Beginning Python for Bioinformatics

Let me start by saying that Python for Bioinformatics (Chapman & Hall/Crc Mathematical & Computational Biology) is a massive book, massive in a way that it contains a lot of material. I still didn’t have enough time to check everything, but I’m well into the first section of the book that gives an initial view of Python and how to program it.

The initial section of the book is well written (I’m not going criticize the book in terms of good/poor English, as I’m not well qualified to do that), and gives a clear perspective on how to program Python for scientists, who are the main target demographic of the book. Of course, it always help to have some basic knowledge of command line shells, but the book also includes some explanations of IDLE and other Python-capable IDEs. I cannot say that I read this section with the enough care and attention, but what I can say is that you won’t miss a beat with PfB, as it has more material than I expected. I still have to start with the more advanced topics, like BioPython and so forth, what I plan to do in the coming month, and as I don’t have a lot of experience with BioPython, I’m looking forward to it.

On the other hand I have a small-ish complaint, that maybe is more about style than substance. I don’t like the design of the book, the way the code interleaves with the text and the way the code explanations are presented. Most of the code blocks are followed by a careful explanation, but this explanation works as a figure label for the code block. That is quite annoying because there are too many stops in the text fluidity as one tends to lose attention to it (my case, not exactly everyone’s).

Another minor detail is the use of “he” every time scientists are referred (one example is on page 3 on the second phrase of the introduction). The (politically) correct would be to use “he or she” or “she or he” (but that’s OK with me).

I will try to post more complete reviews of the sections that I don’t master. I would also like to thank Sebastian for sending me a copy of the book.

So, I’m closing the blog, maybe for good, maybe not. I haven’t been updating it and some other responsibilities are consuming my spare time.

I would like to thank everyone that contributed, commented and read it. You have my deepest appreciation. My work is fulfilled if I helped at least one person along the way.

Cheers
Paulo

PS: there’s the wiki, so register and help me improve it.
PS II: sorry that I couldn’t finish the last project. Maybe some other time.

I’m slowly moving the posts from the blog to a wiki. It makes easier to display post series and allows people to modify/enhance/discuss.

The wiki address is http://wiki.genedrift.org.

Thanks to the comments and suggestions to the last post, it’s possible to make now a more pythonic and clearly generic database update class. Let’s check how the “generic” update/edit entry function is currently:

def update_data(self, values_list):
    '''edits and updates fields'''

    if sys.platform == 'darwin':
        (cursor, database) = link_db(self.db_path)
    else:
        (cursor, database) = link_db()

    cursor.execute("UPDATE bac SET  projects = ?, comments = ?, temperature = ?, cell = ?, box = ?, tubes = ?, chromosome = ?, sdate = ?, clone = ?, source
	= ?, location1 = ?, startpos = ?, endpos = ?,
	gene = ?, genelink = ?, dnaex = ?, validation = ?, pcr = ?, refs = ?, antibiotic = ? WHERE idbac = ?",
    values_list['projects'], values_list['comments'], values_list['temperature'], values_list['cell'], values_list['box'], values_list['tubes'],
    values_list['chromo'], values_list['date'], values_list['clone'], values_list['source'], values_list['location'], values_list['start']
    values_list['end'], values_list['gene'], values_list['genelink'], values_list['dna'], values_list['validation'], values_list['pcr'],
    values_list['refs'], values_list['antibiotic'], values_list['idbac']))

    database.commit()
    database.close()

which is really ugly and, although it works, is not really useful outside this small project. Based on the comments the best option was to use placeholders and a dictionary, similar to the approach used on the insert data function. Pre-formatting a string to have both the field name to be updated and a placeholder (for instance :idbac) that will receive the values

update = ','.join(['%s=:%s' % (y, y) for y in values_list])

where update is the string we want and values_list is the dictionary with all the key-value pairs. I tried this approach, using this structure in the generic function, but then I decided that the best alternative was to put this join in the derived class function and pre-populate the string with the values and then send this string directly to the update function. In the end I opted to use this

update = ','.join(['%s=\"%s\"' % (y, values_list[y]) for y in values_list])

The latter is slightly different to what was suggested. The original one would create a tuple with the keys from the dictionary, making for instance sdate:sdate. With all these place holders just pass the dictionary and you have all the values inserted. This would be handy if the insert string was being created on the “generic” function. If we move this to the derived class, we can use the the alternative, keeping in mind that the values parsed should be surrounded by quotes, otherwise the SQL UPDATE statement will have problems with spaces and other foreign characters that should not be there. So instead of placeholders we will have gene:"PTEN" and we can attache this joined string to the actual commands. We then can move all the machinery from the “generic” function that can be written as

def update_data(self, update_string):
    '''edits and updates fields'''

    if sys.platform == 'darwin':
        (cursor, database) = link_db(self.db_path)
    else:
        (cursor, database) = link_db()
    cursor.execute(update_string)

    database.commit()
    database.close()

