options in full help menu.
Call options:
--rename Rename FASTA headers based on file name.
Example: sample1.fa --> (fasta header renamed to) > sample1......
Why? DRAM2 output is focused on scaffolds/contigs with respect to each provided input sample.
Thus, without renaming FASTA headers, the individual scaffolds/contigs will not be distinguashable.
*If you have already renamed your FASTA headers, do not include '--call'.
--prodigal_mode STRING <single|meta>
Default: 'single'
--prodigal_tras_table <1|2|3|4|5|6|7|8|9|10|11|12|13|14|15|16|17|18|19|20|21|22|23|24|25>
Specify a translation table to use (default: '1').
--min_contig_len NUMBER <number in base pairs>
Default: '2500'
Main options:
--input_fasta PATH <path/to/fasta/directory/>
Directory containing input fasta files.
Default: './input_fasta/'
--outdir PATH <path/to/output/directory>
Default: './DRAM2_output/'
--threads NUMBER Number of threads to use for processing.
Default: '10'
--slurm_node STRING <node_name>
Example --slurm_queue c001
--slurm_queue STRING <slurm partition name>
Example: --slurn_queue 'smith-hi,smith-low'
-with-trace OPTION Nextflow option to output a process-by-process report of the run. (TEXT)
-with-report OPTION Nextflow option to output a process-by-process report of the run. (HTML)
-with-timeline: OPTION Nextflow option to output a process-by-process HTML timeline report of the run. (HTML)
Annotate description: The purpose of DRAM2 '--annotate' is to annotate called genes on input (nucleotide) FASTA (fa*) files.
Usage:
Annotate called genes using input called genes and the KOFAM database:
nextflow run DRAM2.nf --annotate --input_genes <path/to/called/genes/directory> --use_kofam
Annotate called genes using input fasta files and the KOFAM database:
nextflow run DRAM2.nf --annotate --input_fasta <path/to/called/genes/directory> --use_kofam
REQUIRED DRAM2 profile options:
-profile STRING <conda, conda_slurm, singularity, singularity_conda>
Runs DRAM2 either using Conda (must be installed) or Singularity (must be installed).
Runs DRAM2 with no scheduling or scheduling via SLURM.
See SLURM options in full help menu.
Annotate options:
--use_<db-name> STRING <camper|cant_hyd|dbcan|fegenie|kegg|kofam|merops|methyl|heme|pfam|sulfur|uniref]
Specify databases to use. Can use more than one. Can be used in combination with --use_dbset.
--use_dbset STRING <metabolism_kegg_set|metabolism_set|adjectives_kegg_set|adjectivs set>
metabolism_kegg_set = kegg, dbcan, merops, pfam, heme
metabolism_set = kofam, dbcan, merops, pfam, heme
adjectives_kegg_set = kegg, dbcan, merops, pfam, heme, sulfur, camper, methyl, fegenie
adjectives_set = kofam, dbcan, merops, pfam, heme, sulfur, camper, methyl, fegenie
*Only one set can be used. Can be used in combination with --use_[db-name]
--add_annotations PATH <path/to/old-annoations.tsv>
Used to add in old annotations to the current run. (See example for format.)
--generate_gff OPTION Will generate an output GFF for each sample based on the raw-annotations.tsv.
--generate_gbk OPTION Will generate an output GBK for each sample based on the raw-annotations.tsv.
Main options:
--input_fasta PATH <path/to/fasta/directory/>
Directory containing input fasta files.
Default: './input_fasta/'
Either '--input_fasta' or '--input_genes' may be used - not both.
--input_genes PATH <path/to/called/genes/directory/>
Directory containing called genes (.fna)
Either '--input_fasta' or '--input_genes' may be used - not both.
--outdir PATH <path/to/output/directory/>
Default: './DRAM2_output/'
--threads NUMBER Number of threads to use for processing.
Default '10'
--slurm_node string <node_name>
Example --slurm_queue c001
--slurm_queue string <slurm partition name>
Example: --slurn_queue 'smith-hi,smith-low'
-with-trace OPTION Nextflow option to output a process-by-process report of the run. (TEXT)
-with-report OPTION Nextflow option to output a process-by-process report of the run. (HTML)
-with-timeline: OPTION Nextflow option to output a process-by-process HTML timeline report of the run. (HTML)
DRAM2 Nextflow Pipeline
===================================
Distill description: The purpose of DRAM2 --distill is to distill down annotations based on curated distillation summary form(s).
User's may also provide a custom distillate via --distill_custom <path/to/file> (TSV forms).
Distill can be ran independent of --call and --annotate however, annotations must be provided (--annotations <path/to/annotations.tsv>).
Optional tRNA, rRNA and bin quality may also be provided.
Usage:
nextflow run DRAM2.nf --distill_<topic|ecosystem|custom> --annotations <path/to/annotations.tsv> --outdir <path/to/output/directory/> --threads <threads>
*Important: if more than one topic or ecosystem is included, they must be enclosed in single quotes. Example: --distill_topic 'carbon transport'
Example:
Call and Annotate genes using input fastas and KOFAM database. Distill using carbon topic and AG ecosystem:
nextflow run DRAM2.nf --input_fasta_dir <path/to/fasta/directory/> --outdir <path/to/output/directory/> --call --annotate --distill_topic carbon --distill_ecosystem ag --threads <threads> --use_kofam
REQUIRED DRAM2 profile options:
-profile STRING <conda, conda_slurm, singularity, singularity_conda>
Runs DRAM2 either using Conda (must be installed) or Singularity (must be installed).
