# So much to do, so little time

Trying to squeeze sense out of chemical data

## Molecules & MongoDB – Numbers and Thoughts

In my previous post I had mentioned that key/value or non-relational data stores could be useful in certain cheminformatics applications. I had started playing around with MongoDB and following Rich’s example, I thought I’d put it through its paces using data from PubChem.

Installing MongoDB was pretty trivial. I downloaded the 64 bit version for OS X, unpacked it and then simply started the server process:

 1 $MONGO_HOME/bin/mongod --dbpath=$HOME/src/mdb/db

where $HOME/src/mdb/db is the directory in which the database will store the actual data. The simplicity is certainly nice. Next, I needed the Python bindings. With easy_install, this was quite painless. At this point I had everything in hand to start playing with MongoDB. ### Getting data The first step was to get some data from PubChem. This is pretty easy using via their FTP site. I was a bit lazy, so I just made calls to wget, rather than use ftplib. The code below will retrieve the first 80 PubChem SD files and uncompress them into the current directory.  12345678910111213 import glob, sys, os, time, random, urllib def getfiles(): n = 0 nmax = 80 for o in urllib.urlopen('ftp://ftp.ncbi.nlm.nih.gov/pubchem/Compound/CURRENT-Full/SDF/').read() o = o.strip().split()[5] os.system('wget %s/%s' % ('ftp://ftp.ncbi.nlm.nih.gov/pubchem/Compound/CURRENT-Full/SDF/', o)) os.system('gzip -d %s' % (o)) n += 1 sys.stdout.write('Got n = %d, %s\r' % (n,o)) sys.stdout.flush() if n == nmax: return This gives us a total of 1,641,250 molecules. ### Loading data With the MongoDB instance running, we’re ready to connect and insert records into it. For this test, I simply loop over each molecule in each SD file and create a record consisting of the PubChem CID and all the SD tags for that molecule. In this context a record is simply a Python dict, with the SD tags being the keys and the tag values being the values. Since i know the PubChem CID is unique in this collection I set the special document key “_id” (essentially, the primary key) to the CID. The code to perform this uses the Python bindings to OpenBabel:  1234567891011121314151617181920212223242526272829303132333435363738394041424344454647 from openbabel import * import glob, sys, os from pymongo import Connection from pymongo import DESCENDING def loadDB(recreate = True): conn = Connection() db = conn.chem if 'mol2d' in db.collection_names(): if recreate: print 'Deleting mol2d collection' db.drop_collection('mol2d') else: print 'mol2d exists. Will not reload data' return coll = db.mol2d obconversion = OBConversion() obconversion.SetInFormat("sdf") obmol = OBMol() n = 0 files = glob.glob("*.sdf") for f in files: notatend = obconversion.ReadFile(obmol,f) while notatend: doc = {} sdd = [toPairData(x) for x in obmol.GetData() if x.GetDataType()==PairData] for entry in sdd: key = entry.GetAttribute() value = entry.GetValue() doc[key] = value doc['_id'] = obmol.GetTitle() coll.insert(doc) obmol = OBMol() notatend = obconversion.Read(obmol) n += 1 if n % 100 == 0: sys.stdout.write('Processed %d\r' % (n)) sys.stdout.flush() print 'Processed %d molecules' % (n) coll.create_index([ ('PUBCHEM_HEAVY_ATOM_COUNT', DESCENDING) ]) coll.create_index([ ('PUBCHEM_MOLECULAR_WEIGHT', DESCENDING) ]) Note that this example loads each molecule on its own and takes a total of 2015.020 sec. It has been noted that bulk loading (i.e., insert a list of documents, rather than individual documents) can be more efficient. I tried this, loading 1000 molecules at a time. But this time round the load time was 2224.691 sec – certainly not an improvement! Note that the “_id” key is a “primary key’ and thus queries on this field are extremely fast. MongoDB also supports indexes and the code above implements an index on the PUBCHEM_HEAVY_ATOM_COUNT field. ### Queries The simplest query is to pull up records based on CID. I selected 8000 CIDs randomly and evaluated how long it’d take to pull up the records from the database:  12345678 from pymongo import Connection def timeQueryByCID(cids): conn = Connection() db = conn.chem coll = db.mol2d for cid in cids: result = coll.find( {'_id' : cid} ).explain() The above code takes 2351.95 ms, averaged over 5 runs. This comes out to about 0.3 ms per query. Not bad! Next, lets look at queries that use the heavy atom count field that we had indexed. For this test I selected 30 heavy atom count values randomly and for each value performed the query. I retrieved the query time as well as the number of hits via explain().  