Table of Contents
- SASpector: a tool for analysis of missing regions in (bacterial) draft genomes"
- Introduction
- System Requirements
- Contact
- References
SASpector: a tool for analysis of missing regions in (bacterial) draft genomes"
A bioinformatics tool to extract and analyze missing regions of short-read assemblies by mapping the contigs to a reference genome.
Publication: to be added (doi)
Introduction
SASpector is a tool that compares a short-reads assembly with a reference bacterial genome (for example obtained via hybrid assembly) by extracting missing (unmapped) regions from the reference and analyzing them to see functional and compositional pattern. The aim of the analysis is to explain why these regions are missed by the short-read assembly and if important parts of the genome are missed when a resolved genome is lacking.
The tool takes as global inputs the reference genome and a short-read assembly as contigs/draft genome, both in FASTA format. This repository contains a command-line tool `SASpector` to obtain missing regions and several script for evaluation and analysis for those missing regions:
- mapper.py: mapping of the short-reads assembly against the reference assembly using progressiveMauve (Mauve aligner).
- summary.py: extraction of the mapped, unmapped (missing) and conflict regions to FASTA files. Also, creates summary statistic for the missing regions and reference which are written to separate tab-delimited files.
- gene_predict.py: prediction of genes in the missing regions using Prokka. Optionally, run BLASTX to the predicted genes with an user-defined protein database in FASTA file. In this repository you can find protein database retrieved from UniprotKB for bacterial species: Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, Bacillus subtillis and Staphylococcus aureus.
Optionally, some scripts provide additional analysis:
- kmer.py: tandem repeats in the missing regions and pairwise comparison between kmers of missing and mapped regions using Sourmash.
- coverage.py: calculates the average coverage of the missing and mapped regions with a provided BAM file.
- quastunmap.py: performs a genome quality assessment for the missing regions to the reference and provides a genome viewer using QUAST and Icarus.
System Requirements
Linux 64-bit and OS X are supported.
General dependencies
- Python3 (3.4+)
- shutil
- shlex
- Seaborn
- BioPython
- matplotlib
- Pandas
- numpy
- sourmash
Third-party tools (available on bioconda)
Installation
First you need to ensure that the above tools are available in your environment. This can be achieved by means of conda virtual environments as follows:
$ conda install mamba -n base -c conda-forge
$ mamba create -n SASpector
$ mamba activate SASpector
$ mamba install -c conda-forge -c bioconda -c defaults blast emboss progressivemauve prokka quast samtools sourmash trf mash pyani
We recommend to install SASpector using pip:
$ git clone https://github.com/LoGT-KULeuven/SASpector
$ cd SASpector
$ python setup.py install --user
Command line options
For basic functionalities, run `SASpector`:
usage: SASpector - Short-read Assembly inSpector [-h] [-p PREFIX]
[-dir OUTDIR] [-f [LENGTH]]
[-db [PROTEIN-DB]] [-k [K]]
[-q] [-msh] [-c [BAMFILE]]
--draft draft_assembly.fasta
--reference reference_assembly.fasta
Required arguments:
-draft, --draft Short-read assembly FASTA file as contigs/draft genome.
optional arguments:
-ref, --reference Reference assembly FASTA file. If this is not provided, you can
try to use the -msh option to automatically serach refseq
-h, --help show this help message and exit
-p PREFIX, --prefix PREFIX
Genome ID
-dir OUTDIR, --outdir OUTDIR
Output directory
-f [LENGTH], --flanking [LENGTH]
Add flanking regions to the extracted missing regions
[Default = 100 bp]
-db [PROTEIN-DB], --proteindb [PROTEIN-DB]
BLAST protein database FASTA file to use for checking
the Prokka gene prediction
-k [K], --kmers [K] Choose k to calculate kmer frequencies
-q, --quast Run QUAST with unmapped regions against reference
assembly
-c [BAMFILE], --coverage [BAMFILE]
Run SAMtools bedcov to look at short-read coverage in
the missing regions. Needs alignment of reads to the
reference genome in BAM format
-msh [PATH], --mash_selection [PATH]
run automated reference selection (experimental feature)
needs a sketch file (provided in SASpector/assets)
First, Mauve performs an alignment of both genomes with the progressiveMauve algorithm. This generates the subdirectory prefix.alignment with several output files and importantly the backbone file with coordinates of the mapped and unmapped regions in the reference genome.
This backbone file is parsed and we extract the sequences that are not covered in the short-read assembly from the reference genome. They are written to a multi-fasta file with the prefix and coordinates in the headers, which is done equally for the mapped and conflict regions (regions that didn't map correctly due to gaps or indels). Two tab-delimited summary files are generated in a subdirectory called summary. One for the reference, with the amount of gapped and ungapped regions, the fraction of the reference genome that they represent, the GC content and the length. The other one for the unmapped regions, with for each region the GC content and length and then for each amino acid the occurence frequency averaged over all 6 reading frames. As an optional input, the user can add flanking regions to the extracted missing regions.
Finally, SASpector predicts genes that are in the missing regions using Prokka and if a protein FASTA file database is provided, SASpector will BLAST the output sequences from Prokka to the database generating a tab-delimited summary with the hits of the sequences. You can use our defined database saspector_proteindb.fasta.
As optional analysis:
-
kmer analysis and tandem repeats: SASpector runs Tandem Repeats Finder and will generate HTML reports for the missing regions with tandem repeats. Finally, SASpector will perform a pairwise comparison between kmers of missing regions and mapped regions (k-size = 31) for comparative studies, using sourmash.
-
Coverage analysis: if a BAM file is provided, SASpector will calculate the average coverage of the missing and the mapped regions, using SAMtools. It will generate a sorted BAM file and tab-delimited reports of the coverage for both regions.
-
QUAST: SASpector will run QUAST for the missing regions against the reference genome for genome quality assessment and will provide Icarus as genome viewer.
Usage
SASpector [Reference genome].fasta [Contigs].fasta -p [Genome ID] -dir [Output directory] -f [Length] -db [Protein database].fasta -k [kmer size] -c [reference genome].bam -q
Output
[Output directory]/
[Genome ID]_unmappedregions.fasta FASTA file of the missing regions
[Genome ID]_mappedregions.fasta FASTA file of the mapped regions
[Genome ID]_conflictregions.fasta FASTA file of regions that did not map correctly
[Genome ID]_referencesummary.tsv tab-delimited summary report of the reference genome
[Genome ID]_unmapsummary.tsv tab-delimited summary report of the missing regions
[Genome ID]_length_missing.jpg Distribution plot of the length of the missing missing regions
[Genome ID]_gc_content_missing.jpg Distribution plot of the GC content of the missing regions
[Genome ID]_codons_missing.jpg Boxplot of the averaged frequency for each amino acid (for all 6 reading frames) of the missing regions
alignment/
[Genome ID].alignment Alignment output from progressiveMauve
[Genome ID].bbcols Coordinates of the mapped and unmapped regions from Mauve (not used)
[Genome ID].backbone Coordinates of the mapped, unmapped and conflicts regions from progressiveMauve
[Genome ID].sslist SSlists of short-reads assembly and reference genome
genesprediction/
[Genome ID].predictedgenes.gff Genes annotation GFF3 file
[Genome ID].predictedgenes.gbk Genbank file
[Genome ID].predictedgenes.fna Nucleotide FASTA file of the missing regions
[Genome ID].predictedgenes.faa Protein FASTA file of the translated CDS sequences
[Genome ID].predictedgenes.ffn Nucleotide FASTA file of all the prediction transcripts
[Genome ID].predictedgenes.sqn Sequin file for submission to Genbank
[Genome ID].predictedgenes.fsa Nucleotide FASTA file of the missing regions, used by 'tbl2asn' for the .sqn file
[Genome ID].predictedgenes.tbl Feature table file, used by 'tbl2asn' for the .sqn file
[Genome ID].predictedgenes.err NCBI discrepancy report
[Genome ID].predictedgenes.log Output report of Prokka during its run
[Genome ID].predictedgenes.txt Statistics of the annotated features
[Genome ID].predictedgenes.tsv tab-delimited report of all features
[Genome ID]_blastxresults.tsv tab-delimited report of BLASTX
kmer/
[Genome ID]_sourmash tab-delimited output of pairwise comparison between missing regions and mapped regions
_distances.tsv
sourmash Clustermap of pairwise comparison
_clustermap.jpg
trf/
*.html Tandem Repeat Finder HTML interactive reports
coverage/
[Genome ID].sorted.bam Sorted BAM file of the reference genome
[Genome ID].sorted.bam.bai Sorted BAM index file
[Genome ID]_mappedregions.bed BED file of the mapped regions
[Genome ID]_unmappedregions.bed BED file of the missing regions
[Genome ID]_mapcvg.tsv tab-delimited report of the average coverage of the mapped regions
[Genome ID]_unmappedcvg.tsv tab-delimited report of the average coverage of the missing regions
[Genome ID]_coverageresults.tsv tab-delimited summary report of the average coverage, total depth per base and locations for both regions
coverage_boxplots.jpg Boxplot comparison of the average coverage for both regions
quast/
report.txt QUAST summary table
report.tsv tab-delimited summary report
report.tex LaTex summary report
report.html HTML interactive report, includes all tables and plots for statistics
report.pdf PDF report
icarus.html Icarus genome viewer
Contact
Laboratory of Computational Systems Biology, KU Leuven
References
- Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990). Basic local alignment search tool. Journal of Molecular Biology, 215(3), 403-410.
- Darling, A. C. E. (2004). Mauve: Multiple Alignment of Conserved Genomic Sequence With Rearrangements. Genome Research, 14(7), 1394-1403.$
- Brown and Irber (2016), sourmash: a library for MinHash sketching of DNA Journal of Open Source Software, 1(5), 27.
- Pierce NT, Irber L, Reiter T et al. Large-scale sequence comparisons with sourmash [version 1; peer review: 2 approved]. F1000Research 2019, 8:1006
- Gurevich, A., Saveliev, V., Vyahhi, N., & Tesler, G. (2013). QUAST: Quality assessment tool for genome assemblies. Bioinformatics, 29(8), 1072-1075.
- Li H.*, Handsaker B.*, Wysoker A., Fennell T., Ruan J., Homer N., Marth G., Abecasis G., Durbin R. and 1000 Genome Project Data Processing Subgroup (2009) The Sequence alignment/map (SAM) format and SAMtools. Bioinformatics, 25, 2078-9.
- Torsten Seemann, Prokka: rapid prokaryotic genome annotation, Bioinformatics, Volume 30, Issue 14, 15 July 2014, Pages 2068-2069.
- Benson G. (1999). Tandem repeats finder: a program to analyze DNA sequences. Nucleic acids research, 27(2), 573-580.
