bioinformatics-one-linersBioinformatics one liners from Ming Tang
bioinformatics-one-liners
my collection of bioinformatics one liners that is useful in my day-to-day work
I came across the bioinformatics one-liners on the biostar forum and gathered them here.
I also added some of my own tricks
05/21/2015.
get the sequences length distribution form a fastq file using awk
zcat file.fastq.gz | awk 'NR%4 == 2 {lengths[length($0)]++} END {for (l in lengths) {print l, lengths[l]}}'
add barcode to 10x single cell R1 read
cat test.fq | awk 'NR%4 == 2 {$0="xxx"$0}{print}'
@D00365:1187:HMM2FBCX2:1:1103:1258:2132 1:N:0:CGTGCAGA
xxxTATTACCAGATGAGAGCATGGTTAGG
+
DDDDDIIIIIIIHIIIIIIIIIIIII
@D00365:1187:HMM2FBCX2:1:1103:1472:2136 1:N:0:CGTGCAGA
xxxAACCATGAGTGTCCCGCTGGCATCGC
+
DDDADGHHIIHIIGIHHHFCHHIIII
@D00365:1187:HMM2FBCX2:1:1103:1822:2139 1:N:0:CGTGCAGA
xxxGTGCATATCATGTAGCGTATTATACT
+
DDDDDIIIIIIIIIIIIIIIIIIIII
@D00365:1187:HMM2FBCX2:1:1103:1943:2145 1:N:0:CGTGCAGA
xxxGATTCAGTCTCCAACCTCTCCTTTGT
+
DDDDDHIIIIIIIIIIHIIIIHIIII
@D00365:1187:HMM2FBCX2:1:1103:1917:2147 1:N:0:CGTGCAGA
xxxCCTTCGACAAGTTGTCAGGTGCGGTC
+
DDDDDHIIIIIIIIIIIIIIGIIHHH
Reverse complement a sequence (I use that a lot when I need to design primers)
echo 'ATTGCTATGCTNNNT' | rev | tr 'ACTG' 'TGAC'
split a multifasta file into single ones with csplit:
csplit -z -q -n 4 -f sequence_ sequences.fasta /\>/ {*}
Split a multi-FASTA file into individual FASTA files by awk
awk '/^>/{s=++d".fa"} {print > s}' multi.fa
linearize multiline fasta
cat file.fasta | awk '/^>/{if(N>0) printf("\n"); ++N; printf("%s\t",$0);next;} {printf("%s",$0);}END{printf("\n");}'
awk 'BEGIN{RS=">"}NR>1{sub("\n","\t"); gsub("\n",""); print RS$0}' file.fa
fastq2fasta
zcat file.fastq.gz | paste - - - - | perl -ane 'print ">$F[0]\n$F[2]\n";' | gzip -c > file.fasta.gz
bam2bed
samtools view file.bam | perl -F'\t' -ane '$strand=($F[1]&16)?"-":"+";$length=1;$tmp=$F[5];$tmp =~ s/(\d+)[MD]/$length+=$1/eg;print "$F[2]\t$F[3]\t".($F[3]+$length)."\t$F[0]\t0\t$strand\n";' > file.bed
bam2wig
samtools mpileup -BQ0 file.sorted.bam | perl -pe '($c, $start, undef, $depth) = split;if ($c ne $lastC || $start != $lastStart+1) {print "fixedStep chrom=$c start=$start step=1 span=1\n";}$_ = $depth."\n";($lastC, $lastStart) = ($c, $start);' | gzip -c > file.wig.gz
Number of reads in a fastq file
cat file.fq | echo $((`wc -l`/4))
Single line fasta file to multi-line fasta of 60 characteres each line
awk -v FS= '/^>/{print;next}{for (i=0;i<=NF/60;i++) {for (j=1;j<=60;j++) printf "%s", $(i*60 +j); print ""}}' file
fold -w 60 file
Sequence length of every entry in a multifasta file
awk '/^>/ {if (seqlen){print seqlen}; print ;seqlen=0;next; } { seqlen = seqlen +length($0)}END{print seqlen}' file.fa
Reproducible subsampling of a FASTQ file. srand() is the seed for the random number generator - keeps the subsampling the same when the script is run multiple times. 0.01 is the % of reads to output.
