Dataset Information

DNA-COMPACT: DNA COMpression based on a pattern-aware contextual modeling technique.

ABSTRACT: Genome data are becoming increasingly important for modern medicine. As the rate of increase in DNA sequencing outstrips the rate of increase in disk storage capacity, the storage and data transferring of large genome data are becoming important concerns for biomedical researchers. We propose a two-pass lossless genome compression algorithm, which highlights the synthesis of complementary contextual models, to improve the compression performance. The proposed framework could handle genome compression with and without reference sequences, and demonstrated performance advantages over best existing algorithms. The method for reference-free compression led to bit rates of 1.720 and 1.838 bits per base for bacteria and yeast, which were approximately 3.7% and 2.6% better than the state-of-the-art algorithms. Regarding performance with reference, we tested on the first Korean personal genome sequence data set, and our proposed method demonstrated a 189-fold compression rate, reducing the raw file size from 2986.8 MB to 15.8 MB at a comparable decompression cost with existing algorithms. DNAcompact is freely available at https://sourceforge.net/projects/dnacompact/for research purpose.

SUBMITTER: Li P

PROVIDER: S-EPMC3840021 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

DNA-COMPACT: DNA COMpression based on a pattern-aware contextual modeling technique.

Li Pinghao P Wang Shuang S Kim Jihoon J Xiong Hongkai H Ohno-Machado Lucila L Jiang Xiaoqian X

PloS one 20131125 11

Genome data are becoming increasingly important for modern medicine. As the rate of increase in DNA sequencing outstrips the rate of increase in disk storage capacity, the storage and data transferring of large genome data are becoming important concerns for biomedical researchers. We propose a two-pass lossless genome compression algorithm, which highlights the synthesis of complementary contextual models, to improve the compression performance. The proposed framework could handle genome compre ...[more]

PMID: 24282536

Similar Datasets

Project description:Importance:Many women dread undergoing mammography, and some may not attend or reattend breast cancer screening because of the discomfort or pain induced by breast compression. Objective:To evaluate the noninferiority of the self-compression mammography technique for reducing breast thickness compared with standard compression. Design, Setting, and Participants:This prospective, parallel-group, noninferiority randomized clinical trial was conducted from May 7, 2013, to October 26, 2015, at 6 cancer care centers in France. Participants were women aged 50 to 75 years, without a history of recent breast surgical procedure or treatment, and who could perform self-compression. Analyses were performed on intention-to-treat basis from January 27, 2017, to March 30, 2018. Interventions:Patients were randomized 1:1 to the self-compression group or the standard compression group. Main Outcomes and Measures:Primary end point was breast thickness expressed as the mean of 4 views: right and left craniocaudal and right and left mediolateral oblique. The predefined noninferiority margin was a difference of 3 mm, with a 1-sided 95% CI. Secondary end points included compression force, image quality, requirement for additional views, pain, and patient satisfaction and radiographer assessment questionnaires. Results:Among the 549 women randomized, 548 (97.3%) completed the trial. Of these, 275 (48.8%) (mean [SD] age, 61.35 [6.34] years) were randomized to the self-compression arm and 273 (48.5%) (mean [SD] age, 60.84 [6.41] years) to the standard compression arm. The difference in the mean thickness between the 2 arms was lower than the noninferiority margin, with an upper 1-sided 95% CI less than 3 mm (-0.17; 95% CI,-? to 1.89 mm; P?<?.05). Compression force was higher in the self-compression group compared with the standard compression arm for the 4 mammographic views. Pain was statistically significantly lower in the self-compression group (n?=?274) compared with the standard compression group (n?=?269) (median [interquartile range (IQR)] score, 2 [1-5] vs 3 [1-5]; P?=?.009). No difference was reported in the image quality scores of the 2 groups or in the number of additional views performed (median [IQR] extra views, 2 [2-2] vs 2 [2-3] extra views; P?=?.64), whatever the indication, including insufficient image quality (29 [16.8%] vs 27 [15.0%] insufficient quality views; P?=?.65). No adverse effects or pain were reported by the participants after the self-compression mammography. Conclusions and Relevance:Self-compression does not appear to be inferior to standard compression mammography in achieving minimal breast thickness without increasing pain or compromising image quality; this technique may be an effective option for women who want to be involved in their breast examination. Trial Registration:ClinicalTrials.gov identifier: NCT02866591.

Project description:BACKGROUND: High-throughput short read sequencing is revolutionizing genomics and systems biology research by enabling cost-effective deep coverage sequencing of genomes and transcriptomes. Error detection and correction are crucial to many short read sequencing applications including de novo genome sequencing, genome resequencing, and digital gene expression analysis. Short read error detection is typically carried out by counting the observed frequencies of kmers in reads and validating those with frequencies exceeding a threshold. In case of genomes with high repeat content, an erroneous kmer may be frequently observed if it has few nucleotide differences with valid kmers with multiple occurrences in the genome. Error detection and correction were mostly applied to genomes with low repeat content and this remains a challenging problem for genomes with high repeat content. RESULTS: We develop a statistical model and a computational method for error detection and correction in the presence of genomic repeats. We propose a method to infer genomic frequencies of kmers from their observed frequencies by analyzing the misread relationships among observed kmers. We also propose a method to estimate the threshold useful for validating kmers whose estimated genomic frequency exceeds the threshold. We demonstrate that superior error detection is achieved using these methods. Furthermore, we break away from the common assumption of uniformly distributed errors within a read, and provide a framework to model position-dependent error occurrence frequencies common to many short read platforms. Lastly, we achieve better error correction in genomes with high repeat content. AVAILABILITY: The software is implemented in C++ and is freely available under GNU GPL3 license and Boost Software V1.0 license at "http://aluru-sun.ece.iastate.edu/doku.php?id = redeem". CONCLUSIONS: We introduce a statistical framework to model sequencing errors in next-generation reads, which led to promising results in detecting and correcting errors for genomes with high repeat content.

Dataset Information

DNA-COMPACT: DNA COMpression based on a pattern-aware contextual modeling technique.

Publications

DNA-COMPACT: DNA COMpression based on a pattern-aware contextual modeling technique.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure