fastv

fastv is an ultra-fast tool for identification of SARS-CoV-2 and other microbes from sequencing data. It detects microbial sequences from FASTQ data, generates JSON reports and visualizes the result in HTML reports. This tool can be used to detect viral infectious diseases, like COVID-19. This tool supports both short reads (Illumina, BGI, etc.) and long reads (ONT, PacBio, etc.)

Examples
How it works?
Understand the input
Understand the output
Download or build fastv
Screenshot
Options
Citation
Tutorials
---- mNGS data analysis
---- SARS-CoV-2 identification
---- influenza A virus subtyping

take a quick glance of the informative report

Sample HTML report (Illumina): http://opengene.org/fastv/fastv.html
Sample HTML report (ONT): http://opengene.org/fastv/ont.html
Sample JSON report: http://opengene.org/fastv/fastv.json

quick example for SARS-CoV-2 identification

download FASTQ file for testing: http://opengene.org/fastv/testdata.fq.gz
get fastv and use following command for testing:

# make sure that SARS-CoV-2.kmer.fa and SARS-CoV-2.genomes.fa are in the ./data folder
./fastv -i testdata.fq.gz

how it works?

fastv accepts FASTQ files as input, and then:

performs data QC and quality filtering as fastp does (cut adapters, remove low quality reads, correct wrong bases).
scans the clean data to collect the sequences that containing any unique k-mer, or can be mapped to any reference microbial genomes.
makes statistics, visualizes the result in HTML format, and output the results in JSON format.
outputs the on-target sequencing reads so that they can be analyzed by downstream tools.

understand the input

fastv accepts following files as input:

FASTQ file (required) to be scanned, can be single-end (-i) or paired-end (-i and -I), can be short reads (Illumina, MGI, etc.) or long reads (PacBio, ONT, etc.)
genomes file (optional): a FASTA file containing one or many reference genomes of the target microorganism (-g).
k-mer file (optional): a FASTA file containing the UNIQUE k-mer of the target microbial genomes (-k).
k-mer collection file (optional): a FASTA containing the unique k-mers of many microorganisms (-c). See an example: http://opengene.org/kmer_collection.fasta

If none of (k-mer, k-mer collection, genomes) files is specified, fastv will try to load the SARS-CoV-2 Genomes/k-mer files in the data folder to detect SARS-CoV-2 sequences.

Besides the HTML/JSON reports, fastv also can output the sequence reads that contains any unique k-mer or can be mapped to any of the target reference genomes. The output data:

is in FASTQ format
is clean data after quality filtering
the file names can be specified by -o for SE data, or -o and -O for PE data.

get the pre-built k-mer file, genomes file or k-mer collection file for viruses

You can download k-mer files and genomes files of viruses from http://opengene.org/uniquekmer/viral/index.html. This is generated by extracting unique k-mers for all genomes in a big FASTA (http://opengene.org/viral.genomic.fasta), which contains all NCBI complete RefSeq release of viral sequences that can be found from https://ftp.ncbi.nlm.nih.gov/refseq/release/viral/. The k-mers that can be mapped to human reference genome (GRCh38) with edit_distance <= 3 have already been filtered out.
You can download the k-mer collection file for viral genomes from: http://opengene.org/viral.kc.fasta.gz

get the pre-built k-mer file, genomes file or k-mer collection file for viruses and human microorganisms

You can download k-mer files and genomes files of viruses from http://opengene.org/uniquekmer/microbial/index.html. This is generated by extracting unique k-mers for all genomes in a big FASTA (http://opengene.org/microbial.genomic.fasta), which contains genomes for the viruses above and common human microorganisms. The k-mers that can be mapped to human reference genome (GRCh38) with edit_distance <= 3 have already been filtered out.
You can download the k-mer collection file for viral and microbial genomes from: http://opengene.org/microbial.kc.fasta.gz
you can get the k-mer file and genomes file for SARS-CoV-2 by git clone https://github.com/OpenGene/fastv.git. If you don't use git, you can also download these two files from http://opengene.org/fastv/SARS-CoV-2.kmer.fa and http://opengene.org/fastv/SARS-CoV-2.genomes.fa

If you want to generate your own unique k-mer files and k-mer collection files, please use UniqueKMER: https://github.com/OpenGene/UniqueKMER

understand the output

fastv outputs reports in HTML and JSON formats.

Sample HTML report (Illumina): http://opengene.org/fastv/fastv.html
Sample JSON report: http://opengene.org/fastv/fastv.json
If the k-mer file is specified, there will be a POSITIVE or NEGATIVE result, which is determined by comparing the mean depth of the k-mer keys to the threshold (--positive_threshold).

