Q:

How do I generate a recursive tree-like dictionary from a flat file (Gene Ontology OBO file)?

I'm trying to write code to parse a Gene Ontology (GO) OBO file and push the go term IDs (e.g. GO:0003824) into a tree-like nested dictionary. The hierarchical go structure in an OBO file is indicated with the "is_a" identifier, which is used to mark each parent of each GO term. A GO term might have multiple parents, and the highest go terms in the hierarchy don't have parents. 
A small example of a GO OBO file is shown below:
[Term]
id: GO:0003674
name: molecular_function
namespace: molecular_function
alt_id: GO:0005554
def: "A molecular process that can be carried out by the action of a single macromolecular machine, usually via direct physical interactions with other molecular entities. Function in this sense denotes an action, or activity, that a gene product (or a complex) performs. These actions are described from two distinct but related perspectives: (1) biochemical activity, and (2) role as a component in a larger system/process." [GOC:pdt]
comment: Note that, in addition to forming the root of the molecular function ontology, this term is recommended for use for the annotation of gene products whose molecular function is unknown. When this term is used for annotation, it indicates that no information was available about the molecular function of the gene product annotated as of the date the annotation was made; the evidence code "no data" (ND), is used to indicate this. Despite its name, this is not a type of 'function' in the sense typically defined by upper ontologies such as Basic Formal Ontology (BFO). It is instead a BFO:process carried out by a single gene product or complex.
subset: goslim_aspergillus
subset: goslim_candida
subset: goslim_chembl
subset: goslim_generic
subset: goslim_metagenomics
subset: goslim_pir
subset: goslim_plant
subset: goslim_yeast
synonym: "molecular function" EXACT []

[Term]
id: GO:0003824
name: catalytic activity
namespace: molecular_function
def: "Catalysis of a biochemical reaction at physiological temperatures. In biologically catalyzed reactions, the reactants are known as substrates, and the catalysts are naturally occurring macromolecular substances known as enzymes. Enzymes possess specific binding sites for substrates, and are usually composed wholly or largely of protein, but RNA that has catalytic activity (ribozyme) is often also regarded as enzymatic." [GOC:vw, ISBN:0198506732]
subset: goslim_chembl
subset: goslim_flybase_ribbon
subset: goslim_metagenomics
subset: goslim_pir
subset: goslim_plant
synonym: "enzyme activity" EXACT [GOC:dph, GOC:tb]
xref: Wikipedia:Enzyme
is_a: GO:0003674 ! molecular_function

[Term]
id: GO:0005198
name: structural molecule activity
namespace: molecular_function
def: "The action of a molecule that contributes to the structural integrity of a complex or its assembly within or outside a cell." [GOC:mah, GOC:vw]
subset: goslim_agr
subset: goslim_aspergillus
subset: goslim_candida
subset: goslim_chembl
subset: goslim_flybase_ribbon
subset: goslim_generic
subset: goslim_pir
subset: goslim_plant
subset: goslim_yeast
is_a: GO:0003674 ! molecular_function

[Term]
id: GO:0005488
name: binding
namespace: molecular_function
def: "The selective, non-covalent, often stoichiometric, interaction of a molecule with one or more specific sites on another molecule." [GOC:ceb, GOC:mah, ISBN:0198506732]
comment: Note that this term is in the subset of terms that should not be used for direct, manual gene product annotation. Please choose a more specific child term, or request a new one if no suitable term is available. For ligands that bind to signal transducing receptors, consider the molecular function term 'receptor binding ; GO:0005102' and its children.
subset: gocheck_do_not_manually_annotate
subset: goslim_pir
subset: goslim_plant
synonym: "ligand" NARROW []
xref: Wikipedia:Binding_(molecular)
is_a: GO:0003674 ! molecular_function

