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# Tutorial
## Some manipulations on big genomes
A script [memusg](https://github.com/shenwei356/memusg) is
used to check the peek memory usage of seqkit. Usage: `memusg [-t] command`.
1. Human genome
$ seqkit stat hsa.fa
file format type num_seqs sum_len min_len avg_len max_len
hsa.fa FASTA DNA 194 3,099,750,718 970 15,978,096.5 248,956,422
1. Build FASTA index (***optional***, when using flag `-2` (`--two-pass`),
some commands will automaticlly build it).
For some commands, including `subseq`, `split`, `sort` and `shuffle`,
when input files are (plain or gzipped) FASTA files or stdin,
FASTA index would be optional used for
rapid acccess of sequences and reducing memory occupation.
***ATTENTION***: the `.seqkit.fai` file created by SeqKit is a little different from .fai file
created by samtools. SeqKit uses full sequence head instead of just ID as key.
$ memusg -t seqkit faidx --id-regexp "^(.+)$" hsa.fa -o hsa.fa.seqkit.fai
elapsed time: 10.011s
peak rss: 177.21 MB
Create common .fai file:
$ memusg -t seqkit faidx hsa.fa -o hsa.fa.fai2
elapsed time: 10.454s
peak rss: 172.82 MB
Performance of samtools:
$ memusg -t samtools faidx hsa.fa
elapsed time: 9.574s
peak rss: 1.45 MB
Exactly same content:
$ md5sum hsa.fa.fai*
21e0c25b4d817d1c19ee8107191b9b31 hsa.fa.fai
21e0c25b4d817d1c19ee8107191b9b31 hsa.fa.fai2
1. Sorting by sequence length
$ memusg -t seqkit sort --by-length --reverse --two-pass hsa.fa > hsa.sorted.fa
[INFO] create and read FASTA index ...
[INFO] read sequence IDs and lengths from FASTA index ...
[INFO] 194 sequences loaded
[INFO] sorting ...
[INFO] output ...
elapsed time: 4.892s
peak rss: 500.15 MB
Detail:
$ seqkit fx2tab --length hsa.sorted.fa --name --only-id | cut -f 1,4 | more
1 248956422
2 242193529
3 198295559
4 190214555
5 181538259
6 170805979
7 159345973
X 156040895
8 145138636
9 138394717
11 135086622
10 133797422
12 133275309
13 114364328
14 107043718
15 101991189
16 90338345
17 83257441
18 80373285
20 64444167
19 58617616
Y 57227415
22 50818468
21 46709983
KI270728.1 1872759
KI270727.1 448248
...
real 0m10.697s
user 0m11.153s
sys 0m0.917s
1. Shuffling sequences
$ memusg -t seqkit shuffle hsa.fa --two-pass > hsa.shuffled.fa
[INFO] create and read FASTA index ...
[INFO] read sequence IDs from FASTA index ...
[INFO] 194 sequences loaded
[INFO] shuffle ...
[INFO] output ...
elapsed time: 6.632s
peak rss: 528.3 MB
1. Spliting into files with single sequence
$ memusg -t seqkit split --by-id hsa.fa --two-pass
[INFO] split by ID. idRegexp: ^([^\s]+)\s?
[INFO] create and read FASTA index ...
[INFO] read sequence IDs from FASTA index ...
[INFO] 194 sequences loaded
[INFO] write 1 sequences to file: hsa.id_KI270743.1.fa
[INFO] write 1 sequences to file: hsa.id_KI270706.1.fa
[INFO] write 1 sequences to file: hsa.id_KI270717.1.fa
[INFO] write 1 sequences to file: hsa.id_KI270718.1.fa
[INFO] write 1 sequences to file: hsa.id_KI270468.1.fa
...
elapsed time: 18.807s
peak rss: 1.36 GB
1. Geting subsequence of some chromesomes
$ memusg -t seqkit subseq -r 1:10 --chr X --chr Y hsa.fa
>X_1-10 X dna_sm:chromosome chromosome:GRCh38:X:1:156040895:1 REF
nnnnnnnnnn
>Y_1-10 Y dna_sm:chromosome chromosome:GRCh38:Y:2781480:56887902:1 REF
NNNNNNNNNN
elapsed time: 1.276s
peak rss: 640.92 MB
1. Geting CDS sequence of chr 1 by GTF files
$ memusg -t seqkit subseq --gtf Homo_sapiens.GRCh38.84.gtf.gz --chr X --feature cds hsa.fa > chrX.gtf.cds.fa
[INFO] read GTF file ...
