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Binning fasta sequences by size?

Hi everyone,

I have a large fasta file and need to bin it by size: everything 200 nt and up in one file, and everything 199 or smaller in another. I have found some useful scripts that will remove the smaller sequences, but they are discarded and I need to keep them in a separate file.

Can anyone suggest a perl script (or anything similar) that will sort out sequences into two files by size cutoff?

Thanks in advance for any advice.

Stacy

fasta
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updated by Ram Ram • written by stacy734 • stacy734

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written by WouterDeCoster WouterDeCoster

4 answers

Linearize FASTA if you have multiple lines of sequences between each header. 

#!/usr/bin/env python

import sys
lt200 = open(sys.argv[2])
gt200 = open(sys.argv[3])

with open(sys.argv[1], 'r') as f:
    for line in f:
        if line.startswith(">"):
            header = line.strip()
            seq = next(f).strip()
            if len(seq) >= 200:
                gt200.write(header + '\n' + seq)
            else:
                lt200.write(header + '\n' + seq)

lt200.close()
gt200.close()

save as bin_by_len.py, run as python bin_by_len.py input.fasta lt200.fasta gt200.fasta.

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written by st.ph.n st.ph.n

Use reformat.sh from BBMap suite.

reformat.sh in=your_file.fa minlen=200 out=more_than_200.fa
reformat.sh in=your_file.fa maxlen=200 out=less_than_200.fa
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written by GenoMax GenoMax

linearize and dispatch with awk:

rm -f file1.fa file2.fa
awk '/^>/ {printf("%s%s\t",(N>0?"\n":""),$0);N++;next;} {printf("%s",$0);} END {printf("\n");}' input.fa   |\
awk -F '\t' '{out=length($2)<200?"file1.fa":"file2.fa";printf(">%s\n%s\n",$1,$2) >> out;}'
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written by Pierre Lindenbaum Pierre Lindenbaum

The faidx utility in pyfaidx has this capability built-in:

$ pip install pyfaidx
$ faidx --size-range 1,199 file.fa > smalls.fa
$ faidx --size-range 200,200000000 file.fa > bigs.fa

An advantage of this approach is that you read the sizes from the .fai index file each time instead of computing the sequence lengths, so for lots of bins this will be much faster.

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written by Matt Shirley Matt Shirley

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