[Term]
id: GO:0005515
name: protein binding
namespace: molecular_function
alt_id: GO:0001948
alt_id: GO:0045308
def: "Interacting selectively and non-covalently with any protein or protein complex (a complex of two or more proteins that may include other nonprotein molecules)." [GOC:go_curators]
subset: goslim_aspergillus
subset: goslim_candida
subset: goslim_chembl
subset: goslim_metagenomics
subset: goslim_pir
subset: goslim_plant
synonym: "glycoprotein binding" NARROW []
synonym: "protein amino acid binding" EXACT []
xref: reactome:R-HSA-170835 "An anchoring protein, ZFYVE9 (SARA), recruits SMAD2/3"
xref: reactome:R-HSA-170846 "TGFBR2 recruits TGFBR1"
xref: reactome:R-HSA-3645786 "TGFBR2 mutant dimers recruit TGFBR1"
xref: reactome:R-HSA-3656484 "TGFBR2 recruits TGFBR1 KD Mutants"
xref: reactome:R-HSA-3702153 "An anchoring protein, ZFYVE9 (SARA), recruits SMAD2/3 MH2 domain mutants"
xref: reactome:R-HSA-3713560 "An anchoring protein, ZFYVE9 (SARA), recruits SMAD2/3 phosphorylation motif mutants"
is_a: GO:0005488 ! binding

[Term]
id: GO:0005549
name: odorant binding
namespace: molecular_function
def: "Interacting selectively and non-covalently with an odorant, any substance capable of stimulating the sense of smell." [GOC:jl, ISBN:0721662544]
subset: goslim_pir
is_a: GO:0005488 ! binding

[Term]
id: GO:0005550
name: pheromone binding
namespace: molecular_function
def: "Interacting selectively and non-covalently with a pheromone, a substance, or characteristic mixture of substances, that is secreted and released by an organism and detected by a second organism of the same or a closely related species, in which it causes a specific reaction, such as a definite behavioral reaction or a developmental process." [GOC:ai]
is_a: GO:0005549 ! odorant binding

Below is an attempt at a recursive function (and some supporting code) for storing GO term IDs in a tree-like dictionary:
import pandas as pd
import re

with open("tiny_go.obo", 'rt') as f:
    content = f.read()    

# Clean GO terms list
def clean_go_terms(terms):
    l = []
    for term in terms:
        if (len(re.findall('is_obsolete: true', term))==0) and (len(re.findall('id: GO:\d+', term)) > 0):
            l.append(term)
    return l

def get_top_nodes(terms):
    l = []
    for term in terms: 
        if len(re.findall('is_a: GO:\d+', term)) == 0:
            l.append(term)
    return l

split_terms = content.split('\n\n')
split_terms_clean = clean_go_terms(split_terms)
top_nodes = get_top_nodes(split_terms_clean)
len(top_nodes)

# Find every term that has the top node as a parent; apply recursively to entire list of terms
# * Keys with empty lists will be leaves
def generate_go_tree(parent_nodes, all_go_terms, switch=True):
    go_dict = {}
    for node in parent_nodes:
        parent_go_id = re.findall('id: (GO:\d+)', node)[0]
        go_dict[parent_go_id] = {}
        for go_term in all_go_terms:
            go_id = re.findall('id: (GO:\d+)', go_term)[0]
            parent_list = re.findall('is_a: (GO:\d+)', go_term)
            if (parent_go_id in parent_list):
                go_dict[parent_go_id][go_id] = generate_go_tree([go_term], all_go_terms, True)
    return go_dict

go_tree = generate_go_tree(top_nodes, split_terms_clean)

Clearly I have not constructed the recursive function correctly because I'm seeing key duplication in the output: 
{'GO:0003674': {'GO:0003824': {'GO:0003824': {}},
  'GO:0005198': {'GO:0005198': {}},
  'GO:0005488': {'GO:0005488': {'GO:0005515': {'GO:0005515': {}},
    'GO:0005549': {'GO:0005549': {'GO:0005550': {'GO:0005550': {}}}}}}}}

Suggestions on how to fix the recursive function will be greatly appreciated! Thank you!

