TChan/Notebook/2007-5-3

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=Presentation=
=Presentation=
-
==Lessons learned==
 
-
* In order to build something in a large group like this one:
 
-
** (visual, if possible) plot out:
 
-
***general framework
 
-
***what needs to be done
 
-
***what is being done, and
 
-
***by whom.
 
-
**(This was also an iGEM lesson.)
 
-
* Scientific foresight
 
-
** We're pretty darn near-sighted, in general.
 
-
* Don't bite off more than you can chew.
 
-
* (Corollary) Don't let tasks go by you.
 
-
* IDLE is useful.
 
-
* Front end is more fun than back end.
 
-
* Programming is fun for the brain - much more so than writing papers.
 
-
 
-
===Things I now know exist===
 
-
* BioPython! (And its API.)
 
-
* IDLE
 
-
* Help sites for Python
 
-
** Especially for interfaces with other data
 
-
* Python: __init__, XML parsers, installed code
 
-
* NCBI: multiple forms of BLAST, GenBank, OMIM
 
-
* GeneCards, HapMap, PolyPhenk, MeSH Terms
 
-
* POST and GET
 
-
* Locally-kept databases
 
-
* Interesting methods for strings
 
-
 
-
===Things I now know how to do===
 
-
* Use BioPython to program simple stuff
 
-
* Use Python to access search sites
 
-
** URL (cheap way)
 
-
* Parse XML, the nice way
 
-
* Parse HTML, the brute force way
 
-
* Read HTML forms
 
-
* Look at installed code to figure out how to program my own tasks
 
-
* Write functions
 
-
 
-
* Do research online to figure out how to complete a programming task
 
-
* (Related) How to decide whether or not and how to complete a programming task
 
-
* Break programs down
 
-
 
==Stuff done==
==Stuff done==
===1. General output===
===1. General output===
'''INPUT''': Disease name
'''INPUT''': Disease name
 +
'''OUTPUT''': Targeted URLs and lists of data that would be of interest to the patient
'''OUTPUT''': Targeted URLs and lists of data that would be of interest to the patient
Line 64: Line 23:
===2. Allelic frequency===
===2. Allelic frequency===
'''INPUT''': RS#
'''INPUT''': RS#
 +
'''OUTPUT''': parsed allelic frequency data from dbSNP
'''OUTPUT''': parsed allelic frequency data from dbSNP
 +
* Though started by looking at GeneCards, saw that GeneCards takes its data from dbSNP, so decided to go to the source.
* Though started by looking at GeneCards, saw that GeneCards takes its data from dbSNP, so decided to go to the source.
Line 82: Line 43:
## HWP - ?
## HWP - ?
## Alleles - frequency of the individual alleles
## Alleles - frequency of the individual alleles
 +
 +
 +
==Lessons learned==
 +
* In order to build something in a large group like this one:
 +
** (visually, if possible) plot out:
 +
***general framework
 +
***what needs to be done
 +
***what is being done, and
 +
***by whom.
 +
**(This was also an iGEM lesson.)
 +
* Scientific foresight
 +
** We're pretty darn near-sighted, in general.
 +
* Don't bite off more than you can chew.
 +
* (Corollary) Don't let tasks go by you.
 +
* IDLE is useful.
 +
* Front end is more fun than back end.
 +
* Programming is fun for the brain - much more so than writing papers.
 +
 +
 +
===Things I now know how to do===
 +
* Use BioPython to program simple stuff
 +
* Use Python to access search sites
 +
** URL (cheap way)
 +
* Parse XML, the nice way
 +
* Parse HTML, the brute force way
 +
* Read HTML forms
 +
* Look at installed code to figure out how to program my own tasks
 +
* Write functions
 +
 +
* Do research online to figure out how to complete a programming task
 +
* (Related) How to decide whether or not and how to complete a programming task
 +
* Break programs down
 +
 +
 +
===Things I now know exist===
 +
* BioPython! (And its API.)
 +
* IDLE
 +
* Help sites for Python
 +
** Especially for interfaces with other data
 +
* Python: __init__, XML parsers, installed code
 +
* NCBI: multiple forms of BLAST, GenBank, OMIM
 +
* GeneCards, HapMap, PolyPhenk, MeSH Terms
 +
* POST and GET
 +
* Locally-kept databases
 +
* Interesting methods for strings

Revision as of 04:47, 3 May 2007

Contents

Presentation

Stuff done

1. General output

INPUT: Disease name

OUTPUT: Targeted URLs and lists of data that would be of interest to the patient

Targeted data (from MedStory)

