Biological data compression

Data compression, on the other hand, involves reducing the size of data by removing redundancies and compressing the data using compression algorithms.
Compressed data can be decompressed back to its original form using decompression algorithms.
Shannon formulated the theory of data compression.
Shannon established that there is a fundamental limit to lossless data compression.
This limit, called the entropy rate, is denoted by H.
The exact value of H depends on the information source --- more specifically, the statistical nature of the source.

The concepts behind data compression have been very useful in understanding how information is organized in a number of signals used in multimedia Entropy and BiologyCompression and PhylogenyGrammar and Biology

We briefly review compression algorithms developed for compressing biological sequences, however, our main focus is on how conceptual tools in the data Entropy and BiologyCompression and PhylogenyGrammar and Biology

Is there a lossless DNA compression algorithm based on a single-block encoding scheme?

A new lossless dna compression algorithm based on a single-block encoding scheme International Conference on Neural Information Processing, Springer ( 2018), pp. 378 - 386 A comparative study and survey on existing DNA compression techniques Int.
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What are the different types of DNA sequence compression algorithms?

DNA sequence compression algorithms can be categorized as lossy or lossless.
The algorithms may be standard, vertical mode or horizontal mode .
Standard algorithms follow standard text compression methods.
However, these algorithms cannot take advantage of redundancy in biological data.
Example of such algorithms includes ,gzip and bzip2.

What is compression ratio?

Compression Ratio = Encoded Bits/Bases.
Thus, in the first phase of the algorithm, a modified version of the RLE is applied.
In this modified version, the runs of data are stored in a separate file and the index of the repeated character in another file instead of keeping one file of RLE sequence.

Why is compression important in DNA sequencing?

Compression plays a vital role in dealing with increasing size of sequencing data.
DNA sequences can be compressed using generic approaches, as compression has natural representation as a string of characters for which a rich literature exists.

What are the different types of DNA compression algorithms?

Usually, the DNA sequence is substantially the most abundant part of these data, and, hence, multiple tools use specialized DNA compression algorithms combined with simple header coding, namely, Deliminate [ 65 ], MFCompress [ 66 ], and NAF [ 67 ]

Why is data compression important for DNA sequence analysis?

Although data compression is the natural approach for decreasing the storage of DNA sequences losslessly [ 9 ], it can also be efficiently applied to sequence analysis and prediction using special-purpose compressors [ 10–12 ]

Therefore, this improvement also enables increasing the precision of DNA sequence compression–based analysis tools