Besides the HTML/JSON reports, fastv also can output the sequence reads that contains any unique k-mer or can be mapped to any of the target reference genomes. The output data:

is in FASTQ format
is clean data after quality filtering
the file names can be specified by -o for SE data, or -o and -O for PE data.

get fastv

download binary

This binary is only for Linux systems: http://opengene.org/fastv/fastv

# this binary was compiled on CentOS, and tested on CentOS/Ubuntu
wget http://opengene.org/fastv/fastv
chmod a+x ./fastv

or compile from source

# step 1: get the code
git clone https://github.com/OpenGene/fastv.git

# step 2: build
cd fastv
make

# step 3: install it to system if you have a sudo permission
make install

screenshot

options

Key options:

  -i, --in1                                        read1 input file name (string [=])
  -I, --in2                                        read2 input file name (string [=])
  -o, --out1                                       file name to store read1 with on-target sequences (string [=])
  -O, --out2                                       file name to store read2 with on-target sequences (string [=])
  -c, --kmer_collection                            the unique k-mer collection file in fasta format, see an example: http://opengene.org/kmer_collection.fasta (string [=])
  -k, --kmer                                       the unique k-mer file of the detection target in fasta format. data/SARS-CoV-2.kmer.fa will be used if none of k-mer/Genomes/k-mer_Collection file is specified (string [=])
  -g, --genomes                                    the genomes file of the detection target in fasta format. data/SARS-CoV-2.genomes.fa will be used if none of k-mer/Genomes/k-mer_Collection file is specified (string [=])
  -p, --positive_threshold                         the data is considered as POSITIVE, when its mean coverage of unique kmer >= positive_threshold (0.001 ~ 100). 0.1 by default. (float [=0.1])
  -d, --depth_threshold                            For coverage calculation. A region is considered covered when its mean depth >= depth_threshold (0.001 ~ 1000). 1.0 by default. (float [=1])
  -E, --ed_threshold                               If the edit distance of a sequence and a genome region is <=ed_threshold, then consider it a match (0 ~ 50). 8 by default. (int [=8])
      --long_read_threshold                        A read will be considered as long read if its length >= long_read_threshold (100 ~ 10000). 200 by default. (int [=200])
      --read_segment_len                           A long read will be splitted to read segments, with each <= read_segment_len (50 ~ 5000, should be < long_read_threshold). 100 by default. (int [=100])
      --bin_size                                   For coverage calculation. The genome is splitted to many bins, with each bin has a length of bin_size (1 ~ 100000), default 0 means adaptive. (int [=0])
      --kc_coverage_threshold                      For each genome in the k-mer collection FASTA, report it when its coverage > kc_coverage_threshold. Default is 0.01. (double [=0.01])
      --kc_high_confidence_coverage_threshold      For each genome in the k-mer collection FASTA, report it as high confidence when its coverage > kc_high_confidence_coverage_threshold. Default is 0.9. (double [=0.9])
      --kc_high_confidence_median_hit_threshold    For each genome in the k-mer collection FASTA, report it as high confidence when its median hits > kc_high_confidence_median_hit_threshold. Default is 5. (int [=5])
  -j, --json                                       the json format report file name (string [=fastv.json])
  -h, --html                                       the html format report file name (string [=fastv.html])
  -R, --report_title                               should be quoted with ' or ", default is "fastv report" (string [=fastv report])
  -w, --thread                                     worker thread number, default is 4 (int [=4])

Other I/O options:

  -6, --phred64                       indicate the input is using phred64 scoring (it'll be converted to phred33, so the output will still be phred33)
  -z, --compression                   compression level for gzip output (1 ~ 9). 1 is fastest, 9 is smallest, default is 4. (int [=4])
      --stdin                         input from STDIN. If the STDIN is interleaved paired-end FASTQ, please also add --interleaved_in.
      --stdout                        stream passing-filters reads to STDOUT. This option will result in interleaved FASTQ output for paired-end output. Disabled by default.
      --interleaved_in                indicate that <in1> is an interleaved FASTQ which contains both read1 and read2. Disabled by default.
      --reads_to_process              specify how many reads/pairs to be processed. Default 0 means process all reads. (int [=0])
      --dont_overwrite                don't overwrite existing files. Overwritting is allowed by default.
  -V, --verbose                       output verbose log information (i.e. when every 1M reads are processed).