Runs DRAM2 with no scheduling or scheduling via SLURM.
See SLURM options in full help menu.
Distill options:
--annotations PATH <path/to/annotations.tsv>
Required if you are running distill without --call and --annotate.
--rrnas PATH <path/to/rRNA.tsv> (See example for format.)
rRNA information will be included in distill output.
--trnas PATH <path/to/tRNA.tsv> (See example for format.)
tRNA information will be included in distill output.
--bin_quality PATH <path/to/bin-quality.tsv> (See example for format.)
CheckM and CheckM2 compatible.
--taxa PATH <path/to/bin-taxonomy.tsv>
Compatible with GTDB. (See example for format.)
--distill_topic STRING <carbon|energy|misc|nitrogen|transport> OR <default = carbon, energy, misc, nitrogen, transport>
If more than one topic included, they must be enclosed in single quotes
--distill_ecosystem STRING <eng_sys|ag>
If more than one ecosystem included, they must be enclosed in single quotes
--distill_custom STRING <path/to/custom_distillate.tsv> (See example for format and options.)
As of now, only one custom distillate may be included.
Main options:
--outdir PATH <path/to/output/directory/>
Default: './DRAM2_output/'
--threads NUMBER Number of threads to use for processing.
Default '10'
--slurm_node string <node_name>
Example --slurm_queue c001
--slurm_queue string <slurm partition name>
Example: --slurn_queue 'smith-hi,smith-low'
-with-trace OPTION Nextflow option to output a process-by-process report of the run. (TEXT)
-with-report OPTION Nextflow option to output a process-by-process report of the run. (HTML)
-with-timeline: OPTION Nextflow option to output a process-by-process HTML timeline report of the run. (HTML)
- BBTools v39.01
- Bowtie2 v2.5.1
- Prodigal v2.6.3
- Python v3.10
- Pandas v1.5.2
- Pytest v7.2.0
- Scikit-bio v0.5.7
- MMseqs2 v14.7e284
- HMMER v3.3.2
- SciPy v1.8.1
- SQLAlchemy v1.4.46
- Barrnap v0.9
- Altair v4.2.0
- OpenPyXL v3.0.10
- NetworkX v2.8.8
- Ruby v3.1.2
- GNU Parallel v20221122
- tRNAscan-SE v2.0.12
- Samtools v1.17
- CD-HIT v4.6
- CoverM v0.6.1
- Subread v2.0.6
- XlsxWriter v3.1.6
- Numpy v1.26.0
In Nextflow DSL2, the -resume option offers a powerful feature that allows you to efficiently manage and modify your workflow runs. It enables you to resume a run after it has finished, make changes to parameters, and reuse previously generated data, enhancing the flexibility and reusability of your workflow. Here are some common scenarios where the -resume option comes in handy:
- I Want to run DRAM2 again but with additional databases
- By using the
-resumeoption, if you have not deleted yourwork/directory, running your previous command with the desired changes will allow you to reuse your called genes and existing annotations. - For example, if you intially used
--use_kofam --use_dbcan, you can add in additional annotation databases like,--use_kegg --use_uniref. This will reuse the existing called genes to annotate the newly added databases and will retain the existing annotations (assuming they were retained within the modified command).
- By using the
These Nextflow tips and tricks demonstrate how the -resume option can optimize your workflow, save time, and improve the reusability of previously computed data.
How the resume option does NOT work:
EDIT
Depending on which databases you download, and if you choose to use Singularity or Conda, you will need adequate storage space on your machine.
DRAM2 is implemented in Nextflow.
Nextflow is able to scale horizontally on a given machine.
What does this mean?
Horizontal scaling refers to the ability to distribute computational tasks across multiple computing resources, such as cores or nodes, in parallel. In the context of Nextflow, this means that a single workflow can leverage the computational power of multiple CPUs or nodes, allowing for faster execution of tasks and improved overall performance.
By utilizing horizontal scaling, Nextflow can efficiently manage and execute workflows that require significant computational resources, such as those involved in genomic data analysis. This enables DRAM2 to process large datasets and complex analyses in a timely manner, making it suitable for a wide range of research and bioinformatics applications.
DRAM2 comes with configuration files which have the option to change how many "things" can happen at a time in the pipeline.
A user can modify these, "maxForks", parameters within the nextflow.config to increase the number of "things" which DRAM2 can perform at a given time.
NOTE: Development is in progress to enable different DRAM2 modes: "lite", "medium" and "heavy". Where "lite" would be for a good laptop and "heavy" for a HPC. These options will alter the CPU and memory (RAM) requirements for each process.
Please visit the Read the Docs page. page for more detailed inforamtion on how to run DRAM2 efficiently.
The DRAM was published in Nucleic Acids Research in 2020 and is available here. If DRAM helps you out in your research, please cite it.