Comparison of the two major classes of assembly algorithms: overlap-layout-consensus and de-bruijn-graph

Li, Zhenyu; Chen, Yanxiang; Mu, Desheng; Yuan, Jianying; Shi, Yujian; Zhang, Hao; Gan, Jun; Li, Nan; Hu, Xuesong; Liu, Binghang; Yang, Bicheng; Fan, Wei

doi:10.1093/bfgp/elr035

Cited by 228 publications

(179 citation statements)

References 49 publications

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“…Current assembling algorithms for NGS consist of two main types [66]: (i) Overlap-layout-consensus (OLC) methods; and (ii) Eulerian/ de Bruijn Graph (DBG) methods. Both types utilise a graph theory to represent NGS data, but OLC considers reads to be nodes, while DBG takes k-mer for a node.…”

Section: Platform-specific Biasesmentioning

confidence: 99%

Computational Errors and Biases in Short Read Next Generation Sequencing

Abnizova¹,

Boekhorst²,

Orlov³

2017

J Proteomics Bioinform

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Section: Platform-specific Biasesmentioning

confidence: 99%

Computational Errors and Biases in Short Read Next Generation Sequencing

Abnizova¹,

Boekhorst²,

Orlov³

2017

J Proteomics Bioinform

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“…This algorithm ensures that if two PCR amplimers share same genomic areas, only the collective area will be reported and redundant area will be removed. The same algorithm was used for genome sequence assembly (Li et al, 2012). For calculating the total coverage for each PCR marker type, all in silico PCR amplimers belonging to each marker type were processed collectively .The same procedure was conducted in calculating the total primer set (83 primers) coverage.…”

Section: In Silico Pcr Analysismentioning

confidence: 99%

Selective Amplification of Start codon Polymorphic Loci (SASPL): a new PCR-based molecular marker in olive

Alsamman¹,

Adawy²,

Ibrahim³

et al. 2017

Plant Omics

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The Selective Amplification of Start Codon Polymorphic Loci (SASPL) has been developed as a new PCR-based molecular marker. SASPL was validated for the analysis of varietal diversity on ten olive varieties. Validation included in vitro comparison against RAPD, SCoT and SAMPL markers. Assessment of these techniques included primer selectivity, genome coverage and the ability to target genic regions through in silico PCR analysis. Candidate PCR fragments were further sequenced to annotate non-identified genes in olive. Eight SASPL primers were compared to 24 RAPD, 39 SCoT and 12 SAMPL primers. The TA produced by the RAPD, SCoT, SAMPL and the eight SASPL primers were 359, 642, 571 and 269 amplicons, respectively. The highest average number of TA was revealed by SAMPL (47.6), average of PA (18.1) and genetic similarity (GS) (96%) among the olive varieties. On the other hand, SASPL analysis provided higher average number of TA (33.6), average of PA (16.2) and GS (93%) than SCoT and RAPD. The highest average of (PIC) (0.2909) was exhibited by SASPL analysis and the lowest average (0.2038) was revealed by SCoT. The highest number of UB (111) was revealed by SCoT and the lowest UB (43) was obtained by SASPL. Across the four marker types, variety Maraki was characterized by the highest number of unique markers (74). Meanwhile, the variety Manzanillo was characterized by the lowest number of unique markers (8). In addition, in the in silico analysis SASPL exhibited the highest chromosomal coverage (0.59%) and targeted genes (1090) using the lowest number of primers. Additionally, the average area covered by the SASPL primers (354kb) was larger than SCoT and SAMPL. RAPD analysis provided the lowest potential, chromosomal coverage (0.04%) and number of targeted genes (17) compared to SASPL, SCoT and SAMPL analysis. The total coverage of the genome, revealed by combined data was higher (1.21%) than that of each technique separately. Meanwhile, the difference between the actual and the total genomic regions covered by the combined data was about 652kb. Our results suggested that the newly developed SASPL marker is the most adequately and each of the studied marker target different genomic areas, while some areas are shared. Two SCoT and one RAPD fragment were sequenced and showed a high similarity to genes of high physiological functions; such as cyclic plant-specific DNA-binding transcription factor, SANT domain and Copia-type retrotransposon.

