将文件解析为 JSON 文件的父/子格式

Question

我想要一些关于如何为Gene ontology (.obo)解析此文件的帮助/建议

我正在 D3 中创建一个可视化，需要创建一个 JSON 格式的“树”文件 -

{
 "name": "flare",
 "description": "flare",
 "children": [
  {
   "name": "analytic",
   "description": "analytics",
   "children": [
    {
     "name": "cluster",
     "description": "cluster",
     "children": [
      {"name": "Agglomer", "description": "AgglomerativeCluster", "size": 3938},
      {"name": "Communit", "description": "CommunityStructure", "size": 3812},
      {"name": "Hierarch", "description": "HierarchicalCluster", "size": 6714},
      {"name": "MergeEdg", "description": "MergeEdge", "size": 743}
     ]
    }, etc..

这种格式似乎很容易在 python 的字典中复制，每个条目有 3 个字段：名称、描述和子项[]。

我的问题实际上是如何提取数据。上面链接的文件的“对象”结构如下：

[Term]
id: GO:0000001
name: mitochondrion inheritance
namespace: biological_process
def: "The distribution of mitochondria, including the mitochondrial genome, into daughter cells after mitosis or meiosis, mediated by interactions between mitochondria and the cytoskeleton." [GOC:mcc, PMID:10873824, PMID:11389764]
synonym: "mitochondrial inheritance" EXACT []
is_a: GO:0048308 ! organelle inheritance
is_a: GO:0048311 ! mitochondrion distribution

我需要 id、is_a 和 name 字段。我曾尝试使用 python 来解析这个，但我似乎无法找到一种方法来定位每个对象。

有什么想法吗？

Answer 1

这是解析“.obo”文件中对象的一种相当简单的方法。它将对象数据保存到dict 中，id 作为键，name 和is_a 数据保存在列表中。然后它使用标准的json 模块的.dumps 函数漂亮地打印它。

出于测试目的，我在您的链接中使用了该文件的截断版本，最多只包含id: GO:0000006。

此代码忽略任何包含is_obsolete 字段的对象。它还会从is_a 字段中删除描述信息；我想您可能想要这样做，但禁用该功能很容易。

#!/usr/bin/env python

''' Parse object data from a .obo file

    From http://stackoverflow.com/q/32989776/4014959

    Written by PM 2Ring 2015.10.07
'''

from __future__ import print_function, division

import json
from collections import defaultdict

fname = "go-basic.obo"
term_head = "[Term]"

#Keep the desired object data here
all_objects = {}

def add_object(d):
    #print(json.dumps(d, indent = 4) + '\n')
    #Ignore obsolete objects
    if "is_obsolete" in d:
        return

    #Gather desired data into a single list,
    # and store it in the main all_objects dict
    key = d["id"][0]
    is_a = d["is_a"]
    #Remove the next line if you want to keep the is_a description info
    is_a = [s.partition(' ! ')[0] for s in is_a]
    all_objects[key] = d["name"] + is_a


#A temporary dict to hold object data
current = defaultdict(list)

with open(fname) as f:
    #Skip header data
    for line in f:
        if line.rstrip() == term_head:
            break

    for line in f:
        line = line.rstrip()
        if not line:
            #ignore blank lines
            continue
        if line == term_head:
            #end of term
            add_object(current)
            current = defaultdict(list)
        else:
            #accumulate object data into current
            key, _, val = line.partition(": ")
            current[key].append(val)

if current:
    add_object(current)    

print("\nall_objects =")
print(json.dumps(all_objects, indent = 4, sort_keys=True))

输出

all_objects =
{
    "GO:0000001": [
        "mitochondrion inheritance", 
        "GO:0048308", 
        "GO:0048311"
    ], 
    "GO:0000002": [
        "mitochondrial genome maintenance", 
        "GO:0007005"
    ], 
    "GO:0000003": [
        "reproduction", 
        "GO:0008150"
    ], 
    "GO:0000006": [
        "high-affinity zinc uptake transmembrane transporter activity", 
        "GO:0005385"
    ]
}