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Note: these benchmarks was performed in 2016.


  1. seqkit. (Go). Version v0.3.1.1. Compiled with Go 1.7rc5.
  2. fasta_utilities. (Perl). Version 3dcc0bc. Lots of dependencies to install.
  3. fastx_toolkit. (Perl). Version 0.0.13. Can't handle multi-line FASTA files.
  4. seqmagick. (Python). Version 0.6.1
  5. seqtk. (C). Version 1.1-r92-dirty.

Not used:

  1. pyfaidx. (Python). Version Not used, because it exhausted my memory (10G) when computing reverse-complement on a 5GB fasta file of 250 bp.

A Python script memusg was used to compute running time and peak memory usage of a process.


Categories Features seqkit fasta_utilities fastx_toolkit pyfaidx seqmagick seqtk
Formats supports Multi-line FASTA Yes Yes -- Yes Yes Yes
FASTQ Yes Yes Yes -- Yes Yes
Multi-line FASTQ Yes Yes -- -- Yes Yes
Validating sequences Yes -- Yes Yes -- --
Supporting RNA Yes Yes -- -- Yes Yes
Functions Searching by motifs Yes Yes -- -- Yes --
Sampling Yes -- -- -- Yes Yes
Extracting sub-sequence Yes Yes -- Yes Yes Yes
Removing duplicates Yes -- -- -- Partly --
Splitting Yes Yes -- Partly -- --
Splitting by seq Yes -- Yes Yes -- --
Shuffling Yes -- -- -- -- --
Sorting Yes Yes -- -- Yes --
Locating motifs Yes -- -- -- -- --
Common sequences Yes -- -- -- -- --
Cleaning bases Yes Yes Yes Yes -- --
Transcription Yes Yes Yes Yes Yes Yes
Translation -- Yes Yes Yes Yes --
Filtering by size Indirect Yes -- Yes Yes --
Renaming header Yes Yes -- -- Yes Yes
Other features Cross-platform Yes Partly Partly Yes Yes Yes
Reading STDIN Yes Yes Yes -- Yes Yes
Reading gzipped file Yes Yes -- -- Yes Yes
Writing gzip file Yes -- -- -- Yes --

Note 2: See usage for detailed options of seqkit.


All test data is available here: seqkit-benchmark-data.tar.gz (2.2G)

dataset_A.fa - large number of short sequences

Dataset A is reference genomes DNA sequences of gastrointestinal tract from NIH Human Microbiome Project: Gastrointestinal_tract.nuc.fsa (FASTA format, ~2.7G).

dataset_B.fa - small number of large sequences

Dataset B is Human genome from ensembl.

dataset_C.fq – Illumina single end reads (SE100)

Dataset C is Illumina single end (SE 100bp) reads file (~2.2G).


$ seqkit stat *.fa
file          format  type   num_seqs        sum_len  min_len       avg_len      max_len
dataset_A.fa  FASTA   DNA      67,748  2,807,643,808       56      41,442.5    5,976,145
dataset_B.fa  FASTA   DNA         194  3,099,750,718      970  15,978,096.5  248,956,422
dataset_C.fq  FASTQ   DNA   9,186,045    918,604,500      100           100          100

Sequence ID list

Parts of sequences IDs was sampled and shuffled from original data. They were used in test of extracting sequences by ID list.


$ seqkit sample -p 0.3  dataset_A.fa | seqkit seq --name --only-id | shuf > ids_A.txt
$ seqkit sample -p 0.3  dataset_B.fa | seqkit seq --name --only-id | shuf > ids_B.txt    
$ seqkit sample -p 0.03 dataset_C.fq | seqkit seq --name --only-id | shuf > ids_C.txt


$ wc -l ids*.txt
    20138 ids_A.txt
    58 ids_B.txt
2754516 ids_C.txt

BED file

Only BED data of chromosome 19 was used in test of subsequence with BED file:

$ zcat Homo_sapiens.GRCh38.84.bed.gz | grep -E "^19" | gzip -c > chr19.bed.gz



  • CPU: Intel Core i5-3320M @ 2.60GHz, two cores/4 threads
  • RAM: DDR3 1600MHz, 12GB
  • SSD: SAMSUNG 850 EVO 250G, SATA-3
  • OS: Fedora 24 (Scientific KDE spin), Kernal: 4.6.4-301.fc24.x86_64


  • Perl: perl 5, version 22, subversion 2 (v5.22.2) built for x86_64-linux-thread-multi
  • Python: Python 2.7.11 (default, Jul 10 2016, 20:58:20) [GCC 6.1.1 20160621 (Red Hat 6.1.1-3)] on linux2


Automatic benchmark and plotting scripts are available at:

All tests were repeated 3 times, and average time and peak memory ware used for plotting.

All data were readed once before tests began to minimize the influence of page cache.

Output sequences of all softwares were not wrapped to fixed length.

Test 1. Reverse Complement

revcom_biogo (source, binary ), a tool written in Golang (compiled with Go 1.6.3) using biogo (Version 7ebd71b) package, was also used for comparison of FASTA file parsing performance.

Note that some softwares (fasta_utilities and biogo) have different converting rules of computing complement sequence on ambiguous bases, there fore the results are different from others.


Test 2. Extracting sequences by ID list


Test 3. Sampling by number

Note that different softwares have different sampling strategies, the peak memory depends on size of sampled sequences and the results may not be the same.


Test 4. Removing duplicates by sequence content


Test 5. Subsequence with BED file



seqkit version: v0.3.1.1



Test of multiple threads:

From the results, 2 threads/CPU is enough, so the default threads of seqkit is 2.

Tests on different file sizes

Files are generated by replicating Human genome chr1 for N times.