PREMIER &amp;#x2014; PRobabilistic error-correction using Markov inference in errored reads

Yin, Xin; Song, Zhao; Dorman, Karin S.; Ramamoorthy, Aditya

doi:10.1109/isit.2013.6620502

Cited by 6 publications

(22 citation statements)

References 24 publications

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“…In our previous work [17], we presented a hidden Markov model for the DNA sequencer outputs. The reads were corrected by first fitting the HMM model parameters from the observed data and then posing the problem of read correction as one of maximum likelihood sequence detection (MLSD).…”

Section: Introductionmentioning

confidence: 99%

PREMIER Turbo: Probabilistic error-correction using Markov inference in errored reads using the turbo principle

Yin

Song

Dorman

et al. 2013

2013 IEEE Global Conference on Signal and Information Processing

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We present a probabilistic algorithm for error cor rection for high throughput DNA sequencing data. Our approach leverages our prior algorithm PREMIER where sequencer out puts are modeled as independent realizations of a Hidden Markov Model (HMM) and the problem of error correction is posed as one of maximum likelihood sequence detection over this HMM. In this work we propose an algorithm called PREMIER Turbo which can be viewed as an iterative application of the PREMIER approach. Specifically, we apply error correction in both the forward and the backward directions in a given read.We also present a heuristic inspired by turbo-equalization that incorporates the prior belief on a nucleotide position returned by the Baum-WeIch algorithm into the error correction steps.Our approach significantly improves the correction of nucleotides in the beginning of the read. Our test results on the real C. elegans and E. coli datasets show that PREMIER Turbo achieves a significantly better error correction performance than the other competing methods.

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Section: Introductionmentioning

confidence: 99%

PREMIER Turbo: Probabilistic error-correction using Markov inference in errored reads using the turbo principle

Yin

Song

Dorman

et al. 2013

2013 IEEE Global Conference on Signal and Information Processing

Self Cite

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“…Premier [136] models a sequencer as a Hidden Markov Model, with each read being an independent realization of the HMM. It uses Expectation Maximization (EM) Baum-Welch to fit a HMM to a read.…”

Section: Probabilistic Models Based (Pmb)mentioning

confidence: 99%

“…They present two algorithms for error correction: Viterbi and Fano. Premier Turbo [137] expands upon the previous version from [136]. It allows errors in the first k-mer.…”

Section: Probabilistic Models Based (Pmb)mentioning

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%

“…[9,136] A too low value for k would result in many k-mers appearing in most reads, thus joining in groups reads without any real relation. On the contrary, very large values would generate too many unique k-mers, which also have a higher probability of including more errors, therefore introducing noise into the grouping.…”

Section: K-mermentioning

confidence: 99%

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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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Objective review of de novo stand‐alone error correction methods for NGS data

Alic

Ruzafa

Dopazo

et al. 2016

WIREs Comput Mol Sci

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The sequencing market has increased steadily over the last years, with different approaches to read DNA information, prone to different types of errors. Multiple studies demonstrated the impact of sequencing errors on different applications of Next Generation Sequencing (NGS), making error correction a fundamental initial step. Different methods in the literature use different approaches and fit different types of problems. We analysed a number of 50 methods divided into five main approaches (k-spectrum, suffix arrays, multiple sequence alignment, read clustering and probabilistic models). They are not published as a part of a suite (stand-alone) and target raw, unprocessed data without an existing reference genome (de Novo). These correctors handle one or more sequencing 1 Correspondence to: asalic@posgrado.upv.es 1 technologies using the same or different approaches. They face general challenges (sometimes with specific traits for specific technologies) such as repetitive regions, uncalled bases and ploidy. Even assessing their performance is a challenge in itself because of the approach taken by various authors, the unknown factor (de Novo) and the behaviour of the third party tools employed in the benchmarks. This work aims at helping the researcher in the field to advance the state-of-the-art, the educator to have a brief but comprehensive companion and the bioinformatician to choose the right tool for the right job.The Next Generation Sequencing (NGS) appeared in 2005 and since then its market has increased steadily, with various technologies being developed. The NGS has evolved faster than the the Moore's law in Computer Science, allowing us to sequence and assemble large genomes like the Loblolly Pine with 22 Gb 1 or the Norway Spruce with 20 Gb 2 for a reasonable cost in time and resources. However, there are many other species (e.g. the Amoeba Dubia with a 670 Gb estimated genome size 3 , 200x human genome's size) that are still challenging to assemble. The errors introduced by the sequencing process are one of the main reasons NGS data has to be corrected before any further use. Multiple studies have demonstrated the impact of sequencing errors on different applications of NGS, making error correction a fundamental initial step. [4][5][6][7] There are many error correction tools in the literature that cope with different technologies and error types. However, to our knowledge, there is no complete, objective review of the modern methods that could help researchers, educators and users at the same time. There are benchmarks summarizing a number of methods, but there is none extensively focusing on the implementation, features and the overall domain (including challenges). Our work synthesises 50 de Novo stand-alone error-correction software. The Supplementary Material includes the description of the approach used to search the literature along with the inclusion criteria.The article continues with the motivation (also containing a brief description of the sequencing technologies and various err...

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PREMIER — PRobabilistic error-correction using Markov inference in errored reads

Cited by 6 publications

References 24 publications

PREMIER Turbo: Probabilistic error-correction using Markov inference in errored reads using the turbo principle

PREMIER Turbo: Probabilistic error-correction using Markov inference in errored reads using the turbo principle

Improved Error Correction of NGS Data

Objective review of de novo stand‐alone error correction methods for NGS data

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