That’s it, very elegant (we will see the derived class in the next post). And finishing our generic class, we would need a delete function, so the user can eliminate entries that he/she doesn’t want anymore. It’s also a very simple function

def delete_data(self, delete_string):
    '''deletes one field'''

    if sys.platform == 'darwin':
        (cursor, database) = link_db(self.db_path)
    else:
        (cursor, database) = link_db()
    cursor.execute(delete_string)

    database.commit()
    database.close()

We will check the delete string next time. Again, I would like to thank for all the comments, it has been really helpful for me.

Previously in the series:
Part 1
Part 2
Part 3
Part 4
Part 5
Part 6
Part 7

And we’re back. After a couple of weeks of inactivity we will get back to our small soap-opera pf Python, wxPython and SQLite. Continuing in our database management code let’s check two other functions that changed since our first inception of the code. The first one is the insert_data function that looks like this now

def insert_data(self, values_list, insert_string):
    '''inserts data in the database'''

    if sys.platform == 'darwin':
        (cursor, database) = link_db(self.db_path)
    else:
        (cursor, database) = link_db()

    cursor.execute(insert_string % self.table_name, values_list)

    database.commit()
    database.close()

Basically no changes, apart from the obvious check for the current running operating system, which was explained in the last post. The other function to check is the update_data. This function is new and it wasn’t in the first version, but as it can be seen it has a problem being a “generic” function, because it contains information pertained to the table and database being used in the interface. This function basically received information that needs to be updated in the table’s fields and by using the SQL UPDATE ... SET edits and updates data in the changed fields. I have tried several different syntaxes to make the execute generic, mainly trying to pre-format the string without success. IF anyone reading this can help, I’d appreciate.

def update_data(self, values_list):
    '''edits and updates fields'''

    if sys.platform == 'darwin':
        (cursor, database) = link_db(self.db_path)
    else:
        (cursor, database) = link_db()

    cursor.execute("UPDATE bac SET  projects = ?, comments = ?, temperature = ?, cell = ?, box = ?, tubes = ?, chromosome = ?, sdate = ?, clone = ?, source = ?, location1 = ?, startpos = ?, endpos = ?,
	gene = ?, genelink = ?, dnaex = ?, validation = ?, pcr = ?, refs = ?, antibiotic = ? WHERE idbac = ?",
    values_list['projects'], values_list['comments'], values_list['temperature'], values_list['cell'], values_list['box'], values_list['tubes'],
    values_list['chromo'], values_list['date'], values_list['clone'], values_list['source'], values_list['location'], values_list['start'],  values_list['end'],
    values_list['gene'], values_list['genelink'], values_list['dna'], values_list['validation'], values_list['pcr'],
    values_list['refs'], values_list['antibiotic'], values_list['idbac']))

    database.commit()
    database.close()

Anyway, I will explain the logic of the command (OK for a stop gap, but not as a definite solution). values_list is a dictionary that is passed to the function and contains the field names as keys and the new/changed information as values. The execute method simply parses the values from each key in the update string which is then sent to the database and table to be changed. Everything is committed and the database is closed.

As this is a “generic” function from a “generic” class the ideal scenario would be to the function to receive a pre-formatted string with all the information, as in the insert data function, and update the information in the database.

I would like to thank in advance anyone that can comment on this. Next time we will continue checking the generic class and finalize this part in order to start with the interface build process.

Previously in the series:
Part 1
Part 2
Part 3
Part 4
Part 5
Part 6

Let’s get back to our SQLite and wxPython project. We haven’t seen anything on wxPython yet, and we will check the interface only on the next post. For now, let’s see some extra code added to the SQLite access class. Remember that we have a generic class and one class derived from it that would work on accessing specific tables in our database file.

