# Tutorials

## Simple distance estimation

Download example E. coli genomes:

genome1.fna
genome2.fna

Run:

    mash dist genome1.fna genome2.fna

The results are tab delimited lists of Reference-ID, Query-ID, Mash-distance, P-value, and Matching-hashes:

    genome1.fna   genome2.fna     0.0222766       0       456/1000

## Saving time by sketching first

    mash sketch genome1.fna
    mash sketch genome2.fna
    mash dist genome1.fna.msh genome2.fna.msh

## Pairwise comparisons with compound sketch files

Download additional example E. coli genome:

genome3.fna

Sketch the first two genomes to create a combined archive, use `mash info` to verify its contents, and estimate pairwise distances:

    mash sketch -o reference genome1.fna genome2.fna
    mash info reference.msh
    mash dist reference.msh genome3.fna

This will estimate the distance from each query (which there is one of) to each reference (which there are two of in the sketch file):

    genome1.fna   genome3.fna     0       0       1000/1000
    genome2.fna   genome3.fna     0.0222766       0       456/1000

## Querying read sets against an existing RefSeq sketch

Download the pre-sketched RefSeq archive (reads not provided here; 10x-100x coverage of a single genome with any sequencing technology should work):

refseq.genomes.k21.s1000.msh

Plasmids also available:

refseq.genomes+plasmids.k21.s1000.msh refseq.plasmids.k21.s1000.msh

Concatenate paired ends (this could also be piped to `mash` to save space by specifying `-` for standard input, zipped or unzipped):

    cat reads_1.fastq reads_2.fastq > reads.fastq

Sketch the reads, using `-m 2` to improve results by ignoring single-copy k-mers, which are more likely to be erroneous:

    mash sketch -m 2 reads.fastq

Run `mash dist` with the RefSeq archive as the reference and the read sketch as the query:

    mash dist refseq.genomes.k21.s1000.msh reads.fastq.msh > distances.tab

Sort the results to see the top hits and their p-values:

    sort -gk3 distances.tab | head

## Screening a read set for containment of RefSeq genomes

(new in Mash v2.0)

If a read set potentially has multiple genomes, it can be “screened” against the database to estimate how well each genome is contained in the read set. We can use the SRA Toolkit to download ERR024951:

    fastq-dump ERR024951

…and screen it against Refseq Genomes (link above), sorting the results:

    mash screen refseq.genomes.k21s1000.msh ERR024951.fastq > screen.tab
    sort -gr screen.tab | head

We see the expected organism, Salmonella enterica, but also an apparent contaminant, Klebsiella pneumoniae. The fields are [identity, shared-hashes, median-multiplicity, p-value, query-ID, query-comment]:

    0.99957       991/1000        26      0       GCF_000841985.1_ViralProj14228_genomic.fna.gz   NC_004313.1 Salmonella phage ST64B, complete genome
    0.99957       991/1000        24      0       GCF_002054545.1_ASM205454v1_genomic.fna.gz      [57 seqs] NZ_MYON01000010.1 Salmonella enterica strain BCW_4905 NODE_10_length_152932_cov_1.77994, whole genome shotgun sequence [...]
    0.999522      990/1000        102     0       GCF_900086185.1_12082_4_85_genomic.fna.gz       [51 seqs] NZ_FLIP01000001.1 Klebsiella pneumoniae strain k1037, whole genome shotgun sequence [...]
    0.999329      986/1000        24      0       GCF_002055205.1_ASM205520v1_genomic.fna.gz      [72 seqs] NZ_MYOO01000010.1 Salmonella enterica strain BCW_4904 NODE_10_length_177558_cov_3.07217, whole genome shotgun sequence [...]
    0.999329      986/1000        24      0       GCF_002054075.1_ASM205407v1_genomic.fna.gz      [88 seqs] NZ_MYNK01000010.1 Salmonella enterica strain BCW_4936 NODE_10_length_177385_cov_3.78874, whole genome shotgun sequence [...]
    0.999329      986/1000        24      0       GCF_000474475.1_CFSAN001184_01.0_genomic.fna.gz [45 seqs] NZ_AUQM01000001.1 Salmonella enterica subsp. enterica serovar Typhimurium str. CDC_2009K1158 isolate 2009K-1158 SEET1158_1, whole genome shotgun sequence [...]
    0.999329      986/1000        24      0       GCF_000474355.1_CFSAN001186_01.0_genomic.fna.gz [46 seqs] NZ_AUQN01000001.1 Salmonella enterica subsp. enterica serovar Typhimurium str. CDC_2009K1283 isolate 2009K1283 (Typo) SEET1283_1, whole genome shotgun sequence [...]
    0.999329      986/1000        24      0       GCF_000213635.1_ASM21363v1_genomic.fna.gz       [2 seqs] NC_016863.1 Salmonella enterica subsp. enterica serovar Typhimurium str. UK-1, complete genome [...]
    0.999281      985/1000        24      0       GCF_001271965.1_Salmonella_enterica_CVM_N43825_v1.0_genomic.fna.gz      [67 seqs] NZ_LIMN01000001.1 Salmonella enterica subsp. enterica serovar Typhimurium strain CVM N43825 N43825_contig_1, whole genome shotgun sequence [...]
    0.999281      985/1000        24      0       GCF_000974215.1_SALF-297-3.id2_v1.0_genomic.fna.gz      [90 seqs] NZ_LAPO01000001.1 Salmonella enterica subsp. enterica serovar Typhimurium strain SALF-297-3 NODE_1, whole genome shotgun sequence [...]

