kmcp

README

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

What can we do?

1. Accurate metagenomic profiling

KMCP adopts a novol metagenomic profiling strategy, by splitting reference genomes into 10 chunks and mappings reads to these chunks via fast k-mer matching. KMCP performs well on both prokaryotic and viral organisms, with higher sensitivity and specificity than other k-mer-based tools (check the benchmark).

2. Fast sequence search against large scales of genomic datasets

KMCP can be used for fast sequence search against large scales of genomic dataset 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 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 (Scaled MinHash) (Sourmash). KMCP utilizes multiple k-mer sketches (Minimizer, FracMinHash (previously named Scaled MinHash) and Closed Syncmers) for genome similarity estimation. KMCP is 4x-7x faster than Mash/Sourmash (check the tutorial and benchmark).

Features

• Easy to install
  • Statically linked executable binaries for multiple platforms (Linux/Windows/macOS, amd64).
  • No dependencies, no configurations.
• Easy to use
  • Supporting shell autocompletion.
  • Detailed usage and tutorials.
• Building database is easy and fast
  • ~25 min for 47894 genomes from GTDB-r202 on a sever with 40 CPU threads and solid disk drive.
• Fast searching speed
  • The index structure is modified from COBS, while KMCP is 4x-10x faster.
  • Automatically scales to exploit all available CPU cores.
  • Searching time is linearly related to the number of reference genomes.
• Scalable searching. Searching results against multiple databases can be fast merged. This brings many benefits:
  • There's no need to re-built the database with newly added reference genomes.
  • HPC cluster could linearly accelerate searching with each computation node hosting a database built with a part of reference genomes.
  • Computers with limited main memory would also support searching by building small databases.
• Accurate taxonomic profiling
  • Some k-mer based taxonomic profilers suffers from high false positive rates, while KMCP adopts multiple strategies to improve specificity and keeps high sensitivity at the same time.
  • Except for archaea and bacteria, KMCP performed well on virus/phages.
  • KMCP also provides confident infectious pathogen detection.
  • Preset six modes for multiple scenarios.
  • Supports CAMI and MetaPhlAn profiling format.

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Documents

• Installation
• Databases
• Tutorials
  • Taxonomic profiling
  • Sequence and genome searching
• Usage
• Benchmarks
• FAQs

Installation

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.

Commands

subcommand	function
compute	Generate k-mers (sketches) 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

Quickstart

# 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 --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.tsv.gz

# profile and binning
kmcp profile search.tsv.gz \
    --taxid-map        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

Support

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

License

MIT License

Acknowledgments

• 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.