When we last covered the db access routines, there was no search for an entry (the function returned everything in the table no matter what), there was no update function in case someone would want to modify an entry and there was no delete method if you wanted to delete something. In the meantime, I added all of this functionality (and some other) to the generic class and extended it to the class derived from it. Let’s check how the generic class is now (you will notice that there is an issue in one of the methods, if someone can help me I’d appreciate. More details later.)

class DB_Generic():
    '''generic class to add DB functionality'''
    def __init__(self, table_name, db_path = ''):
        #par= name of the table to be used
        self.table_name = table_name
        if len(db_path) > 0:
            self.db_path = db_path
            print db_path

    def get_data_generic(self, range = 1, bac_to_get = 0):
        '''gets the data from the database'''       

        if sys.platform == 'darwin':
            (cursor, database) = link_db(self.db_path)
        else:
            (cursor, database) = link_db()

        if range == 1:
            cursor.execute("""SELECT * FROM %s""" % self.table_name)
        elif range == 2:
            cursor.execute("""SELECT * FROM %s where idbac = %d""" % (self.table_name, bac_to_get))

        table_data = cursor.fetchall()
        raw_data = []
        for i in table_data:
            raw_data.append(list(i))

        self.table_data = raw_data
        database.close()

    def insert_data(self, values_list, insert_string):
        '''inserts data in the database'''

        if sys.platform == 'darwin':
            (cursor, database) = link_db(self.db_path)
        else:
            (cursor, database) = link_db()

        cursor.execute(insert_string % self.table_name, values_list)

        database.commit()
        database.close()

    def update_data(self, values_list):
        '''edits and updates fields'''

        if sys.platform == 'darwin':
            (cursor, database) = link_db(self.db_path)
        else:
            (cursor, database) = link_db()

        #change this to generic!!!!!!!!!!!!
        cursor.execute("UPDATE bac SET  projects = ?, comments = ?, temperature = ?, cell = ?, box = ?, tubes = ?, chromosome = ?, sdate = ?, clone = ?, source = ?, location1 = ?, startpos = ?, endpos = ?,
		gene = ?, genelink = ?, dnaex = ?, validation = ?, pcr = ?, refs = ?, antibiotic = ? WHERE idbac = ?",
        (values_list['projects'], values_list['comments'], values_list['temperature'], values_list['cell'], values_list['box'], values_list['tubes'],
         values_list['chromo'], values_list['date'], values_list['clone'], values_list['source'], values_list['location'], values_list['start'], values_list['end'],
         values_list['gene'], values_list['genelink'], values_list['dna'], values_list['validation'], values_list['pcr'],
         values_list['refs'], values_list['antibiotic'], values_list['idbac']))

        database.commit()
        database.close()

    def delete_data(self, delete_string):
        '''deletes one field'''

        if sys.platform == 'darwin':
            (cursor, database) = link_db(self.db_path)
        else:
            (cursor, database) = link_db()
        cursor.execute(delete_string)

        database.commit()
        database.close()

In the next couple of posts we’ll dissect each function and see what’s going on. The class definition wasn’t changed, so we start with get_data_generic

def get_data_generic(self, range = 1, bac_to_get = 0):
	'''gets the data from the database'''       

	if sys.platform == 'darwin':
		(cursor, database) = link_db(self.db_path)
	else:
		(cursor, database) = link_db()

	if range == 1:
		cursor.execute("""SELECT * FROM %s""" % self.table_name)
	elif range == 2:
		cursor.execute("""SELECT * FROM %s where idbac = %d""" % (self.table_name, bac_to_get))

	table_data = cursor.fetchall()
	raw_data = []
	for i in table_data:
		raw_data.append(list(i))

	self.table_data = raw_data
	database.close()

The first difference we notice here is the sys.platform usage. This is required if we intend to package our application as an OS X app, using py2app. When a Python/wxPython application is packaged in OS X, the actual application executable is inside the a directory named after the application (or whatever you set up). In our case here we don’t provide a way for the Python script to receive the path and name for the database on a command line, as we expect it to be in the executable’s current directory. Because of that we need to provide a “config” file (in our case here a one-line text file with the database path) inside the application wrapper, something we will see in the end of the series.

Another modification here is the range parameter and the addition of the bac_to_get parameter. Notice that both parameters have a value assigned to it. This means that they are optional, the function’s call can pass them or not. If it doesn’t pass, their value will be the one assigned on the function declaration. So, here if we are interested in getting all bacs, range will have the value of 1 and we don’t need to worry about it. If we want an specific bac we will pass range as 2 and then pass the bac_to_get ID to be returned.

A final change/addition is that we added a new select statement for the cases when range equals 2. This time we are adding the bac ID to be returned.

Previously in the series:
Part 1
Part 2
Part 3
Part 4
Part 5

Just illustrating my point (or lack of), an animation about the commits of RoR to its repository. Notice the jump after it was migrated to Github

Ruby on Rails from Ilya Grigorik on Vimeo.

Sorry for the non-Python post.

I start this post with an apology. I usually don’t rant or vent here, which are feelings that I usually reserve to my personal blog.

