Correction Algorithm

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Robert G Grossman - One of the best experts on this subject based on the ideXlab platform.

Joachim K Krauss - One of the best experts on this subject based on the ideXlab platform.

David E King - One of the best experts on this subject based on the ideXlab platform.

Ercument A Cicek - One of the best experts on this subject based on the ideXlab platform.

  • hercules a profile hmm based hybrid error Correction Algorithm for long reads
    Nucleic Acids Research, 2018
    Co-Authors: Can Firtina, Ziv Barjoseph, Can Alkan, Ercument A Cicek
    Abstract:

    Choosing whether to use second or third generation sequencing platforms can lead to trade-offs between accuracy and read length. Several types of studies require long and accurate reads. In such cases researchers often combine both technologies and the erroneous long reads are corrected using the short reads. Current approaches rely on various graph or alignment based techniques and do not take the error profile of the underlying technology into account. Efficient machine learning Algorithms that address these shortcomings have the potential to achieve more accurate integration of these two technologies. We propose Hercules, the first machine learning-based long read error Correction Algorithm. Hercules models every long read as a profile Hidden Markov Model with respect to the underlying platform's error profile. The Algorithm learns a posterior transition/emission probability distribution for each long read to correct errors in these reads. We show on two DNA-seq BAC clones (CH17-157L1 and CH17-227A2) that Hercules-corrected reads have the highest mapping rate among all competing Algorithms and have the highest accuracy when the breadth of coverage is high. On a large human CHM1 cell line WGS data set, Hercules is one of the few scalable Algorithms; and among those, it achieves the highest accuracy.

  • hercules a profile hmm based hybrid error Correction Algorithm for long reads
    bioRxiv, 2017
    Co-Authors: Can Firtina, Ziv Barjoseph, Can Alkan, Ercument A Cicek
    Abstract:

    Abstract Motivation Choosing whether to use second or third generation sequencing platforms can lead to trade-offs between accuracy and read length. Several studies require long and accurate reads including de novo assembly, fusion and structural variation detection. In such cases researchers often combine both technologies and the more erroneous long reads are corrected using the short reads. Current approaches rely on various graph based alignment techniques and do not take the error profile of the underlying technology into account. Memory- and time-efficient machine learning Algorithms that address these shortcomings have the potential to achieve better and more accurate integration of these two technologies. Results We designed and developed Hercules, the first machine learning-based long read error Correction Algorithm. The Algorithm models every long read as a profile Hidden Markov Model with respect to the underlying platform’s error profile. The Algorithm learns a posterior transition/emission probability distribution for each long read and uses this to correct errors in these reads. Using datasets from two DNA-seq BAC clones (CH17-157L1 and CH17-227A2), and human brain cerebellum polyA RNA-seq, we show that Hercules-corrected reads have the highest mapping rate among all competing Algorithms and highest accuracy when most of the basepairs of a long read are covered with short reads. Availability Hercules source code is available at https://github.com/BilkentCompGen/Hercules

Roy A.e. Bakay - One of the best experts on this subject based on the ideXlab platform.

  • alignment Correction Algorithm for transformation of stereotactic anteriorcommissure posterior commissure based coordinates into frame coordinates fo r image guided functional neurosurgery author s reply
    Neurosurgery, 1998
    Co-Authors: Joachim K Krauss, David E King, Robert G Grossman, R. R. Tasker, Robert J. Maciunas, Kim J. Burchiel, Patrick J. Kelly, Roy A.e. Bakay
    Abstract:

    OBJECTIVE: The goal was to describe an alignment Correction Algorithm for the transformation of stereotactic atlas-derived anterior commissure/posterior commissure-based coordinates into frame coordinates for image-guided functional stereotactic neurosurgery. TECHNIQUE: The Algorithm was developed for the calculation of targets that are referenced to the intercommissural line. It corrects for deviations of the axis of the intercommissural line in relation to the stereotactic frame (x, y, and z coordinates). The Algorithm is easily implemented on a personal computer with a spreadsheet program. The calculation is fast and effective. CONCLUSION: The procedure is universally applicable for functional stereotactic neurosurgery, and it can be used with different stereotactic frames, different imaging techniques, and different workstations.

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