0. Requirments
	Opal requires Vowpal Wabbit (VW) and Genetic Data Analysis Library (GDL) libraries. 
	We provided a script (see "Installation" below) to install these dependencies.

1. Directory
data/
	A1.10.1000/:
		train/: fasta and tax id used for training the model, 
				fasta records of 10 species.
		test/: short fragments (length 100bp) and corresponding tax id used for testing.

	B1.20.500/: 
		train/: fasta and tax id used for training the model, 
				fasta records of 20 species.
		test/: short fragments (length 100bp) and corresponding tax id used for testing.
	simHC.20.500/:
		train/: fasta and tax id used for training the model, 
				fasta records of 20 species.
		test/: short fragments (length 200bp) and corresponding tax id used for testing.
				Fragments were simulated from the original reads such that each position 
				in the read was covered once on average.
src/	
	build-models
		src/:
			opal-train.sh: train a multi-class svm using VW library.
	3-make-predictions:
		src/:
			opal-predict.sh: classify fragments.
			vw-class-to-taxid.py: map VW labels to tax id.
			eval_full.py: evaluation of supervised classification.
util/
	ext/: installation directory of external libararies.
	drawfrag.c: draw fragments from fasta records.
	fasta2skm.c: construct feature (spaced k-mer profile), and convert to VW input format.
	ldpc.py: generate LSH function using LDPC code.
	kseq.h: parse FASTA files

2. Installation
	Run the following script in the root directory to install Opal and required external softwares.
	$ bash opal_install.sh

3. Usage
	1) Run experiments on the A1.10.1000 dataset
		$ bash run_A1.10.1000.sh
	2) Run experiments on the B1.20.500 dataset
		$ bash run_B1.20.500.sh
	3) Run experiments on the simHC.20.500 dataset
		$ bash run_simHC.20.500.sh
