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structural-variations
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Speeding up of Genetic Structural Variation Detection
, M.Sc. Thesis Sharif University of Technology ; Goudarzi, Maziar (Supervisor)
Abstract
Large differences in chromosome structures, compared to the reference genome, are one of the essential reasons for genetic variations. These differences that are called structural variations are associated with numerous diseases, including schizophrenia, cancer development, and autism. Therefore, calling these variations is of utmost importance in the next stages of analysis. However, due to computationally intensive tasks of discovering these variations, calling structural variations is lagging behind data produced by sequencers. Hence, discovering these variations with proper accuracy and in a reasonable time is of paramount importance. In this research, we implement a fast, yet accurate,...
Catalyst deactivation in industrial combined steam and dry reforming of natural gas
, Article Fuel Processing Technology ; Vol. 120 , 2014 , pp. 96-105 ; ISSN: 03783820 ; Baghalha, M ; Sharif University of Technology
Abstract
The catalyst's performance and deactivation in a Midrex® industrial fixed bed reactor were investigated for the combined steam and dry reforming of natural gas using a one-dimensional heterogeneous model. The results demonstrate that there is a strong tendency for the catalyst's deactivation by carbon formation originating from methane decomposition. However, kinetic modelling of the combined reforming process shows that only a fraction of the catalyst in the industrial reactor is required for the reactions to reach an equilibrium state in the reformer. Hence, as the catalyst is deactivated at the reactor entrance area, the reaction zone gradually moves forward and still allows for...
Preparation of nitrogen-doped aluminium titanate (Al2TiO5) nanostructures: Application to removal of organic pollutants from aqueous media
, Article Advanced Powder Technology ; Volume 31, Issue 8 , 2020 , Pages 3328-3341 ; Zekavat, M ; Soltaninejad, M ; Bakhshandeh, F ; Reza Madaah Hosseini, H ; Kashani, S ; Amutha, C ; Khatiboleslam Sadrnezhaad, S ; Ramakrishna, S ; Sharif University of Technology
Elsevier B.V
2020
Abstract
Recently, aluminum titanate (Al2TiO5)-based nanostructures have been proved to serve as an efficient photocatalytic material with satisfactory photodegradation capacity. In this study, the citrate sol–gel method was used to synthesize these nanostructures and inspect the significant impacts of nitrogen-doping-originated crystalline defects on their photocatalytic performance in some details for the first time. The results indicated that the penetration of nitrogen atoms into AT crystal lattice, depending on the nitriding time and temperature, can induce a great deal of the residual stress and result in propagating the existing cracks and breaking down the particles. The XPS and FTIR results...
SVNN: an efficient PacBio-specific pipeline for structural variations calling using neural networks
, Article BMC Bioinformatics ; Volume 22, Issue 1 , 2021 ; 14712105 (ISSN) ; 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) ; 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...