The Experts below are selected from a list of 79419 Experts worldwide ranked by ideXlab platform
Jan Vijg - One of the best experts on this subject based on the ideXlab platform.
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single cell analysis reveals different age related Somatic Mutation profiles between stem and differentiated cells in human liver
bioRxiv, 2019Co-Authors: Kristina Brazhnik, Xiao Dong, Jan Vijg, Shixiang Sun, Omar Alani, Milan Kinkhabwala, Allan W Wolkoff, Alexander Y MaslovAbstract:Accumulating Somatic Mutations have been implicated in age-related cellular degeneration and death. Because of their random nature and low abundance, Somatic Mutations are difficult to detect except in single cells or clonal lineages. Here we show that in single hepatocytes from human liver, an organ normally exposed to high levels of genotoxic stress, Somatic Mutation frequencies are high and increase substantially with age. Significantly lower Mutation frequencies were observed in liver stem cells and organoids derived from them. These results could explain the increased age-related incidence of liver disease in humans and stress the importance of stem cells in maintaining genome integrity.
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Differences between germline and Somatic Mutation rates in humans and mice.
Nature Communications, 2017Co-Authors: Brandon Milholland, Xiao Dong, Lei Zhang, Jan VijgAbstract:The germline Mutation rate has been extensively studied and has been found to vary greatly between species, but much less is known about the Somatic Mutation rate in multicellular organisms, which remains very difficult to determine. Here, we present data on Somatic Mutation rates in mice and humans, obtained by sequencing single cells and clones derived from primary fibroblasts, which allows us to make the first direct comparison with germline Mutation rates in these two species. The results indicate that the Somatic Mutation rate is almost two orders of magnitude higher than the germline Mutation rate and that both Mutation rates are significantly higher in mice than in humans. Our findings demonstrate both the privileged status of germline genome integrity and species-specific differences in genome maintenance. Germline Mutation rates are known to vary between species but Somatic Mutation rates are less well understood. Here the authors compare mice and humans, observing that Somatic Mutation rates were nearly two orders of magnitude higher in both species, with both Mutation rates significantly higher in mice.
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differences between germline and Somatic Mutation rates in humans and mice
Nature Communications, 2017Co-Authors: Brandon Milholland, Xiao Dong, Lei Zhang, Xiaoxiao Hao, Yousin Suh, Jan VijgAbstract:The germline Mutation rate has been extensively studied and has been found to vary greatly between species, but much less is known about the Somatic Mutation rate in multicellular organisms, which remains very difficult to determine. Here, we present data on Somatic Mutation rates in mice and humans, obtained by sequencing single cells and clones derived from primary fibroblasts, which allows us to make the first direct comparison with germline Mutation rates in these two species. The results indicate that the Somatic Mutation rate is almost two orders of magnitude higher than the germline Mutation rate and that both Mutation rates are significantly higher in mice than in humans. Our findings demonstrate both the privileged status of germline genome integrity and species-specific differences in genome maintenance.
Prasanth S. Ariyannur - One of the best experts on this subject based on the ideXlab platform.
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Somatic Mutation detection efficiency in EGFR : a comparison between high resolution melting analysis and Sanger sequencing
BMC cancer, 2020Co-Authors: Reenu Anne Joy, Sukrishna Kamalasanan Thelakkattusserry, Narendranath Vikkath, Renjitha Bhaskaran, Damodaran Vasudevan, Sajitha Krishnan, Prasanth S. AriyannurAbstract:High resolution melting curve analysis is a cost-effective rapid screening method for detection of Somatic gene Mutation. The performance characteristics of this technique has been explored previously, however, analytical parameters such as limit of detection of mutant allele fraction and total concentration of DNA, have not been addressed. The current study focuses on comparing the Mutation detection efficiency of High-Resolution Melt Analysis (HRM) with Sanger Sequencing in Somatic Mutations of the EGFR gene in non-small cell lung cancer. The minor allele fraction of Somatic Mutations was titrated against total DNA concentration using Sanger sequencing and HRM to determine the limit of detection. The mutant and wildtype allele fractions were validated by multiplex allele-specific real-time PCR. Somatic Mutation detection efficiency, for exons 19 & 21 of the EGFR gene, was compared in 116 formalin fixed paraffin embedded tumor tissues, after screening 275 tumor tissues by Sanger sequencing. The limit of detection of minor allele fraction of exon 19 Mutation was 1% with sequencing, and 0.25% with HRM, whereas for exon 21 Mutation, 0.25% MAF was detected using both methods. Multiplex allele-specific real-time PCR revealed that the wildtype DNA did not impede the amplification of mutant allele in mixed DNA assays. All Mutation positive samples detected by Sanger sequencing, were also detected by HRM. About 28% cases in exon 19 and 40% in exon 21, detected as mutated in HRM, were not detected by sequencing. Overall, sensitivity and specificity of HRM were found to be 100 and 67% respectively, and the negative predictive value was 100%, while positive predictive value was 80%. The comparative series study suggests that HRM is a modest initial screening test for Somatic Mutation detection of EGFR, which must further be confirmed by Sanger sequencing. With the modification of annealing temperature of initial PCR, the limit of detection of Sanger sequencing can be improved.
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Somatic Mutation detection efficiency in EGFR: a comparison between High Resolution Melting Analysis and Sanger Sequencing
2020Co-Authors: Reenu Anne Joy, Sukrishna Kamalasanan Thelakkattusserry, Narendranath Vikkath, Renjitha Bhaskaran, Damodaran Vasudevan, Sajitha Krishnan, Prasanth S. AriyannurAbstract:Abstract Background: High resolution melting curve analysis is a cost-effective rapid screening method for detection of Somatic gene Mutation. The performance characteristics of this technique has been explored previously, however, analytical parameters such as limit of detection of mutant allele fraction and total concentration of DNA, have not been addressed. The current study focuses on comparing the Mutation detection efficiency of High-Resolution Melt Analysis (HRM) with Sanger Sequencing in Somatic Mutations of the EGFR gene in non-small cell lung cancer .Methods: The minor allele fraction of Somatic Mutations was titrated against total DNA concentration using Sanger sequencing and HRM to determine the limit of detection. The mutant and wildtype allele fractions were validated by multiplex allele-specific real-time PCR. Somatic Mutation detection efficiency, for exons 19 & 21 of the EGFR gene, was compared in 116 formalin fixed paraffin embedded tumor tissues, after screening 275 tumor tissues by Sanger sequencing.Results: The limit of detection of minor allele fraction of exon 19 Mutation was 1% with Sequencing, and 0.25% with HRM, whereas for exon 21 Mutation, 0.25% MAF was detected using both methods. Multiplex allele-specific real-time PCR revealed that the wildtype DNA did not impede the amplification of mutant allele in mixed DNA assays. All Mutation positive samples detected by Sanger sequencing, were also detected by HRM. About 28% cases in exon 19 and 40% in exon 21, detected as mutated in HRM, were not detected by sequencing. Overall, sensitivity and specificity of HRM were found to be 100% and 67% respectively, and the negative predictive value was 100%, while positive predictive value was 80%. Conclusion: The comparative series study suggests that HRM is a modest initial screening test for Somatic Mutation detection of EGFR, which must further be confirmed by Sanger sequencing. With the modification of annealing temperature of initial PCR, the limit of detection of Sanger sequencing can be improved.
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Somatic Mutation detection efficiency in EGFR: a comparison between High Resolution Melting Analysis and Sanger Sequencing
2020Co-Authors: Reenu Anne Joy, Sukrishna Kamalasanan Thelakkattusserry, Narendranath Vikkath, Renjitha Bhaskaran, Damodaran Vasudevan, Sajitha Krishnan, Prasanth S. AriyannurAbstract:Abstract Background: The current study focuses on comparing the Mutation detection efficiency of High-Resolution Melt Analysis (HRM) with Sanger Sequencing in EGFR gene.Methods: The minor allele frequency of Somatic Mutations was titrated against total DNA concentration using Sanger sequencing and HRM. Somatic Mutation detection efficiency, for exons 19 & 21 of the EGFR gene, was compared in 116 formalin fixed paraffin embedded tumor tissues, after screening 275 tumor tissues by Sanger sequencing.Results: The limit of detection of minor allele fraction of exon 19 Mutation was 1% with Sequencing, and 0.25% with HRM, whereas for exon 21 Mutation, 0.25% MAF was detected using both methods. All Mutation positive samples detected by Sanger sequencing, were also detected by HRM. About 28% cases in exon 19 and 40% in exon 21, detected as mutated in HRM, were not detected by sequencing. Overall, sensitivity and specificity of HRM were found to be 100% and 67% respectively, and the negative predictive value was 100%, while positive predictive value was 80%.Conclusion: The comparative series study suggests that HRM is a modest initial screening test for Somatic Mutation detection of EGFR, which must further be confirmed by Sanger sequencing.
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Somatic Mutation detection efficiency in EGFR: A comparison between High Resolution Melting Analysis and Sanger Sequencing
2020Co-Authors: Reenu Anne Joy, Sukrishna Kamalasanan Thelakkattusserry, Narendranath Vikkath, Renjitha Bhaskaran, Damodaran Vasudevan, Sajitha Krishnan, Prasanth S. AriyannurAbstract:Abstract Background: The current study focuses on comparing the Mutation detection efficiency of High-Resolution Melt Analysis (HRM) with Sanger Sequencing in EGFR gene.Methods: The minor allele frequency of Somatic Mutations was titrated against total DNA concentration using Sanger sequencing and HRM. Somatic Mutation detection efficiency, for exons 19 & 21 of the EGFR gene, was compared in 116 formalin fixed paraffin embedded tumor tissues, after screening 275 tumor tissues by Sanger sequencing.Results: The limit of detection of minor allele fraction of exon 19 Mutation was 1% with Sequencing, and 0.25% with HRM, whereas for exon 21 Mutation, 0.25% MAF was detected using both methods. Mutation positive samples detected by Sanger sequencing, and wild-type samples detected by HRM were corresponding to each other. About 28% cases in exon 19 and 40% in exon 21, detected as mutated in HRM, was not detected by sequencing. Overall, sensitivity and specificity of HRM were found to be 100% and 67% respectively, and the negative predictive value was 100%, while positive predictive value was 80%.Conclusion: The comparative series study suggests that HRM is a modest initial screening test for Somatic Mutation detection of EGFR, which must further be confirmed by Sanger sequencing.
Rachel Karchin - One of the best experts on this subject based on the ideXlab platform.
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a machine learning approach for Somatic Mutation discovery
Science Translational Medicine, 2018Co-Authors: Derrick E Wood, James R White, Andrew Georgiadis, Beth O Van Emburgh, Sonya Parpartli, Jason Mitchell, Valsamo Anagnostou, Noushin Niknafs, Rachel KarchinAbstract:Variability in the accuracy of Somatic Mutation detection may affect the discovery of alterations and the therapeutic management of cancer patients. To address this issue, we developed a Somatic Mutation discovery approach based on machine learning that outperformed existing methods in identifying experimentally validated tumor alterations (sensitivity of 97% versus 90 to 99%; positive predictive value of 98% versus 34 to 92%). Analysis of paired tumor-normal exome data from 1368 TCGA (The Cancer Genome Atlas) samples using this method revealed concordance for 74% of Mutation calls but also identified likely false-positive and false-negative changes in TCGA data, including in clinically actionable genes. Determination of high-quality Somatic Mutation calls improved tumor Mutation load–based predictions of clinical outcome for melanoma and lung cancer patients previously treated with immune checkpoint inhibitors. Integration of high-quality machine learning Mutation detection in clinical next-generation sequencing (NGS) analyses increased the accuracy of test results compared to other clinical sequencing analyses. These analyses provide an approach for improved identification of tumor-specific Mutations and have important implications for research and clinical management of cancer patients.
Patricio Yankilevich - One of the best experts on this subject based on the ideXlab platform.
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A pan-cancer Somatic Mutation embedding using autoencoders
BMC Bioinformatics, 2019Co-Authors: Martin Palazzo, Pierre Beauseroy, Patricio YankilevichAbstract:Background Next generation sequencing instruments are providing new opportunities for comprehensive analyses of cancer genomes. The increasing availability of tumor data allows to research the complexity of cancer disease with machine learning methods. The large available repositories of high dimensional tumor samples characterised with germline and Somatic Mutation data requires advance computational modelling for data interpretation. In this work, we propose to analyze this complex data with neural network learning, a methodology that made impressive advances in image and natural language processing. Results Here we present a tumor Mutation profile analysis pipeline based on an autoencoder model, which is used to discover better representations of lower dimensionality from large Somatic Mutation data of 40 different tumor types and subtypes. Kernel learning with hierarchical cluster analysis are used to assess the quality of the learned Somatic Mutation embedding, on which support vector machine models are used to accurately classify tumor subtypes. Conclusions The learned latent space maps the original samples in a much lower dimension while keeping the biological signals from the original tumor samples. This pipeline and the resulting embedding allows an easier exploration of the heterogeneity within and across tumor types and to perform an accurate classification of tumor samples in the pan-cancer Somatic Mutation landscape.
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A pan-cancer Somatic Mutation embedding using autoencoders.
BMC bioinformatics, 2019Co-Authors: Martin Palazzo, Pierre Beauseroy, Patricio YankilevichAbstract:Next generation sequencing instruments are providing new opportunities for comprehensive analyses of cancer genomes. The increasing availability of tumor data allows to research the complexity of cancer disease with machine learning methods. The large available repositories of high dimensional tumor samples characterised with germline and Somatic Mutation data requires advance computational modelling for data interpretation. In this work, we propose to analyze this complex data with neural network learning, a methodology that made impressive advances in image and natural language processing. Here we present a tumor Mutation profile analysis pipeline based on an autoencoder model, which is used to discover better representations of lower dimensionality from large Somatic Mutation data of 40 different tumor types and subtypes. Kernel learning with hierarchical cluster analysis are used to assess the quality of the learned Somatic Mutation embedding, on which support vector machine models are used to accurately classify tumor subtypes. The learned latent space maps the original samples in a much lower dimension while keeping the biological signals from the original tumor samples. This pipeline and the resulting embedding allows an easier exploration of the heterogeneity within and across tumor types and to perform an accurate classification of tumor samples in the pan-cancer Somatic Mutation landscape.
Brandon Milholland - One of the best experts on this subject based on the ideXlab platform.
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Differences between germline and Somatic Mutation rates in humans and mice.
Nature Communications, 2017Co-Authors: Brandon Milholland, Xiao Dong, Lei Zhang, Jan VijgAbstract:The germline Mutation rate has been extensively studied and has been found to vary greatly between species, but much less is known about the Somatic Mutation rate in multicellular organisms, which remains very difficult to determine. Here, we present data on Somatic Mutation rates in mice and humans, obtained by sequencing single cells and clones derived from primary fibroblasts, which allows us to make the first direct comparison with germline Mutation rates in these two species. The results indicate that the Somatic Mutation rate is almost two orders of magnitude higher than the germline Mutation rate and that both Mutation rates are significantly higher in mice than in humans. Our findings demonstrate both the privileged status of germline genome integrity and species-specific differences in genome maintenance. Germline Mutation rates are known to vary between species but Somatic Mutation rates are less well understood. Here the authors compare mice and humans, observing that Somatic Mutation rates were nearly two orders of magnitude higher in both species, with both Mutation rates significantly higher in mice.
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differences between germline and Somatic Mutation rates in humans and mice
Nature Communications, 2017Co-Authors: Brandon Milholland, Xiao Dong, Lei Zhang, Xiaoxiao Hao, Yousin Suh, Jan VijgAbstract:The germline Mutation rate has been extensively studied and has been found to vary greatly between species, but much less is known about the Somatic Mutation rate in multicellular organisms, which remains very difficult to determine. Here, we present data on Somatic Mutation rates in mice and humans, obtained by sequencing single cells and clones derived from primary fibroblasts, which allows us to make the first direct comparison with germline Mutation rates in these two species. The results indicate that the Somatic Mutation rate is almost two orders of magnitude higher than the germline Mutation rate and that both Mutation rates are significantly higher in mice than in humans. Our findings demonstrate both the privileged status of germline genome integrity and species-specific differences in genome maintenance.
