• Reading a pre-existing reference genome from one or more FASTA files.
• Alternatively, generating an artificial reference genome based on input parameters (length, repeat count, repeat length, repeat variability rate).
• Simulating reads from random locations in the genome based on input parameters of read length, coverage, sequencing error rate, and indel rate.
• Applying alignment tools to the genome and the reads through a standardized interface.
• Parsing the output of the alignment tool and calculating the number of reads that were correctly or incorrectly mapped.
• Computing run times and measures of accuracy.

Seal has interfaces to evaluate the following software packages:

• Bowtie
• BWA
• MAQ
• mrFAST
• mrsFAST
• Novoalign
• SHRiMP
• SOAPv2

Address of the bookmark: http://compbio.case.edu/seal/

RaGOO is a tool for coalescing genome assembly contigs into pseudochromosomes via minimap2 alignments to a closely related reference genome. The focus of this tool is on practicality and therefore has the following features:

1. Good performance. On a MacBook Pro using Arabidopsis data, pseudochromosome construction takes less than a minute and the whole pipeline with SV calling takes ~2 minutes.
2. Intact ordering and orienting of contigs.
3. Misassembly correction
4. GFF lift-over
5. Structural variant calling with and integrated version of Assemblytics
6. Confidence scores associated with the grouping, localization, and orientation for each contig.

Address of the bookmark: https://github.com/malonge/RaGOO

Address of the bookmark: https://github.com/ParBLiSS/FastANI

Address of the bookmark: http://www.seqan.de/apps/stellar/

per-base depth about 2x as fast samtools depth--about 25 minutes of CPU time for a 30X genome.
mean per-window depth given a window size--as would be used for CNV calling.
the mean per-region given a BED file of regions.
a distribution of proportion of bases covered at or above a given threshold for each chromosome and genome-wide.
quantized output that merges adjacent bases as long as they fall in the same coverage bins e.g. (10-20)
threshold output to indicate how many bases in each region are covered at the given thresholds.
A summary of mean depths per chromosome and within specified regions per chromosome.

Address of the bookmark: https://github.com/brentp/mosdepth

USAGE:

• -query: sequence A in fasta format
• -db: sequence B in fasta format
• -out: output matrix
• -kmer Integer: k>1 (default 32) Use 32 for chromosomes and genomes and 16 for small bacteria
• -diffuse Integer: z>0 (default 4) Use 4 for everything - if using large plant genomes you can try using 1
• -dimension Size of the output matrix and plot. Integer: d>0 (default 1000) Use 1000 for everything that is not full genome size, where 2000 is recommended

Address of the bookmark: https://github.com/estebanpw/chromeister

Address of the bookmark: http://www.nature.com/srep/2015/150707/srep11996/full/srep11996.html

Address of the bookmark: http://schatzlab.cshl.edu/data/nanocorr/

Porechop also supports demultiplexing of Nanopore reads that were barcoded with the Native Barcoding Kit, PCR Barcoding Kit or Rapid Barcoding Kit.

The known Nanopore adapters that Porechop looks for are defined

https://github.com/rrwick/Porechop/blob/master/porechop/adapters.py

They are:

• Ligation kit adapters
• Rapid kit adapters
• PCR kit adapters
• Barcodes
• Native barcoding
• Rapid barcoding

Address of the bookmark: https://github.com/rrwick/Porechop/blob/master/porechop/adapters.py

Complete paper at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5321748/

Address of the bookmark: https://github.com/mdcao/npScarf