Thanks this works! Appreciate it!
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Computing chunk of fasta file before moving to next chunks using python
I have a fasta file with 100 random permutations of a genome. It is then broken up into ORFs. Here is a sample:
>Shuffled1_1 [5 - 79] Based on NC_000908.2
>MNGLISMLIYFDEYENEIFVEKKDL
>Shuffled1_2 [36 - 101] Based on NC_000908.2
>MMSMKMKFSLKKKICNYIPKLQ
>Shuffled1_3 [19 - 111] Based on NC_000908.2
>MNVNLLWWVWKWNFRWKKRFVTTYQSSNKLK
>Shuffled1_4 [105 - 134] Based on NC_000908.2
>MKVERQISTL
>Shuffled1_5 [148 - 177] Based on NC_000908.2
>MINNSSSPAL
>Shuffled1_6 [174 - 242] Based on NC_000908.2
>MINKFPELLLTQLSYVRYYLFAI
There are over 3 million records. I need to calculate the 95th percentile by averaging the estimates of each shuffled permutation. I am having trouble figuring out how to get the 95th percentile of shuffled1 before moving onto shuffled2 and so on. Any help would be appreciated. I was able to pull out just the first shuffled permutation using awk and get the percentile using this code:
from Bio import SeqIO
import numpy as np
seqLen=[]
fasta_sequence = SeqIO.parse(open("firstperm.fasta"),"fasta")
for fasta in fasta_sequence:
name,sequence = fasta.id, str(fasta.seq)
seqLen.append(len(fasta))
a = np.array(seqLen)
p = np.percentile(a,95)
print p
I am just trying to figure out a way to duplicate this over the entire fasta file for all 100 shuffled permutations. Basically I am looking for 100 numbers in a list for each 95th percentile.
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george.ry
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ToastedGoat •
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ToastedGoat •
1 answer
from collections import defaultdict as dd
from Bio import SeqIO
import numpy as np
shuffles = dd(list)
for record in SeqIO.parse(open('firstperm.fasta'), 'fasta'):
id = record.id.split('_')[0]
shuffles[id].append(len(record))
for k, v in shuffles.items():
print(k, np.percentile(v, 95))
Something like that? (Not tested ... on the wrong machine)
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