[INFO] 22420 GTF features loaded
elapsed time: 8.643s
peak rss: 846.14 MB
## Remove contaminated reads
1. Mapping with reads on some potential contaminate genomes, and get the reads IDs list.
$ wc -l contaminate.list
244 contaminate.list
$ head -n 2 contaminate.list
HWI-D00523:240:HF3WGBCXX:1:1101:2574:2226
HWI-D00523:240:HF3WGBCXX:1:1101:12616:2205
1. Remove contaminated reads
$ seqkit grep -f contaminate.list -v reads_1.fq.gz -o reads_1.clean.fq.gz
$ seqkit grep -f contaminate.list -v reads_2.fq.gz -o reads_2.clean.fq.gz
$ seqkit stat *.fq.gz
file seq_format seq_type num_seqs min_len avg_len max_len
reads_1.clean.fq.gz FASTQ DNA 2,256 226 227 229
reads_1.fq.gz FASTQ DNA 2,500 226 227 229
reads_2.clean.fq.gz FASTQ DNA 2,256 223 224 225
reads_2.fq.gz FASTQ DNA 2,500 223 224 225
## Handling of aligned sequences
1. Some mock sequences (usually they will be much longer)
$ cat seqs.fa
>seq1
ACAACGTCTACTTACGTTGCATCGTCATGCTGCATTACGTAGTCTGATGATG
>seq2
ACACCGTCTACTTTCATGCTGCATTACGTAGTCTGATGATG
>seq3
ACAACGTCTACTTACGTTGCATCGTCATGCTGCACTGATGATG
>seq4
ACAACGTCTACTTACGTTGCATCTTCGGTCATGCTGCATTACGTAGTCTGATGATG
1. Run multiple sequence alignment (clustalo)
clustalo -i seqs.fa -o seqs.msa.fa --force --outfmt fasta --threads=4
1. Convert FASTA format to tabular format.
$ seqkit fx2tab seqs.msa.fa
seq1 ACAACGTCTACTTACGTTGCAT----CGTCATGCTGCATTACGTAGTCTGATGATG
seq2 ---------------ACACCGTCTACTTTCATGCTGCATTACGTAGTCTGATGATG
seq3 ACAACGTCTACTTACGTTGCATCGTCATGCTGCACTGATGATG-------------
seq4 ACAACGTCTACTTACGTTGCATCTTCGGTCATGCTGCATTACGTAGTCTGATGATG
or
$ seqkit fx2tab seqs.msa.fa | cut -f 2
ACAACGTCTACTTACGTTGCAT----CGTCATGCTGCATTACGTAGTCTGATGATG
---------------ACACCGTCTACTTTCATGCTGCATTACGTAGTCTGATGATG
ACAACGTCTACTTACGTTGCATCGTCATGCTGCACTGATGATG-------------
ACAACGTCTACTTACGTTGCATCTTCGGTCATGCTGCATTACGTAGTCTGATGATG
For me, it's useful when 1) manually assembling Sanger sequencing result,
2) designing site specific PCR primers.
1. Remove gaps
$ seqkit seq seqs.msa.fa -g
>seq1
ACAACGTCTACTTACGTTGCATCGTCATGCTGCATTACGTAGTCTGATGATG
>seq2
ACACCGTCTACTTTCATGCTGCATTACGTAGTCTGATGATG
>seq3
ACAACGTCTACTTACGTTGCATCGTCATGCTGCACTGATGATG
>seq4
ACAACGTCTACTTACGTTGCATCTTCGGTCATGCTGCATTACGTAGTCTGATGATG
## Play with miRNA hairpins
### Dataset
[`hairpin.fa.gz`](ftp://mirbase.org/pub/mirbase/21/hairpin.fa.gz)
from [The miRBase Sequence Database -- Release 21](ftp://mirbase.org/pub/mirbase/21/)
### Quick glance
1. Sequence number
$ seqkit stat hairpin.fa.gz
file format type num_seqs sum_len min_len avg_len max_len
hairpin.fa.gz FASTA RNA 28,645 2,949,871 39 103 2,354
1. First 10 bases
$ zcat hairpin.fa.gz \
| seqkit subseq -r 1:10 \
| seqkit sort -s
| seqkit seq -s \
| head -n 10
AAAAAAAAAA
AAAAAAAAAA
AAAAAAAAAG
AAAAAAAAAG
AAAAAAAAAG
AAAAAAAAAU
AAAAAAAAGG
AAAAAAACAU
AAAAAAACGA
AAAAAAAUUA
hmm, nothing special, non-coding RNA~
### Repeated hairpin sequences
We may want to check how may identical hairpins among different species there are.
`seqkit rmdup` could remove duplicated sequences by sequence content,
and save the replicates to another file (here is `duplicated.fa.gz`),
as well as replicating details (`duplicated.detail.txt`,
1th column is the repeated number,
2nd column contains sequence IDs seperated by comma).