  • Drugs
  • Experts
  • Drugs in clinical trials
  • Procedures

Targeted URL outputs

  • MedStory
  • eMedicine
  • Google (general)
  • Google (treatment)
  • Wikipedia
  • WHO
  • GeneCards

2. Allelic frequency

INPUT: RS#

OUTPUT: parsed allelic frequency data from dbSNP

  • Though started by looking at GeneCards, saw that GeneCards takes its data from dbSNP, so decided to go to the source.
  1. Download dbSNP HTML file targeted to the RS#
  2. Extract the line of HTML describing allelic frequency
    1. Provision: if no allelic frequency data, will tell user
  3. Break it down into HTML table-row chunks, convenient because the different rows stand for different population groups
  4. Extract the categories of data
  5. Extract all the data from the populations
    1. ss#
      1. Provisions: if no ss# in that row (because multiple population groups are combined under one ss#), will return in the ss# position in the list
    2. Population Name - technical name of the population
    3. Individual Group - race of people in population
    4. Chromosome Sample Count - number of chromosomes analyzed in the population
    5. Source - ?
    6. Allele Combinations - SNP means that there will be differing nucleotides in the population
    7. HWP - ?
    8. Alleles - frequency of the individual alleles


Lessons learned

  • In order to build something in a large group like this one:
    • (visually, if possible) plot out:
      • general framework
      • what needs to be done
      • what is being done, and
      • by whom.
    • (This was also an iGEM lesson.)
  • Scientific foresight
    • We're pretty darn near-sighted, in general.
  • Don't bite off more than you can chew.
  • (Corollary) Don't let tasks go by you.
  • IDLE is useful.
  • Front end is more fun than back end.
  • Programming is fun for the brain - much more so than writing papers.


Things I now know how to do

  • Use BioPython to program simple stuff
  • Use Python to access search sites
    • URL (cheap way)
  • Parse XML, the nice way
  • Parse HTML, the brute force way
  • Read HTML forms
  • Look at installed code to figure out how to program my own tasks
  • Write functions
  • Do research online to figure out how to complete a programming task
  • (Related) How to decide whether or not and how to complete a programming task
  • Break programs down


Things I now know exist

  • BioPython! (And its API.)
  • IDLE
  • Help sites for Python
    • Especially for interfaces with other data
  • Python: __init__, XML parsers, installed code
  • NCBI: multiple forms of BLAST, GenBank, OMIM
  • GeneCards, HapMap, PolyPhenk, MeSH Terms
  • POST and GET
  • Locally-kept databases
  • Interesting methods for strings



Allelic Frequency

  • Input: rs# (string)
  • Output: allelic frequency data (list of lists (of lists, in some cases))

Sample Input

"rs11200538"

Code

import urllib

# Definitions of functions

# Returns the dbSNP URL for the search term
def parse_for_dbSNP_search(search_term):
    #search_term will be initial input, the RS# in a string (ie. "rs11200538" or "11200538")
    parsed_term = search_term.replace("rs", "")
    return "http://www.ncbi.nlm.nih.gov/SNP/snp_ref.cgi?rs=%s" % parsed_term
    
# Grabs the dbSNP HTML search_file
def get_dbSNP_search_file(URL, genl_search_file):
    URL_stream_genl = urllib.urlopen(URL)
    page = URL_stream_genl.read()
    URL_stream_genl.close()
    genl_search_file.write(page)

# Extracts out the relevant allelic frequency line from the dbSNP HTML search file
def extract_allelic_freq_line(dbSNP_file):
    for line in dbSNP_file:
        if line.find('''Alleles</TH></TR><TR ><TH  bgcolor="silver">ss''') != -1:
            return line
        elif line.find('''There is no frequency data.''') != -1:
            better_luck_next_time = ''
            return better_luck_next_time

# Divides the relevant allelic frequency line into separate HTML-table 'rows', which delineate the populations
def divide_freq_line_into_TRs(freq_line):
    TR_list = []
    while freq_line.rfind("<TR") != -1:
        TR_instance = freq_line.rfind("<TR")
        TR_list.insert(0, freq_line[TR_instance:(len(freq_line))])
        freq_line = freq_line[0:TR_instance]
    TR_list.insert(0, freq_line)
    return TR_list

# Parses out (1) categories, and (2) population rows
def extract_categories_and_population_TRs(categories, population_list, TR_list):
    for element in TR_list:
        if element.find('''ss#''') != -1:
            categories = element
        elif element.find('''<td ><a href="snp_viewTable.cgi?pop=''') != -1:
            population_list.append(element)
    return categories, population_list

def parse_IMG_tags_out_of_category(category):
    if "<IMG" in category:
        category = category[0:category.find("<IMG")]
    return category

def parse_BR_tags_out_of_category(category):
    br = "<BR>", "<br>"
    if category.endswith(br):
        category = category[0:len(category)-4]
    category = category.replace("<BR>", ' ')
    category = category.replace("<br>", ' ')
    return category

