Burrows-Wheeler Transform

Definition of Burrows-Wheeler Transform

The Burrows-Wheeler Transform (BWT) is a data transformation algorithm used in data compression and bioinformatics. It reorganizes strings of characters into runs, resulting in an efficiently compressible format. BWT is reversible, meaning the original data can be easily reconstructed from the transformed data without loss of information.


The phonetics of the keyword “Burrows-Wheeler Transform” can be represented as:ˈbʌroʊz-ˈhwiːlər trænsˈfɔrm

Key Takeaways

  1. Burrows-Wheeler Transform (BWT) is a data preprocessing algorithm used primarily for improving data compression and assisting in pattern matching tasks, such as searching for substrings within a text.
  2. BWT rearranges the characters of the input string in a specific way that groups similar characters together, making it easier to compress. It also enables the recovery of the original string without any loss of data.
  3. Due to high data compression efficiency, BWT is utilized in various applications such as bioinformatics for genome sequence analysis, as well as in the bzip2 compression tool for compressing files.

Importance of Burrows-Wheeler Transform

The Burrows-Wheeler Transform (BWT) is an important algorithm in technology as it plays a vital role in data compression and bioinformatics.

It rearranges data in a specific way that efficiently groups similar characters, enabling more effective compression. This transformed data can then be compressed using other techniques, such as move-to-front encoding and Huffman coding, resulting in significantly reduced file size.

In bioinformatics, BWT has been instrumental in efficiently handling the storage and processing of large volumes of biological data, particularly in genome sequencing and read alignment tasks.

Overall, BWT contributes to increased efficiency in data handling, faster data transmission, and reduced storage requirements, making it a valuable and prominent tool in the technology landscape.


The Burrows-Wheeler Transform (BWT) is an ingenious algorithm designed to make data compression more efficient, primarily used in the realms of data storage and transmission. The primary purpose of the BWT is to enhance the compressibility of data by reorganizing the input sequence in a way that clusters similar characters together, making it easier for subsequent compression algorithms to run more efficiently.

By transforming the original data into a format with more repetitive patterns, BWT significantly contributes to reducing the storage space required or achieving faster data transfer speeds. This transformation is reversible, ensuring the original data can be accurately reconstructed without any loss of information.

Implemented in various applications, including the widely recognized bzip2 compression utility, the Burrows-Wheeler Transform has become a vital aspect of modern data compression techniques. Additionally, BWT has found remarkable utility in the field of bioinformatics, specifically in genome sequence alignment and assembly.

As genomic data typically consists of long, repetitive sequences, the BWT’s ability to group similar characters together becomes exceedingly beneficial for compressing such data, thereby facilitating efficient storage and analysis of genetic information. Ultimately, the Burrows-Wheeler Transform stands as a unique and powerful algorithm that has made significant advancements in both data compression and the expanding realm of bioinformatics.

Examples of Burrows-Wheeler Transform

Data Compression Algorithms: BWT is a key component of block-sorting compression algorithms such as bzip2, which is used for file compression. bzip2 compresses data more effectively than other popular compression algorithms, like gzip, by employing BWT as a pre-processing step, followed by Move-to-Front Transform (MTF), and Run-Length Encoding (RLE). It is widely used in Unix and Linux systems for compressing files and data.

Bioinformatics and Genome Sequencing: BWT is also used in bioinformatics for compressing DNA sequence data and performing efficient searching for gene patterns in large genome databases. For example, BWT is used in FM-index, an efficient data structure in compressed full-text indexes, which allow pattern matching queries in compressed text. FM-index is utilized by tools like Bowtie and BWA (Burrows-Wheeler Aligner), which are widely used in DNA sequence alignment and analysis.

Data Mining and String Matching: BWT has been used for efficient string matching in data mining applications, such as searching for patterns and keywords in large text databases. By organizing data in a more compact format, BWT enables fast pattern matching and querying even in compressed texts. This application is useful in web search engines, text retrieval systems, and various string matching computational problems.

Frequently Asked Questions: Burrows-Wheeler Transform

1. What is Burrows-Wheeler Transform?

The Burrows-Wheeler Transform (BWT) is an algorithm used in data compression and genomic data storage. It rearranges a string’s characters into runs of similar characters and is reversible, meaning the original string can be reconstructed from the transformed data.

2. How does Burrows-Wheeler Transform work?

BWT works by generating a matrix of cyclically shifted copies of the input string, sorting the rows in lexicographic order, and then taking the last column of the matrix as the output. The sorted rows’ indices can be used to reverse the transformation and obtain the original string.

3. What are the main benefits of using Burrows-Wheeler Transform?

The main benefits of using BWT include better data compression and reduced storage space requirements. The transformation groups similar characters together, making it easier for compression algorithms like run-length encoding to compress the data efficiently.

4. Can the Burrows-Wheeler Transform be reversed?

Yes, the Burrows-Wheeler Transform can be reversed. The indices of the sorted rows in the original matrix are used to reconstruct the original string. Reversing the transformation is the decoding process in data compression algorithms that rely on BWT.

5. What are some applications of the Burrows-Wheeler Transform?

BWT has various applications, primarily in data compression and genomic data storage. It is widely used in lossless data compression algorithms such as bzip2, and genomic data formats such as CRAM for managing and storing genomic sequencing data efficiently.

Related Technology Terms

  • Data Compression
  • Block-sorting Algorithm
  • Inversion Algorithm
  • Lossless Compression
  • Sequence Alignment

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