The Experts below are selected from a list of 7665 Experts worldwide ranked by ideXlab platform
Ron Kikinis - One of the best experts on this subject based on the ideXlab platform.
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dcmqi an open source library for standardized communication of quantitative image analysis results using dicom
Cancer Research, 2017Co-Authors: Christian Herz, Jeanchristophe Fillionrobin, Michael D Onken, Jorg Riesmeier, Andras Lasso, Csaba Pinter, Gabor Fichtinger, Steve Pieper, David A Clunie, Ron KikinisAbstract:Quantitative analysis of clinical image data is an active area of research that holds promise for precision medicine, early assessment of treatment response, and objective characterization of the disease. Interoperability, data sharing, and the ability to mine the resulting data are of increasing importance, given the explosive growth in the number of quantitative analysis methods being proposed. The Digital Imaging and Communications in Medicine (DICOM) standard is widely adopted for image and metadata in radiology. dcmqi (DICOM for Quantitative Imaging) is a free, open source library that implements conversion of the data stored in commonly used research formats into the standard DICOM representation. dcmqi source code is distributed under BSD-style license. It is freely available as a precompiled Binary Package for every major operating system, as a Docker image, and as an extension to 3D Slicer. Installation and usage instructions are provided in the GitHub repository at https://github.com/qiicr/dcmqi. Cancer Res; 77(21); e87–90. ©2017 AACR .
Sonali Bahl - One of the best experts on this subject based on the ideXlab platform.
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RNA2DNAlign: nucleotide resolution allele asymmetries through quantitative assessment of RNA and DNA paired sequencing data.
Nucleic acids research, 2016Co-Authors: Mercedeh Movassagh, Nawaf Alomran, Prakriti Mudvari, Merve Dede, Cem Dede, Kamran Kowsari, Paula Restrepo, Edmund Cauley, Sonali BahlAbstract:We introduce RNA2DNAlign, a computational framework for quantitative assessment of allele counts across paired RNA and DNA sequencing datasets. RNA2DNAlign is based on quantitation of the relative abundance of variant and reference read counts, followed by binomial tests for genotype and allelic status at SNV positions between compatible sequences. RNA2DNAlign detects positions with differential allele distribution, suggesting asymmetries due to regulatory/structural events. Based on the type of asymmetry, RNA2DNAlign outlines positions likely to be implicated in RNA editing, allele-specific expression or loss, somatic mutagenesis or loss-of-heterozygosity (the first three also in a tumor-specific setting). We applied RNA2DNAlign on 360 matching normal and tumor exomes and transcriptomes from 90 breast cancer patients from TCGA. Under high-confidence settings, RNA2DNAlign identified 2038 distinct SNV sites associated with one of the aforementioned asymetries, the majority of which have not been linked to functionality before. The performance assessment shows very high specificity and sensitivity, due to the corroboration of signals across multiple matching datasets. RNA2DNAlign is freely available from http://github.com/HorvathLab/NGS as a self-contained Binary Package for 64-bit Linux systems.
Mercedeh Movassagh - One of the best experts on this subject based on the ideXlab platform.
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RNA2DNAlign: nucleotide resolution allele asymmetries through quantitative assessment of RNA and DNA paired sequencing data.
Nucleic acids research, 2016Co-Authors: Mercedeh Movassagh, Nawaf Alomran, Prakriti Mudvari, Merve Dede, Cem Dede, Kamran Kowsari, Paula Restrepo, Edmund Cauley, Sonali BahlAbstract:We introduce RNA2DNAlign, a computational framework for quantitative assessment of allele counts across paired RNA and DNA sequencing datasets. RNA2DNAlign is based on quantitation of the relative abundance of variant and reference read counts, followed by binomial tests for genotype and allelic status at SNV positions between compatible sequences. RNA2DNAlign detects positions with differential allele distribution, suggesting asymmetries due to regulatory/structural events. Based on the type of asymmetry, RNA2DNAlign outlines positions likely to be implicated in RNA editing, allele-specific expression or loss, somatic mutagenesis or loss-of-heterozygosity (the first three also in a tumor-specific setting). We applied RNA2DNAlign on 360 matching normal and tumor exomes and transcriptomes from 90 breast cancer patients from TCGA. Under high-confidence settings, RNA2DNAlign identified 2038 distinct SNV sites associated with one of the aforementioned asymetries, the majority of which have not been linked to functionality before. The performance assessment shows very high specificity and sensitivity, due to the corroboration of signals across multiple matching datasets. RNA2DNAlign is freely available from http://github.com/HorvathLab/NGS as a self-contained Binary Package for 64-bit Linux systems.
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SNPlice: variants that modulate Intron retention from RNA-sequencing data.