# Returns cleaned-up categories (ie. ss#, Population, etc.)
def parse_categories(categories):
    categories_list = []
    while categories.rfind('''<TH  bgcolor="silver">''') != -1:
        category_instance = categories.rfind('''<TH  bgcolor="silver">''')
        end_tag_instance = categories.rfind('''</TH>''')
        categories_list.insert(0, categories[(category_instance+22):end_tag_instance])
        categories = categories[0:category_instance]
    
    for index in range(len(categories_list)):
        categories_list[index] = parse_IMG_tags_out_of_category(categories_list[index])
        categories_list[index] = parse_BR_tags_out_of_category(categories_list[index])
    return categories_list


# Extraction functions to parse allelic frequency data from populations

# Returns whether or not the particular population in population_list has an ss_numb
def ss_numb_in_population(population):
    if '''<a href="snp_ss.cgi?ss=''' in population:
        return True
    else:
        return False

def extract_ss_numb(population):
    #SS_numb START: after '''<a href="snp_ss.cgi?ss='''
    #SS_numb END: before the '''">''' immediately after '''<a href="snp_ss.cgi?ss=''' 
    if ss_numb_in_population(population):
        ss_numb = population[population.find('''<a href="snp_ss.cgi?ss=''')+23:population.find('''">''',
                             population.find('''<a href="snp_ss.cgi?ss='''))]
        last_index = population.find('''">''', population.find('''<a href="snp_ss.cgi?ss=''')) + 2
    else:
        ss_numb = ''
        last_index = 0
    return ss_numb, last_index

def extract_population_name(population, last_index):
    #population_name START: after the '''">''' immediately after '''<a href="snp_viewTable.cgi?pop='''
    #population_name END: before the '''</a>''' that occurs after '''<a href="snp_viewTable.cgi?pop='''
    population_name = population[population.find('''">''', population.find('''<a href="snp_viewTable.cgi?pop='''))+2:
                                     population.find('''</a>''', population.find('''<a href="snp_viewTable.cgi?pop='''))]
    last_index = population.find('''</a>''', population.find('''<a href="snp_viewTable.cgi?pop=''')) + 5
    return population_name, last_index

def extract_group(population, last_index):
    start_point = population.find('''<td >''', last_index) + 5
    group = population[start_point:population.find('''</td>''', start_point)]
    last_index = population.find('''</td>''', start_point) + 5
    return group, last_index

def extract_chrom_cnt(population, last_index):
    start_point = population.find('''<td >''', last_index) + 5
    chrom_cnt = population[start_point:population.find('''</td>''', start_point)]
    chrom_cnt = chrom_cnt.strip()
    last_index = population.find('''</td>''', start_point)
    return chrom_cnt, last_index

def extract_source(population, last_index):
    start_point = population.find('''<td >''', last_index) + 5
    source = population[start_point:population.find('''</td>''', start_point)]
    source = source.strip()
    last_index = population.find('''</td>''', start_point)
    return source, last_index

def extract_allele_combos(num_of_allele_combos, population, last_index):
    # This function works even if there are identical allele combos
    allele_combos = []
    start_point = population.find('''<FONT  size="-1">''', last_index) + 17
    for i in range(num_of_allele_combos):
        allele_combo = population[start_point:population.find('''</FONT>''', start_point)]
        allele_combos.append(allele_combo)
        last_index = start_point + 5
        start_point = population.find('''<FONT  size="-1">''', population.find('''</FONT>''', start_point)) + 17
    for j in range(num_of_allele_combos):
        allele_combos[j] = allele_combos[j].strip()
    return allele_combos, last_index

def extract_HWP(population, last_index):
    # This function works even if the last allele_combo was ''
    start_point = population.find('''<FONT  size="-1">''', last_index) + 17
    HWP = population[start_point:population.find('''</FONT>''', start_point)]
    HWP = HWP.strip()
    last_index = population.find('''</FONT>''', start_point)
    return HWP, last_index
    
def extract_alleles(num_of_alleles, population, last_index):
    alleles = []
    start_point = population.find('''<FONT  size="-1">''', last_index) + 17
    for i in range(num_of_alleles):
        if start_point != 16:   #ie. if the population.find returned -1 because no more '''<FONT  size="-1">'''s were found, + 17
            allele = population[start_point:population.find('''</FONT>''', start_point)]
            alleles.append(allele)
            last_index = start_point + 5
            start_point = population.find('''<FONT  size="-1">''', population.find('''</FONT>''', start_point)) + 17
        else:
            alleles.append('')
    for j in range(num_of_alleles):
        alleles[j] = alleles[j].strip()
    return alleles, last_index

