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

    , Article BMC Bioinformatics ; Volume 18, Issue 1 , 2017 ; 14712105 (ISSN) Nashta Ali, D ; Aliyari, A ; Ahmadian Moghadam, A ; Edrisi, M. A ; Motahari, S. A ; Khalaj, B. H ; Sharif University of Technology
    Abstract
    Background: Current development of sequencing technologies is towards generating longer and noisier reads. Evidently, accurate alignment of these reads play an important role in any downstream analysis. Similarly, reducing the overall cost of sequencing is related to the time consumption of the aligner. The tradeoff between accuracy and speed is the main challenge in designing long read aligners. Results: We propose Meta-aligner which aligns long and very long reads to the reference genome very efficiently and accurately. Meta-aligner incorporates available short/long aligners as subcomponents and uses statistics from the reference genome to increase the performance. Meta-aligner estimates... 

    SVNN: an efficient PacBio-specific pipeline for structural variations calling using neural networks

    , Article BMC Bioinformatics ; Volume 22, Issue 1 , 2021 ; 14712105 (ISSN) Akbarinejad, S ; Hadadian Nejad Yousefi, M ; Goudarzi, M ; Sharif University of Technology
    BioMed Central Ltd  2021
    Abstract
    Background: Once aligned, long-reads can be a useful source of information to identify the type and position of structural variations. However, due to the high sequencing error of long reads, long-read structural variation detection methods are far from precise in low-coverage cases. To be accurate, they need to use high-coverage data, which in turn, results in an extremely time-consuming pipeline, especially in the alignment phase. Therefore, it is of utmost importance to have a structural variation calling pipeline which is both fast and precise for low-coverage data. Results: In this paper, we present SVNN, a fast yet accurate, structural variation calling pipeline for PacBio long-reads... 

    SVNN: an efficient PacBio-specific pipeline for structural variations calling using neural networks

    , Article BMC Bioinformatics ; Volume 22, Issue 1 , 2021 ; 14712105 (ISSN) Akbarinejad, S ; Hadadian Nejad Yousefi, M ; Goudarzi, M ; Sharif University of Technology
    BioMed Central Ltd  2021
    Abstract
    Background: Once aligned, long-reads can be a useful source of information to identify the type and position of structural variations. However, due to the high sequencing error of long reads, long-read structural variation detection methods are far from precise in low-coverage cases. To be accurate, they need to use high-coverage data, which in turn, results in an extremely time-consuming pipeline, especially in the alignment phase. Therefore, it is of utmost importance to have a structural variation calling pipeline which is both fast and precise for low-coverage data. Results: In this paper, we present SVNN, a fast yet accurate, structural variation calling pipeline for PacBio long-reads...