Using Hadoop for Transcriptomics - An Example to Get Started

Using Hadoop for Transcriptomics - An Example to Get Started


In our previous commentary, we explained the advantages provided by Hadoop in distributed analysis of large data sets. Today we will work on a real life problem, namely, finding all K-mers in a set of short reads. Being true to our style (standing on the shoulder of giants), we shall only explain things that are not explained elsewhere in the web by other experts. When others have done better job than we can ever do, we are content with providing links.

Hadoop is a framework developed primarily for Java programmers. Those, who have familiarity with Java or C++ but never used Hadoop, will find the following example the most useful. Others will learn the general concepts, but may have to do a bit of work, if they want to port our code to their favorite languages. It is possible to write Hadoop codes in python by using APIs developed for python.

Let us get started. Finding all K-mers from a large set of sequences using Hadoop requires two steps - (i) installing Hadoop or finding computers where Hadoop is already installed, and (ii) writing, compiling and running Java code for the problem at hand. If you follow the example presented here mechanically, you should be able to install and run the code in Hadoop in less than 30 minutes. Then the hard work is to understand all the steps.

We tested our code in Windows (cygwin) and Linux (Fedora release 9, 64 bit), and encountered every possible pitfall a human being could possibly come across. Hopefully, you will not make the same mistakes as us, but if you do, the ‘Pitfalls’ section in the next commentary will be helpful. The Hadoop documentation written by Apache foundation is another excellent source of information, and we urge you to read it. One point of caution - some examples provided in the official documentation are not updated for the latest version of Hadoop. We will mention those differences in our ‘Pitfalls’ section.

i) Installing Hadoop

In our work, we used hadoop-0.20.2 that can be downloaded from here or here. We encountered some difficulties with a newer version - hadoop-0.20.203. Please check ‘Pitfalls’ section for details.

Hadoop is meant to be run in a computer cluster in distributed manner, but one can also set it up on a single node in standalone or pseudo-distributed manner. Our example will be based on single node standalone installation. Although single node operation does not take any advantage of Hadoop, it is great for testing codes and learning the general concepts. The Java code itself need not be changed for running it in a distributed cluster. We expect that you will not have to install Hadoop in a cluster, and you will find help from either your local computer administrator or Amazon cloud. If you have to do multi-node installation, Apache documentation is an excellent place to get started.

Please follow these steps for standalone installation of Hadoop -

a) Make sure Java is installed properly in your machine by compiling (‘javac’) and running (‘java’) HelloWorldApp.java from here. You will see ‘Hello World’ printed on your screen.

b) Download stable version of Hadoop from an Apache Download Mirror. We used hadoop-0.20.2.tar.gz.

c) Uncompress and untar the hadoop-0.20.2.tar.gz file. Your installation is complete. No need to do ‘configure’, ‘make’, etc. It is that easy (thanks to Java).

d) At this point, you will have ‘hadoop-0.20.2’ folder in your directory. Go inside ‘hadoop-0.20.2’ folder and run ‘ls’. You will see the following files and folders.

Hadoop executable is located in the ‘bin’ directory.

The ‘txt’ files are for general information.

The ‘jar’ files are compiled versions of various codes and libraries that we will use for testing and building our own code.

The ‘lib’ directory has additional libraries.

The ‘conf’ directory has configuration files.

The ‘docs’ directory has a copy of Apache documentation that you will find useful, if you are stuck in Yellowstone NP without any internet.

The ‘src’ directory has various source-codes. Among them, you can check the ‘src/examples’ for many excellent Hadoop examples.

e) Edit the file conf/hadoop-env.sh to make ‘JAVA_HOME’ to be the root of your Java installation. Alternatively, you can also define JAVA_HOME as an environment variable from you .cshrc or .bashrc file.

f) From inside ‘hadoop-0.20.2’ directory, run the following commands.

% mkdir input

% cp conf/*.xml input

% bin/hadoop jar hadoop-0.20.2-examples.jar grep input output ‘This’

% cat output/*

In the above steps, you executed an example provided by Hadoop creators. You copied the content of ‘conf’ directory to ‘input’, ran the example in the jar file ‘hadoop-0.20.2-examples.jar’ to count number of word ‘This’ within all files of ‘input’ directory. The output of your run is stored in ‘output’ directory.

Here is term-by-term explanation of the command on third line (bin/hadoop jar hadoop-0.20.2-examples.jar grep input output ‘This’).

bin/hadoop - hadoop executable

jar - option to hadoop executable that tells it that we are running a jar file

hadoop-0.20.2-examples.jar - the jar file being run

grep - class within jar file that is run

input - location of input files

output - location of output files

If we can create a jar file for our K-mer application, we can run it in the same manner to find all K-mers in a set of sequences. That is explained in the next section.

ii) Code for finding all K-mers from a short read library

If you never used Hadoop, a Hadoop code may appear daunting. We will make the task real easy for you. We suggest that you learn Hadoop coding in two steps.

