Short-read Assembly inSpector
Integrated Bioinformatics Project 19-20
"SASpector: a tool for analysis of missing regions in (bacterial) draft genomes" Alejandro Correa Rojo, Deniz Sinar and Emma Verkinderen
Advisor: Cédric Lood
Master of Bioinformatics - KU Leuven, Belgium
Tool: SASpector (Short-read Assembly inSpector)
A bioinformatics tool to extract and analyze missing regions of short-read assemblies by mapping the contigs to a reference genome.
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:
SASpectorcheck: checks if the third-party tools used by SASpector are installed in the system.
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: extraction of kmers and tandem repeats in the missing regions. Additionally, performs a 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 BED file.
quastunmap.py: performs a genome quality assessment for the missing regions to the reference and provides a genome viewer using QUAST and Icarus.
- Linux 64-bit and OS X are supported.
- Python3 (3.4+)
- Mauve or pogressiveMauve (v2.4.0) (https://darlinglab.org/mauve/mauve.html)
- Prokka (v1.14.5) (https://github.com/tseemann/prokka)
- QUAST (v5.02) (https://github.com/ablab/quast)
- SAMtools (v1.7) (http://samtools.sourceforge.net/)
- Tandem Repeats Finder (TRF) (v4.09) Note: SASpector run TRF with the name
We recommend to install SASpector using pip:
pip install SASpector
Command line options
For basic functionalities, run
usage: SASpector - Short-read Assembly inSpector [-h] [-p PREFIX] [-dir OUTDIR] [-f [LENGTH]] [-db [PROTEIN-DB]] [-k [K]] [-q] [-c [BAMFILE]] reference contigs positional arguments: reference Reference genome FASTA file, e.g. from hybrid assembly. If the file contains multiple seqences, only the first one is used, so make sure to concatenate if needed. contigs Short-read assembly FASTA file as contigs/draft genome. optional arguments: -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
First, Mauve performs an alignment of both genomes with the progressiveMauve algorithm. It will generate a subdirectory prefix.alignment with several output files but most importantly the backbone file with coordinates of the mapped and unmapped regions in the reference genome.
Afterwards, this script will parse the backbone file and 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 will predict 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
As optional analysis:
kmer analysis and tandem repeats: if a kmer size is provided, SASpector will calculate the frequency of the kmers per missing regions and will generate summary tables and barplots for those kmers. Additionally, it will run 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.
Before running SASpector, be sure to have your reference genome as a single FASTA file not a multi-FASTA file. If your reference genome is a multi-FASTA file (e.g. output from Unicycler), you can concatenate your sequences using Union command by EMBOSS.
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
[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/ *.tsv tab-delimited reports of the frequency of kmer per missing region *.jpg Barplots of the frequency of kmer per missing region [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
For questions or issues, go to this repository Issues tab.
- 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.
- Mapleson, D., Garcia Accinelli, G., Kettleborough, G., Wright, J., & Clavijo, B. J. (2016). KAT: a K-mer analysis toolkit to quality control NGS datasets and genome assemblies. Bioinformatics, 33(4).
- Gurevich, A., Saveliev, V., Vyahhi, N., & Tesler, G. (2013). QUAST: Quality assessment tool for genome assemblies. Bioinformatics, 29(8), 1072–1075.
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