Markov Clustering (MCL): a cluster algorithm for graphs

MCL implements Markov cluster algorithm. Among its applications is the assignment of proteins into families based on precomputed sequence similarity information. This approach does not suffer from the problems that normally hinder other protein sequence clustering algorithms, such as the presence of multi-domain proteins, promiscuous domains and fragmented proteins.

References:

Documentation
Important Notes

Interactive job
Interactive jobs should be used for debugging, graphics, or applications that cannot be run as batch jobs.

Allocate an interactive session and run the program. Sample session:

[user@biowulf]$ sinteractive 
[user@cn3200 ~]$ module load mcl 
[+] Loading MCL 14-137  ...
[user@cn3200 ~]$ cp $MCL_DATA/* .
[user@cn3200 ~]$ mcl cathat  --abc -o out2.cathat 
[mcl] new tab created
[mcl] pid 10660
 ite ------  chaos  time hom(avg,lo,hi) m-ie m-ex i-ex fmv
  1  ......   0.47  0.00 0.87/0.80/0.95 1.33 1.33 1.33 100
  2  ......   0.53  0.00 0.86/0.69/0.95 1.24 1.21 1.67 100
  3  ......   0.35  0.00 0.95/0.88/1.00 1.00 0.67 1.14 100
  4  ......   0.44  0.00 0.94/0.88/1.00 1.08 0.68 0.81 100
  5  ......   0.24  0.00 0.89/0.78/1.00 1.17 0.67 0.57 100
  6  ......   0.20  0.00 0.89/0.80/0.99 0.92 0.92 0.57 100
  7  ......   0.12  0.00 0.95/0.95/0.95 0.92 0.92 0.57 100
  8  ......   0.20  0.00 0.92/0.85/1.00 0.92 0.92 0.57 100
  9  ......   0.25  0.00 0.88/0.76/1.00 0.92 0.69 0.43 100
 10  ......   0.15  0.00 0.93/0.85/1.00 0.90 0.90 0.43 100
 11  ......   0.02  0.00 0.99/0.98/1.00 0.90 0.90 0.43 100
 12  ......   0.00  0.00 1.00/1.00/1.00 0.90 0.90 0.43 100
 13  ......   0.00  0.00 1.00/1.00/1.00 0.90 0.60 0.29 100
[mcl] jury pruning marks: <100,99,99>, out of 100
[mcl] jury pruning synopsis: <99.6 or perfect> (cf -scheme, -do log)
[mcl] output is in out.cathat
[mcl] 2 clusters found
[mcl] output is in out.cathat
...
...

End the interactive session:
[user@cn3200 ~]$ exit
salloc.exe: Relinquishing job allocation 46116226
[user@biowulf ~]$