GitLocker: The Coding Marketplace

Description:

phamclust 1.3.3

phamclust
PhamClust is a tool for performing gene phamily based clustering of bacteriophage genomes. It makes use of a novel genome
similarity index, the proteomic equivalence quotient (PEQ) to cluster genomes according to their global similarity. It was
published in mSphere in 2023.
Installation
The most straightforward way to install PhamClust is from the Python Package Index (PyPI): pip install phamclust
Usage
Once installed, invoking phamclust at the commandline with no arguments will display the help menu:
(phamclust) chg60 % phamclust
usage: phamclust [-h] [-g] [-k] [-s] [-sl] [-c] [-cl] [-nr] [-nl] [-m] [-d] [-n] [-r] [-t] infile outdir

Cluster phage genomes using gene content similarity-based metrics.

positional arguments:
infile path to a TSV file mapping genomes to phams and translations
outdir path to which output files should be written

options:
-h, --help show this help message and exit
-g, --genome-dir interpret `infile` as a directory of genome FASTA files instead of TSV
-d, --debug increase verbosity of logging for debug purposes
-n, --no-sub do not perform sub-clustering
-r, --remove-tmp remove temporary files (not recommended if repeated runs are planned on the same dataset)
-t , --threads number of CPU cores to use [default: 16]

clustering arguments:
-k , --k-min minimum cluster size to perform subclustering [default: 6]
-s , --sub-thresh similarity threshold to use for sub-clustering [default: 0.6]
-sl , --sub-linkage linkage type to use for sub-clustering [default: single]
-c , --clu-thresh similarity threshold to use for clustering [default: 0.25]
-cl , --clu-linkage linkage type to use for clustering [default: average]
-nr , --nr-thresh similarity threshold above which to pre-group very similar genomes that must be clustered together [default: 0.75]
-nl , --nr-linkage linkage type to use for pre-grouping very similar genomes [default: complete]
-m , --metric relatedness index to use for pairwise genome comparisons [default: peq]

heatmap arguments:
-hc , --heatmap-colors
comma-separated list of 2 or 3 colors to use in heatmaps [default: red,yellow,green]
-hm , --heatmap-midpoint
midpoint to use for color gradient in heatmaps [default: same as clustering threshold]

Available metrics:

Acronym Name Reference
(1) gcs gene content similarity https://doi.org/10.1038/nmicrobiol.2017.112
(2) jc jaccard coefficient https://doi.org/10.1111/j.1469-8137.1912.tb05611.x
(3) pocp percentage of conserved proteins https://doi.org/10.1128/JB.01688-14
(4) af alignment fraction https://doi.org/10.1093/nar/gkv657
(5) aai average aminoacid identity https://doi.org/10.1073/pnas.0409727102
(6) peq proteomic equivalence quotient https://doi.org/10.1128/msystems.00443-23

