我有一个带有GI编号的文件,希望从ncbi获得FASTA
个序列。
from Bio import Entrez
import time
Entrez.email ="eigtw59tyjrt403@gmail.com"
f = open("C:\\bioinformatics\\gilist.txt")
for line in iter(f):
handle = Entrez.efetch(db="nucleotide", id=line, retmode="xml")
records = Entrez.read(handle)
print ">GI "+line.rstrip()+" "+records[0]["GBSeq_primary-accession"]+" "+records[0]["GBSeq_definition"]+"\n"+records[0]["GBSeq_sequence"]
time.sleep(1) # to make sure not many requests go per second to ncbi
f.close()
这个脚本运行正常但是在几个序列之后我突然收到此错误消息。
Traceback (most recent call last):
File "C:/Users/Ankur/PycharmProjects/ncbiseq/getncbiSeq.py", line 7, in <module>
handle = Entrez.efetch(db="nucleotide", id=line, retmode="xml")
File "C:\Python27\lib\site-packages\Bio\Entrez\__init__.py", line 139, in efetch
return _open(cgi, variables)
File "C:\Python27\lib\site-packages\Bio\Entrez\__init__.py", line 455, in _open
raise exception
urllib2.HTTPError: HTTP Error 500: Internal Server Error
当然我可以使用http://www.ncbi.nlm.nih.gov/sites/batchentrez
,但我正在尝试创建一个管道,并希望自动化。
如何防止ncbi“踢我”
答案 0 :(得分:0)
我不熟悉ncbi API,但我的猜测是你违反了某种速率限制规则(即使使用“sleep(1)”),所以你之前的请求有效,但是在一些请求之后服务器发现你经常打它并阻止你。这对您来说是有问题的,因为您的代码中没有错误处理。
我建议在try / except块中包装数据,以使脚本等待更长时间,然后再遇到问题再试一次。如果所有其他方法都失败了,请将导致错误的id写入文件并继续(如果id在某种程度上是罪魁祸首,可能导致Entrez库生成错误的URL)。
尝试将代码更改为此类代码(未经测试):
from urllib2 import HTTPError
from Bio import Entrez
import time
def get_record(_id):
handle = Entrez.efetch(db="nucleotide", id=_id, retmode="xml")
records = Entrez.read(handle)
print ">GI "+line.rstrip()+" "+records[0]["GBSeq_primary-accession"]+" "+records[0]["GBSeq_definition"]+"\n"+records[0]["GBSeq_sequence"]
time.sleep(1) # to make sure not many requests go per second to ncbi
Entrez.email ="eigtw59tyjrt403@gmail.com"
f = open("C:\\bioinformatics\\gilist.txt")
for id in iter(f):
try:
get_record(id)
except HTTPError:
print "Error fetching", id
time.sleep(5) # we have angered the API! Try waiting longer?
try:
get_record(id)
except:
with open('error_records.bad','a') as f:
f.write(str(id)+'\n')
continue #
f.close()
答案 1 :(得分:0)
有一种叫做efetch的工作。您可以将列表拆分为200个批次(直觉感觉这是一个正确的批量大小)并使用efetch一次性发送所有这些ID。
首先,这比发送200个单独查询要快得多。其次,它还有效地符合“每秒3个查询”规则,因为每个查询的处理时间超过0.33秒但不会太长。
但是,你确实需要一种机制来捕捉“坏苹果”。即使你的200个ID中有一个是坏的,NCBI也会返回0结果。换句话说,当且仅当所有200个ID都有效时,NCBI才返回结果。
如果是坏苹果,我会逐个遍历200个ID并忽略坏苹果。这个“如果糟糕的苹果”情景也告诉你不要让批次太大,只是在坏苹果的情况下。如果它很大,首先,有一个坏苹果的机会更大,也就是说,你经常需要迭代整个事情。其次,批次越大,您需要迭代的单个项目就越多。
我使用以下代码下载CAZy蛋白,效果很好:
import urllib2
prefix = "http://www.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=protein&rettype=fasta&id="
id_per_request = 200
def getSeq (id_list):
url = prefix + id_list[:len(id_list)-1]
temp_content = ""
try:
temp_content += urllib2.urlopen(url).read()
### if there is a bad apple, try one by one
except:
for id in id_list[:len(id_list)-1].split(","):
url = prefix + id
#print url
try:
temp_content += urllib2.urlopen(url).read()
except:
#print id
pass
return temp_content
content = ""
counter = 0
id_list = ""
#define your accession numbers first, here it is just an example!!
accs = ["ADL19140.1","ABW01768.1","CCQ33656.1"]
for acc in accs:
id_list += acc + ","
counter += 1
if counter == id_per_request:
counter = 0
content += getSeq(id_list)
id_list = ""
if id_list != "":
content += getSeq(id_list)
id_list = ""
print content
答案 2 :(得分:0)
这是“正常”的Entrez API临时失败,即使您应用了所有Entrez API规则也可能发生。 Biopython文档在this section中介绍了一种处理它的方法。
有时您会从Entrez收到间歇性错误,HTTPError 5XX,我们使用了一个尝试,除了“暂停重试”块来解决此问题。例如,
# This assumes you have already run a search as shown above, # and set the variables count, webenv, query_key try: from urllib.error import HTTPError # for Python 3 except ImportError: from urllib2 import HTTPError # for Python 2 batch_size = 3 out_handle = open("orchid_rpl16.fasta", "w") for start in range(0, count, batch_size): end = min(count, start+batch_size) print("Going to download record %i to %i" % (start+1, end)) attempt = 0 while attempt < 3: attempt += 1 try: fetch_handle = Entrez.efetch(db="nucleotide", rettype="fasta", retmode="text", retstart=start, retmax=batch_size, webenv=webenv, query_key=query_key, idtype="acc") except HTTPError as err: if 500 <= err.code <= 599: print("Received error from server %s" % err) print("Attempt %i of 3" % attempt) time.sleep(15) else: raise data = fetch_handle.read() fetch_handle.close() out_handle.write(data) out_handle.close()
因此您不必为此错误感到内and,只需抓住它即可。