14 results on '"PCAWG Drivers And Functional Interpretation Working Group"'
Search Results
2. Pathway and network analysis of more than 2500 whole cancer genomes
- Author
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Matthew A. Reyna, David Haan, Marta Paczkowska, Lieven P. C. Verbeke, Miguel Vazquez, Abdullah Kahraman, Sergio Pulido-Tamayo, Jonathan Barenboim, Lina Wadi, Priyanka Dhingra, Raunak Shrestha, Gad Getz, Michael S. Lawrence, Jakob Skou Pedersen, Mark A. Rubin, David A. Wheeler, Søren Brunak, Jose M. G. Izarzugaza, Ekta Khurana, Kathleen Marchal, Christian von Mering, S. Cenk Sahinalp, Alfonso Valencia, PCAWG Drivers and Functional Interpretation Working Group, Jüri Reimand, Joshua M. Stuart, Benjamin J. Raphael, and PCAWG Consortium
- Subjects
Science - Abstract
Understanding deregulation of biological pathways in cancer can provide insight into disease etiology and potential therapies. Here, as part of the PanCancer Analysis of Whole Genomes (PCAWG) consortium, the authors present pathway and network analysis of 2583 whole cancer genomes from 27 tumour types.
- Published
- 2020
- Full Text
- View/download PDF
3. Combined burden and functional impact tests for cancer driver discovery using DriverPower
- Author
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Shimin Shuai, PCAWG Drivers and Functional Interpretation Working Group, Steven Gallinger, Lincoln Stein, and PCAWG Consortium
- Subjects
Science - Abstract
Analysis of cancer genome sequencing data has enabled the discovery of driver mutations. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium the authors present DriverPower, a software package that identifies coding and non-coding driver mutations within cancer whole genomes via consideration of mutational burden and functional impact evidence.
- Published
- 2020
- Full Text
- View/download PDF
4. Author Correction: Combined burden and functional impact tests for cancer driver discovery using DriverPower
- Author
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Shimin Shuai, PCAWG Drivers and Functional Interpretation Working Group, Steven Gallinger, Lincoln D. Stein, and PCAWG Consortium
- Subjects
Science - Published
- 2022
- Full Text
- View/download PDF
5. Author Correction: Pathway and network analysis of more than 2500 whole cancer genomes
- Author
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Matthew A. Reyna, David Haan, Marta Paczkowska, Lieven P. C. Verbeke, Miguel Vazquez, Abdullah Kahraman, Sergio Pulido-Tamayo, Jonathan Barenboim, Lina Wadi, Priyanka Dhingra, Raunak Shrestha, Gad Getz, Michael S. Lawrence, Jakob Skou Pedersen, Mark A. Rubin, David A. Wheeler, Søren Brunak, Jose M. G. Izarzugaza, Ekta Khurana, Kathleen Marchal, Christian von Mering, S. Cenk Sahinalp, Alfonso Valencia, PCAWG Drivers and Functional Interpretation Working Group, Jüri Reimand, Joshua M. Stuart, Benjamin J. Raphael, and PCAWG Consortium
- Subjects
Science - Published
- 2022
- Full Text
- View/download PDF
6. Author Correction: Integrative pathway enrichment analysis of multivariate omics data
- Author
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Marta Paczkowska, Jonathan Barenboim, Nardnisa Sintupisut, Natalie S. Fox, Helen Zhu, Diala Abd-Rabbo, Miles W. Mee, Paul C. Boutros, PCAWG Drivers and Functional Interpretation Working Group, Jüri Reimand, and PCAWG Consortium
