HiTEC: accurate error correction in high-throughput sequencing data

Ilie, Lucian; Fazayeli, Farideh; Ilie, Silvana

doi:10.1093/bioinformatics/btq653

Cited by 111 publications

(112 citation statements)

References 35 publications

(67 reference statements)

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“…There also exist a number of standalone software packages for these tasks. Error correction tools include Quake [48,106] and HiTEC [49,107]. For scaffolding with NGS data, there are SSPACE [50], SOPRA [51], Bambus [52] and the recently released MIP scaffolder [53].…”

Section: Next-generation Assemblersmentioning

confidence: 99%

Next-Generation Sequencing and Large Genome Assemblies

2012

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The next-generation sequencing (NGS) revolution has drastically reduced time and cost requirements for sequencing of large genomes, and also qualitatively changed the problem of assembly. This article reviews the state of the art in de novo genome assembly, paying particular attention to mammalian-sized genomes. The strengths and weaknesses of the main sequencing platforms are highlighted, leading to a discussion of assembly and the new challenges associated with NGS data. Current approaches to assembly are outlined and the various software packages available are introduced and compared. The question of whether quality assemblies can be produced using short-read NGS data alone, or whether it must be combined with more expensive sequencing techniques, is considered. Prospects for future assemblers and tests of assembly performance are also discussed. Keywordsde novo assembly; genomics; next-generation sequencing; whole-genome shotgun Genome assembly continues to be one of the central problems of bioinformatics. This is owing, in large part, to the continuing development of the sequencing technology that provides 'reads' of short sequences of DNA, from which the genome is inferred. Larger sets of data, and changes in the properties of reads such as length and errors, bring with them new challenges for assembly. For the earliest sequencing efforts using the whole-genome shotgun (WGS) approach, in which reads are generated from random locations across the entire genome, assembly could be dealt with by arranging print-outs of the reads by hand. Through the next three decades, Sanger capillary sequencing gained substantially in throughput, and WGS became practical for increasingly large and complex genomes, from tens of kilobases in the early 1980s to gigabases by 2001 [1]. In line with this, assembly went on to use not only increasingly powerful computational means, but also increasingly time and memory-efficient assemblers.A further revolution in sequencing began around 2005, when second-generation sequencing (SGS) technologies began to produce massive throughput at far lower costs than Sanger sequencing, enabling a mammalian genome to be sequenced in a matter of days [2]. De novo assemblies of the Panda [3] and Turkey [4] genomes have now been made using SGS data alone, and several human resequencing projects have been completed [5][6][7]. The © The Wellcome Trust Sanger Institute * Author for correspondence: Tel.: +44 1223 494705, Fax: +44 1223 494919, zn1@sanger.ac.uk. Financial & competing interests disclosureThe authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript. Assembly is not at all a trivial task. Repeated sequences of DNA make it difficult to infer the relative positions in the genome corresponding to reads, and they occur far more often...

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Section: Next-generation Assemblersmentioning

confidence: 99%

Next-Generation Sequencing and Large Genome Assemblies

2012

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“…Determine csets, the set of all repetitive and non-repetitive c-sets; 3 i fconflicts detected then 4 Remove conflicting c-sets from csets one at a time when attempting correction ; by one position either to the left or to the right (line 18 or line 23). Line 17 in Algorithm 2 checks the same k-mer as found in read without changing it.…”

Section: Identification and Correction Of Sequencing Errorsmentioning

confidence: 99%

“…If the weight of a node deviates significantly from the expectation, the substring corresponding to that node is corrected to one of its siblings. Hitec [4] builds a suffix array of the set of reads and uses the longest common prefix information to count how many times short substrings are present in the input. These counts are used to decide the correct nucleotide following each substring.…”

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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“…As the awareness of sequencing errors increases, many new pipelines have been developed to either correct or remove sequencing errors, such as Blue (Greenfield et al 2014), BLESS (Heo et al 2014), UPARSE (Edgar 2013), Coral (Salmela and Schröder 2011), ECHO (Kao et al 2011), HiTEC (Ilie et al 2011), HSHREC (Salmela 2010), Reptile ) and others (see review by Yang et al 2013). These pipelines have their own advantages to handle certain types of data.…”

Section: Type II Error Caused By Cross-contaminationmentioning

confidence: 99%

Rare biosphere exploration using high-throughput sequencing: research progress and perspectives

Zhan

MacIsaac

2014

Conserv Genet

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Identification of rare species and mapping their distributions is crucial for understanding natural species distributions and causes and consequences of accelerating species declines. However, detection of rare species in both terrestrial and especially aquatic communities typically dominated by numerous microscopic species (i.e. rare biosphere) represents a formidable technical challenge. Rapid advances in high-throughput sequencing (HTS) technologies have revolutionized biodiversity studies in the rare biosphere, and also stimulated associated debates. Here we summarize research progress, discuss debates and problems, and propose possible solutions and future studies to address these issues. In addition, we provide take-home messages for experimental design and data interpretation when utilizing HTS techniques for rare biosphere exploration in ecology and conservation biology.

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HiTEC: accurate error correction in high-throughput sequencing data

Abstract: The source code of HiTEC is freely available at www.csd.uwo.ca/~ilie/HiTEC/.

Cited by 111 publications

References 35 publications

Next-Generation Sequencing and Large Genome Assemblies

Next-Generation Sequencing and Large Genome Assemblies

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

Rare biosphere exploration using high-throughput sequencing: research progress and perspectives

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