12345678910111213 from pymongo import Connection def timeQueryByHeavyAtom(natom): conn = Connection() db = conn.chem coll = db.mol2d o = open('time-natom.txt', 'w') for i in natom: c = coll.find( {'PUBCHEM_HEAVY_ATOM_COUNT' : i} ).explain() nresult = c['n'] elapse = c['millis'] o.write('%d\t%d\t%f\n' % (i, nresult, elapse)) o.close() A summary of these queries is shown in the graphs below. One of the queries is anomalous – there are 93K molecules with 24 heavy atoms, but the query is performed in 139 ms. This might be due to priming while I was testing code. ### Some thoughts One thing that was apparent from the little I’ve played with MongoDB is that it’s extremely easy to use. I’m sure that larger installs (say on a cluster) could be more complex, but for single user apps, setup is really trivial. Furthermore, basic operations like insertion and querying are extremely easy. The idea of being able to dump any type of data (as a document) without worrying whether it will fit into a pre-defined schema is a lot of fun. However, it’s advantages also seem to be its limitations (though this is not specific to MongoDB). This was also noted in a comment on my previous post. It seems that MongoDB is very efficient for simplistic queries. One of the things that I haven’t properly worked out is whether this type of system makes sense for a molecule-centric database. The primary reason is that molecules can be referred by a variety of identifiers. For example, when searching PubChem, a query by CID is just one of the ways one might pull up data. As a result, any database holding this type of data will likely require multiple indices. So, why not stay with an RDBMS? Furthermore, in my previous post, I had mentioned that a cool feature would be able to dump molecules from arbitrary sources into the DB, without worrying about fields. While very handy when loading data, it does present some complexities at query time. How does one perform a query over all molecules? This can be addressed in multiple ways (registration etc.) but is essentially what must be done in an RDBMS scenario. Another things that became apparent is the fact that MongoDB and its ilk don’t support JOINs. While the current example doesn’t really highlight this, it is trivial to consider adding say bioassay data and then querying both tables using a JOIN. In contrast, the NoSQL approach is to perform multiple queries and then do the join in your own code. This seems inelegant and a bit painful (at least for the types of applications that I work with). Finally, one of my interests was to make use of the map/reduce functionality in MongoDB. However, it appears that such queries must be implemented in Javascript. As a result, performing cheminformatics operations (using some other language or external libraries) within map or reduce functions is not currently possible. But of course, NoSQL DB’s were not designed to replace RDBMS. Both technologies have their place, and I don’t believe that one is better than the other. Just that one might be better suited to a given application than the other. Written by Rajarshi Guha February 8th, 2010 at 2:18 am ## Cheminformatics and Non-Relational Datastores with 9 comments Over the past year or so I’ve been seeing a variety of non-relational data stores coming up. They also go by terms such as document databases or key/value stores (or even NoSQL databases). These systems are alternatives to traditional RDBMS’s in that they do not require explicit schema defined a priori. While they do not offer transactional guarantees (ACID) compared to RDBMS’s, they claim flexibility, speed and scalability. Examples include CouchDB, MongoDB and Tokyo Cabinet. Pierre and Brad have described some examples of using CouchDB with bioinformatics data and Rich has started a series on the use of CouchDB to store PubChem data. Having used RDBMS’s such as PostgreSQL and Oracle for some time, I’ve wondered how or why one might use these systems for cheminformatics applications. Rich’s posts describe how one might go about using CouchDB to store SD files, but it wasn’t clear to me what advantage it provided over say, PostgreSQL. I now realize that if you wanted to store arbitrary chemical data from multiple sources a document oriented database makes life significantly easier compared to a traditional RDBMS. While Rich’s post considers SD files from PubChem (which will have the same set of SD tags), CouchDB and its ilk become really useful when one considers, say, SD files from arbitrary sources. Thus, if one were designing a chemical registration system, the core would involve storing structures and an associated identifier. However, if the compounds came with arbitrary fields attached to them, how can we easily and efficiently store them? It’s certainly doable via SQL (put each field name into ‘dictionary’ table etc) but it seems a little hacky. On the other hand, one could trivially transform an SD formatted structure to a JSON-like document and then dump that into CouchDB. In other words, one need not worry about updating a schema. Things become more interesting when storing associated non-structural data – assays, spectra and so on. When I initially set up the IU PubChem mirror, it was tricky to store all the bioassay data since the schema for assays was not necessarily identical. But I now see that such a scenario is perfect for a document oriented database. However some questions still remain. Most fundamentally, how does not having a schema affect query performance? Thus if I were to dump all compounds in PubChem into CouchDB, pulling out details for a given compound ID should be very fast. But what if I wanted to retrieve