Archive for the ‘Burrows-Wheeler Transform (BWT)’ Category

Elegant exact string match using BWT

Thursday, May 17th, 2012

Elegant exact string match using BWT by Santhosh Kumar.

From the post:

This post describes an elegant and fast algorithm to perform exact string match. Why another string matching algorithm? To answer the question, let’s first understand the problem we are trying to solve.

In short, the problem is to match billions of short strings (about 50-100 characters long) to a text which is 3 billion characters long. The 3 billion character string (also called reference) is known ahead and is fixed (at least for a species). The shorter strings (also called reads) are generated as a result of an experiment. The problem arises due to the way the sequencing technology works, which in its current form, breaks the DNA into small fragments and ‘reads’ them. The information about where the fragments came from is lost and hence the need to ‘map’ them back to the reference sequence.

We need an algorithm that allows repeatedly searching on a text as fast as possible. We are allowed to perform some preprocessing on the text once if that will help us achieve this goal. BWT search is one such algorithm. It requires a one-time preprocessing of the reference to build an index, after which the query time is of the order of the length of the query (instead of the reference).

Burrows Wheeler transform is a reversible string transformation that has been widely used in data compression. However the application of BWT to perform string matching was discovered fairly recently in this paper. This technique is the topic of this post. Before we get to the searching application, a little background on how BWT is constructed and some properties of BWT.

Complete with careful illustrations of the operation of the Burrows Wheeler transform (BWT).

A separate post to follow details finding the BWT index of a long string efficiently.

Definitely a series to follow.

Large-scale compression of genomic sequence databases with the Burrows-Wheeler transform

Wednesday, May 2nd, 2012

Large-scale compression of genomic sequence databases with the Burrows-Wheeler transform by Anthony J. Cox, Markus J. Bauer, Tobias Jakobi, and Giovanna Rosone.

Abstract:

Motivation

The Burrows-Wheeler transform (BWT) is the foundation of many algorithms for compression and indexing of text data, but the cost of computing the BWT of very large string collections has prevented these techniques from being widely applied to the large sets of sequences often encountered as the outcome of DNA sequencing experiments. In previous work, we presented a novel algorithm that allows the BWT of human genome scale data to be computed on very moderate hardware, thus enabling us to investigate the BWT as a tool for the compression of such datasets.

Results

We first used simulated reads to explore the relationship between the level of compression and the error rate, the length of the reads and the level of sampling of the underlying genome and compare choices of second-stage compression algorithm.

We demonstrate that compression may be greatly improved by a particular reordering of the sequences in the collection and give a novel `implicit sorting’ strategy that enables these benefits to be realised without the overhead of sorting the reads. With these techniques, a 45x coverage of real human genome sequence data compresses losslessly to under 0.5 bits per base, allowing the 135.3Gbp of sequence to fit into only 8.2Gbytes of space (trimming a small proportion of low-quality bases from the reads improves the compression still further).

This is more than 4 times smaller than the size achieved by a standard BWT-based compressor (bzip2) on the untrimmed reads, but an important further advantage of our approach is that it facilitates the building of compressed full text indexes such as the FM-index on large-scale DNA sequence collections.

Important work for several reasons.

First, if the human genome is thought of as “big data,” it opens the possibility that compressed full text indexes can be build for other instances of “big data.”

Second, indexing is similar to topic mapping in the sense that pointers to information about a particular subject are gathered to a common location. Indexes often account for synonyms (see also) and distinguish the use of the same word for different subjects (polysemy).

Third, depending on the granularity of tokenizing and indexing, index entries should be capable of recombination to create new index entries.

Source code for this approach:

Code to construct the BWT and SAP-array on large genomic data sets is part of the BEETL library, available as a github respository at git@github.com:BEETL/BEETL.git.

Comments?