I don’t use BioPython, never used it. I have it installed in my systems, but I never wrote a piece of code importing BioPython routines. But I subscribe to their mailing lists, both user and developer. I maybe have written once to the list, and I just follow the discussions there.

Since last year one of the main topics has been the possibility of moving BioPython from CVS to another version control system. Yes, you read it right. It’s 2009 and BioPython uses CVS and their version control system. Soon, CVS will be like typewriters and LPs to young developers. Last stable release of CVS was sometime in 2005, what in interwebs time is equivalent to something like 1972. Since 2005, Subversion has taken the world of version control by storm, and Git is getting also very strong, not to mention Bazaar, Darcs, Mercurial and some others that I might not be aware of.

This is a discussion that have been dragging for sometime in the list. And it’s a shame, a clear lack of leadership from whoever is (not) leading the project. BioRuby is Git, BioPerl SVN and BioPython is CVS, because they “need to care for the legacy developers”. It’s like MSFT keeping two copies of the Notepad executable because they needed to cater to legacy applications, but with a different scale of course. With the current Python steam in the non-bioinformatics and bioinformatics community is very sad to see BioPython not evolving (before you ask me, no, I’m not interested in helping, not the way things are now). Perl which is language forever-in-waiting for its holy grail (Perl 6) has a strong community behind it, and more important an excellent leadership, that’s not scare of making decisions.

So, if you’re still using CVS, it’s 2009!

We have seen how to connect, get and insert data (at least theoretically) in the database. Now, a little not about the SQL engine of choice here: SQLite. SQLite databases have the main characteristic that they are self-contained files. Also it does not require an installation, works without a server and works pretty well in most operating systems.

Basically for the type of application we’re developing here, SQLite seems ideal. It eliminates a lot of infrastructure that would be needed if we were working with MySQL or postgresql. We don’t need a server or know how to configure users or manage the databases and tables. All we need is contained in a single file that can be transported from system to system and can be accesed from the computers used in the lab, mainly XP and OS X. Also some web frameworks (Rails and Django, for instance) can use SQLite, so in the end we can have a desktop application and a web application accessing the same file without extra configuration.

Now the database created for this application has 8 tables and almost no relationships among them. SQLite allows the creation of relationships but in our case only a couple of cases were required. For the table we are using at the moment (bac) there is no need for relationships, although there are some fileds that can benefit from a more relational structure. Also SQLite don’t have the same data types that are found on the bigger SQL engines. All values can be stored as text, integer, real (floating point numbers), null and blob (verbose type, what you store is what you get). As actual types, you can set columns as Boolean and Data for instance and SQLite will understand them. If you have no experience in creating databases, let’s check again the table we are using in this small project. First, I would recommend the use of some SQLite database editor. You can find pretty good ones for any computer system and there is even a Firefox extension that allows you to edit some files. Editors make it easier to generate the SQL table creation scripts and make easier to visualize what we are doing.

So, the table bac looks like

CREATE TABLE bac
(idbac INTEGER PRIMARY KEY,
clone Text,
sdate Date,
source Text,
gene Text,
chromosome Text,
startpos Integer,
endpos Integer,
antibiotic Text,
location1 Text,
temperature Integer,
tubes Integer,
box Integer,
cell Integer,
dnaex Boolean,
validation Boolean,
pcr Boolean,
projects Text,
comments Text,
genelink Text,
refs Text);

If you go back to our last post, you will see that in the insert statement there is no mention of the idbac field. We don’t actually insert ay value there, the values that populate this field are created automatically. And idbac is our primary key, meaning it’s the unique identifier of each bac we insert in this table. And in SQLite a integer primary key is automatically incremented whenever values are inserted in the table. So our first insertion will create idbac 1, the second will create idbac 2 and so on.

I’m not going to enter in details about database development and administration, but it’s usual and safe to create tables with an auto-incremental integer primary keys. These fields, apart from make it easier t identify records, make access to such records faster and are great when relationships among tables are set. Let’s say that we had a column user in our bac table. And let’s say we had an user table with two columns: user_id and name, user_id being a auto-increment primary key. The user column in back could be linked with the user_id column in the user table, in what we call a one-to-many relationship (one user can insert as many bacs as he wants). One day we want to know who is actually working in the lab and we want to check how many bacs were catalogued by each user. We can easily search the user table and extract information from bacs at the same time thanks to the relationship between the tables. And the result should be returned quite quickly, as we are only searching integers.