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“…Present-day assembling algorithms for NGS comprise two main groups (Li Z. et al, 2012): (i) Overlap-layout-consensus (OLC) methods; and (ii) Eulerian/de Bruijn Graph (DBG) methods. Both groups apply a graph theory to deal with NGS data, but in OLC notation reads are nodes, while in DBG notation a k-mer is a node.…”

Section: Mapping/aligning To a Reference Genome And/or Assemblymentioning

confidence: 99%

Computational problems of analysis of short next generation sequencing reads

Boekhorst¹,

Naumenko²,

Орлова³

et al. 2016

Vestn. VOGiS

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Секвенирование следующего поколения (NGS) с помощью корот ких прочтений ДНК вносит большой вклад в решение задач современной геномики, генетики, клеточной биологии и медицины, особенно в исследования метагеномики, сравни-тельной геномики, определение полиморфизмов, скрининг мутаций, транскриптомное профилирование, изучение ремо-делирования хроматина и многие другие приложения. Секве-ниро вание неустойчиво к техническим ошибкам, которые могут влиять на научные выводы. NGS технологии состоят из создания коллекции многочисленных коротких фрагментов ДНК, имену емой «библиотекой», получения молекулярных колоний и их дальнейшего массового параллельного секвени-рования. Такие секвенированные фрагменты называются «про-чтениями», они собираются (ассемблируются) в протяженные строки. Протяжен ные последовательности, в свою очередь, собираются в геномы для дальнейшего анализа. Вычислитель-ные/процессинговые ошибки и сбои секвенирования -это ошибки, возникающие при последующей цифровой обработке секвенированных образцов. Последующая обработка (процес-сирование) включает процедуры оценки качества, картирова-ния, ассемблирования и даже корректировки ошибочных дан ных. Данная статья рас сматривает вычислительные ошибки процессирования, компью терные и статистические подходы для их определения, а также представляет словарь терминоло-гии секвенирования. Рассмот рены задачи идентификации мутаций («Определение вариаций») в данных секвенирования и контроль качества их определения. Определение вариаций включает локальные вариации, такие как одиночные нуклео-тидные полиморфизмы, короткие вставки и делеции (инделы), и масштабные вариации (инверсии, трансло кации или боль шие Short read next generation sequencing (NGS) has significant impacts on modern genomics, genetics, cell biology and medicine, especially on meta-genomics, comparative genomics, polymorphism detection, mutation screening, transcriptome profiling, methylation profiling, chromatin remodelling and many more applications. However, NGS are prone for errors which complicate scientific conclusions. NGS technologies consist of shearing DNA molecules into collection of numerous small fragments, called a 'library' , and their further extensive parallel sequencing. These sequenced overlapping fragments are called 'reads' , they are assembled into contiguous strings. The contiguous sequences are in turn assembled into genomes for further analysis. Computational sequencing problems are those arising from numerical processing of sequenced samples. The numerical processing involves procedures such as: quality-scoring, mapping/assembling, and surprisingly, error-correction of a data. This paper is reviewing post-processing errors and computational methods to discern them. It also includes sequencing dictionary. We present here quality control of raw data, errors arising at the steps of alignment of sequencing reads to a reference genome and assembly. Finally this work presents identification of mutations ("Variant calling") in sequencing data and its quality control.

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Comparison of the two major classes of assembly algorithms: overlap-layout-consensus and de-bruijn-graph

Cited by 228 publications

References 49 publications

Computational Errors and Biases in Short Read Next Generation Sequencing

Computational Errors and Biases in Short Read Next Generation Sequencing

Selective Amplification of Start codon Polymorphic Loci (SASPL): a new PCR-based molecular marker in olive

Computational problems of analysis of short next generation sequencing reads

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