A:

You wrote
if (parent_go_id in parent_list):
    go_dict[parent_go_id][go_id] = generate_go_tree([go_term], all_go_terms, True)

correct would be
if (parent_go_id in parent_list):
    go_dict[parent_go_id][go_id] = generate_go_tree([go_term], all_go_terms, True)[go_id]

After this change, it produces:
{
    'GO:0003674': {
        'GO:0003824': {}, 
        'GO:0005198': {}, 
        'GO:0005488': {
            'GO:0005515': {},
            'GO:0005549': {
                'GO:0005550': {}
            }
        }
    }
}

But I would suggest a different approach altogether. Make a class that parses the terms and builds the dependency tree as it does that.
For convenience I've derived it from dict, so you can write term.id instead of term['id']:
class Term(dict):
    __getattr__ = dict.__getitem__
    __setattr__ = dict.__setitem__
    __delattr__ = dict.__delitem__

    registry = {}
    single_valued = 'id name namespace alt_id def comment synonym is_a'.split()
    multi_valued = 'subset xref'.split()

    def __init__(self, text):
        self.children = []
        self.parent = None

        for line in text.splitlines():
            if not ': ' in line:
                continue
            key, val = line.split(': ', 1)
            if key in Term.single_valued:
                self[key] = val
            elif key in Term.multi_valued:
                if not key in self:
                    self[key] = [val]
                else:
                    self[key].append(val)
            else:
                print('unclear property: %s' % line)

        if 'id' in self:
            Term.registry[self.id] = self

        if 'alt_id' in self:
            Term.registry[self.alt_id] = self

        if 'is_a' in self:
            key = self.is_a.split(' ! ', 1)[0]
            if key in Term.registry:
                Term.registry[key].children.append(self)
                self.parent = Term.registry[key]

    def is_top(self):
        return self.parent == None

    def is_valid(self):
        return self.get('is_obsolete') != 'true' and self.id != None

Now you can slurp the file in one go:
with open('tiny_go.obo', 'rt') as f:
    contents = f.read()

terms = [Term(text) for text in contents.split('\n\n')]

and recursing the tree becomes easy. For example, a simple "print" function that only outputs non-obsolete nodes:
def print_tree(terms, indent=''):
    valid_terms = [term for term in terms if term.is_valid()]
    for term in valid_terms:
        print(indent + 'Term %s - %s' % (term.id, term.name))
        print_tree(term.children, indent + '  ')

top_terms = [term for term in terms if term.is_top()]

print_tree(top_terms)

This prints:

Term GO:0003674 - molecular_function
  Term GO:0003824 - catalytic activity
  Term GO:0005198 - structural molecule activity
  Term GO:0005488 - binding
    Term GO:0005515 - protein binding
    Term GO:0005549 - odorant binding
      Term GO:0005550 - pheromone binding

You can also do things like Term.registry['GO:0005549'].parent.name, which would get "binding".
I leave producing nested dicts of GO-IDs (like in your own example) as an excercise, but you might not even need that, since Term.registry already is very similar to this.

A:

You can use recursion for a shorter solution:
import itertools, re, json
content = list(filter(None, [i.strip('\n') for i in open('filename.txt')]))
entries = [[a, list(b)] for a, b in itertools.groupby(content, key=lambda x:x== '[Term]')]
terms = [(lambda x:x if 'is_a' not in x else {**x, 'is_a':re.findall('^GO:\d+', x['is_a'])[0]})(dict(i.split(': ', 1) for i in b)) for a, b in entries if not a]
terms = sorted(terms, key=lambda x:'is_a' in x)
def tree(d, _start):
  t = [i for i in d if i.get('is_a') == _start]
  return {} if not t else {i['id']:tree(d, i['id']) for i in t}

print(json.dumps({terms[0]['id']:tree(terms, terms[0]['id'])}, indent=4))

Output:
{
  "GO:0003674": {
    "GO:0003824": {},
    "GO:0005198": {},
    "GO:0005488": {
        "GO:0005515": {},
        "GO:0005549": {
            "GO:0005550": {}
        }
      }
   }
}

This will also work if parent data sets are not defined before their children. For instance, when the parent is positioned three places below its original place, the same result is still generated (see file):
print(json.dumps({terms[0]['id']:tree(terms, terms[0]['id'])}, indent=4))

Output:
{
"GO:0003674": {
    "GO:0003824": {},
    "GO:0005198": {},
    "GO:0005488": {
        "GO:0005515": {},
        "GO:0005549": {
            "GO:0005550": {}
        }
      }
   }
}

