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Phylo-k-mers databases for SHERPAS

Cite this dataset

Scholz, Guillaume et al. (2021). Phylo-k-mers databases for SHERPAS [Dataset]. Dryad.


SHERPAS is a new program to identify novel recombinant sequences in a large collection of viral sequences, and to provide a first estimate of their recombinant structure. SHERPAS is much faster than other softwares for recombination detection; its main feature is the use of a pre-computed database of "phylogenetically-informed k-mers" (or phylo-k-mers). The computation of this phylo-k-mer database is a heavy computational step, but it only needs to be executed once for a given reference alignment.

A phylo-k-mer database can be built from any reference alignment, and a phylogenetic tree built from that alignment, using RAPPAS2 ( We propose here three ready-to-use databases, for three reference alignments:
-An alignment of 167 sequences of the pol region of the HIV genome, provided with the program SCUEAL, accessible at
-An alignment of 339 sequence of the whole HBV genome, provided with the programm jpHMM, accessible at
-An alignment of 881 sequences of the whole HIV genome, also provided with jpHMM, accessible at

For each of these alignments, we provide a .zip file containing three files: The phylo-k-mer database (.rps file), the reference phylogenetic tree used to build the database (.tree file), and a table associating each reference sequence to a strain of the virus (.csv file). The details of the construction of the database, the construction of the tree, as well as the origin of the information reported in the table, can be found in the Supplementary Materials associated with the original Bioinformatics publication.

Usage notes

Structure of the dataset:

For the pol region of the HIV genome (167 reference sequences):

For the whole HBV genome (339 reference sequences):

For the whole HIV genome (881 reference sequences):


SHERPAS download and documentation:


Agence Nationale de la Recherche

The French National Research Agency

Investissements d'avenir" programme, Award: ANR-16-IDEX-0006