QC and quality pruning options (inherited from fastp: https://github.com/OpenGene/fastp)

  -A, --disable_adapter_trimming      adapter trimming is enabled by default. If this option is specified, adapter trimming is disabled
  -a, --adapter_sequence              the adapter for read1. For SE data, if not specified, the adapter will be auto-detected. For PE data, this is used if R1/R2 are found not overlapped. (string [=auto])
      --adapter_sequence_r2           the adapter for read2 (PE data only). This is used if R1/R2 are found not overlapped. If not specified, it will be the same as <adapter_sequence> (string [=auto])
      --adapter_fasta                 specify a FASTA file to trim both read1 and read2 (if PE) by all the sequences in this FASTA file (string [=])
      --detect_adapter_for_pe         by default, the auto-detection for adapter is for SE data input only, turn on this option to enable it for PE data.
  -f, --trim_front1                   trimming how many bases in front for read1, default is 0 (int [=0])
  -t, --trim_tail1                    trimming how many bases in tail for read1, default is 0 (int [=0])
  -b, --max_len1                      if read1 is longer than max_len1, then trim read1 at its tail to make it as long as max_len1. Default 0 means no limitation (int [=0])
  -F, --trim_front2                   trimming how many bases in front for read2. If it's not specified, it will follow read1's settings (int [=0])
  -T, --trim_tail2                    trimming how many bases in tail for read2. If it's not specified, it will follow read1's settings (int [=0])
  -B, --max_len2                      if read2 is longer than max_len2, then trim read2 at its tail to make it as long as max_len2. Default 0 means no limitation. If it's not specified, it will follow read1's settings (int [=0])
      --poly_g_min_len                the minimum length to detect polyG in the read tail. 10 by default. (int [=10])
  -G, --disable_trim_poly_g           disable polyG tail trimming, by default trimming is automatically enabled for Illumina NextSeq/NovaSeq data
  -x, --trim_poly_x                   enable polyX trimming in 3' ends.
      --poly_x_min_len                the minimum length to detect polyX in the read tail. 10 by default. (int [=10])
  -5, --cut_front                     move a sliding window from front (5') to tail, drop the bases in the window if its mean quality < threshold, stop otherwise.
  -3, --cut_tail                      move a sliding window from tail (3') to front, drop the bases in the window if its mean quality < threshold, stop otherwise.
  -r, --cut_right                     move a sliding window from front to tail, if meet one window with mean quality < threshold, drop the bases in the window and the right part, and then stop.
  -W, --cut_window_size               the window size option shared by cut_front, cut_tail or cut_sliding. Range: 1~1000, default: 4 (int [=4])
  -M, --cut_mean_quality              the mean quality requirement option shared by cut_front, cut_tail or cut_sliding. Range: 1~36 default: 20 (Q20) (int [=20])
      --cut_front_window_size         the window size option of cut_front, default to cut_window_size if not specified (int [=4])
      --cut_front_mean_quality        the mean quality requirement option for cut_front, default to cut_mean_quality if not specified (int [=20])
      --cut_tail_window_size          the window size option of cut_tail, default to cut_window_size if not specified (int [=4])
      --cut_tail_mean_quality         the mean quality requirement option for cut_tail, default to cut_mean_quality if not specified (int [=20])
      --cut_right_window_size         the window size option of cut_right, default to cut_window_size if not specified (int [=4])
      --cut_right_mean_quality        the mean quality requirement option for cut_right, default to cut_mean_quality if not specified (int [=20])
  -Q, --disable_quality_filtering     quality filtering is enabled by default. If this option is specified, quality filtering is disabled
  -q, --qualified_quality_phred       the quality value that a base is qualified. Default 15 means phred quality >=Q15 is qualified. (int [=15])
  -u, --unqualified_percent_limit     how many percents of bases are allowed to be unqualified (0~100). Default 40 means 40% (int [=40])
  -n, --n_base_limit                  if one read's number of N base is >n_base_limit, then this read/pair is discarded. Default is 5 (int [=5])
  -e, --average_qual                  if one read's average quality score <avg_qual, then this read/pair is discarded. Default 0 means no requirement (int [=0])
  -L, --disable_length_filtering      length filtering is enabled by default. If this option is specified, length filtering is disabled
  -l, --length_required               reads shorter than length_required will be discarded, default is 15. (int [=15])
      --length_limit                  reads longer than length_limit will be discarded, default 0 means no limitation. (int [=0])
  -y, --low_complexity_filter         enable low complexity filter. The complexity is defined as the percentage of base that is different from its next base (base[i] != base[i+1]).
  -Y, --complexity_threshold          the threshold for low complexity filter (0~100). Default is 30, which means 30% complexity is required. (int [=30])
      --filter_by_index1              specify a file contains a list of barcodes of index1 to be filtered out, one barcode per line (string [=])
      --filter_by_index2              specify a file contains a list of barcodes of index2 to be filtered out, one barcode per line (string [=])
      --filter_by_index_threshold     the allowed difference of index barcode for index filtering, default 0 means completely identical. (int [=0])
  -C, --correction                    enable base correction in overlapped regions (only for PE data), default is disabled
      --overlap_len_require           the minimum length to detect overlapped region of PE reads. This will affect overlap analysis based PE merge, adapter trimming and correction. 30 by default. (int [=30])
      --overlap_diff_limit            the maximum number of mismatched bases to detect overlapped region of PE reads. This will affect overlap analysis based PE merge, adapter trimming and correction. 5 by default. (int [=5])
      --overlap_diff_percent_limit    the maximum percentage of mismatched bases to detect overlapped region of PE reads. This will affect overlap analysis based PE merge, adapter trimming and correction. Default 20 means 20%. (int [=20])
  -U, --umi                           enable unique molecular identifier (UMI) preprocessing
      --umi_loc                       specify the location of UMI, can be (index1/index2/read1/read2/per_index/per_read, default is none (string [=])
      --umi_len                       if the UMI is in read1/read2, its length should be provided (int [=0])
      --umi_prefix                    if specified, an underline will be used to connect prefix and UMI (i.e. prefix=UMI, UMI=AATTCG, final=UMI_AATTCG). No prefix by default (string [=])
      --umi_skip                      if the UMI is in read1/read2, fastv can skip several bases following UMI, default is 0 (int [=0])

