• Redundans
  • Prerequisites
    • Official conda package
    • UNIX installer
    • Docker image
  • Running the pipeline
    • Parameters
    • Test run
  • Support
  • Citation

Redundans pipeline assists an assembly of heterozygous genomes.
Program takes as input assembled contigs, sequencing libraries and/or reference sequence and returns scaffolded homozygous genome assembly. Final assembly should be less fragmented and with total size smaller than the input contigs. In addition, Redundans will automatically close the gaps resulting from genome assembly or scaffolding.

The pipeline consists of several steps (modules):

1. de novo contig assembly (optional if no contigs are given)
2. redundancy reduction: detection and selective removal of redundant contigs from an initial de novo assembly
3. scaffolding: joining of genome fragments using paired-end reads, mate-pairs, long reads and/or reference chromosomes
4. gap closing: filling the gaps after scaffolding using paired-end and/or mate-pair reads

Redundans is:

• fast & lightweight, multi-core support and memory-optimised, so it can be run even on the laptop for small-to-medium size genomes
• flexible toward many sequencing technologies (Illumina, 454, Sanger, PacBio & Nanopore) and library types (paired-end, mate pairs, fosmids, long reads)
• modular: every step can be omitted or replaced by other tools
• reliable: it has been already used to improve genome assemblies varying in size (several Mb to several Gb) and complexity (fungal, animal & plants)

For more information have a look at the documentation, poster, publication, test dataset or manual.

Redundans uses several programs (all except the interpreters and its submodules are provided within this repository):

On most Linux distros, the installation should be as easy as:

git clone --recursive https://github.com/Gabaldonlab/redundans/
cd redundans && bin/.compile.sh

If it fails, make sure you have below dependencies installed:

• Perl [SSPACE3]
• make, gcc & g++ [BWA, GFAstats, Miniasm & LAST] ie. sudo apt-get install make gcc g++
• zlib including zlib.h headers [BWA] ie. sudo apt-get install zlib1g-dev
• R ≥ 3.6 and additional packages [ggplot2, scales, argparser] for plotting the Merqury results.
• optionally for additional plotting numpy and matplotlib ie. sudo -H pip install -U matplotlib numpy

For user convenience, we provide UNIX installer and Docker image, that can be used instead of manually installation.

If you are familiar with conda, this will be by far the easiest way of installing redundans:

# create new Python3 >=3.8,<3.11 environment
conda create -n redundans python=3.10
# activate it
conda activate redundans
# and install redundans
conda install -c bioconda redundans 

UNIX installer will automatically fetch, compile and configure Redundans together with all dependencies. It should work on all modern Linux systems, given Python >= 3, commonly used programmes (ie. wget, make, curl, git, perl, gcc, g++, ldconfig) and libraries (zlib including zlib.h) are installed.

source <(curl -Ls https://github.com/Gabaldonlab/redundans/raw/master/INSTALL.sh)

First, you need to install docker: wget -qO- https://get.docker.com/ | sh
Then, you can run the test example by executing:

#Pull the image directly from dockerhub
docker pull cgenomics/redundans:latest

# process the data inside the image - all data will be lost at the end
docker run -it -w /root/src/redundans cgenomics/redundans:latest ./redundans.py -v -i test/{600,5000}_{1,2}.fq.gz -f test/contigs.fa -o test/run1

# if you wish to process local files, you need to mount the volume with -v
## make sure you are in redundans repo directory (containing test/ directory)
docker run -v `pwd`/test:/test:rw -it cgenomics/redundans:latest /root/src/redundans/redundans.py -v -i test/*.fq.gz -f test/contigs.fa -o test/run1

Redundans is also supported by singularity. First install singularity.

You can either use our singularity repository to build the image or to build the image out of the docker image. Then run the first example:

#Pull from the singularity repo
singularity pull --arch amd64 library://cgenomics/redundans/redundans:2.0

#Build the image based on the docker repo
singularity build redundans.sif docker://cgenomics/redundans

#Use exec instead of run to account for shell-based wildcarsds * and ?
singularity exec redundans.sif bash -c "/root/src/redundans/redundans.py -v -i /root/src/redundans/test/*_?.fq.gz -f /root/src/redundans/test/contigs.fa -o /tmp/run1"

Redundans input consists of any combination of:

• assembled contigs (FastA)
• paired-end and/or mate pairs reads (FastQ*)
• long reads (FastQ/FastA*) - both PacBio and Nanopore are supported for the scaffolding
• and/or reference chromosomes/contigs (FastA).

• gzipped files are also accepted.

Redundans will return homozygous genome assembly in scaffolds.filled.fa (FastA). It will also report the heterozygous contigs that were not discarded during the reduction step. In addition, the program reports statistics for every pipeline step, including number of contigs that were removed, GC content, N50, N90 and size of gap regions.

For the user convenience, Redundans is equipped with a wrapper that automatically estimates run parameters and executes all steps/modules. You should specify some sequencing libraries (FastA/FastQ) or reference sequence (FastA) in order to perform scaffolding. If you don't specify -f contigs (FastA), Redundans will assemble contigs de novo, but you'll have to provide paired-end and/or mate pairs reads (FastQ). Most of the pipeline parameters can be adjusted manually (default values are given in square brackets []):
HINT: If you run fails, you may try to resume it, by adding --resume parameter.

