Spades Genome Assembler Download: A Guide for Beginners
Genome assembly is the process of reconstructing the complete DNA sequence of an organism from short fragments of sequencing data. It is a challenging computational problem that requires sophisticated algorithms and software tools. Genome assembly is essential for studying the structure, function, evolution, and diversity of genomes, as well as for applications in biotechnology, medicine, and agriculture.
spades genome assembler download
Spades genome assembler is one of the most popular and widely used tools for genome assembly. It is a de novo assembler that can handle various types of sequencing data, such as Illumina, IonTorrent, PacBio, Oxford Nanopore, and Sanger. Spades can also perform hybrid assembly using multiple data sources, as well as specialized assembly for metagenomes, plasmids, transcripts, biosynthetic gene clusters, and viruses. Spades has been shown to produce high-quality assemblies with high contiguity and completeness.
How to download spades genome assembler
Spades genome assembler is freely available under the GPLv2 license and can be downloaded from . There are different ways to download spades depending on your operating system and preferences.
Downloading spades binaries for Linux or Mac
The easiest way to download spades is to use the pre-compiled binaries for Linux or Mac. You can find the latest version of spades (3.15.5) in the following links:
for Linux (64-bit only)
for Mac
To install spades from the binaries, you need to download the corresponding file, extract it, and add the bin directory to your PATH environment variable. For example, on Linux you can do:
wget
tar -xzf SPAdes-3.15.5-Linux.tar.gz
export PATH=$PATH:$PWD/SPAdes-3.15.5-Linux/bin
Downloading and compiling spades source code
If you prefer to compile spades from the source code, you need to download the source code file from and follow the instructions in the README.md file. You will need a C++ compiler (gcc >= 5.3.1 or clang >= 3.8), cmake (>= 2.8), zlib, bzip2, and Python (>= 2.7) libraries installed on your system.
To compile spades from the source code, you need to download the file, extract it, create a build directory, run cmake, and run make. For example, on Linux you can do:
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wget
tar -xzf SPAdes-3.15.5.tar.gz
cd SPAdes-3.15.5
mkdir build
cd build
make
make install
Verifying the installation
To verify that spades is installed correctly, you can run the following command:
spades.py --test
This will run a test assembly on a small dataset and check the results. If everything is OK, you should see a message like this:
======= SPAdes pipeline finished.
SPAdes log can be found here: /home/user/SPAdes-3.15.5/build/spades_test/corrected/configs/config.info
Thank you for using SPAdes!
How to use spades genome assembler
Spades genome assembler is a command-line tool that takes sequencing data as input and produces assembly files as output. To use spades, you need to know the type and format of your input data, the options and parameters that control the assembly process, and the output files and formats that spades generates.
Input data types and formats
Spades can handle various types of sequencing data, such as:
Illumina paired-end (PE) or mate-pair (MP) reads
IonTorrent PE or MP reads
PacBio single-molecule real-time (SMRT) reads
Oxford Nanopore long reads
Sanger reads
Hybrid data from multiple sources
The input data should be in FASTA or FASTQ format, compressed or uncompressed. Spades can automatically detect the format of the input files, but you need to specify the type of the data using the following prefixes:
Data typePrefix
Illumina PE reads-1 and -2
Illumina MP reads-m1 and -m2
IonTorrent PE reads-i1 and -i2
IonTorrent MP reads-mi1 and -mi2
PacBio SMRT reads--pacbio
Oxford Nanopore long reads--nanopore
Sanger reads--sanger
Hybrid data from multiple sourcesUse multiple prefixes accordingly
Command line options and parameters
Spades has many command line options and parameters that can be used to customize the assembly process. Some of the most important ones are:
-o: the output directory where spades will store the assembly files
-k: the k-mer sizes to use for assembly (comma-separated list of odd numbers between 21 and 127)
--careful: the mode to reduce the number of mismatches and short indels in the assembly
--only-assembler: the mode to skip error correction and read filtering steps
--cov-cutoff: the coverage cutoff value to discard low-covered and high-covered k-mers
--meta: the mode to perform metagenomic assembly
--plasmid: the mode to perform plasmid assembly
--rna: the mode to perform transcriptome assembly
--gene-finding: the option to enable gene prediction on the assembled contigs
--help: the option to display the help message with all the available options and parameters
For example, to run spades on Illumina PE reads with k-mer sizes of 21, 33, and 55, in careful mode, with a coverage cutoff of 10, and gene finding enabled, you can use the following command:
spades.py -1 reads_1.fq -2 reads_2.fq -k 21,33,55 --careful --cov-cutoff 10 --gene-finding -o output_dir
Output files and formats
Spades produces several output files and formats in the output directory specified by the -o option. Some of the most important ones are:
spades.log: the log file that contains information about the spades run, such as parameters, steps, timings, and errors
corrected/: the directory that contains the error-corrected reads (if error correction is enabled)
assembly_graph.fastg: the file that contains the assembly graph in FASTG format
scaffolds.fasta: the file that contains the final scaffolds in FASTA format
contigs.fasta: the file that contains the final contigs in FASTA format
genes/: the directory that contains the predicted genes on the contigs (if gene finding is enabled)
You can use these files for further analysis and evaluation of your assembly.
How to evaluate the quality of the assembly
Evaluating the quality of the assembly is an important step to assess how well spades performed on your data. There are different tools and metrics that can be used to evaluate the quality of the assembly, such as:
Quast: a tool that computes various assembly statistics, such as number of contigs, N50, GC content, and misassemblies. You can download quast from and run it on your assembly file using the following command:
quast.py scaffolds.fasta -o quast_output
Comparative genome viewer: a tool that visualizes the alignment of the assembly to a reference genome or another assembly. You can use tools such as Mauve, IGV, or Bandage to compare and explore your assembly graphically. For example, you can download Mauve from and run it on your assembly file and a reference genome file using the following command:
mauve scaffolds.fasta reference.fasta
GenomeQC: a tool that compares the assemblies and annotations of different genomes and reports the quality scores and rankings. You can download genomeQC from and run it on your assembly file and a set of reference genomes using the following command:
genomeqc.py scaffolds.fasta -r reference_genomes_dir -o genomeqc_output
Conclusion
In this article, we have learned how to download, install, use, and evaluate spades genome assembler. Spades is a powerful and versatile tool that can handle various types of sequencing data and perform different types of assembly tasks. Spades has many advantages, such as high accuracy, speed, scalability, and flexibility. However, spades also has some limitations, such as high memory requirements, sensitivity to sequencing errors, and lack of support for circular genomes. Spades is constantly being updated and improved by its developers and users, so you can expect new features and enhancements in the future.
FAQs
What is the difference between contigs and scaffolds?
Contigs are contiguous sequences of DNA that are assembled from overlapping reads. Scaffolds are ordered and oriented collections of contigs that are connected by gaps. The gaps represent unknown sequences or regions that are not covered by reads.
How can I improve the quality of my assembly?
There are several factors that can affect the quality of your assembly, such as the quality, quantity, diversity, and coverage of your sequencing data, the choice of k-mer sizes, the parameters and options of spades, and the post-processing steps. You can try to optimize these factors by using quality control tools, increasing the depth and breadth of your data, experimenting with different k-mer sizes and spades modes, and applying filtering and polishing tools.
How can I cite spades in my publication?
If you use spades in your research, please cite the following papers:
Bankevich A., Nurk S., Antipov D., Gurevich A.A., Dvorkin M., Kulikov A.S., Lesin V.M., Nikolenko S.I., Pham S., Prjibelski A.D., Pyshkin A.V., Sirotkin A.V., Vyahhi N., Tesler G., Alekseyev M.A., Pevzner P.A. (2012) SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing. Journal of Computational Biology 19(5): 455-477.
Nurk S., Meleshko D., Korobeynikov A., Pevzner P.A. (2017) metaSPAdes: a new versatile metagenomic assembler. Genome Research 27(5): 824-834.
Antipov D., Korobeynikov A., McLean J.S., Pevzner P.A. (2016) hybridSPAdes: an algorithm for hybrid assembly of short and long reads. Bioinformatics 32(7): 1009-1015.
Where can I find more information and support for spades?
You can find more information and support for spades on the following websites:
: the official website of spades with documentation, tutorials, downloads, and updates
: the GitHub repository of spades with source code, issues , and pull requests
: the Google group of spades users with discussions, questions, and answers
What are some alternative tools for genome assembly?
There are many other tools for genome assembly, each with its own strengths and weaknesses. Some of the most popular ones are:
ABySS: a de novo assembler for short reads that uses a parallelized k-mer graph approach
Canu: a fork of Celera Assembler that can assemble long reads from PacBio or Oxford Nanopore
MEGAHIT: a fast and memory-efficient assembler for metagenomic data that uses succinct de Bruijn graphs
SOAPdenovo2: an improved version of SOAPdenovo that can assemble large and complex genomes from short reads
Trinity: a de novo assembler for transcriptome data that uses de Bruijn graphs and k-mer coverage
You can compare and choose the best tool for your data and needs by using benchmarking studies, reviews, and tutorials. 44f88ac181
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