Meta-aligner: Long-read alignment based on genome statistics

Nashta-ali, Damoun; Aliyari, Ali; Edrisi, Mohammadamin; Moghadam, Ahmad Ahmadian; Motahari, Seyed Abolfazl; Khalaj, Babak Hossein

doi:10.1101/060129

Cited by 6 publications

(14 citation statements)

References 7 publications

(9 reference statements)

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“…manner. For two cases, we use Meta-aligner [3] to align reads with their two fragments of length ℓ and not using all their bases. We consider only the first stage Meta-aligner.…”

Section: Resultsmentioning

confidence: 99%

“…A mosaic model for capturing the repeat structure of the genome is presented in [3]. In this model, the reference genome consists of two types of intervals: repeat and random intervals.…”

Section: Methodsmentioning

confidence: 99%

“…log f ℓ R ) for Human genome hg19 based on Meta-aligner model with ( ℓ , d ) = (30, 0) defined in [3]. …”

Section: Methodsmentioning

confidence: 99%

“…We have shown significant improvement on coverage bound for real genome is achieved by using this method. For processing machine, Meta-aligner [3] scheme is used to align reads on the reference genome.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Breaking Lander-Waterman’s Coverage Bound

Nashta-ali¹,

Motahari²,

Hosseinkhalaj³

2016

PLoS ONE

Self Cite

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Lander-Waterman’s coverage bound establishes the total number of reads required to cover the whole genome of size G bases. In fact, their bound is a direct consequence of the well-known solution to the coupon collector’s problem which proves that for such genome, the total number of bases to be sequenced should be O(G ln G). Although the result leads to a tight bound, it is based on a tacit assumption that the set of reads are first collected through a sequencing process and then are processed through a computation process, i.e., there are two different machines: one for sequencing and one for processing. In this paper, we present a significant improvement compared to Lander-Waterman’s result and prove that by combining the sequencing and computing processes, one can re-sequence the whole genome with as low as O(G) sequenced bases in total. Our approach also dramatically reduces the required computational power for the combined process. Simulation results are performed on real genomes with different sequencing error rates. The results support our theory predicting the log G improvement on coverage bound and corresponding reduction in the total number of bases required to be sequenced.

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“…manner. For two cases, we use Meta-aligner [3] to align reads with their two fragments of length ℓ and not using all their bases. We consider only the first stage Meta-aligner.…”

Section: Resultsmentioning

confidence: 99%

“…A mosaic model for capturing the repeat structure of the genome is presented in [3]. In this model, the reference genome consists of two types of intervals: repeat and random intervals.…”

Section: Methodsmentioning

confidence: 99%

“…log f ℓ R ) for Human genome hg19 based on Meta-aligner model with ( ℓ , d ) = (30, 0) defined in [3]. …”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Breaking Lander-Waterman’s Coverage Bound

Nashta-ali¹,

Motahari²,

Hosseinkhalaj³

2016

PLoS ONE

Self Cite

View full text Add to dashboard Cite

show abstract

“…There exists many read mapping packages suitable for short/long/very long reads such as Bowtie [5], Bowtie2 [6], BWA [7,8], SOAP [9], SOAP2 [10], MAQ [11], mrsFAST [12], Stampy [13], and Meta-aligner [14]. A single location or a list of locations of possible origins of each read is reported by these packages.…”

Section: Introductionmentioning

confidence: 99%

SureMap: Versatile, Error Tolerant, and High Sensitive Read Mapper

2017

Preprint

Self Cite

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SureMap is a versatile, error tolerant and high sensitive read mapper which is able to map "difficult" reads, those requiring many edit operations to be mapped to the reference genome, with acceptable time complexity. Mapping real datasets reveal that many variants unidentifiable by other mappers can be called using Suremap. Moreover SureMap has a very good running time and accuracy in aligning very long and noisy reads like PacBio and Nanopore against reference genome.

show abstract

Breaking Lander-Waterman’s Coverage Bound

Nashta-ali

Motahari

Khalaj

2016

Preprint

View full text Add to dashboard Cite

Lander-Waterman's coverage bound establishes the total number of reads required to cover the whole genome of size G bases. In fact, their bound is a direct consequence of the well-known solution to the coupon collector's problem which proves that for such genome, the total number of bases to be sequenced should be O (G ln G). Although the result leads to a tight bound, it is based on a tacit assumption that the set of reads are first collected through a sequencing process and then are processed through a computation process, i.e., there are two different machines: one for sequencing and one for processing. In this paper, we present a significant improvement compared to Lander-Waterman's result and prove that by combining the sequencing and computing processes, one can resequence the whole genome with as low as O(G) sequenced bases in total. Our approach also dramatically reduces the required computational power for the combined process. Simulation results are performed on real genomes with different sequencing error rates. The results support our theory predicting the log G improvement on coverage bound and corresponding reduction in the total number of bases required to be sequenced.

show abstract

Meta-aligner: Long-read alignment based on genome statistics

Cited by 6 publications

References 7 publications

Breaking Lander-Waterman’s Coverage Bound

Breaking Lander-Waterman’s Coverage Bound

SureMap: Versatile, Error Tolerant, and High Sensitive Read Mapper

Breaking Lander-Waterman’s Coverage Bound

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