~$ ./sviper -s short-reads.bam -l long-reads.bam -r ref.fa -c variants.vcf -o polished_variants

This will output a polished_variants.vcf file, that contains all the refined variants.

Sometimes it is helpful to look at the polished sequence, e.g. with the IGV browser. In that case you want SViper to output the polished and aligned sequences in a bam file via the option --output-polished-bam:

~$ ./sviper -s short-reads.bam -l long-reads.bam -r ref.fa -c variants.vcf -o polished_variants --output-polished-bam

Address of the bookmark: https://github.com/smehringer/SViper

ftp://ftp.ncbi.nih.gov/genomes/

https://hgdownload.soe.ucsc.edu/downloads.html

Address of the bookmark: ftp://ftp.ncbi.nih.gov/genomes/

More at https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03666-4

Address of the bookmark: https://github.com/aghozlane/shaman

Address of the bookmark: https://github.com/Megagenomics/FastProNGS

You can try this here, or try it later on your own data.

Get data

We will use the same Illumina data as we used above:

• illumina_R1.fastq.gz: the Illumina forward reads
• illumina_R2.fastq.gz: the Illumina reverse reads

Assemble

Run Spades:

spades.py -1 illumina_R1.fastq.gz -2 illumina_R2.fastq.gz --careful --cov-cutoff auto -o spades_assembly_all_illumina

• -1 is input file of forward reads
• -2 is input file of reverse reads
• --careful minimizes mismatches and short indels
• --cov-cutoff auto computes the coverage threshold (rather than the default setting, “off”)
• -o is the output directory

Results

Move into the output directory and look at the contigs:

infoseq contigs.fasta

http://www.ncbi.nlm.nih.gov/pubmed/23307812

http://bioen-compbio.bioen.illinois.edu/RACA/

Address of the bookmark: http://bioen-compbio.bioen.illinois.edu/RACA/

More at http://www.bioinf.uni-leipzig.de/Software/segemehl/

Manual http://www.bioinf.uni-leipzig.de/Software/segemehl/segemehl_manual_0_1_7.pdf

Address of the bookmark: http://hoffmann.bioinf.uni-leipzig.de/LIFE/segemehl.html

Run Maq Now

Follow these steps to try Maq. All you need is a reference sequence file in the FASTA format.

1. Prepare a reference sequence (ref.fasta). Better a bacterial genome.
2. Download maq, maq-data and maqview at the download page.
3. Copy maq, maq.pl and maq_eval.pl to the $PATH or to the same directory.
4. Simulate diploid reference and read sequences, map reads, call variants and evaluate the results in one go:

   maq.pl demo ref.fasta calib-30.dat
   

   where calib-30.dat is contained in maq-data.
5. View the alignment:

   cd maqdemo/easyrun;
   maqindex -i -c consensus.cns all.map;
   maqview -c consensus.cns all.map

Even for advanced maq users, running `maq.pl demo' is recommended. You may find something helpful.

Address of the bookmark: http://maq.sourceforge.net

Training Resources:

• Bioinformatics Workshop (Webinars)
• Bioinformatics Training Slides

Specialized Applications:

• De Novo Assembly
• Transcriptome analysis
• Base Modification Analysis
• Barcoding
• Data Analysis Tools
• Minor Variants and Phasing Analysis

Address of the bookmark: https://github.com/PacificBiosciences/Bioinformatics-Training/wiki

Address of the bookmark: https://github.com/marbl/Krona/wiki