UST is a bioinformatics tool for constructing a spectrum-preserving string set (SPSS) representation from sets of k-mers.

To install, compile from source:

git clone https://github.com/jermp/UST
cd UST
make

After compiling, use

./ust -i [unitigs.fa] -k [kmer_size]

e.g.

./ust -i examples/k11.unitigs.fa -k 11

The important parameters are:

• k [int] : The k-mer size that was used to generate the input, i.e. the length of the nodes of the node-centric de Bruijn graph.
• i [input-file] : Unitigs file produced by BCALM2 in FASTA format.
• a [0 or 1] : Default is 0. A value of 1 tells UST to preserve abundance. Use this option when the input file was generated with the -all-abundance-counts option of BCALM2.

The output is a FASTA file with extenstion "ust.fa" in the working folder, which is the SPSS representaiton of the input.

If the program is run with the option -a 1, then the header line of each sequence will also contain the abundance counts as in the provided BCALM input file.

In order to build a SPSS representation for your k-mer set, you must first run BCALM2 on your set of k-mers. BCALM2 will construct a set of unitigs. Those unitigs are then fed as input to ust, which outputs a FASTA file with the SPSS representation. Note that the k parameter to ust must match the -kmer-size used when running BCALM2.

If you would like to store the data on disk in compressed form (like UST-Compress in our paper), you can then install and run MFCompress on the output of UST as follows: MFCompressC mykmers.ust.fa

If you would like to build a membership data structure based on UST, then see the SSHash repository.

If using UST in your research, please cite

• Amatur Rahman and Paul Medvedev, Representation of k-mer sets using spectrum-preserving string sets, RECOMB 2020.
• Here is the bibtex entry:

@inproceedings{RahmanMedvedevRECOMB20,
  author    = {Amatur Rahman and Paul Medvedev},
  title     = {Representation of $k$-mer sets using spectrum-preserving string sets},
  booktitle = {Research in Computational Molecular Biology - 24th Annual International Conference, {RECOMB} 2020, Padua, Italy, May 10-13, 2020, Proceedings},
  series    = {Lecture Notes in Computer Science},
  volume    = {12074},
  pages     = {152--168},
  publisher = {Springer},
  year      = {2020
}

Note that the general notion of an SPSS was independently introduced under the name of simplitigs. Therefore, if citing this general notion, please also cite:

• Brinda K, Baym M, and Kucherov G, Simplitigs as an efficient and scalable representation of de Bruijn graphs, bioRxiv 2020.