citation

If you use fastv, UniqueKMER or the pre-generated resources provided by this repository, please cite our work as:

Shifu Chen, Changshou He, Yingqiang Li, Zhicheng Li, Charles E Melancon III. A Computational Toolset for Rapid Identification of SARS-CoV-2, other Viruses, and Microorganisms from Sequencing Data. bioRxiv 2020.05.12.092163; doi: https://doi.org/10.1101/2020.05.12.092163

tutorials

analyze metagenomics sequencing (mNGS) data

download or build fastv
download the k-mer collection for all viruses and microorganisms that have reference genomes in NCBI RefSeq:

wget http://opengene.org/microbial.kc.fasta.gz

run fastv:

./fastv -i filename.fastq.gz -c microbial.kc.fasta.gz

check the result in HTML report The result is presented in the section: Detection result for k-mer collection file.
See an example: http://opengene.org/fastv/mngs.html
check the result in JSON report
The result is presented in a list: kmer_collection_scan_result.
See an example: http://opengene.org/fastv/mngs.json

identify SARS-CoV-2

download or build fastv
download the k-mer file and genomes file for SARS-CoV-2

wget http://opengene.org/fastv/data/SARS-CoV-2.kmer.fa
wget http://opengene.org/fastv/data/SARS-CoV-2.genomes.fa

run fastv:

./fastv -i filename.fastq.gz -k SARS-CoV-2.kmer.fa -g SARS-CoV-2.genomes.fa

check the result in SHELL The detection result will be POSITIVE if the SARS-CoV-2 coverage is higher than the threshold.
check the result in HTML report The detection result is presented in the section Detection result for target unique k-mer file. The genome coverage result is presented in the section Genome coverages for file.
See an example: http://opengene.org/fastv/sarscov2.html
check the result in JSON report
The result is presented in: kmer_detection_result.
See an example: http://opengene.org/fastv/sarscov2.json

influenza A virus subtyping

Influenza A viruses are divided into subtypes on the basis of two proteins on the surface of the virus: hemagglutinin (HA) and neuraminidase (NA). For example, “H5N1” virus has an hemagglutinin type 5 protein and an neuraminidase type 1 protein. fastv can be used to identify the hemagglutinin type and neuraminidase type for influenza A virus from sequencing data.

download or build fastv
download the k-mer collection for all viruses and microorganisms that have reference genomes in NCBI RefSeq:

wget http://opengene.org/influenzaA.kc.fasta.gz

run fastv:

./fastv -i filename.fastq.gz -c influenzaA.kc.fasta.gz

check the result in HTML report The result is presented in the section: Detection result for k-mer collection file.
See an example (influenza A H3N2): http://opengene.org/fastv/H3N2.html

Name		Name	Last commit message	Last commit date
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data		data
src		src
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LICENSE		LICENSE
Makefile		Makefile
README.md		README.md

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fastv

take a quick glance of the informative report

quick example for SARS-CoV-2 identification

how it works?

understand the input

get the pre-built k-mer file, genomes file or k-mer collection file for viruses

get the pre-built k-mer file, genomes file or k-mer collection file for viruses and human microorganisms

understand the output

get fastv

download binary

or compile from source

screenshot

options

citation

tutorials

analyze metagenomics sequencing (mNGS) data

identify SARS-CoV-2

influenza A virus subtyping

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SongDabao/fastv

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fastv

take a quick glance of the informative report

quick example for SARS-CoV-2 identification

how it works?

understand the input

get the pre-built k-mer file, genomes file or k-mer collection file for viruses

get the pre-built k-mer file, genomes file or k-mer collection file for viruses and human microorganisms

understand the output

get fastv

download binary

or compile from source

screenshot

options

citation

tutorials

analyze metagenomics sequencing (mNGS) data

identify SARS-CoV-2

influenza A virus subtyping

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