# Master function to compile the list of lists (of lists) that holds all the interesting allelic frequency data
def parse_population_list(num_of_allele_combos, num_of_alleles, population_list, master_data_list):
    for index in range(len(population_list)):
        last_index = 0
        ss_numb = ''
        ss_numb, last_index = extract_ss_numb(population_list[index])
        population_name, last_index = extract_population_name(population_list[index], last_index)
        group, last_index = extract_group(population_list[index], last_index)
        chrom_cnt, last_index = extract_chrom_cnt(population_list[index], last_index)
        source, last_index = extract_source(population_list[index], last_index)
        allele_combos, last_index = extract_allele_combos(num_of_allele_combos, population_list[index], last_index)
        HWP, last_index = extract_HWP(population_list[index], last_index)
        alleles, last_index = extract_alleles(num_of_alleles, population_list[index], last_index)
            
        master_data_list.append([ss_numb, population_name, group, chrom_cnt, source, allele_combos, HWP, alleles])
    return master_data_list


#BEGIN ACTUAL PROGRAM
search_term = "rs185079"     # example search_term for now; will be returned by rest of program when finished    
search_file_name = "%s_dbSNP.html" % search_term

dbSNP_file = open(search_file_name, 'w')
URL = parse_for_dbSNP_search(search_term)
get_dbSNP_search_file(URL, dbSNP_file)
dbSNP_file.close()

dbSNP_file = open(search_file_name, 'r')
freq_line = extract_allelic_freq_line(dbSNP_file)
dbSNP_file.close()

if freq_line != '':
    TR_list = divide_freq_line_into_TRs(freq_line)
    categories = ''
    population_list = []

    categories, population_list = extract_categories_and_population_TRs(categories, population_list, TR_list)

    categories_list = []
    categories_list = parse_categories(categories)
    num_of_categories = len(categories_list)
    num_of_allele_combos = categories_list.count('A/A') + categories_list.count('A/T') + categories_list.count('A/C') + categories_list.count('A/G') + categories_list.count('T/A') + categories_list.count('T/T') +categories_list.count('T/C') + categories_list.count('T/G') + categories_list.count('C/A') + categories_list.count('C/T') + categories_list.count('C/C') + categories_list.count('C/G') + categories_list.count('G/A') + categories_list.count('G/T') + categories_list.count('G/C') + categories_list.count('G/G')
    num_of_alleles = categories_list.count('A') + categories_list.count('T') + categories_list.count('C') + categories_list.count('G')

    master_data_list = []
    master_data_list.append(categories_list)

    master_data_list = parse_population_list(num_of_allele_combos, num_of_alleles, population_list, master_data_list)

    for row in master_data_list:
        print row
else:
    print '''Sorry, there is no frequency data.'''

Sample Output

['ss#', 'Population', 'Individual Group', 'Chrom. Sample Cnt.', 'Source', 'A/A', 'A/G', 'G/G', 'HWP', 'A', 'G']
['ss16081968', 'HapMap-CEU', 'European', '118', 'IG', ['0.983', '0.017', ''], '1.000', ['0.992', '0.008']]
['', 'HapMap-HCB', 'Asian', '90', 'IG', ['0.556', '0.356', '0.089'], '0.584', ['0.733', '0.267']]
['', 'HapMap-JPT', 'Asian', '90', 'IG', ['0.533', '0.356', '0.111'], '0.371', ['0.711', '0.289']]
['', 'HapMap-YRI', 'Sub-Saharan African', '120', 'IG', ['1.000', '', ''], '1.000', ['', '']]
['', 'CHMJ', 'Asian', '74', 'IG', ['', '', ''], '0.757', ['0.243', '']]
['ss24106683', 'AFD_EUR_PANEL', 'European', '48', 'IG', ['0.917', '0.083', ''], '1.000', ['0.958', '0.042']]
['', 'AFD_AFR_PANEL', 'African American', '44', 'IG', ['1.000', '', ''], '1.000', ['', '']]
['', 'AFD_CHN_PANEL', 'Asian', '48', 'IG', ['0.583', '0.333', '0.083'], '0.655', ['0.750', '0.250']]
  • The categories in master_data_list[0] correspond to the data items in each of the following rows.
  • For convenience, the allele_combos frequencies and the allele frequencies were collected in to their own lists.

If no frequency data is given by dbSNP, the following will be output:

Sorry, there is no frequency data.
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