Trends in Genome Compression

Abstract

Technological advancements in high-throughput sequencing have lead to a tremendous increase in the amount of genomic data produced. With the cost being down to 2,000 USD for a single human genome, sequencing dozens of individuals is a task that is feasible even for smaller project or organizations already today. However, generating the sequence is only one issue; another one is storing, managing, and analyzing it. These tasks become more and more challenging due to the sheer size of the data sets and are increasingly considered to be the most severe bottlenecks in larger genome projects. One possible countermeasure is to compress the data; compression reduces costs in terms of requiring less hard disk storage and in terms of requiring less bandwidth if data is shipped to large compute clusters for parallel analysis. Accordingly, sequence compression has recently attracted much interest in the scientific community. In this paper, we explain the different basic techniques for sequence compression, point to distinctions between different compression tasks (e.g., genome versus read compression), and present a comparison of current approaches and tools. To further stimulate progress in genome compression research, we also identify key challenges for future systems.Keywords: genome compression, read compression, survey Key messages of the article:• Overview of trends in genome compression: bit manipulation, dictionarybased, statistical, and referential compression schemes• Wide ranges of compression performance (compression ratio, compression time, memory requirements)• Comparing compression schemes for evaluation purposes is difficult• Many minor improvements in recent contributions

A Survey on Data Compression Methods for Biological Sequences

“…For this purpose, the target is aligned to the reference and the mismatches between these sequences are encoded. Since the decompressor has access to the reference sequence(s), the reference-based methods can obtain very high compression rates [26,28,53,54].…”

“…Many studies have been carried out on the topic of genomic sequence compression, taking into account the characteristics of these sequences, such as small alphabet size (i.e., four, namely the nucleotides A (adenine), T (thymine), C (cytosine) and G (guanine)), repeats and palindromes [25][26][27][28]. In this section, two categories of reference-free methods, that are based only on the characteristics of the target sequences, and reference-based methods, that exploit a (set of) reference sequence(s), are considered for describing these studies.…”

Using DECIPHER v2.0 to Analyze Big Biological Sequence Data in R

“…Compression with nbit uses a combination of 2-bit encoding for gapless bases (i.e., A=00, C=01, G=10, T=11), and 3-bit encoding for gappy regions. Although this encoding was inspired by prior work (Wandelt et al, 2014), DECIPHER uses a unique implementation that is customized to the package's goals.…”

Human Induced Pluripotent Stem Cells as a Platform for Personalized and Precision Cardiovascular Medicine

“…Sequencing throughputs have rapidly outpaced even Moore's Law for computing power, which states that the number of transistors in a dense integrated circuit doubles approximately every two years (195). Improvement of data compression algorithms would help to address the growing data-to-central processing unit (CPU) ratio (287). To date, thousands of WG and hundreds of thousands of exomes have been sequenced.…”