Sequence Factorization with Multiple References

Vertical lossless genomic data compression tools for assembled genomes: A systematic literature review

The recent decrease in cost and time to sequence and assemble of complete genomes created an increased demand for data storage. As a consequence, several strategies for assembled biological data compression were created. Vertical compression tools implement strategies that take advantage of the high level of similarity between multiple assembled genomic sequences for better compression results. However, current reviews on vertical compression do not compare the execution flow of each tool, which is constituted by phases of preprocessing, transformation, and data encoding. We performed a systematic literature review to identify and compare existing tools for vertical compression of assembled genomic sequences. The review was centered on PubMed and Scopus, in which 45726 distinct papers were considered. Next, 32 papers were selected according to the following criteria: to present a lossless vertical compression tool; to use the information contained in other sequences for the compression; to be able to manipulate genomic sequences in FASTA format; and no need prior knowledge. Although we extracted performance compression results, they were not compared as the tools did not use a standardized evaluation protocol. Thus, we conclude that there's a lack of definition of an evaluation protocol that must be applied by each tool.

HRCM: An Efficient Hybrid Referential Compression Method for Genomic Big Data

With the maturity of genome sequencing technology, huge amounts of sequence reads as well as assembled genomes are generating. With the explosive growth of genomic data, the storage and transmission of genomic data are facing enormous challenges. FASTA, as one of the main storage formats for genome sequences, is widely used in the Gene Bank because it eases sequence analysis and gene research and is easy to be read. Many compression methods for FASTA genome sequences have been proposed, but they still have room for improvement. For example, the compression ratio and speed are not so high and robust enough, and memory consumption is not ideal, etc. Therefore, it is of great significance to improve the efficiency, robustness, and practicability of genomic data compression to reduce the storage and transmission cost of genomic data further and promote the research and development of genomic technology. In this manuscript, a hybrid referential compression method (HRCM) for FASTA genome sequences is proposed. HRCM is a lossless compression method able to compress single sequence as well as large collections of sequences. It is implemented through three stages: sequence information extraction, sequence information matching, and sequence information encoding. A large number of experiments fully evaluated the performance of HRCM. Experimental verification shows that HRCM is superior to the best-known methods in genome batch compression. Moreover, HRCM memory consumption is relatively low and can be deployed on standard PCs.

Opportunities for community awareness platforms in personal genomics and bioinformatics education

Precision and personalized medicine will be increasingly based on the integration of various type of information, particularly electronic health records and genome sequences. The availability of cheap genome sequencing services and the information interoperability will increase the role of online bioinformatics analysis. Being on the Internet poses constant threats to security and privacy. While we are connected and we share information, websites and internet services collect various types of personal data with or without the user consent. It is likely that genomics will merge with the internet culture of connectivity. This process will increase incidental findings, exposure and vulnerability. Here we discuss the social vulnerability owing to the genome and Internet combined security and privacy weaknesses. This urges more efforts in education and social awareness on how biomedical data are analysed and transferred through the internet and how inferential methods could integrate information from different sources. We propose that digital social platforms, used for raising collective awareness in different fields, could be developed for collaborative and bottom-up efforts in education. In this context, bioinformaticians could play a meaningful role in mitigating the future risk of digital-genomic divide.

The SeqAn C++ template library for efficient sequence analysis: A resource for programmers

BACKGROUND

The use of novel algorithmic techniques is pivotal to many important problems in life science. For example the sequencing of the human genome (Venter et al., 2001) would not have been possible without advanced assembly algorithms and the development of practical BWT based read mappers have been instrumental for NGS analysis. However, owing to the high speed of technological progress and the urgent need for bioinformatics tools, there was a widening gap between state-of-the-art algorithmic techniques and the actual algorithmic components of tools that are in widespread use. We previously addressed this by introducing the SeqAn library of efficient data types and algorithms in 2008 (Döring et al., 2008).

RESULTS

The SeqAn library has matured considerably since its first publication 9 years ago. In this article we review its status as an established resource for programmers in the field of sequence analysis and its contributions to many analysis tools.

CONCLUSIONS

We anticipate that SeqAn will continue to be a valuable resource, especially since it started to actively support various hardware acceleration techniques in a systematic manner.