All the other fields/columns in our table are straightforward to understand. They are basically related to the type of data they need to store. validation is a boolean because the bac might have been validated or not, just as danex (DNA extraction). At the same time, the number of tubes stored in the freezer will always be an integer. So, why does temperature is an integer? Because we can only store bacs in two type of freezers: -80 (ultra freezers) or -20 (regular freezer that we can have at home), and we don’t need to worry about fractional numbers.

Well, this is a very short and limited explanation of tables and SQLite. The web is full of resources about it, so next time we will get back to Python.

Previously in the series:
Part 1
Part 2
Part 3
Part 4

Let’s continue building our small db app. As mentioned in the previous post we need now to instantiate a specific class from our generic SQLite access class. In order to do this we just have to declare a new class and its type will be DB_Generic.

class Bac(DB_Generic)

This new class is called Bac because it’s linked to the bac table in our database file. A side note, bacs are Bacterial Artificial Chromosomes and are used in different molecular biology techniques. Mainly in our case bacs have incorporated human DNA segments and are used as probes for deletion, duplication, etc studies.

Now, back to our Python code, as soon as we instantiate our generic class, the object (class) we create has access to all methods and functions from the parent class (by using self), but we still need to create functionality and expose other methods that can be accessed from a class object derived from Bac.

Our instantiated class will be

class Bac(DB_Generic):
    def __init_(self):
        self.bac_data = []
        DB_Generic.__init__(self, 'bac')

    def get_data(self):
        return self.get_data_generic()

    def load_data(self):
        pass

    def add_data(self, values_list):
        insert_string = """INSERT INTO %s (projects, comments, temperature, cell, box, tubes, chromosome, sdate, clone, source,
        location1, startpos, endpos, gene, genelink, dnaex, validation, pcr, refs, antibiotic)
        VALUES (:projects, :comments, :temperature, :cell, :box, :tubes, :chromo, :date, :clone, :source, :location, :start,
        :end, :gene, :genelink, :dna, :validation, :pcr, :refs, :antibiotic)"""
        self.insert_data(values_list, insert_string)

Pretty simple so far, as we don’t have a lot of declared methods. Let’s check one by one

def __init_(self):
    DB_Generic.__init__(self, 'bac')

The only line is the initialization required by the parent class, and we’re passing the value that is the table to be accessed.

def get_data(self):
	self.get_data_generic()
	return self.table_data

The get_data function returns the all elements in our table (So far, we still don’t have an elegant range option) and has one too many lines in it. We will get rid of some useless code here in the future, but it’s OK the way it is. Basically this code access the get_data_generic from the parent class and gets all the values stored in the table.

There is a function not yet complete that will load data, and will be used in the future. And the last one is the function that actually adds the data to the table with a SQL insert statement

def add_data(self, values_list):
	insert_string = """INSERT INTO %s (projects, comments, temperature, cell, box, tubes, chromosome, sdate, clone, source,
	location1, startpos, endpos, gene, genelink, dnaex, validation, pcr, refs, antibiotic)
	VALUES (:projects, :comments, :temperature, :cell, :box, :tubes, :chromo, :date, :clone, :source, :location, :start,
	:end, :gene, :genelink, :dna, :validation, :pcr, :refs, :antibiotic)"""
	self.insert_data(values_list, insert_string)

In this function, we have a large string with all the SQL insert options. A SQL insert statement is divided into two parts, one where you point where to insert the values and another where you input the values. Usually simple insert statements will have this structure

INSERT INTO my_table_name (table_column1, table_column2) VALUES (value1, value2);

So, we have the table we want to insert values into, its columns and the values we set for each column. After executed this will put value1 into table_column1 and value2 into table_column2. The actual syntax can vary a bit for different SQL engines but the structure is identical in most cases. Pretty simple.

For our insert string above, there are some aspects to call for attention. Again note the triple quote around the statement. This make sure that it’s not changed and parsed correctly. We also have a %s for the table name, which will be parsed by the parent class function that insert values, then a list of all the tables in the database and then a list of values to insert. And why the values to be inserted have this :value syntax? Because we are previously storing the values in a dictionary, and the “:” indicates that we need to get the dictionary value for the correspondent key.

The insert string, and the list of values (actually a dictionary, not the best variable/object name I must admit) is then sent to the parent class to be inserted. Storing the values to be inserted in a dictionary is OK for a one time insert case, where the values are obtained from a form. If you are parsing a large CSV or TSV file, ideally it’s better to put it in a list, and dump them at the same time.

We’re progressing. Next we will take a look on some simple SQL table structure and then move to create the form to insert the values and check the table.

Previously in the series:
Part 1
Part 2
Part 3

evi