Self-organizing maps (SOM) portray molecular phenotypes with individual resolution. We present an analysis pipeline based on SOM machine learning which allows the comprehensive study of large scale clinical data. The potency of the method is demonstrated in selected applications studying the diversity of gene expression in Glioblastoma Multiforme (GBM) and prostate cancer progression. Our method characterizes relationships between the samples, disentangles the expression patterns into well separated groups of co-regulated genes, extracts their functional contexts using enrichment techniques, and enables the detection of contaminations and outliers in the samples. We found that the four GBM subtypes can be divided into two “localized” and two “intermediate” ones. The localized subtypes are characterized by the antagonistic activation of processes related to immune response and cell division, commonly observed also in other cancers. In contrast, each of the “intermediate” subtypes forms a heterogeneous continuum of expression states linking the “localized” subtypes. Both “intermediate” subtypes are characterized by distinct expression patterns related to translational activity and innate immunity as well as nervous tissue and cell function. We show that SOM portraits provide a comprehensive framework for the description of the diversity of expression landscapes using concepts of molecular function.
{"title":"Portraying the expression landscapes of cancer subtypes","authors":"L. Hopp, H. Wirth, M. Fasold, H. Binder","doi":"10.4161/sysb.25897","DOIUrl":"https://doi.org/10.4161/sysb.25897","url":null,"abstract":"Self-organizing maps (SOM) portray molecular phenotypes with individual resolution. We present an analysis pipeline based on SOM machine learning which allows the comprehensive study of large scale clinical data. The potency of the method is demonstrated in selected applications studying the diversity of gene expression in Glioblastoma Multiforme (GBM) and prostate cancer progression. Our method characterizes relationships between the samples, disentangles the expression patterns into well separated groups of co-regulated genes, extracts their functional contexts using enrichment techniques, and enables the detection of contaminations and outliers in the samples. We found that the four GBM subtypes can be divided into two “localized” and two “intermediate” ones. The localized subtypes are characterized by the antagonistic activation of processes related to immune response and cell division, commonly observed also in other cancers. In contrast, each of the “intermediate” subtypes forms a heterogeneous continuum of expression states linking the “localized” subtypes. Both “intermediate” subtypes are characterized by distinct expression patterns related to translational activity and innate immunity as well as nervous tissue and cell function. We show that SOM portraits provide a comprehensive framework for the description of the diversity of expression landscapes using concepts of molecular function.","PeriodicalId":90057,"journal":{"name":"Systems biomedicine (Austin, Tex.)","volume":"1 1","pages":"121 - 99"},"PeriodicalIF":0.0,"publicationDate":"2013-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4161/sysb.25897","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70654895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Karol Baca-López, María D. Correa-Rodríguez, R. Flores-Espinosa, R. García-Herrera, Claudia Hernandez-Armenta, A. Hidalgo-Miranda, Aldo Huerta-Verde, Ivan Imaz-Rosshandler, Ana V Martinez-Rubio, Alejandra Medina-Escareno, R. Mendoza-Smith, M. Rodríguez-Dorantes, I. Salido-Guadarrama, E. Hernández-Lemus, C. Rangel-Escareño
Background: Genomic technologies have allowed a large-scale molecular characterization of living organisms, involving the generation and interpretation of data at an unprecedented scale. Advanced platforms for the detection of different types of genomic alterations have been developed and applied to analyses of living organisms and, in particular, cancer genomes. It is clear now that studies based on a single platform are limited compared with the extent of knowledge gain possible when exploiting different platforms together. There is therefore a need for systematic methodologies facilitating data management, visualization, and integration. Materials and Methods: We present a 3-state model (3-MDI) that integrates several technological platforms, visualizing and prioritizing different biological scenarios, and thus enables researchers to pursue data exploration in an educated way, where some or all of the explored avenues could be used to determine thresholds for differential changes in the examined platforms, or may help identify genes that follow an interesting pattern. Conclusion: Each additional genomic data dimension increases both the amount of information and consequently the biological and computational complexity of the analysis. We have demonstrated here, however, that multidimensional genomic data driven approaches can facilitate finding relevant genes that would otherwise largely remain unexplored because they would be overlooked in traditional analyses of individual biological experiments.