- Subjects
Science - Published
- 2022
- Full Text
- View/download PDF
7. Integrative pathway enrichment analysis of multivariate omics data.
- Author
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Paczkowska, Marta, Barenboim, Jonathan, Sintupisut, Nardnisa, Fox, Natalie S, Zhu, Helen, Abd-Rabbo, Diala, Mee, Miles W, Boutros, Paul C, PCAWG Drivers and Functional Interpretation Working Group, Reimand, Jüri, and PCAWG Consortium
- Subjects
PCAWG Drivers and Functional Interpretation Working Group ,PCAWG Consortium - Abstract
Multi-omics datasets represent distinct aspects of the central dogma of molecular biology. Such high-dimensional molecular profiles pose challenges to data interpretation and hypothesis generation. ActivePathways is an integrative method that discovers significantly enriched pathways across multiple datasets using statistical data fusion, rationalizes contributing evidence and highlights associated genes. As part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumor types, we integrated genes with coding and non-coding mutations and revealed frequently mutated pathways and additional cancer genes with infrequent mutations. We also analyzed prognostic molecular pathways by integrating genomic and transcriptomic features of 1780 breast cancers and highlighted associations with immune response and anti-apoptotic signaling. Integration of ChIP-seq and RNA-seq data for master regulators of the Hippo pathway across normal human tissues identified processes of tissue regeneration and stem cell regulation. ActivePathways is a versatile method that improves systems-level understanding of cellular organization in health and disease through integration of multiple molecular datasets and pathway annotations.
- Published
- 2020
8. Analyses of non-coding somatic drivers in 2,658 cancer whole genomes.
- Author
-
Rheinbay, Esther, Nielsen, Morten Muhlig, Abascal, Federico, Wala, Jeremiah A, Shapira, Ofer, Tiao, Grace, Hornshøj, Henrik, Hess, Julian M, Juul, Randi Istrup, Lin, Ziao, Feuerbach, Lars, Sabarinathan, Radhakrishnan, Madsen, Tobias, Kim, Jaegil, Mularoni, Loris, Shuai, Shimin, Lanzós, Andrés, Herrmann, Carl, Maruvka, Yosef E, Shen, Ciyue, Amin, Samirkumar B, Bandopadhayay, Pratiti, Bertl, Johanna, Boroevich, Keith A, Busanovich, John, Carlevaro-Fita, Joana, Chakravarty, Dimple, Chan, Calvin Wing Yiu, Craft, David, Dhingra, Priyanka, Diamanti, Klev, Fonseca, Nuno A, Gonzalez-Perez, Abel, Guo, Qianyun, Hamilton, Mark P, Haradhvala, Nicholas J, Hong, Chen, Isaev, Keren, Johnson, Todd A, Juul, Malene, Kahles, Andre, Kahraman, Abdullah, Kim, Youngwook, Komorowski, Jan, Kumar, Kiran, Kumar, Sushant, Lee, Donghoon, Lehmann, Kjong-Van, Li, Yilong, Liu, Eric Minwei, Lochovsky, Lucas, Park, Keunchil, Pich, Oriol, Roberts, Nicola D, Saksena, Gordon, Schumacher, Steven E, Sidiropoulos, Nikos, Sieverling, Lina, Sinnott-Armstrong, Nasa, Stewart, Chip, Tamborero, David, Tubio, Jose MC, Umer, Husen M, Uusküla-Reimand, Liis, Wadelius, Claes, Wadi, Lina, Yao, Xiaotong, Zhang, Cheng-Zhong, Zhang, Jing, Haber, James E, Hobolth, Asger, Imielinski, Marcin, Kellis, Manolis, Lawrence, Michael S, von Mering, Christian, Nakagawa, Hidewaki, Raphael, Benjamin J, Rubin, Mark A, Sander, Chris, Stein, Lincoln D, Stuart, Joshua M, Tsunoda, Tatsuhiko, Wheeler, David A, Johnson, Rory, Reimand, Jüri, Gerstein, Mark, Khurana, Ekta, Campbell, Peter J, López-Bigas, Núria, PCAWG Drivers and Functional Interpretation Working Group, PCAWG Structural Variation Working Group, Weischenfeldt, Joachim, Beroukhim, Rameen, Martincorena, Iñigo, Pedersen, Jakob Skou, Getz, Gad, and PCAWG Consortium
- Subjects
PCAWG Drivers and Functional Interpretation Working Group ,PCAWG Structural Variation Working Group ,PCAWG Consortium ,Humans ,Neoplasms ,Gene Expression Regulation ,Neoplastic ,Mutation ,Genome ,Human ,Databases ,Genetic ,DNA Breaks ,INDEL Mutation ,Genome-Wide Association Study ,Gene Expression Regulation ,Neoplastic ,Genome ,Human ,Databases ,Genetic ,General Science & Technology - Abstract
The discovery of drivers of cancer has traditionally focused on protein-coding genes1-4. Here we present analyses of driver point mutations and structural variants in non-coding regions across 2,658 genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium5 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, we developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods. For structural variants, we present two methods of driver discovery, and identify regions that are significantly affected by recurrent breakpoints and recurrent somatic juxtapositions. Our analyses confirm previously reported drivers6,7, raise doubts about others and identify novel candidates, including point mutations in the 5' region of TP53, in the 3' untranslated regions of NFKBIZ and TOB1, focal deletions in BRD4 and rearrangements in the loci of AKR1C genes. We show that although point mutations and structural variants that drive cancer are less frequent in non-coding genes and regulatory sequences than in protein-coding genes, additional examples of these drivers will be found as more cancer genomes become available.
