シークエンスデータに含まれるウイルスや微生物の検出・同定は、病原体の診断や研究において重要な役割を担っている。しかし、この問題のための既存のツールは、しばしば高い実行時間とメモリ消費に悩まされている。
本著者らは、ユニークなk-merの高速同定に基づき、イルミナシーケンスデータセット中のウイルスや微生物を迅速に検出するツール、RabbitVを発表する。マルチスレッド、ベクトル化、高速データ解析を用いることで、最新のマルチコアCPUのパワーを活用できる。実験によると、RabbitVはユニークk-merの生成（RabbitUniq）および病原体の同定（RabbitV）において、それぞれ少なくとも42.5倍および14.4倍fastvを上回った。さらに、RabbitVは40サンプルのシーケンスデータ（FASTQ形式で255GB）から、わずか320秒でCOVID-19を検出することができた。
インストール
AMDの5950xのCPUを積んだubuntu18でテストした。
#RabbitV
git clone https://github.com/RabbitBio/RabbitV
cd RabbitV/
make -j20
#RabbitUniq
git clone https://github.com/RabbitBio/RabbitUniq
cd RabbitUniq
make -j20
cd bin/
> python RabbitUniq.py -h
usage: RabbitUniq.py [-h] [--workspace WORKSPACE] --infile_list INFILE_LIST
--outfile OUTFILE [--gu_worker GU_WORKER]
[--kmer_len KMER_LEN] [--bin_num BIN_NUM]
[--exclude_last] [--uniq_ref_num UNIQ_REF_NUM]
[--output_char]
RabbitUniq
optional arguments:
-h, --help show this help message and exit
--workspace WORKSPACE, -w WORKSPACE
workspace directory the bin files stored [default:
workspace]
--infile_list INFILE_LIST, -l INFILE_LIST
input file list, one line per file
--outfile OUTFILE, -o OUTFILE
out put file
--gu_worker GU_WORKER, -n GU_WORKER
The number of worker thread when generate unique kmer
[default: 20]
--kmer_len KMER_LEN, -k KMER_LEN
Unique k-mer length [default: 25]
--bin_num BIN_NUM, -b BIN_NUM
Number of bin files to be store, from 64 to
2000[default: 512]
--exclude_last, -e Exclude the last element in infile_list when output
--uniq_ref_num UNIQ_REF_NUM, -u UNIQ_REF_NUM
Threshold considered as unique kmer, default is 1
--output_char, -c Output the unique k-mer collection in character-based
file instead of binary file (slower, so not
recommended)
> ./RabbitV
RabbitV version: 0.1.0
usage: ./RabbitV [options] ...
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 0.7. (double [=0.7])
--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 [=RabbitV.json])
-h, --html the html format report file name (string [=RabbitV.html])
-R, --report_title should be quoted with ' or ", default is "RabbitV report" (string [=RabbitV report])
-w, --thread worker thread number, default is 4 (int [=4])
-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).
-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, RabbitV can skip several bases following UMI, default is 0 (int [=0])
-K, --kmer_len the uniuqe k-mer length, default is 25 (int [=25])
-?, --help print this message
実行方法
１、リファレンスゲノムリストからunique k-mer の生成
./RabbitUniq.py --infile_list ${infile_list} --outfile kmer.fa \
-n 20 -k 25 -b 2000
- --infile_list input file list, one line per file
- --outfile out put file
- -n The number of worker thread when generate unique kmer [default: 20]
- -k Unique k-mer length [default: 25]
- -b Number of bin files to be store, from 64 to 2000[default: 512]
kmer.faが出力される。
２、RabbitVのラン
RabbitV -i paired_*.fq -k SARS-CoV-2.kmer.fa -g SARS-CoV-2.genomes.fa
RabbitV -i filename.fastq -k SARS-CoV-2.kmer.fa -g SARS-CoV-2.genomes.fa
引用
RabbitV: fast detection of viruses and microorganisms in sequencing data on multi-core architectures Get access Arrow
Hao Zhang, Qixin Chang, Zekun Yin, Xiaoming Xu, Yanjie Wei, Bertil Schmidt, Weiguo Liu
Bioinformatics, Published: 25 March 2022
関連
