Reptile: representative tiling for short read error correction

Yang, Xiao; Dorman, Karin S.; Aluru, Srinivas

doi:10.1093/bioinformatics/btq468

Cited by 133 publications

(149 citation statements)

References 15 publications

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“…for every organism in the bacterial database. This database is populated by extracting the ubiquitous 16S ribosomal RNA genes from every organism (using an SQL query over the bacterial database) and passing these gene sequences to RDP 10 for classification. The resulting taxonomic hierarchies are then loaded into the taxonomy database.…”

Section: A Bacterial K-mer Databasementioning

confidence: 99%

Answering biological questions by querying k‐mer databases

Greenfield

Roehm

2012

Concurrency and Computation

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SUMMARYThis paper describes a k-mer approach to analysing DNA data and quickly answering certain types of ad hoc biological questions. These k-mers (short DNA strings) are stored in a conventional relational database and indexed to support efficient exact match operations. We show that k-mers around 20 -25 bases long have interesting and useful uniqueness properties that can be used to compute a 'relatedness' metric and also allow k-mers to be used as 'unique enough' tags to identify organisms and genes. This relatedness metric is used in SQL queries that can directly answer questions such as how two related species differ, and what genes are unique to an organism. The k-mer tags have proven useful in applications, largely metagenomic ones that can quickly process large volumes of sequencing data to say something about what organisms and genes might be present in an environmental sample. All of this work is based on simple and fast exact matches of k-mer strings using a database, rather than conventional alignment based on inexact matches of much longer strings. These k-mer tools provide ways of rapidly exploring large genome spaces and handling large volumes of sequence data, and complement rather than replace existing alignment and assembly tools.

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Section: A Bacterial K-mer Databasementioning

confidence: 99%

Answering biological questions by querying k‐mer databases

Greenfield

Roehm

2012

Concurrency and Computation

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“…Several other error-correction methods are based on k-mer decomposition of the reads, e.g., Sga [14], Reptile [17] and Quake [6]. Sga uses a simple frequency threshold to separate "trusted"k-mers from "untrusted" ones, then performs base changes until untrusted k-mers can become trusted.…”

Section: Introductionmentioning

confidence: 99%

Scrible: Ultra-Accurate Error-Correction of Pooled Sequenced Reads

Duma

Cordero

Beccuti

et al. 2015

Lecture Notes in Computer Science

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Abstract.We recently proposed a novel clone-by-clone protocol for de novo genome sequencing that leverages combinatorial pooling design to overcome the limitations of DNA barcoding when multiplexing a large number of samples on second-generation sequencing instruments. Here we address the problem of correcting the short reads obtained from our sequencing protocol. We introduce a novel algorithm called Scrible that exploits properties of the pooling design to accurately identify/correct sequencing errors and minimize the chance of "over-correcting". Experimental results on synthetic data on the rice genome demonstrate that our method has much higher accuracy in correcting short reads compared to state-of-the-art error-correcting methods. On real data on the barley genome we show that Scrible significantly improves the decoding accuracy of short reads to individual BACs.

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“…SP and SE first appeared in [64]. The gain [98], represents the percentage of eliminated errors. They are all based on counting:…”

Section: Methodsmentioning

confidence: 99%

“…Reptile [98] employs approximate multiple alignments of segments (tiles) of reads allowing only substitutions. This software…”

Section: K-spectrum Based (Ksb)mentioning

confidence: 99%

“…The main reasons stated to avoid artificial datasets are the lack of simulators capable of producing meaningful data and the non-existence of some real challenges that only appear in already existing real data [15,98]. To generate the artificial data, the authors of BLESS used simLibrary and simNGS [209], for Lighter and Hector -Mason [210] and for Pluribus -ART [211].…”

Section: Real Vs Artificial Datasetsmentioning

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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Reptile: representative tiling for short read error correction

Abstract: aluru@iastate.edu.

Cited by 133 publications

References 15 publications

Answering biological questions by querying k‐mer databases

Answering biological questions by querying k‐mer databases

Scrible: Ultra-Accurate Error-Correction of Pooled Sequenced Reads

Improved Error Correction of NGS Data

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