- Published
- 2020
9. Pathway and network analysis of more than 2500 whole cancer genomes.
- Author
-
Reyna, Matthew A, Haan, David, Paczkowska, Marta, Verbeke, Lieven PC, Vazquez, Miguel, Kahraman, Abdullah, Pulido-Tamayo, Sergio, Barenboim, Jonathan, Wadi, Lina, Dhingra, Priyanka, Shrestha, Raunak, Getz, Gad, Lawrence, Michael S, Pedersen, Jakob Skou, Rubin, Mark A, Wheeler, David A, Brunak, Søren, Izarzugaza, Jose MG, Khurana, Ekta, Marchal, Kathleen, von Mering, Christian, Sahinalp, S Cenk, Valencia, Alfonso, PCAWG Drivers and Functional Interpretation Working Group, Reimand, Jüri, Stuart, Joshua M, Raphael, Benjamin J, and PCAWG Consortium
- Subjects
PCAWG Drivers and Functional Interpretation Working Group ,PCAWG Consortium ,Humans ,Neoplasms ,Computational Biology ,Chromatin Assembly and Disassembly ,Gene Expression Regulation ,Neoplastic ,RNA Splicing ,Mutation ,Genome ,Human ,Databases ,Genetic ,Metabolic Networks and Pathways ,Promoter Regions ,Genetic - Abstract
The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancer across 38 tumor types, we perform multi-faceted pathway and network analyses of non-coding mutations across 2583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project that was motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes. While few non-coding genomic elements are recurrently mutated in this cohort, we identify 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53, TLE4, and TCF4. We find that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing is primarily altered by non-coding mutations in this cohort, and samples containing non-coding mutations in well-known RNA splicing factors exhibit similar gene expression signatures as samples with coding mutations in these genes. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments.
- Published
- 2020
10. Combined burden and functional impact tests for cancer driver discovery using DriverPower.
- Author
-
Shuai, Shimin, Shuai, Shimin, PCAWG Drivers and Functional Interpretation Working Group, Gallinger, Steven, Stein, Lincoln D, PCAWG Consortium, Shuai, Shimin, Shuai, Shimin, PCAWG Drivers and Functional Interpretation Working Group, Gallinger, Steven, Stein, Lincoln D, and PCAWG Consortium
- Abstract
The discovery of driver mutations is one of the key motivations for cancer genome sequencing. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumour types, we describe DriverPower, a software package that uses mutational burden and functional impact evidence to identify driver mutations in coding and non-coding sites within cancer whole genomes. Using a total of 1373 genomic features derived from public sources, DriverPower's background mutation model explains up to 93% of the regional variance in the mutation rate across multiple tumour types. By incorporating functional impact scores, we are able to further increase the accuracy of driver discovery. Testing across a collection of 2583 cancer genomes from the PCAWG project, DriverPower identifies 217 coding and 95 non-coding driver candidates. Comparing to six published methods used by the PCAWG Drivers and Functional Interpretation Working Group, DriverPower has the highest F1 score for both coding and non-coding driver discovery. This demonstrates that DriverPower is an effective framework for computational driver discovery.