$ seqkit rmdup -s -i hairpin.fa.gz -o clean.fa.gz -d duplicated.fa.gz -D duplicated.detail.txt
$ head -n 5 duplicated.detail.txt
18 dre-mir-430c-1, dre-mir-430c-2, dre-mir-430c-3, dre-mir-430c-4, dre-mir-430c-5, dre-mir-430c-6, dre-mir-430c-7, dre-mir-430c-8, dre-mir-430c-9, dre-mir-430c-10, dre-mir-430c-11, dre-mir-430c-12, dre-mir-430c-13, dre-mir-430c-14, dre-mir-430c-15, dre-mir-430c-16, dre-mir-430c-17, dre-mir-430c-18
16 hsa-mir-29b-2, mmu-mir-29b-2, rno-mir-29b-2, ptr-mir-29b-2, ggo-mir-29b-2, ppy-mir-29b-2, sla-mir-29b, mne-mir-29b, ppa-mir-29b-2, bta-mir-29b-2, mml-mir-29b-2, eca-mir-29b-2, aja-mir-29b, oar-mir-29b-1, oar-mir-29b-2, rno-mir-29b-3
15 dme-mir-125, dps-mir-125, dan-mir-125, der-mir-125, dgr-mir-125-1, dgr-mir-125-2, dmo-mir-125, dpe-mir-125-2, dpe-mir-125-1, dpe-mir-125-3, dse-mir-125, dsi-mir-125, dvi-mir-125, dwi-mir-125, dya-mir-125
13 hsa-mir-19b-1, ggo-mir-19b-1, age-mir-19b-1, ppa-mir-19b-1, ppy-mir-19b-1, ptr-mir-19b-1, mml-mir-19b-1, sla-mir-19b-1, lla-mir-19b-1, mne-mir-19b-1, bta-mir-19b, oar-mir-19b, chi-mir-19b
13 hsa-mir-20a, ssc-mir-20a, ggo-mir-20a, age-mir-20, ppa-mir-20, ppy-mir-20a, ptr-mir-20a, mml-mir-20a, sla-mir-20, lla-mir-20, mne-mir-20, bta-mir-20a, eca-mir-20a
The result shows the most conserved miRNAs among different species,
`mir-29b`, `mir-125`, `mir-19b-1` and `mir-20a`.
And the `dre-miR-430c` has the most multicopies in *Danio rerio*.
### Hairpins in different species
1. Before spliting by species, let's take a look at the sequence names.
$ seqkit seq hairpin.fa.gz -n | head -n 3
cel-let-7 MI0000001 Caenorhabditis elegans let-7 stem-loop
cel-lin-4 MI0000002 Caenorhabditis elegans lin-4 stem-loop
cel-mir-1 MI0000003 Caenorhabditis elegans miR-1 stem-loop
The first three letters (e.g. `cel`) are the abbreviation of species names.
So we could split hairpins by the first letters by defining custom
sequence ID parsing regular expression `^([\w]+)\-`.
By default, `seqkit` takes the first non-space letters as sequence ID.
For example,
| FASTA head | ID |
|:--------------------------------------------------------------|:--------------------------------------------------|
| >123456 gene name | 123456 |
| >longname | longname |
| >gi|110645304|ref|NC_002516.2| Pseudomona | gi|110645304|ref|NC_002516.2| |
But for some sequences from NCBI,
e.g. `>gi|110645304|ref|NC_002516.2| Pseudomona`, the ID is `NC_002516.2`.
In this case, we could set sequence ID parsing regular expression by flag
`--id-regexp "\|([^\|]+)\| "` or just use flag `--id-ncbi`. If you want
the `gi` number, then use `--id-regexp "^gi\|([^\|]+)\|"`.
1. Split sequences by species.
A custom ID parsing regular expression is used, `^([\w]+)\-`.
$ seqkit split hairpin.fa.gz -i --id-regexp "^([\w]+)\-" --two-pass
***To reduce memory usage when splitting big file, we should always use flag `--two-pass`***
2. Species with most miRNA hairpins. Third column is the sequences number.
$ cd hairpin.fa.gz.split/;
$ seqkit stat hairpin.id_* \
| csvtk space2tab \
| csvtk -t sort -k num_seqs:nr \
| csvtk -t pretty \
| more
file format type num_seqs sum_len min_len avg_len max_len
hairpin.id_hsa.fasta FASTA RNA 1,881 154,242 82 82 82
hairpin.id_mmu.fasta FASTA RNA 1,193 107,370 90 90 90
hairpin.id_bta.fasta FASTA RNA 808 61,408 76 76 76
hairpin.id_gga.fasta FASTA RNA 740 42,180 57 57 57
hairpin.id_eca.fasta FASTA RNA 715 89,375 125 125 125
hairpin.id_mtr.fasta FASTA RNA 672 231,840 345 345 345
Here, a CSV/TSV tool [csvtk](https://github.com/shenwei356/csvtk)
is used to sort and view the result.
