In genome sequence alignment problem, a reference string and number of query strings referred as short reads, are given, goal is to seek out occurrences of these query strings in the reference string. Huge amount of reads generated by new sequencing technologies (Illumina/Solexa) need the development of an efficient algorithm requiring both less memory and computational time. There are number of indexing and string matching techniques to align short reads on reference string (genome). Size of index of the reference string in each of existing techniques is large. In this paper, a new self compressed index technique (BWT-WT) is proposed. BWT-WT scheme is based on Burrow Wheeler Transform (BWT) and Wavelet tree (WT). BWT-WT also supports exact alignment of DNA sequence reads. Performances of BWT-WT with other BWT based tools of short read alignments are compared. Experiments show that BWT-WT based program achieves more compression and also faster searching in comparison to other existing tools.

Efficient Read Alignment Using Burrows Wheeler Transform and Wavelet Tree

In this paper, we discuss an efficient and effective index mechanism for the string matching with k differences, by which we will find all the substrings of a target string y of length n that align...

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On the string matching with k differences in DNA databases

Many modern sequence alignment tools implement fast string matching using the space efficient data structure called FM-index. The succinct nature of this data structure presents unique challenges for the algorithm designers. In this paper, we explore the opportunities for parallelization of the exact and inexact matches and present an efficient SIMD solution for the Occ portion of the algorithm. Our implementation computes all eight Occ values required for the inexact match algorithm step in a single pass. We showcase the algorithm performance in a multi-core genome aligner and discuss effects of the memory prefetch.

Vectorized Character Counting for Faster Pattern Matching

In this paper we present a new lossless audio coding algorithm using Burrows-Wheeler Transform (BWT) and Run Length Encoding (RLE).Audio signals used are assumed to be of floating point values. The BWT is applied to the audio signals to get the transformed coefficients and then these resulting coefficients are better compressed using Run Length Encoding. Two entropy coding are used which are Run Length Encoding and Huffman coding. Proposed compression algorithm is experimented and analyzed for two different stereo type audio signals. Compression ratio and Bit rate for audio coding has been used as a comparison parameter for proposed audio coding algorithm. Experimental result shows that the lossless audio coding algorithm outperforms other lossless audio coding methods; using combined Burrows Wheeler Transform & Move to front coding method ,using combined Burrows Wheeler Transform and Huffman coding method, and using Burrows Wheeler Transform ,Move to front coding method & Run Length Encoding method.

http://www.internationaljournalssrg.org/IJECE/2015/Volume2-Issue10/IJECE-V2I10P106.pdf

Lossless Audio Coding based on Burrows Wheeler Transform and Run Length Encoding Algorithm

Burrows-Wheeler Transform (BWT) is a new data transform method, firstly introduced by Burrows and Wheeler in 1994 and used in a lossless data compression algorithm. In the original version of data compression algorithm based on BWT, Burrows and Wheeler used the Move-To-Front (MTF) transform after the BWT and entropy coding (Huffman coding) for encoding the transform result in the last stage. Ferragina and Manzini combined the compression algorithm based on BWT and the suffix array data structure, and proposed a new opportunistic data structure. They shortly called it FM-index in that it is a Full-text index and occupies Minute space. This paper mainly describes the methods about compression and index based on BWT, and relationship between them. At last, the paper compares some tools.

Yuewan Wu

Papers

Compression and Indexing Based on BWT: A Survey