Wgsim is a small tool for simulating sequence reads from a reference genome. It is able to simulate diploid genomes with SNPs and insertion/deletion (INDEL) polymorphisms, and simulate reads with uniform substitution sequencing errors. It does not generate INDEL sequencing errors, but this can be partly compensated by simulating INDEL polymorphisms.

Wgsim outputs the simulated polymorphisms, and writes the true read coordinates as well as the number of polymorphisms and sequencing errors in read names. One can evaluate the accuracy of a mapper or a SNP caller with wgsim_eval.pl that comes with the package.

Address of the bookmark: https://github.com/lh3/wgsim

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

C Chiang, R M Layer, G G Faust, M R Lindberg, D B Rose, E P Garrison, G T Marth, A R Quinlan, and I M Hall. SpeedSeq: ultra-fast personal genome analysis and interpretation. Nat Meth (2015). doi:10.1038/nmeth.3505.

http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.3505.html

Address of the bookmark: https://github.com/hall-lab/speedseq

The data are available using NCBI accession IDs: BioProject: (PRJNA483067), assembly: [RBJD00000000] and sequence data (SRP155659).

Additional Resources

• Interactive map showcasing global initiatives underway to generate reference-quality human genome assemblies for diverse populations
• BioReport Podcast on the value of ethnic-specific reference genomes
• Nature Reviews Genetics paper from NHGRI: Prioritizing diversity in human genomics research
• Article in The Journal of Precision Medicine: “Minority Report – Ethnic Diversity and the Real Promise for Precision Medicine”
• Article in Bio-IT World: “Genomic Data Standards Are a Necessity”
• NHGRI Project Award: High Quality Human and Non-Human Primate Genome Assemblies

More details are available on the PacBio website:

• Blog post: Data Release: Highest-Quality, Most Contiguous Individual Human Genome Assembly to Date
• Blog post: For Reference-Grade Human Genome Assemblies, SMRT Sequencing Yields Optimal Results
• Webinar:  Assembling High-Quality Human Reference Genomes for Global Populations
• FALCON-Phase press release and article preprint
• PacBio research focus webpage about Human Population Genetics

 Ref: https://stockguru.com/2018/10/08/pacific-biosciences-releases-highest-quality-most-contiguous-individual-human-genome-assembly-to-date/

I made Rebaler for bacterial genomes (specifically for the task of testing basecallers). It should in principle work for non-bacterial genomes as well, but I haven't tested it.

Address of the bookmark: https://github.com/rrwick/Rebaler

By default, Kaiju uses either the available complete genomes from NCBI RefSeq or the microbial subset of the non-redundant protein database nr used by NCBI BLAST, optionally also including fungi and microbial eukaryotes.

Kaiju translates reads into amino acid sequences, which are then searched in the database using a modified backward search on a memory-efficient implementation of the Burrows-Wheeler transform, which finds maximum exact matches (MEMs), optionally allowing mismatches in the protein alignment. The search can process up to millions of reads per minute using, for example, only 10 GB RAM with a protein database comprising 4821 microbial genomes. Kaiju can also be used for querying any other protein database without taxonomic classification, using either protein or nucleotide queries.

Kaiju is described in Menzel, P. et al. (2016) Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat. Commun. 7:11257 (open access).

Address of the bookmark: http://kaiju.binf.ku.dk/

Note that the information on this page is targeted at end-users. For developers, the source code, building instructions and implementation/development resources are available on GitHub.

The Picard toolkit is open-source under the MIT license and free for all uses.

Enjoy!

Address of the bookmark: http://broadinstitute.github.io/picard/

• Whole-genome sequencing
• Whole-exome sequencing
• RNA-seq (speical mode available)
• ChIP-seq

Address of the bookmark: http://qualimap.bioinfo.cipf.es/

Manual at http://spades.bioinf.spbau.ru/release3.7.1/manual.html

Address of the bookmark: http://bioinf.spbau.ru/spades