Note, however, that multiple strains of Salmonella enterica have good identity. This is because they are each contained well when considered independently. For this reason `mash screen` is not a true classifier. However, we can remove much of the redundancy for interpreting the results using the winner-take-all strategy (`-w`). And while we’re at it, let’s throw some more cores at the task to speed it up (`-p 4`):

    mash screen -w -p 4 refseq.genomes.k21s1000.msh ERR024951.fastq > screen.tab
    sort -gr screen.tab | head

The output is now much cleaner, with just the two whole genomes, plus phages (a lot of other hits to viruses and assembly contigs would appear further down):

    0.99957       991/1000        24      0       GCF_002054545.1_ASM205454v1_genomic.fna.gz      [57 seqs] NZ_MYON01000010.1 Salmonella enterica strain BCW_4905 NODE_10_length_152932_cov_1.77994, whole genome shotgun sequence [...]
    0.99899       979/1000        26      0       GCF_000841985.1_ViralProj14228_genomic.fna.gz   NC_004313.1 Salmonella phage ST64B, complete genome
    0.998844      976/1000        101     0       GCF_900086185.1_12082_4_85_genomic.fna.gz       [51 seqs] NZ_FLIP01000001.1 Klebsiella pneumoniae strain k1037, whole genome shotgun sequence [...]
    0.923964      190/1000        40      0       GCF_000900935.1_ViralProj181984_genomic.fna.gz  NC_019545.1 Salmonella phage SPN3UB, complete genome
    0.900615      111/1000        100     0       GCF_001876675.1_ASM187667v1_genomic.fna.gz      [137 seqs] NZ_MOXK01000132.1 Klebsiella pneumoniae strain AWD5 Contig_(1-18003), whole genome shotgun sequence [...]
    0.887722      82/1000 31      3.16322e-233    GCF_001470135.1_ViralProj306294_genomic.fna.gz  NC_028699.1 Salmonella phage SEN34, complete genome
    0.873204      58/1000 22      1.8212e-156     GCF_000913735.1_ViralProj227000_genomic.fna.gz  NC_022749.1 Shigella phage SfIV, complete genome
    0.868675      52/1000 57      6.26251e-138    GCF_001744215.1_ViralProj344312_genomic.fna.gz  NC_031129.1 Salmonella phage SJ46, complete genome
    0.862715      45/1000 1       1.05185e-116    GCF_001882095.1_ViralProj353688_genomic.fna.gz  NC_031940.1 Salmonella phage 118970_sal3, complete genome
    0.856856      39/1000 21      6.70643e-99     GCF_000841165.1_ViralProj14230_genomic.fna.gz   NC_004348.1 Enterobacteria phage ST64T, complete genome