Deep Sequencing of a Genetically Heterogeneous Sample: Local Haplotype Reconstruction and Read Error Correction

Zagordi, Osvaldo; Geyrhofer, Lukas; Röth, Volker; Beerenwinkel, Niko

doi:10.1007/978-3-642-02008-7_21

Cited by 21 publications

(39 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Subsequently, we configure our method to discard l-tuples that appear below a cutoff threshold defined by the first minimum in the frequency spectrum. More tailored approaches for error correction have been discussed elsewhere (Li et al, 2008;Zagordi et al, 2010a;Zhao et al, 2010;Yang et al, 2012). We observe from Table 1 that the outlined error correction scheme resulted in significant performance gain.…”

Section: Resultsmentioning

confidence: 86%

“…We consider populations consisting of 2 to 5 haplotypes. Figures 5 and 6 compares the estimated haplotype frequencies of Mutant-Bin and state-of-the-art ShoRAH (Zagordi et al, 2010a) on the same datasets. ShoRAH aligns the reads to a reference genome to extract a minimal subset of haplotypes that explain the observed reads, while Mutant-Bin finds the minimal set of basic frequencies that explain the observed frequencies of l-tuples and clusters the l-tuples of haplotypes with similar frequency together, without any sort of alignment.…”

Section: Resultsmentioning

confidence: 97%

“…Our final evaluation on real deep-sequencing HIV data obtained from 454/Roche FLX pyrosequencing platform allows for hard assessment of the performance (Zagordi et al, 2010a). Of the four datasets, two are of subtype A and two of subtype B.…”

Section: Resultsmentioning

confidence: 99%

“…We assess its performance in isolating regions containing polymorphic sites into clusters and estimating haplotype frequencies. Also, we compare our method to the existing state of the art, ShoRAH (Zagordi et al, 2010a).…”

Section: Introductionmentioning

confidence: 97%

“…In Eriksson et al (2008) and Zagordi et al (2010a), the authors proposed a set of methodologies based on a graph theoretic solution, where a set of haplotypes were obtained by calculating a minimal coverage set of paths over a graph of aligned reads. In Prosperi et al (2011), a reconstruction algorithm based on combinations of multinomial distributions was designed to account for the overlaps between reads with similar frequencies.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Mutant-Bin: Unsupervised Haplotype Estimation of Viral Population Diversity Without Reference Genome

Prabhakara

Malhotra

Acharya

et al. 2013

Journal of Computational Biology

View full text Add to dashboard Cite

High genetic variability in viral populations plays an important role in disease progression, pathogenesis, and drug resistance. The last few years has seen significant progress in the development of methods for reconstruction of viral populations using data from next-generation sequencing technologies. These methods identify the differences between individual haplotypes by mapping the short reads to a reference genome. Much less has been published about resolving the population structure when a reference genome is lacking or is not well-defined, which severely limits the application of these new technologies to resolve virus population structure. We describe a computational framework, called Mutant-Bin, for clustering individual haplotypes in a viral population and determining their prevalence based on a set of deep sequencing reads. The main advantages of our method are that: (i) it enables determination of the population structure and haplotype frequencies when a reference genome is lacking; (ii) the method is unsupervised-the number of haplotypes does not have to be specified in advance; and (iii) it identifies the polymorphic sites that co-occur in a subset of haplotypes and the frequency with which they appear in the viral population. The method was evaluated on simulated reads with sequencing errors and 454 pyrosequencing reads from HIV samples. Our method clustered a high percentage of haplotypes with low false-positive rates, even at low genetic diversity.

show abstract

Section: Resultsmentioning

confidence: 86%

Section: Resultsmentioning

confidence: 97%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Mutant-Bin: Unsupervised Haplotype Estimation of Viral Population Diversity Without Reference Genome

Prabhakara

Malhotra

Acharya

et al. 2013

Journal of Computational Biology

View full text Add to dashboard Cite

show abstract

Multiple Sequence Alignment System for Pyrosequencing Reads

Saeed

Khokhar

Zagordi

et al. 2009

Bioinformatics and Computational Biology

Self Cite

View full text Add to dashboard Cite

Pyrosequencing is among the emerging sequencing techniques, capable of generating upto 100,000 overlapping reads in a single run. This technique is much faster and cheaper than the existing state of the art sequencing technique such as Sanger. However, the reads generated by pyrosequencing are short in size and contain numerous errors. Furthermore, each read has a specific position in the reference genome. In order to use these reads for any subsequent analysis, the reads must be aligned . Existing multiple sequence alignment methods cannot be used as they do not take into account the specific positions of the sequences with respect to the genome, and are highly inefficient for large number of sequences. Therefore, the common practice has been to use either simple pairwise alignment despite its poor accuracy for error prone pyroreads, or use computationally expensive techniques based on sequential gap propagation. In this paper, we develop a computationally efficient method based on domain decomposition, referred to as pyro-align, to align such large number of reads. The proposed alignment algorithm accurately aligns the erroneous reads in a short period of time, which is orders of magnitude faster than any existing method. The accuracy of the alignment is confirmed from the consensus obtained from the multiple alignments.

show abstract