- Published
- 2020
11. Pathway and network analysis of more than 2500 whole cancer genomes
- Author
-
Reyna, Matthew A, Haan, David, Paczkowska, Marta, Verbeke, Lieven PC, Vazquez, Miguel, Kahraman, Abdullah, Pulido-Tamayo, Sergio, Barenboim, Jonathan, Wadi, Lina, Dhingra, Priyanka, Shrestha, Raunak, Getz, Gad, Lawrence, Michael S, Pedersen, Jakob Skou, Rubin, Mark A, Wheeler, David A, Brunak, Søren, Izarzugaza, Jose MG, Khurana, Ekta, Marchal, Kathleen, Von Mering, Christian, Sahinalp, S Cenk, Valencia, Alfonso, PCAWG Drivers And Functional Interpretation Working Group, Reimand, Jüri, Stuart, Joshua M, Raphael, Benjamin J, and PCAWG Consortium
- Subjects
Gene Expression Regulation, Neoplastic ,Genome, Human ,Neoplasms ,RNA Splicing ,Databases, Genetic ,Mutation ,Computational Biology ,Humans ,Chromatin Assembly and Disassembly ,Promoter Regions, Genetic ,Metabolic Networks and Pathways ,3. Good health - Abstract
The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancer across 38 tumor types, we perform multi-faceted pathway and network analyses of non-coding mutations across 2583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project that was motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes. While few non-coding genomic elements are recurrently mutated in this cohort, we identify 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53, TLE4, and TCF4. We find that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing is primarily altered by non-coding mutations in this cohort, and samples containing non-coding mutations in well-known RNA splicing factors exhibit similar gene expression signatures as samples with coding mutations in these genes. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments.
12. Integrative pathway enrichment analysis of multivariate omics data
- Author
-
Paczkowska, Marta, Barenboim, Jonathan, Sintupisut, Nardnisa, Fox, Natalie S., Zhu, Helen, Abd-Rabbo, Diala, Mee, Miles W., Boutros, Paul C., PCAWG Drivers and Functional Interpretation Working Group, Reimand, Jüri, and PCAWG Consortium
- Subjects
3. Good health - Abstract
Multi-omics datasets represent distinct aspects of the central dogma of molecular biology. Such high-dimensional molecular profiles pose challenges to data interpretation and hypothesis generation. ActivePathways is an integrative method that discovers significantly enriched pathways across multiple datasets using statistical data fusion, rationalizes contributing evidence and highlights associated genes. As part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumor types, we integrated genes with coding and non-coding mutations and revealed frequently mutated pathways and additional cancer genes with infrequent mutations. We also analyzed prognostic molecular pathways by integrating genomic and transcriptomic features of 1780 breast cancers and highlighted associations with immune response and anti-apoptotic signaling. Integration of ChIP-seq and RNA-seq data for master regulators of the Hippo pathway across normal human tissues identified processes of tissue regeneration and stem cell regulation. ActivePathways is a versatile method that improves systems-level understanding of cellular organization in health and disease through integration of multiple molecular datasets and pathway annotations., Nature Communications, 11 (1), ISSN:2041-1723
13. Analyses of non-coding somatic drivers in 2,658 cancer whole genomes
- Author
-
Rheinbay, Esther, Nielsen, Morten Muhlig, Abascal, Federico, Wala, Jeremiah A, Shapira, Ofer, Tiao, Grace, Hornshøj, Henrik, Hess, Julian M, Juul, Randi Istrup, Lin, Ziao, Feuerbach, Lars, Sabarinathan, Radhakrishnan, Madsen, Tobias, Kim, Jaegil, Mularoni, Loris, Shuai, Shimin, Lanzós, Andrés, Herrmann, Carl, Maruvka, Yosef E, Shen, Ciyue, Amin, Samirkumar B, Bandopadhayay, Pratiti, Bertl, Johanna, Boroevich, Keith A, Busanovich, John, Carlevaro-Fita, Joana, Chakravarty, Dimple, Chan, Calvin Wing Yiu, Craft, David, Dhingra, Priyanka, Diamanti, Klev, Fonseca, Nuno A, Gonzalez-Perez, Abel, Guo, Qianyun, Hamilton, Mark P, Haradhvala, Nicholas J, Hong, Chen, Isaev, Keren, Johnson, Todd A, Juul, Malene, Kahles, Andre, Kahraman, Abdullah, Kim, Youngwook, Komorowski, Jan, Kumar, Kiran, Kumar, Sushant, Lee, Donghoon, Lehmann, Kjong-Van, Li, Yilong, Liu, Eric Minwei, Lochovsky, Lucas, Park, Keunchil, Pich, Oriol, Roberts, Nicola D, Saksena, Gordon, Schumacher, Steven E, Sidiropoulos, Nikos, Sieverling, Lina, Sinnott-Armstrong, Nasa, Stewart, Chip, Tamborero, David, Tubio, Jose