cat file.fq | paste - - - - | awk 'BEGIN{srand(1234)}{if(rand() < 0.01) print $0}' | tr '\t' '\n' > out.fq
or look at the Hengli's Seqtk
Deinterleaving a FASTQ:
cat file.fq | paste - - - - - - - - | tee >(cut -f1-4 | tr '\t'
'\n' > out1.fq) | cut -f5-8 | tr '\t' '\n' > out2.fq
Using mpileup for a whole genome can take forever. So, handling each chromosome separately and parallely running them on several cores will speed up your pipeline. Using xargs you can easily realize it.
Example usage of xargs (-P is the number of parallel processes started - don't use more than the number of cores you have available):
samtools view -H yourFile.bam | grep "\@SQ" | sed 's/^.*SN://g' | cut -f 1 | xargs -I {} -n 1 -P 24 sh -c "samtools mpileup -BQ0 -d 100000 -uf yourGenome.fa -r {} yourFile.bam | bcftools view -vcg - > tmp.{}.vcf"
To merge the results afterwards, you might want to do something like this:
samtools view -H yourFile.bam | grep "\@SQ" | sed 's/^.*SN://g' | cut -f 1 | perl -ane 'system("cat tmp.$F[0].bcf >> yourFile.vcf");'
split large file by id/label/column
awk '{print >> $1; close($1)}' input_file
split a bed file by chromosome:
cat nexterarapidcapture_exome_targetedregions_v1.2.bed | sort -k1,1 -k2,2n | sed 's/^chr//' | awk '{close(f);f=$1}{print > f".bed"}'
#or
awk '{print $0 >> $1".bed"}' example.bed
sort vcf file with header
cat my.vcf | awk '$0~"^#" { print $0; next } { print $0 | "sort -k1,1V -k2,2n" }'
Rename a file, bash string manipulation
for file in *gz
do zcat $file > ${file/bed.gz/bed}
gnu sed print invisible characters
cat my_file | sed -n 'l'
cat -A
exit a dead ssh session
~.
copy large files, copy the from_dir directory inside the to_dir directory
rsync -av from_dir to_dir
## copy every file inside the frm_dir to to_dir
rsync -av from_dir/ to_dir
##re-copy the files avoiding completed ones:
rsync -avhP /from/dir /to/dir
make directory using the current date
mkdir $(date +%F)
all the folders' size in the current folder (GNU du)
du -h --max-depth=1
this one is a bit different, try it and see the difference
du -ch
the total size of current directory
du -sh .
disk usage
df -h
the column names of the file, install csvkit https://csvkit.readthedocs.org/en/0.9.1/
csvcut -n
open top with human readable size in Mb, Gb. install htop for better visualization
top -M
how many memeory are used in Gb
free -mg
print out unique rows based on the first and second column
awk '!a[$1,$2]++' input_file
sort -u -k1,2 file It will sort based on unique first and second column
do not wrap the lines using less
less -S
pretty output
fold -w 60
cat file.txt | column -t | less -S
pass tab as delimiter http://unix.stackexchange.com/questions/46910/is-it-a-bug-for-join-with-t-t
-t $'\t'
awk with the first line printed always
awk ' NR ==1 || ($10 > 1 && $11 > 0 && $18 > 0.001)' input_file
delete blank lines with sed
sed /^$/d
delete the last line
sed $d
awk to join files based on several columns
my github repo
### select lines from a file based on columns in another file
## http://unix.stackexchange.com/questions/134829/compare-two-columns-of-different-files-and-print-if-it-matches
awk -F"\t" 'NR==FNR{a[$1$2$3]++;next};a[$1$2$3] > 0' file2 file1
Finally learned about the !$ in unix: take the last thing (word) from the previous command.
echo hello, world; echo !$ gives 'world'
Create a script of the last executed command:
echo "!!" > foo.sh
Reuse all parameter of the previous command line:
!*
find bam in current folder (search recursively) and copy it to a new directory using 5 CPUs
find . -name "*bam" | xargs -P5 -I{} rsync -av {} dest_dir
ls -X will group files by extension.