compounds with a molecular weight less than 250? In a traditional RDBMS, the molecular weight would be a column, preferably with an index. So such queries would be fast. But if the molecular weight is just a document property, it’s not clear that such a query would (or could) be very fast in a document oriented DB (would it require linear scans?). I note that I haven’t RTFM so I’d be happy to be corrected! However I’d expect that substructure search performance wouldn’t differ much between the two types of database systems. In fact, with the map/reduce features of CouchDB and MongoDB, such searches could in fact be significantly faster (though Oracle is capable of parallel queries).This also leads to the interesting topic of how one would integrate cheminformatics capabilities into a document-oriented DB (akin to a cheminformatics cartridge for an RDBMS). So it looks like I’m going to have to play around and see how all this works. Written by Rajarshi Guha February 4th, 2010 at 5:51 am ## A GPL3 Oracle Cheminformatics Cartridge with 10 comments Sometime back I had mentioned a new cheminformatics toolkit, Indigo. Recently, Dmitry from SciTouch let me know that they had also developed Bingo, an Oracle cartridge based on Indigo, to perform cheminformatics operations in the database. This expands the current ecosystem of Open Source database cartridges (PGChem, MyChem, OrChem) which pretty much covers all the main RDBMSs (Postgres, MyQSL and Oracle). SciTouch have also provided a live instance of their database and associated cartridge, so you can play with it without requiring a local Oracle install. (It’d be useful to provide some details of the hardware that the DB is running on, so that timing numbers get some context) Written by Rajarshi Guha January 24th, 2010 at 2:35 pm Posted in software Tagged with , , , ## Oracle Notes with 3 comments Some handy settings when running a query from the command line via sqlplus set echo off set heading on set linesize 1024 set pagesize 0 set tab on set trims on set wrap off -- might want to set column formats here -- e.g.: column foo format A10 spool stats -- dump results to stats.lst -- SQL query here spool off exit Written by Rajarshi Guha October 6th, 2009 at 2:26 pm Posted in Uncategorized Tagged with , ## R and Oracle with 2 comments It’s been a while since my last post, but I’m getting up to speed at work. It’s been less than a month, but there’s already a ton of cool stuff going on. One of the first things I’ve been getting to grips with is the data infrastructure at the NCGC, which is based around Oracle. One of my main projects is handling informatics for RNAi screening. As the data comes out of the pilots, they get loaded into the Oracle infrastructure. Being an R aficionado, I’m doing the initial, exploratory analyses (normalization, hit selection, annotation etc.) using R. Thus I needed to have a way to access an Oracle DB from R. This is supported by the ROracle package. But it turns out that the installation is a little non-obvious and I figured I’d describe the procedure (on OS X 10.5) for posterity. The first thing to do is to get Oracle from here. Note that this is the full Oracle installation and while it comes with 32 bit and 64 bit libraries, some of the binaries that are required during the R install are 64 bit only. After getting the zip file, extract the installation files and run the installation script. Since I just needed the libraries (as opposed to running an actual Oracle DB), I just went with the defaults and opted out of the the actual DB creation step. After installation is done, it’s useful to set the following environment variables:  123 export ORACLE_HOME=/Users/foo/oracle export LD_LIBRARY_PATH=$ORACLE_HOME/lib:$LD_LIBRARY_PATH export DYLD_LIBRARY_PATH=$ORACLE_HOME/lib:$DYD_LIBRARY_PATH With Oracle installed, execute the following  1$ORACLE_HOME/bin/genclntst

This will link a variety of object files into a library, which is required by the R package, but doesn’t come in the default Oracle installation.

The next thing is to get a 64 bit version of R from here and simply install as usual. Note that this will require you to reinstall all your packages, if you had a previous version of R around. Specifically, before installing ROracle, make sure to install the DBI package.

After installing R, get the ROracle 0.5-9 source package. Since there’s no binary build for OS X, we have to compile it ourselves. Before building, I like to CHECK the package to make sure that all is OK. Thus, the sequence of commands is

 1234 tar -zxvf ROracle_0.5-9.tar.gz R --arch x86_64 CMD CHECK ROracle R --arch x86_64 CMD BUILD ROracle R --arch x86_64 CMD INSTALL -l  /Users/guhar/Library/R/2.9/library ROracle_0.5-9.tar.gz

When I ran the CHECK, I did get some warnings, but it seems to be safe to ignore them.

At this stage, the ROracle package should be installed and you can start R and load the package. Remember to start R with the –arch x86_64 argument, since the ROracle package will have been built for the 64 bit version of R.

Written by Rajarshi Guha

June 17th, 2009 at 3:19 am

Posted in software

Tagged with , ,