PhamClust takes an input filepath and an output filepath as its primary arguments. By default, the input path is
assumed to be a TSV file mapping genome names/identifiers to pham identifiers and translations.
If the -g/--genome-dir argument is provided, PhamClust interprets the input filepath as a directory containing
one FASTA file per genome, with headers structured as in the example below:
>name=Bipper|pham=pham_1|n=1
MTAPLLQSVTADDGNMITVPTLQFTRWLDETRDKVIGADGAPDPVRDPMSAYRYLKGRRSVIEGAARQRPMLRLFDKNMDPIAQIAGERLASVEEMMSDSGQANVVLRYDNWLTDFILHQTKIHEDLHLVVDPNPTNRTWRTRWGGKITGINAKRDSSGIHTLELEAISNRQHAKHMLFASNPVFPPEIQLPKMWVLPGNTRTILSISMFVNLARRFFPLLSIPTNIFNPMAWVNGWGAGLDPLMWPLQVAFVNPLLDQSRLSVLGSSWTDWHTAMDSMLKDAGVLFRAYTWLTEDADTPHTELVDMVRGLGPLQDTVDNLTRPHRNCVVFALEDKSGVQGPTGTAADGVINLIGATADDMITETLFNLDRDGDGETDPIFRKLLGVAPEKPKTIWYDGQFSGIIESEIRRHKGPVKKIHTGGRSPSILNQAQTYAIRYALSQLAQVISYGIGAYQQYGTEGLDNLYQGQLDNTLFAWQAFDDPIRALQTGDMAWQEHFERGSGTAYTLSGIVTLRVGHYKTRAWQGFTVKVVNGRPHAVDVDITLGDRAGFEQGGIIFVDQITAIKRSWSRTEPVTVQLSIGDDQDKEDPAARGLRAIQAVWTTLGMLLGEGTIF
>name=Bipper|pham=pham_37|n=1
MTSPSGVAVAALKGHTKPRLYTPPLAVNCNIWIAPELSCPCGCGLHAGTSWGFDCIDFLTNVLKWQLIPYQRWLYIHALEKGPGGEGFRFKTLVILIARQNGKTQWLRGLGLWRLYLDSRGRSSPDCPAAKTVVIAAQGLEYAEGTLGEVVNDVKECRALKREFLRHRQTNGKHAMLLSGRRSWRAVAANRKGGRSMSVDLAELDELREHHDWLAWNAITPTTQARQYSQNVAASNAGDKRSVVLRSLRDGAMAKILARDTEDTKTGLFEYSAPQDANPLERKYWPMANPALGYLPGHDEDALAAKAEAMADNMAGFVTEHLCQWVDTLLPGVMPMEDWNATTDPESRRAEGAPVYAAVDVSHSRSKAYIAVASRRSDGLLHVEVVAAHRGTDWVVPWFKARPGKFVAVAVQARGCPASDLIEPLTEAGVPVMELGGAELVRGAGGVLFDGIRKHAIWHRPSPALDTAAKGTVSRSLGGDTWVLDRKNSPVDAAPLVACAAAAWAEGQGPMVPDKVPEVHEWPDEEEIAEWEKELDELQ
...

If a genome encodes more than one copy of a pham (namely, paralogs), each copy after the first should increment
the value of n.
Six metrics are available for calculating intergenomic similarities (processing speeds estimated in parentheses
as the number of genome pairs calculated per second on an M1 Macbook Pro):

Gene Content Similarity (gcs) (>100,000 pairs/second)
Jaccard Coefficient (jc) (>55,000 pairs/second)
Percentage of Conserved Proteins (pocp) (>25,000 pairs/second)
Alignment Fraction (AF) (>15,000 pairs/second)
Average Aminoacid Identity (aai) (~500 pairs/second)
Proteomic Equivalence Quotient (peq) (~475 pairs/second)

Because of how cheap the first four metrics are, the maximum parallelization benefit is seen with just 4 CPU
cores for datasets consisting of fewer than 2,500 genomes. The last two metrics are considerably more expensive
to calculate, and will see benefit from as many physical cores are available on your machine (i.e., core count,
not thread count), as long as there is enough system memory.
Heatmap settings
Among the outputs from PhamClust are per-cluster matrix heatmaps that nicely illustrate the pairwise similarities
within clusters/subclusters.
For reasonably small datasets (those with fewer than 1000 genomes), a heatmap will also be drawn for the complete
dataset matrix.
Two commandline arguments can be used to alter the appearance of these heatmaps. The -hc/--heatmap-colors
argument allows you to specify either two or three colors that define the heatmap colorscheme. By default, a
3-point gradient is used, where the lowest similarity genome pairs are in red, intermediate similarity genomes
pairs are in yellow, and the highest-similarity genome pairs are green. This default behavior is the same as
invoking phamclust with -hc red,yellow,green. Another visually appealing option might be a 2-color gradient
from white (0% similar) to green (100% identical), which could be applied with -hc white,green.
Any valid CSS named colors can be used:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, saddlebrown, salmon, sandybrown,
seagreen, seashell, sienna, silver, skyblue,
slateblue, slategray, slategrey, snow, springgreen,
steelblue, tan, teal, thistle, tomato, turquoise,
violet, wheat, white, whitesmoke, yellow,
yellowgreen

For 3-point color scales, the -hm/--heatmap-midpoint argument can be used to adjust the similarity threshold
where the middle color goes. For example, to highlight diversity within clusters (i.e., dissimilarity between
subclusters), a midpoint at the subcluster threshold would maximize contrast between intra-subcluster and
inter-subcluster similarity values.