For human miRNA hairpins
1. Length distribution.
`seqkit fx2tab` could show extra information like sequence length, GC content.
[`csvtk`](http://bioinf.shenwei.me/csvtk/) is used to plot.
$ seqkit grep -r -p '^hsa' hairpin.fa.gz \
| seqkit fx2tab -l \
| cut -f 4 \
| csvtk -H plot hist --xlab Length --title "Human pre-miRNA length distribution"
$ seqkit grep -r -p '^hsa' hairpin.fa.gz \
| seqkit fx2tab -l \
| cut -f 4 \
| csvtk -H plot box --xlab Length --horiz --height 1.5
## Bacteria genome
### Dataset
[Pseudomonas aeruginosa PAO1](http://www.ncbi.nlm.nih.gov/nuccore/110645304),
files:
- Genbank file [`PAO1.gb`](/files/PAO1/PAO1.gb)
- Genome FASTA file [`PAO1.fasta`](/files/PAO1/PAO1.fasta)
- GTF file [`PAO1.gtf`](/files/PAO1/PAO1.gtf) was created with [`extract_features_from_genbank_file.py`](https://github.com/shenwei356/bio_scripts/blob/master/file_formats/extract_features_from_genbank_file.py), by
extract_features_from_genbank_file.py PAO1.gb -t . -f gtf > PAO1.gtf
### Motif distribution
Motifs
$ cat motifs.fa
>GTAGCGS
GTAGCGS
>GGWGKTCG
GGWGKTCG
1. Sliding. Remember flag `--id-ncbi`, do you?
By the way, do not be scared by the long flag `--circle-genome`, `--step`
and so on. They have short ones, `-c`, `-s`
$ seqkit sliding --id-ncbi --circular-genome \
--step 20000 --window 200000 PAO1.fasta -o PAO1.fasta.sliding.fa
$ seqkit stat PAO1.fasta.sliding.fa
file format type num_seqs sum_len min_len avg_len max_len
PAO1.fasta.sliding.fa FASTA DNA 314 62,800,000 200,000 200,000 200,000
1. Locating motifs
$ seqkit locate --id-ncbi --ignore-case --degenerate \
--pattern-file motifs.fa PAO1.fasta.sliding.fa -o PAO1.fasta.sliding.fa.motifs.tsv
1. Ploting distribution ([plot_motif_distribution.R](/files/PAO1/plot_motif_distribution.R))
# preproccess
$ perl -ne 'if (/_sliding:(\d+)-(\d+)\t(.+)/) {$loc= $1 + 100000; print "$loc\t$3\n";} else {print}' PAO1.fasta.sliding.fa.motifs.tsv > PAO1.fasta.sliding.fa.motifs.tsv2
# plot
$ ./plot_motif_distribution.R
Result
### Find multicopy genes
1. Get all CDS sequences
$ seqkit subseq --id-ncbi --gtf PAO1.gtf --feature cds PAO1.fasta -o PAO1.cds.fasta
$ seqkit stat *.fasta
file format type num_seqs sum_len min_len avg_len max_len
PAO1.cds.fasta FASTA DNA 5,572 5,593,306 72 1,003.8 16,884
PAO1.fasta FASTA DNA 1 6,264,404 6,264,404 6,264,404 6,264,404
1. Get duplicated sequences
$ seqkit rmdup --by-seq --ignore-case PAO1.cds.fasta -o PAO1.cds.uniq.fasta \
--dup-seqs-file PAO1.cds.dup.fasta --dup-num-file PAO1.cds.dup.text
$ cat PAO1.cds.dup.text
6 NC_002516.2_500104:501120:-, NC_002516.2_2556948:2557964:+, NC_002516.2_3043750:3044766:-, NC_002516.2_3842274:3843290:-, NC_002516.2_4473623:4474639:+, NC_002516.2_5382796:5383812:-
2 NC_002516.2_2073555:2075438:+, NC_002516.2_4716660:4718543:+
2 NC_002516.2_2072935:2073558:+, NC_002516.2_4716040:4716663:+
2 NC_002516.2_2075452:2076288:+, NC_002516.2_4718557:4719393:+
### Flanking sequences
1. Get CDS and 1000 bp upstream sequence
$ seqkit subseq --id-ncbi --gtf PAO1.gtf \
--feature cds PAO1.fasta --up-stream 1000
1. Get 1000 bp upstream sequence of CDS, *NOT* including CDS.
$ seqkit subseq --id-ncbi --gtf PAO1.gtf \
--feature cds PAO1.fasta --up-stream 1000 --only-flank
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