MC, Umer, Husen M, Uusküla-Reimand, Liis, Wadelius, Claes, Wadi, Lina, Yao, Xiaotong, Zhang, Cheng-Zhong, Zhang, Jing, Haber, James E, Hobolth, Asger, Imielinski, Marcin, Kellis, Manolis, Lawrence, Michael S, Von Mering, Christian, Nakagawa, Hidewaki, Raphael, Benjamin J, Rubin, Mark A, Sander, Chris, Stein, Lincoln D, Stuart, Joshua M, Tsunoda, Tatsuhiko, Wheeler, David A, Johnson, Rory, Reimand, Jüri, Gerstein, Mark, Khurana, Ekta, Campbell, Peter J, López-Bigas, Núria, PCAWG Drivers And Functional Interpretation Working Group, PCAWG Structural Variation Working Group, Weischenfeldt, Joachim, Beroukhim, Rameen, Martincorena, Iñigo, Pedersen, Jakob Skou, Getz, Gad, and PCAWG Consortium
- Subjects
Gene Expression Regulation, Neoplastic ,INDEL Mutation ,Genome, Human ,Neoplasms ,DNA Breaks ,Databases, Genetic ,Mutation ,Humans ,3. Good health ,Genome-Wide Association Study - Abstract
The discovery of drivers of cancer has traditionally focused on protein-coding genes1-4. Here we present analyses of driver point mutations and structural variants in non-coding regions across 2,658 genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium5 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, we developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods. For structural variants, we present two methods of driver discovery, and identify regions that are significantly affected by recurrent breakpoints and recurrent somatic juxtapositions. Our analyses confirm previously reported drivers6,7, raise doubts about others and identify novel candidates, including point mutations in the 5' region of TP53, in the 3' untranslated regions of NFKBIZ and TOB1, focal deletions in BRD4 and rearrangements in the loci of AKR1C genes. We show that although point mutations and structural variants that drive cancer are less frequent in non-coding genes and regulatory sequences than in protein-coding genes, additional examples of these drivers will be found as more cancer genomes become available.
14. Integrative pathway enrichment analysis of multivariate omics data
- Author
-
Paczkowska, Marta, Barenboim, Jonathan, Sintupisut, Nardnisa, Fox, Natalie S, Zhu, Helen, Abd-Rabbo, Diala, Mee, Miles W, Boutros, Paul C, PCAWG Drivers And Functional Interpretation Working Group, Reimand, Jüri, and PCAWG Consortium
- Subjects
Chromatin Immunoprecipitation ,ComputingMilieux_THECOMPUTINGPROFESSION ,Databases, Factual ,Sequence Analysis, RNA ,Gene Expression Profiling ,Gene Dosage ,Computational Biology ,Apoptosis ,Breast Neoplasms ,Genomics ,Adenocarcinoma ,Protein Serine-Threonine Kinases ,Prognosis ,3. Good health ,Neoplasms ,Mutation ,ComputingMilieux_COMPUTERSANDEDUCATION ,Humans ,Female ,Gene Regulatory Networks ,Hippo Signaling Pathway ,RNA, Messenger ,GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries) ,Metabolic Networks and Pathways ,Signal Transduction - Abstract
Funder: BioTalent Canada Student Internship, Funder: Canadian Institutes of Health Research (CIHR) Canadian Graduate Scholarship, Funder: Ontario Institute for Cancer Research (OICR) Investigator Awards provided by the Government of Ontario; Terry Fox Research Institute (TFRI) and Canadian Institutes of Health Research (CIHR) New Investigator Awards., Multi-omics datasets represent distinct aspects of the central dogma of molecular biology. Such high-dimensional molecular profiles pose challenges to data interpretation and hypothesis generation. ActivePathways is an integrative method that discovers significantly enriched pathways across multiple datasets using statistical data fusion, rationalizes contributing evidence and highlights associated genes. As part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumor types, we integrated genes with coding and non-coding mutations and revealed frequently mutated pathways and additional cancer genes with infrequent mutations. We also analyzed prognostic molecular pathways by integrating genomic and transcriptomic features of 1780 breast cancers and highlighted associations with immune response and anti-apoptotic signaling. Integration of ChIP-seq and RNA-seq data for master regulators of the Hippo pathway across normal human tissues identified processes of tissue regeneration and stem cell regulation. ActivePathways is a versatile method that improves systems-level understanding of cellular organization in health and disease through integration of multiple molecular datasets and pathway annotations.
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