Prices for the estimated cost Estimated cost. What the search would cost, in US dollars, to annotate 1,000 proteins against one relative at cloud prices. You can change the prices. Lower is cheaper.In detail: Estimated cost in US dollars per 1,000 queries: core-hours × r_cpu + GPU-hours × r_gpu, where r_cpu and r_gpu are prices per hour. The default prices are AWS on-demand rates.how it works →Default prices are AWS on-demand rates for US East (N. Virginia), Linux, read
2026-10-15 from the EC2 on-demand price page (its
price file). CPU: a c7i.large costs $0.08925 per hour for 2 vCPU, so $0.044625 per CPU-hour.
GPU: a g5.xlarge with one NVIDIA A10G costs $1.006 per hour.
Table 1.Tools ranked by Fmax, highest first, best setting of each tool.
aligns lettersfamily profiles3-D shape lettersreduced-alphabet k-mersPfam models× not rungraded on its own models
The marks and colours in Table 1, in full
aligns amino-acid letters: phmmer, MMseqs2builds a profile of a protein family: jackhmmer, MMseqs2, three rounds, HH-suite (hhsearch)compares 3-D shape letters (3Di): Foldseek, ProstT5 + Foldseekcounts shared k-mers in a reduced alphabet: kmerseekscans curated Pfam family models: hmmscan with Pfam
× not run: no row in the results table for these filtersnot measured: the search ran, but no task record holds its costno correct calls: no call was correct, so there is no median IoUcorrect calls only on single residues: Fmax is above 0, but every correct
call is on a one-residue annotation, where an IoU says nothing about where the ends areneeds 3D structure predictionneeds GPU
what the tool needs besides the sequencegraded on its own models hmmscan under the Pfam truth
set: Pfam's domains are defined by the models hmmscan runs, so read its row as an upper limitEach row shows a tool's best setting for the column it is sorted by;
"+N more settings" opens the rest. Click a column name to sort; rows with no value sort last.Cost and core-hours are measured on Stanford's Sherlock cluster, from the run's own task records measured. We timed it ourselves, on Stanford's Sherlock computer cluster.In detail: Measured on Stanford's Sherlock cluster, from the run's own task records.how it works →
In plain words: Fmax asks whether a tool finds the real domains
without adding wrong ones. AUPRC asks whether its correct calls stay at the top of its list.
Median IoU asks how well the ends of its right calls line up with the real domain. All run
0 to 1, and higher is better. Hover or tap a ? for more, or see
How the scores work, which works each through on a made-up protein.
Each tool searched 998 human proteins, most from chromosome 6, against one
relative's proteome and copied its annotations (which proteins).
Estimated cost = core-hours × r_cpu + GPU-hours × r_gpu, per 1,000 queries,
for the search alone (how cost is counted).
How to choose between Fmax and AUPRC, and raw against dedup
Fmax is the score at the single best cutoff on the tool's own score, protein by protein: use it
when you will keep every call above one cutoff. AUPRC sums precision over every cutoff, counting
each call: use it when you will rank calls and read down the list. The two can rank tools in
opposite orders, because AUPRC pools every call of every protein while Fmax averages protein by
protein. A domain reported many times counts against both. Dedup merges those copies; for phmmer against zebrafish (Pfam) it moves Fmax from
0.333 to 0.457 and AUPRC from 0.029 to
0.111. Definitions: Methods.