Correcting Illumina data

Molnar, Michael; Ilie, Lucian

doi:10.1093/bib/bbu029

Cited by 37 publications

(59 citation statements)

References 32 publications

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“…In contrast, the previous Sanger sequencing technology, used to decipher the human genome, required over a decade to deliver the final draft [9]. For analyzing NGS data, some algorithms and software were developed for genome assembly [54], read correction [91], and short read mapping [31].…”

Section: Dna Genes and Chromosomesmentioning

confidence: 99%

NBPMF: Novel network-based inference methods for peptide mass fingerprinting

Liang

Lajoie

Zhang

2017

2017 IEEE 16th International Conference on Cognitive Informatics &Amp; Cognitive Computing (ICCI*CC)

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Proteins are large, complex molecules that perform a vast array of functions in every living cell. A proteome is a set of proteins produced in an organism, and proteomics is the large-scale study of proteomes. Several high-throughput technologies have been developed in proteomics, where the most commonly applied are mass spectrometry (MS) based approaches.MS is an analytical technique for determining the composition of a sample. Recently it has become a primary tool for protein identification, quantification, and post translational modification (PTM) characterization in proteomics research. There are usually two different ways to identify proteins: top-down and bottom-up. Top-down approaches are based on subjecting intact protein ions and large fragment ions to tandem MS directly, while bottom-up methods are based on mass spectrometric analysis of peptides derived from proteolytic digestion, usually with trypsin.In bottom-up techniques, peptide mass fingerprinting (PMF) is widely used to identify proteins from MS dataset. Conventional PMF representatives such as probabilistic MOWSE algorithm, is based on mass distribution of tryptic peptides. In this thesis, we developed a novel network-based inference software termed NBPMF. By analyzing peptide-protein bipartite network, we designed new peptide protein matching score functions. We present two methods: the static one, ProbS, is based on an independent probability framework; and the dynamic one, HeatS, depicts input dataset as dependent peptides. Moreover, we use linear regression to adjust the matching score according to the masses of proteins. In addition, we consider the order of retention time to further correct the score function. In the post processing, we design two algorithms: assignment of peaks, and protein filtration. The former restricts that a peak can only be assigned to one peptide in order to reduce random matches; and the latter assumes each peak can only be assigned to one protein. In the result validation, we propose two new target-decoy search strategies to estimate the false discovery rate (FDR). The experiments on simulated, authentic, and simulated authentic dataset demonstrate that our NBPMF approaches lead to significantly improved performance compared to several state-of-the-art methods.i

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Section: Dna Genes and Chromosomesmentioning

confidence: 99%

NBPMF: Novel network-based inference methods for peptide mass fingerprinting

Liang

Lajoie

Zhang

2017

2017 IEEE 16th International Conference on Cognitive Informatics &Amp; Cognitive Computing (ICCI*CC)

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“…The tests were performed for three datasets described in Table 2. Two of this datasets were taken from the survey by Molnar and Ilie (2015).…”

Section: Real Data Evaluationmentioning

confidence: 99%

“…Therefore, the correction of errors in reads is currently an important and popular issue. The existing solutions are discussed and compared in the recent surveys: (Yang et al, 2013), (Molnar and Ilie, 2015), (Laehnemann et al, 2015). Yang et al classify the correction algorithms into three groups: (i) kspectrum-based, (ii) suffix-tree/array-based, and (iii) multiple-sequencealignment-based.…”

Section: Introductionmentioning

confidence: 99%

RECKONER: read error corrector based on KMC

Dlugosz

Deorowicz

2016

Bioinformatics

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Motivation: Next-generation sequencing tools have enabled producing of huge amount of genomic information at low cost. Unfortunately, presence of sequencing errors in such data affects quality of downstream analyzes. Accuracy of them can be improved by performing error correction. Because of huge amount of such data correction algorithms have to: be fast, memory-frugal, and provide high accuracy of error detection and elimination for variously-sized organisms. Results: We introduce a new algorithm for genomic data correction, capable of processing eucaryotic 300 Mbp-genome-size, high error-rated data using less than 4 GB of RAM in less than 40 minutes on 16-core CPU. The algorithm allows to correct sequencing data at better or comparable level than competitors. This was achieved by using very robust KMC 2 k-mer counter, new method of erroneous regions correction based on both k-mer counts and FASTQ quality indicators as well as careful optimization. Availability: Program is freely available at http://sun.aei.posl.pl/REFRESH.

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“…There are only three reviews [1,2,79] published, with the latest one targeting only Illumina data. They focus on benchmarking the correctors, while this article extracts key information from the work in this field, summing up all important points deemed as important.…”

Section: Approachmentioning

confidence: 99%

“…For instance, Coral [15] is listed in this review in the category of msb algorithms [1,2,79], but it also uses the kmer spectrum to determine the related reads. The same case arises with Premier [136] and Premier Turbo [137] which use k-mers to update the probabilities for the variants on a position.…”

Section: Probabilistic Models Based (Pmb)mentioning

confidence: 99%

Improved Error Correction of NGS Data

Alic¹

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SummaryThe work done for this doctorate thesis focuses on error correction of Next Generation Sequencing (NGS) data in the context of High Performance Computing (HPC).Due to the reduction in sequencing cost, the increasing output of the sequencers and the advancements in the biological and medical sciences, the amount of NGS data has increased tremendously. Humans alone are not able to keep pace with this explosion of information, therefore computers must assist them to ease the handle of the deluge of information generated by the sequencing machines. Since NGS is no longer just a research topic (used in clinical routine to detect cancer mutations, for instance), requirements in performance and accuracy are more stringent. For sequencing to be useful outside research, the analysis software must work accurately and fast. This is where HPC comes into play. NGS processing tools should leverage the full potential of multi-core and even distributed computing, as those platforms are extensively available. Moreover, as the performance of the individual core has hit a barrier, current computing tendencies focus on adding more cores and explicitly split the computation to take advantage of them.This thesis starts with a deep analysis of all these problems in a general and comprehensive way (to reach out to a very wide audience), in the form of an exhaustive and objective review of the NGS error correction field. We dedicate a chapter to this topic to introduce the reader gradually and gently into the world of sequencing. It presents real problems and applications of NGS that demonstrate the impact this technology has on science. The review results in the following conclusions: the need of understanding of the specificities of NGS data samples (given the high variety of technologies and features) and the need of flexible, efficient and accurate tools for error correction as a preliminary step of any NGS postprocessing.As a result of the explosion of NGS data, we introduce MuffinInfo. It is a piece of software capable of extracting information from the raw data produced by the sequencer to help the user understand the data. MuffinInfo uses HTML5, therefore it runs in almost any software and hardware environment. It supports custom statistics to mould itself to specific requirements. MuffinInfo can reload the results of a run which are stored in JSON format for easier integration with third party applications. Finally, our application uses threads to perform the calculations, to load the data from the disk and to handle the UI.In continuation to our research and as a result of the single core performance limitation, we leverage the power of multi-core computers to develop a new error correction tool. The error correction of the NGS data is normally the first step of any analysis targeting NGS. As we conclude from the review performed within the frame of this thesis, many projects in different real-life applications have opted for this step before further analysis. In this sense, we propose MuffinEC, a multi-technology (Illu...

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Correcting Illumina data

Cited by 37 publications

References 32 publications

NBPMF: Novel network-based inference methods for peptide mass fingerprinting

NBPMF: Novel network-based inference methods for peptide mass fingerprinting

RECKONER: read error corrector based on KMC

Improved Error Correction of NGS Data

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