loop through all the chromosomes
for i in {1..22} X Y
do
echo $i
done
for i in in {01..22} will expand to 01 02 ...
change every other newline to tab:
paste is used to concatenate corresponding lines from files: paste file1 file2 file3 .... If one of the "file" arguments is "-", then lines are read from standard input. If there are 2 "-" arguments, then paste takes 2 lines from stdin. And so on.
cat test.txt
0 ATTTTATTNGAAATAGTAGTGGG
0 CTCCCAAAATACTAAAATTATAA
1 TTTTAGTTATTTANGAGGTTGAG
1 CNTAATCTTAACTCACTACAACC
2 TTATAATTTTAGTATTTTGGGAG
2 CATATTAACCAAACTAATCTTAA
3 GGTTAATATGGTGAAATTTAAT
3 ACCTCAACCTCNTAAATAACTAA
cat test.txt| paste - -
0 ATTTTATTNGAAATAGTAGTGGG 0 CTCCCAAAATACTAAAATTATAA
1 TTTTAGTTATTTANGAGGTTGAG 1 CNTAATCTTAACTCACTACAACC
2 TTATAATTTTAGTATTTTGGGAG 2 CATATTAACCAAACTAATCTTAA
3 GGTTAATATGGTGAAATTTAAT 3 ACCTCAACCTCNTAAATAACTAA
ORS: output record seperator in awk var=condition?condition_if_true:condition_if_false is the ternary operator.
cat test.txt| awk 'ORS=NR%2?"\t":"\n"'
0 ATTTTATTNGAAATAGTAGTGGG 0 CTCCCAAAATACTAAAATTATAA
1 TTTTAGTTATTTANGAGGTTGAG 1 CNTAATCTTAACTCACTACAACC
2 TTATAATTTTAGTATTTTGGGAG 2 CATATTAACCAAACTAATCTTAA
3 GGTTAATATGGTGAAATTTAAT 3 ACCTCAACCTCNTAAATAACTAA
awk
We can also use the concept of a conditional operator in print statement of the form print CONDITION ? PRINT_IF_TRUE_TEXT : PRINT_IF_FALSE_TEXT. For example, in the code below, we identify sequences with lengths > 14:
cat data/test.tsv
blah_C1 ACTGTCTGTCACTGTGTTGTGATGTTGTGTGTG
blah_C2 ACTTTATATATT
blah_C3 ACTTATATATATATA
blah_C4 ACTTATATATATATA
blah_C5 ACTTTATATATT
awk '{print (length($2)>14) ? $0">14" : $0"<=14";}' data/test.tsv
blah_C1 ACTGTCTGTCACTGTGTTGTGATGTTGTGTGTG>14
blah_C2 ACTTTATATATT<=14
blah_C3 ACTTATATATATATA>14
blah_C4 ACTTATATATATATA>14
blah_C5 ACTTTATATATT<=14
awk 'NR==3{print "";next}{printf $1"\t"}{print $1}' data/test.tsv
blah_C1 blah_C1
blah_C2 blah_C2
blah_C4 blah_C4
blah_C5 blah_C5
You can also use getline to load the contents of another file in addition to the one you are reading, for example, in the statement given below, the while loop will load each line from test.tsv into k until no more lines are to be read:
awk 'BEGIN{while((getline k <"data/test.tsv")>0) print "BEGIN:"k}{print}' data/test.tsv
BEGIN:blah_C1 ACTGTCTGTCACTGTGTTGTGATGTTGTGTGTG
BEGIN:blah_C2 ACTTTATATATT
BEGIN:blah_C3 ACTTATATATATATA
BEGIN:blah_C4 ACTTATATATATATA
BEGIN:blah_C5 ACTTTATATATT
blah_C1 ACTGTCTGTCACTGTGTTGTGATGTTGTGTGTG
blah_C2 ACTTTATATATT
blah_C3 ACTTATATATATATA
blah_C4 ACTTATATATATATA
blah_C5 ACTTTATATATT
merge multiple fasta sequences in two files into a single file line by line
see post
linearize.awk:
/^>/ {printf("%s%s\t",(N>0?"\n":""),$0);N++;next;} {printf("%s",$0);} END {printf("\n");}
paste <(awk -f linearize.awk file1.fa ) <(awk -f linearize.awk file2.fa )| tr "\t" "\n"
grep fastq reads containing a pattern but maintain the fastq format
grep -A 2 -B 1 'AAGTTGATAACGGACTAGCCTTATTTT' file.fq | sed '/^--$/d' > out.fq
# or
zcat reads.fq.gz \
| paste - - - - \
| awk -v FS="\t" -v OFS="\n" '$2 ~ "AAGTTGATAACGGACTAGCCTTATTTT" {print $1, $2, $3, $4}' \
| gzip > filtered.fq.gz
count how many columns of a tsv files:
cat file.tsv | head -1 | tr "\t" "\n" | wc -l
csvcut -n -t file.tsv (from csvkit)
awk '{print NF; exit}' file.tsv
awk -F "\t" 'NR == 1 {print NF}' file.tsv
combine info to the fasta header
cat myfasta.txt
>Blap_contig79
MSTDVDAKTRSKERASIAAFYVGRNIFVTGGTGFLGKVLIEKLLRSCPDVGEIFILMRPKAGLSI
>Bluc_contig23663
MSTNVDAKARSKERASIAAFYVGRNIFVTGGTGFLGKVLIEKLLRSCPDVGEIFILMRPKAGLSI
>Blap_contig7988
MSTDVDAKTRSKERASIAAFYVGRNIFVTGGTGFLGKVLIEKLLRSCPDVGEIFILMRPKAGLSI
>Bluc_contig1223663
MSTNVDAKARSKERASIAAFYVGRNIFVTGGTGFLGKVLIEKLLRSCPDVGEIFILMRPKAGLSI
cat my_info.txt
info1
info2
info3
info4
paste <(cat my_info.txt) <(cat myfasta.txt| paste - - | cut -c2-) | awk '{printf(">%s_%s\n%s\n",$1,$2,$3);}'
>info1_Blap_contig79
MSTDVDAKTRSKERASIAAFYVGRNIFVTGGTGFLGKVLIEKLLRSCPDVGEIFILMRPKAGLSI
>info2_Bluc_contig23663
MSTNVDAKARSKERASIAAFYVGRNIFVTGGTGFLGKVLIEKLLRSCPDVGEIFILMRPKAGLSI
>info3_Blap_contig7988
MSTDVDAKTRSKERASIAAFYVGRNIFVTGGTGFLGKVLIEKLLRSCPDVGEIFILMRPKAGLSI
>info4_Bluc_contig1223663
MSTNVDAKARSKERASIAAFYVGRNIFVTGGTGFLGKVLIEKLLRSCPDVGEIFILMRPKAGLSI
count how many columns in a tsv file
cat file.tsv | head -1 | tr "\t" "\n" | wc -l
##(from csvkit)
csvcut -n -t file.
## emulate csvcut -n -t
less files.tsv | head -1| tr "\t" "\n" | nl
awk -F "\t" 'NR == 1 {print NF}' file.tsv
awk '{print NF; exit}'
change fasta header
see https://www.biostars.org/p/53212/
The fasta header is like >7 dna:chromosome chromosome:GRCh37:7:1:159138663:1 convert to >7:
cat Homo_sapiens_assembly19.fasta | gawk '/^>/ { b=gensub(" dna:.+", "", "g", $0); print b; next} {print}' > Homo_sapiens_assembly19_reheader.fasta
mkdir and cd into that dir shortcut
mkdir blah && cd $_
cut out columns based on column names in another file
http://crazyhottommy.blogspot.com/2016/10/cutting-out-500-columns-from-26g-file.html
#! /bin/bash
set -e
set -u
set -o pipefail
#### Author: Ming Tang (Tommy)
#### Date 09/29/2016
#### I got the idea from this stackOverflow post http://stackoverflow.com/questions/11098189/awk-extract-columns-from-file-based-on-header-selected-from-2nd-file
# show help
show_help(){
cat << EOF
This is a wrapper extracting columns of a (big) dataframe based on a list of column names in another
file. The column names must be one per line. The output will be stdout. For small files < 2G, one
can load it into R and do it easily, but when the file is big > 10G. R is quite cubersome.
