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KMCP: accurate metagenomic profiling of both prokaryotic and viral populations by pseudo-mapping

The preprint

KMCP: accurate metagenomic profiling of both prokaryotic and viral populations by pseudo-mapping.
Wei Shen, Hongyan Xiang, Tianquan Huang, Hui Tang, Mingli Peng, Dachuan Cai, Peng Hu, Hong Ren.
bioRxiv 2022.03.07.482835; doi:


What can we do?

1. Accurate metagenomic profiling

KMCP adopts a novel metagenomic profiling strategy by splitting reference genomes into 10 or 5 chunks and mappings reads to these chunks via fast k-mer matching, denoted as pseudo-mapping.

Benchmarking results on both simulated and real data indicate that KMCP not only allows for accurate taxonomic profiling of archaea, bacteria, and viral populations from metagenomic shotgun sequence data, but also provides confident pathogen detection in infectious clinical samples of low depth (check the benchmark).

Genome collections with custom taxonomy, e.g., GTDB uses its own taxonomy and MGV uses ICTV taxonomy, are also supported by generating NCBI-style taxdump files with taxonkit create-taxdump.

2. Fast sequence search against large scales of genomic datasets

KMCP can be used for fast sequence search against large scales of genomic datasets as BIGSI and COBS do. We reimplemented and modified the Compact Bit-Sliced Signature index (COBS) algorithm, bringing a smaller index size and much faster searching speed (4x-10x faster than COBS) (check the tutorial and benchmark).

3. Fast genome similarity estimation

KMCP can also be used for fast similarity estimation of assemblies/genomes against known reference genomes.

Genome sketching is a method of utilizing small and approximate summaries of genomic data for fast searching and comparison. Mash and Sourmash provide fast genome distance estimation using MinHash (Mash) or FracMinHash (Sourmash). KMCP supports multiple k-mer sketches (Minimizer, FracMinHash (previously named Scaled MinHash), and Closed Syncmers) for genome similarity estimation. And KMCP is 4x-7x faster than Mash/Sourmash (check the tutorial and benchmark).



Latest Version Github Releases Cross-platform Anaconda Cloud

Download executable binaries, or install using conda:

conda install -c bioconda kmcp

SIMD extensions including AVX512, AVX2, SSE2 are sequentially detected and used in two packages for better searching performance.

  • pand, for accelerating searching on databases constructed with multiple hash functions.
  • pospop, for batch counting matched k-mers in bloom filters.


Subcommand Function
compute Generate k-mers (sketch) from FASTA/Q sequences
index Construct database from k-mer files
search Search sequences against a database
merge Merge search results from multiple databases
profile Generate taxonomic profile from search results
utils filter Filter search results and find species/assembly-specific queries
utils merge-regions Merge species/assembly-specific regions
utils unik-info Print information of .unik file
utils index-info Print information of index file
utils cov2simi Convert k-mer coverage to sequence similarity
utils query-fpr Compute the maximal false positive rate of a query


# compute k-mers
kmcp compute -k 21 --split-number 10 --split-overlap 100 \
    --in-dir genomes/ --out-dir genomes-k21-n10

# index k-mers
kmcp index --false-positive-rate 0.1 --num-hash 1 \
    --in-dir genomes-k21-n10/ --out-dir genomes.kmcp

# delete temporary files
# rm -rf genomes-k21-n10/

# search    
kmcp search --db-dir genomes.kmcp/ test.fa.gz --out-file search.kmcp@db1.kmcp.tsv.gz

# merge search results against multiple databases
kmcp merge -o search.kmcp.tsv.gz search.kmcp@*.kmcp.tsv.gz

# profile and binning
kmcp profile search.kmcp.tsv.gz \
    --taxid-map \
    --taxdump          taxdump/ \
    --out-prefix       search.tsv.gz.k.profile \
    --metaphlan-report search.tsv.gz.m.profile \
    --cami-report      search.tsv.gz.c.profile \
    --binning-result   search.tsv.gz.binning.gz


Please open an issue to report bugs, propose new functions or ask for help.


MIT License


  • Zhi-Luo Deng (Helmholtz Centre for Infection Research, Germany) gave a lot of valuable advice on metagenomic profiling and benchmarking.
  • Robert Clausecker (Zuse Institute Berlin, Germany) wrote the high-performance vectorized positional popcount package (pospop) during my development of KMCP, which greatly accelerated the bit-matrix searching.