Bioinformatics (Oxford England), 2014Co-Authors: Prakriti Mudvari, Mercedeh Movassagh, Kamran Kowsari, Ali Seyfi, Maria Kokkinaki, Nathan Edwards, Nady Golestaneh, Anelia HorvathAbstract:Rationale: The growing recognition of the importance of splicing, together with rapidly accumulating RNA-sequencing data, demand robust high-throughput approaches, which efficiently analyze experimentally derived whole-transcriptome splice profiles. Results: We have developed a computational approach, called SNPlice, for identifying cis-acting, splice-modulating variants from RNA-seq datasets. SNPlice mines RNA-seq datasets to find reads that span single-nucleotide variant (SNV) loci and nearby splice junctions, assessing the co-occurrence of variants and molecules that remain unspliced at nearby exon–intron boundaries. Hence, SNPlice highlights variants preferentially occurring on intron-containing molecules, possibly resulting from altered splicing. To illustrate co-occurrence of variant nucleotide and exon–intron boundary, allele-specific sequencing was used. SNPlice results are generally consistent with splice-prediction tools, but also indicate splice-modulating elements missed by other algorithms. SNPlice can be applied to identify variants that correlate with unexpected splicing events, and to measure the splice-modulating potential of canonical splice-site SNVs. Availability and implementation: SNPlice is freely available for download from https://code.google.com/p/snplice/ as a self-contained Binary Package for 64-bit Linux computers and as python source-code. Contact: ude.uwg@iravdump or ude.uwg@ahtavroh Supplementary information: Supplementary data are available at Bioinformatics online.
Christian Herz - One of the best experts on this subject based on the ideXlab platform.
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dcmqi an open source library for standardized communication of quantitative image analysis results using dicom
Cancer Research, 2017Co-Authors: Christian Herz, Jeanchristophe Fillionrobin, Michael D Onken, Jorg Riesmeier, Andras Lasso, Csaba Pinter, Gabor Fichtinger, Steve Pieper, David A Clunie, Ron KikinisAbstract:Quantitative analysis of clinical image data is an active area of research that holds promise for precision medicine, early assessment of treatment response, and objective characterization of the disease. Interoperability, data sharing, and the ability to mine the resulting data are of increasing importance, given the explosive growth in the number of quantitative analysis methods being proposed. The Digital Imaging and Communications in Medicine (DICOM) standard is widely adopted for image and metadata in radiology. dcmqi (DICOM for Quantitative Imaging) is a free, open source library that implements conversion of the data stored in commonly used research formats into the standard DICOM representation. dcmqi source code is distributed under BSD-style license. It is freely available as a precompiled Binary Package for every major operating system, as a Docker image, and as an extension to 3D Slicer. Installation and usage instructions are provided in the GitHub repository at https://github.com/qiicr/dcmqi. Cancer Res; 77(21); e87–90. ©2017 AACR .
Prakriti Mudvari - One of the best experts on this subject based on the ideXlab platform.
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RNA2DNAlign: nucleotide resolution allele asymmetries through quantitative assessment of RNA and DNA paired sequencing data.
Nucleic acids research, 2016Co-Authors: Mercedeh Movassagh, Nawaf Alomran, Prakriti Mudvari, Merve Dede, Cem Dede, Kamran Kowsari, Paula Restrepo, Edmund Cauley, Sonali BahlAbstract:We introduce RNA2DNAlign, a computational framework for quantitative assessment of allele counts across paired RNA and DNA sequencing datasets. RNA2DNAlign is based on quantitation of the relative abundance of variant and reference read counts, followed by binomial tests for genotype and allelic status at SNV positions between compatible sequences. RNA2DNAlign detects positions with differential allele distribution, suggesting asymmetries due to regulatory/structural events. Based on the type of asymmetry, RNA2DNAlign outlines positions likely to be implicated in RNA editing, allele-specific expression or loss, somatic mutagenesis or loss-of-heterozygosity (the first three also in a tumor-specific setting). We applied RNA2DNAlign on 360 matching normal and tumor exomes and transcriptomes from 90 breast cancer patients from TCGA. Under high-confidence settings, RNA2DNAlign identified 2038 distinct SNV sites associated with one of the aforementioned asymetries, the majority of which have not been linked to functionality before. The performance assessment shows very high specificity and sensitivity, due to the corroboration of signals across multiple matching datasets. RNA2DNAlign is freely available from http://github.com/HorvathLab/NGS as a self-contained Binary Package for 64-bit Linux systems.
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SNPlice: variants that modulate Intron retention from RNA-sequencing data.
Bioinformatics (Oxford England), 2014Co-Authors: Prakriti Mudvari, Mercedeh Movassagh, Kamran Kowsari, Ali Seyfi, Maria Kokkinaki, Nathan Edwards, Nady Golestaneh, Anelia HorvathAbstract:Rationale: The growing recognition of the importance of splicing, together with rapidly accumulating RNA-sequencing data, demand robust high-throughput approaches, which efficiently analyze experimentally derived whole-transcriptome splice profiles. Results: We have developed a computational approach, called SNPlice, for identifying cis-acting, splice-modulating variants from RNA-seq datasets. SNPlice mines RNA-seq datasets to find reads that span single-nucleotide variant (SNV) loci and nearby splice junctions, assessing the co-occurrence of variants and molecules that remain unspliced at nearby exon–intron boundaries. Hence, SNPlice highlights variants preferentially occurring on intron-containing molecules, possibly resulting from altered splicing. To illustrate co-occurrence of variant nucleotide and exon–intron boundary, allele-specific sequencing was used. SNPlice results are generally consistent with splice-prediction tools, but also indicate splice-modulating elements missed by other algorithms. SNPlice can be applied to identify variants that correlate with unexpected splicing events, and to measure the splice-modulating potential of canonical splice-site SNVs. Availability and implementation: SNPlice is freely available for download from https://code.google.com/p/snplice/ as a self-contained Binary Package for 64-bit Linux computers and as python source-code. Contact: ude.uwg@iravdump or ude.uwg@ahtavroh Supplementary information: Supplementary data are available at Bioinformatics online.