Using unix commands on the other hand is better because files do not have to be loaded into memory at once.
e.g. subset a 26G size file for 700 columns takes around 30 mins. Memory footage is very low ~4MB.
usage: ${0##*/} -f < a dataframe > -c < colNames> -d <delimiter of the file>
-h display this help and exit.
-f the file you want to extract columns from. must contain a header with column names.
-c a file with the one column name per line.
-d delimiter of the dataframe: , or \t. default is tab.
e.g.
for tsv file:
${0##*/} -f mydata.tsv -c colnames.txt -d $'\t' or simply ommit the -d, default is tab.
for csv file: Note you have to specify -d , if your file is csv, otherwise all columns will be cut out.
${0##*/} -f mydata.csv -c colnames.txt -d ,
EOF
}
## if there are no arguments provided, show help
if [[ $# == 0 ]]; then show_help; exit 1; fi
while getopts ":hf:c:d:" opt; do
case "$opt" in
h) show_help;exit 0;;
f) File2extract=$OPTARG;;
c) colNames=$OPTARG;;
d) delim=$OPTARG;;
'?') echo "Invalid option $OPTARG"; show_help >&2; exit 1;;
esac
done
## set up the default delimiter to be tab, Note the way I specify tab
delim=${delim:-$'\t'}
## get the number of columns in the data frame that match the column names in the colNames file.
## change the output to 2,5,6,22,... and get rid of the last comma so cut -f can be used
cols=$(head -1 "${File2extract}" | tr "${delim}" "\n" | grep -nf "${colNames}" | sed 's/:.*$//' | tr "\n" "," | sed 's/,$//')
## cut out the columns
cut -d"${delim}" -f"${cols}" "${File2extract}"
or use csvtk from Shen Wei:
csvtk cut -t -f $(paste -s -d , list.txt) data.tsv
merge all bed files and add a column for the filename.
awk '{print $0 "\t" FILENAME}' *bed
add or remove chr from the start of each line
# add chr
sed 's/^/chr/' my.bed
# or
awk 'BEGIN {OFS = "\t"} {$1="chr"$1; print}'
# remove chr
sed 's/^chr//' my.bed
check if a tsv files have the same number of columns for all rows
awk '{print NF}' test.tsv | sort -nu | head -n 1
Parallelized samtools mpileup
https://www.biostars.org/p/134331/
BAM="yourFile.bam"
REF="reference.fasta"
samtools view -H $BAM | grep "\@SQ" | sed 's/^.*SN://g' | cut -f 1 | xargs -I {} -n 1 -P 24 sh -c "samtools mpileup -BQ0 -d 100000 -uf $REF -r \"{}\" $BAM | bcftools call -cv > \"{}\".vcf"
convert multiple lines to a single line
This is better than tr "\n" "\t" because somtimes I do not want to convert the last newline to tab.
cat myfile.txt | paste -s
merge multiple files with same header by keeping the header of the first file
I usually do it in R, but like the quick solution.