• General options:

  -h, --help            show this help message and exit
  -v, --verbose         verbose
  --version             show program's version number and exit
  -i FASTQ, --fastq FASTQ
                        FASTQ PE / MP files
  -f FASTA, --fasta FASTA
                        FASTA file with contigs / scaffolds
  -o OUTDIR, --outdir OUTDIR
                        output directory [redundans]
  -t THREADS, --threads THREADS
                        no. of threads to run [4]
  --resume              resume previous run
  --log LOG             output log to [stderr]
  --nocleaning

De novo assembly options:

  -m MEM, --mem MEM     max memory to allocate (in GB) for the Platanus assembler [2]
  --tmp TMP             tmp directory [/tmp]

• Reduction options:

  --identity IDENTITY   min. identity [0.51]
  --overlap OVERLAP     min. overlap  [0.80]
  --minLength MINLENGTH
                        min. contig length [200]
  --minimap2reduce      Use minimap2 for the initial and final Reduction step. Recommended for input assembled contigs from long reads or larger contigs using --preset[asm5] by default. By default LASTal is used for Reduction.
  -x INDEX, --index INDEX
                        Minimap2 parameter -i used to load at most INDEX target bases into RAM for indexing [4G]. It has to be provided as a string INDEX ending with k/K/m/M/g/G.
  --noreduction         Skip reduction

• Short-read scaffolding options:

  -j JOINS, --joins JOINS
                        min pairs to join contigs [5]
  -a LINKRATIO, --linkratio LINKRATIO
                        max link ratio between two best contig pairs [0.7]
  --limit LIMIT         align subset of reads [0.2]
  -q MAPQ, --mapq MAPQ  min mapping quality [10]
  --iters ITERS         iterations per library [2]
  --noscaffolding       Skip short-read scaffolding
  -b, --usebwa          use bwa mem for alignment [use snap-aligner]

• Long-read scaffolding options:

  -l LONGREADS, --longreads LONGREADS
                        FastQ/FastA files with long reads
  -s, --populateScaffolds
                        Run populateScaffolds mode for long read scaffolding, else generate a dirty assembly for reference-based scaffolding. Not recommended for highly repetitive genomes. Default False.
  --minimap2scaffold         Use Minimap2 for aligning long reads. Preset usage dependant on file name convention (case insensitive): ont, nanopore, pb, pacbio, hifi, hi_fi, hi-fi. ie: s324_nanopore.fq.gz. Else it uses LASTal.

• Reference-based scaffolding options:

  -r REFERENCE, --reference REFERENCE
                        reference FastA file
  --norearrangements    high identity mode (rearrangements not allowed)
  -p PRESET, --preset PRESET
                        Preset option for Minimap2-based Reduction and/or Reference-based scaffolding. Possible options: asm5 (5 percent sequence divergence), asm10 (10 percent sequence divergence) and asm20(20 percent sequence divergence). Default [asm5]

• Gap closing options:

  --nogapclosing                        

• Meryl and Merqury options:

  --runmerqury           Run meryldb and merqury for assembly kmer multiplicity stats. [False] by default.
  -k KMER, --kmer KMER  K-mer size for meryl [21]

Redundans is extremely flexible. All steps of the pipeline can be ommited using: --noreduction, --noscaffolding, --nogapclosing and/or --runmerqury parameters.

To run the test example, execute:

./redundans.py -v -i test/*_?.fq.gz -f test/contigs.fa -o test/run1

#Test it using minimap2 for the reduction step, increasing performance for large genomes
./redundans.py -v -i test/*_?.fq.gz -f test/contigs.fa --minimap2reduce -o test/run2

# if your run failed for any reason, you can try to resume it
rm test/run1/_sspace.2.1.filled.fa
./redundans.py -v -i test/*_?.fq.gz -f test/contigs.fa -o test/run1 --resume

# if you have no contigs assembled, just run without `-f`
./redundans.py -v -i test/*_?.fq.gz -o test/run.denovo

Note, the order of libraries (-i/--input) is not important, as long as read1 and read2 from each library are given one after another i.e. -i 600_1.fq.gz 600_2.fq.gz 5000_1.fq.gz 5000_2.fq.gz would be interpreted the same as -i 5000_1.fq.gz 5000_2.fq.gz 600_1.fq.gz 600_2.fq.gz.

You can play with any combination of inputs ie. paired-end, mate pairs, long reads and / or reference-based scaffolding as well as selecting minimap2 for each step or default to LASTal, for example:

# reduction, scaffolding with paired-end, mate pairs and long reads used to generate a miniasm assembly to do reference-based scaffolding, and gap closing with paired-end and mate pairs using as an aligner minimap2
./redundans.py -v -i test/*_?.fq.gz -l test/nanopore.fa.gz -f test/contigs.fa -o test/run_short_long_ref --minimap2scaffold

# reduction, scaffolding with paired-end, mate pairs and long reads, and gap closing with paired-end and mate pairs using populateScaffolds method using as aligner minimap2
./redundans.py -v -i test/*_?.fq.gz -l test/pacbio.fq.gz test/nanopore.fa.gz -f test/contigs.fa -o test/run_short_long_populatescaffold --minimap2scaffold --populateScaffolds

# scaffolding and gap closing with paired-end and mate pairs (no reduction)
./redundans.py -v -i test/*_?.fq.gz -f test/contigs.fa -o test/run_short-scaffolding-closing --noreduction

# reduction, reference-based scaffolding and gap closing with paired-end reads (--noscaffolding disables only short-read scaffolding)
./redundans.py -v -i test/600_?.fq.gz -r test/ref.fa -f test/contigs.fa -o test/run_ref_pe-closing --noscaffolding

For more details have a look in test directory.

If you have any issues or doubts check documentation and FAQ (Frequently Asked Questions). You may want also to sign to our forum.

Leszek P. Pryszcz and Toni Gabaldón (2016) Redundans: an assembly pipeline for highly heterozygous genomes. NAR. doi: 10.1093/nar/gkw294