Below we included a Hadoop 0.20 template (borrowed from this link) that can be used for most simple codes. You can cut and paste the template, and fill up few sections with code for your favorite application. You will have to fill up the class names (1,2,3) and add codes for map and reduce functions. The code for K-mer application in the next section shows how to do that.

-—————————————

`

package us.homolog;

import java.io.IOException;

import java.lang.InterruptedException;

import java.util.StringTokenizer;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.mapreduce.Job;

import org.apache.hadoop.mapreduce.Mapper;

import org.apache.hadoop.mapreduce.Reducer;

import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;

import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import org.apache.hadoop.util.GenericOptionsParser;

public class __1__

{

public static class 2 extends Mapper

{

INSERT CODE HERE

public void map(Object key, Text value, Context context)

throws IOException, InterruptedException

{

INSERT CODE HERE

context.write(…..);

}

}

public static class 3 extends Reducer

{

public void reduce(Text key, Iterable values, Context context)

throws IOException, InterruptedException

{

INSERT CODE HERE

context.write(…..);

}

}

public static void main(String[] args) throws Exception

{

Configuration conf = new Configuration();

String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();

Job job = new Job(conf, “Example Hadoop 0.20.1 Split Fasta”);

job.setJarByClass(1.class);

job.setMapperClass(__2.class);

job.setReducerClass(3.class);

job.setOutputKeyClass(Text.class);

job.setOutputValueClass(IntWritable.class);

FileInputFormat.addInputPath(job, new Path(otherArgs[0]));

FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));

System.exit(job.waitForCompletion(true) ? 0 : 1);

}

}

`

-—————————————

Here is the code for K-mer application. We used the template and filled the map and reduce functions. The map function goes through the sequences one by one and extracts all K-mers (K=10). The reduce function takes outputs of map function from various files as key-value pairs and adds up the counts.

-—————————————

`

package us.homolog;

import java.io.IOException;

import java.lang.InterruptedException;

import java.util.StringTokenizer;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.mapreduce.Job;

import org.apache.hadoop.mapreduce.Mapper;

import org.apache.hadoop.mapreduce.Reducer;

import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;

import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import org.apache.hadoop.util.GenericOptionsParser;

public class Kmer

{

public static class KmerMapper extends Mapper

{

private final static IntWritable one = new IntWritable(1);

private Text word = new Text();

public void map(Object key, Text value, Context context)

throws IOException, InterruptedException

{

String line=value.toString();

String sub;

for(int i=0; i<=line.length()-10; i++)

{

sub=line.substring(i,i+10);

word.set(sub);

context.write(word, one);

}

}

}

public static class KmerReducer extends Reducer

{

public void reduce(Text key, Iterable values, Context context)

throws IOException, InterruptedException

{

int sum = 0;

for (IntWritable value : values)

{

sum += value.get();

}

context.write(key, new IntWritable(sum));

}

}

public static void main(String[] args) throws Exception

{

Configuration conf = new Configuration();

String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();

Job job = new Job(conf, “Example Hadoop 0.20.1 Kmer”);

job.setJarByClass(Kmer.class);

job.setMapperClass(KmerMapper.class);

job.setReducerClass(KmerReducer.class);

job.setOutputKeyClass(Text.class);

job.setOutputValueClass(IntWritable.class);

FileInputFormat.addInputPath(job, new Path(otherArgs[0]));

FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));

System.exit(job.waitForCompletion(true) ? 0 : 1);

}

}

`

-—————————————

Instructions on how to compile and run the above code are given below.

Create a folder named Kmer in your main directory and save the above code as Kmer.java inside the folder. You already have another folder hadoop-0.20.2 in your main folder with all Hadoop files.

Go inside Kmer folder and run the following commands.

% mkdir kmer_classes

% javac -classpath ../hadoop-0.20.2/hadoop-0.20.2-core.jar:../hadoop-0.20.2/lib/commons- cli-1.2.jar -d kmer_classes Kmer.java

% jar -cvf Kmer.jar -C kmer_classes/ .

The above steps will create the Kmer.jar and we already know how to run it using Hadoop in standalone mode.

% mkdir input

% echo ‘ATATATATATATATAT’ > input/seq-W-strand

% echo ‘ATATATATATATATAT’ > input/seq-C-strand

% ../hadoop-0.20.2/bin/hadoop jar Kmer.jar us.homolog.Kmer input output

% cat output/*

You will see that our input file has 8 ATATATATAT and 6 TATATATATA.

Regarding data format, the files in ‘input’ directory should have all read sequences with one sequence/line. They are not in FASTA format. We also generated reverse complements ourselves and added them in a separate file. You can split the reads and reverse complements in as many files within the ‘input’ folder as you want. There is no need to preserve order.

If you followed the above code, a good homework is to modify it for reverse complement. Another exercise is to make it read FASTA-formatted inputs.

Additional posts on the same topic -

Hadoop Example - FAQ

Contrail - A de Bruijn Genome Assembler that uses Hadoop


Written by M. //

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