awk 'FNR==1 && NR!=1{next;}{print}' *.csv
# or
awk '
FNR==1 && NR!=1 { while (/^<header>/) getline; }
1 {print}
' file*.txt >all.txt
insert a field into the first line
cut -f1-4 F5.hg38.enhancers.expression.usage.matrix | head
CNhs11844 CNhs11251 CNhs11282 CNhs10746
chr10:100006233-100006603 1 0 0
chr10:100008181-100008444 0 0 0
chr10:100014348-100014634 0 0 0
chr10:100020065-100020562 0 0 0
chr10:100043485-100043744 0 0 0
chr10:100114218-100114567 0 0 0
chr10:100148595-100148922 0 0 0
chr10:100182422-100182522 0 0 0
chr10:100184498-100184704 0 0 0
sed '1 s/^/enhancer\t/' F5.hg38.enhancers.expression.usage.matrix | cut -f1-4 | head
enhancer CNhs11844 CNhs11251 CNhs11282
chr10:100006233-100006603 1 0 0
chr10:100008181-100008444 0 0 0
chr10:100014348-100014634 0 0 0
chr10:100020065-100020562 0 0 0
chr10:100043485-100043744 0 0 0
chr10:100114218-100114567 0 0 0
chr10:100148595-100148922 0 0 0
chr10:100182422-100182522 0 0 0
chr10:100184498-100184704 0 0 0
extract PASS calls from vcf file
cat my.vcf | awk -F '\t' '{if($0 ~ /\#/) print; else if($7 == "PASS") print}' > my_PASS.vcf
replace a pattern in a specific column
## column5
awk '{gsub(pattern,replace,$5)}1' in.file
## http://bioinf.shenwei.me/csvtk/usage/#replace
csvtk replace -f 5 -p pattern -r replacement
move a process to a screen session
https://www.linkedin.com/pulse/move-running-process-screen-bruce-werdschinski/
1. Suspend: Ctrl+z
2. Resume: bg
3. Disown: disown %1
4. Launch screen
5. Find pid: prep BLAH
6. Reparent process: reptyr ###
count uinque values in a column and put in a new
# input
blabla_1 A,B,C,C
blabla_2 A,E,G
blabla_3 R,Q,A,B,C,R,Q
# output
blabla_1 3
blabla_2 3
blabla_3 5
awk '{split(x,C); n=split($2,F,/,/); for(i in F) if(C[F[i]]++) n--; print $1, n}' file
get the promoter regions from a gtf file
https://twitter.com/David_McGaughey/status/1106371758142173185
Create TSS bed from GTF in one line:
zcat gencode.v29lift37.annotation.gtf.gz | awk '$3=="gene" {print $0}' | grep protein_coding | awk -v OFS="\t" '{if ($7=="+") {print $1, $4, $4+1} else {print $1, $5-1, $5}}' > tss.bed
or 5kb flanking tss
zcat gencode.v29lift37.annotation.gtf.gz | awk '$3=="gene" {print $0}' | grep protein_coding | awk -v OFS="\t" '{if ($7=="+") {print $1, $4, $4+5000} else {print $1, $5-5000, $5}}' > promoters.bed
caveat: some genes are at the end of the chromosomes, add or minus 5000 may go beyond the point, use bedtools slop with a genome size file to avoid that.
download fetchChromSizes from http://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/
fetchChromSizes hg19 > chrom_size.txt
zcat gencode.v29lift37.annotation.gtf.gz | awk '$3=="gene" {print $0}' | awk -v OFS="\t" '{if ($7=="+") {print $1, $4, $4+1} else {print $1, $5-1, $5}}' | bedtools slop -i - -g chrom_size.txt -b 5000 > promoter_5kb.bed
reverse one column of a txt file
reverse column 3 and put it to column5
awk -v OFS="\t" '{"echo "$3 "| rev" | getline $5}{print $0}'
#or use perl reverse second column
perl -lane 'BEGIN{$,="\t"}{$rev=reverse $F[2];print $F[0],$F[1],$rev,$F[3]}
get the full path of a file
realpath file.txt
readlink -f file.txt
pugz unizp in parallel
https://github.com/Piezoid/pugz
Contrary to the pigz program which does single-threaded decompression (see https://github.com/madler/pigz/blob/master/pigz.c#L232), pugz found a way to do truly parallel decompression.
run singularity on a multi-user HPC
#! /bin/bash
set -euo pipefail
module load singularity
# Need a unique /tmp for this job for /tmp/rstudio-rsession & /tmp/rstudio-server
WORKDIR=/liulab/${USER}/singularity_images
mkdir -m 700 -p ${WORKDIR}/tmp2
mkdir -m 700 -p ${WORKDIR}/tmp
PASSWORD='xyz' singularity exec --bind "${WORKDIR}/tmp2:/var/run/rstudio-server" --bind "${WORKDIR}/tmp:/tmp" --bind="/liulab/${USER}" geospatial_4.0.2.simg rserver --www-port 8888 --auth-none=0 --auth-pam-helper-path=pam-helper --www-address=127.0.0.1
add ServerAliveInterval 60 to avoid dropping from your ssh session
Add the following on the top of your ~/.ssh/config to prevent drop off the ssh session
Host *
ServerAliveInterval 60
I use screen/tmux and also mosh as well.
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