{"title":"A 3-state model for multidimensional genomic data integration","authors":"Karol Baca-López, María D. Correa-Rodríguez, R. Flores-Espinosa, R. García-Herrera, Claudia Hernandez-Armenta, A. Hidalgo-Miranda, Aldo Huerta-Verde, Ivan Imaz-Rosshandler, Ana V Martinez-Rubio, Alejandra Medina-Escareno, R. Mendoza-Smith, M. Rodríguez-Dorantes, I. Salido-Guadarrama, E. Hernández-Lemus, C. Rangel-Escareño","doi":"10.4161/sysb.25898","DOIUrl":"https://doi.org/10.4161/sysb.25898","url":null,"abstract":"Background: Genomic technologies have allowed a large-scale molecular characterization of living organisms, involving the generation and interpretation of data at an unprecedented scale. Advanced platforms for the detection of different types of genomic alterations have been developed and applied to analyses of living organisms and, in particular, cancer genomes. It is clear now that studies based on a single platform are limited compared with the extent of knowledge gain possible when exploiting different platforms together. There is therefore a need for systematic methodologies facilitating data management, visualization, and integration. Materials and Methods: We present a 3-state model (3-MDI) that integrates several technological platforms, visualizing and prioritizing different biological scenarios, and thus enables researchers to pursue data exploration in an educated way, where some or all of the explored avenues could be used to determine thresholds for differential changes in the examined platforms, or may help identify genes that follow an interesting pattern. Conclusion: Each additional genomic data dimension increases both the amount of information and consequently the biological and computational complexity of the analysis. We have demonstrated here, however, that multidimensional genomic data driven approaches can facilitate finding relevant genes that would otherwise largely remain unexplored because they would be overlooked in traditional analyses of individual biological experiments.","PeriodicalId":90057,"journal":{"name":"Systems biomedicine (Austin, Tex.)","volume":"1 1","pages":"122 - 129"},"PeriodicalIF":0.0,"publicationDate":"2013-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4161/sysb.25898","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70654969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
CAMDA has now evolved from its origins at Duke University in the year 2000, founded by Simon Lin and Kimberly Johnson, to an international conference of renown that has been affiliated with ISMB/ECCB since the 2008 meeting in Vienna, and which is now a regular official Satellite Meeting of the ISMB Conference. Since 2011, proceedings are published Open Access in partnership with Systems Biomedicine. At the CAMDA conferences, alternative analyses of annually set Contest Datasets are discussed which have been submitted by different research teams. Selected contributions are collected in the special proceedings volume presented here, including the analyses by the teams of Sol Efroni and Djork-Arné Clevert, which were chosen as the best contributions by secret vote of the delegates of CAMDA 2011 and 2012, respectively. By design, CAMDA analysis goals and the competition are very openended, which is a distinguishing feature of the contest. CAMDA can therefore take on the most challenging data sets. Over the last few years, both complex multi-track data sets and unusually large measurement series have been featured. For CAMDA 2011, the Glioblastoma multiforme subset of The Cancer Genome Atlas (TCGA) had been identified as a particularly interesting challenge. It is unusual in that it provides publicly, for several hundred patients, profiles of gene transcript expression (435 cancer patients vs 11 controls), miRNA expression (426 tumor samples vs 10 controls), genomic DNA methylation (256 tumor samples vs a control), and copy number variation (465 tumor samples vs 430 controls, including 402 matched normals), which are complemented by a variety of clinical parameters and survival outcomes. Sometimes, additional results are available from alternative technologies/platforms. The data can be downloaded at different abstraction levels, from raw (Level 1, publicly available for some platforms) via normalized (Level 2) to processed (Level 3), also facilitating integration and participation by non-domain experts. Typical questions of interest include the investigation to what degree the integration of such large heterogeneous repositories can improve our understanding of complex biomedical questions. For the 2012 contest, a subset of the Japanese Toxicogenomics Project focusing on liver proved the most popular contest data set, containing over 21,000 arrays for rats treated with mainly human drugs and profiled using the Affymetrix RAE230_2.0 GeneChip. Expression profiles (raw and processed data) were complemented by drug information and pathology data that had been compiled by Weida Tong of the US FDA. Typical questions of interest include whether a better prediction of liver-toxicity from animal experiments can be achieved, and to what degree animal studies could be replaced by in-vitro assays. In fact, the data set sparked such interesting discussions at the conference, that it has been offered again also in the following years. As in past years, the CAMDA c
{"title":"Proceedings of the Critical Assessment of Massive Data Analysis conferences: CAMDA 2011 (Vienna, Austria) and CAMDA 2012 (Long Beach, CA USA)","authors":"David P. Kreil, Lanyi Hu","doi":"10.4161/SYSB.28947","DOIUrl":"https://doi.org/10.4161/SYSB.28947","url":null,"abstract":"CAMDA has now evolved from its origins at Duke University in the year 2000, founded by Simon Lin and Kimberly Johnson, to an international conference of renown that has been affiliated with ISMB/ECCB since the 2008 meeting in Vienna, and which is now a regular official Satellite Meeting of the ISMB Conference. Since 2011, proceedings are published Open Access in partnership with Systems Biomedicine. At the CAMDA conferences, alternative analyses of annually set Contest Datasets are discussed which have been submitted by different research teams. Selected contributions are collected in the special proceedings volume presented here, including the analyses by the teams of Sol Efroni and Djork-Arné Clevert, which were chosen as the best contributions by secret vote of the delegates of CAMDA 2011 and 2012, respectively. By design, CAMDA analysis goals and the competition are very openended, which is a distinguishing feature of the contest. CAMDA can therefore take on the most challenging data sets. Over the last few years, both complex multi-track data sets and unusually large measurement series have been featured. For CAMDA 2011, the Glioblastoma multiforme subset of The Cancer Genome Atlas (TCGA) had been identified as a particularly interesting challenge. It is unusual in that it provides publicly, for several hundred patients, profiles of gene transcript expression (435 cancer patients vs 11 controls), miRNA expression (426 tumor samples vs 10 controls), genomic DNA methylation (256 tumor samples vs a control), and copy number variation (465 tumor samples vs 430 controls, including 402 matched normals), which are complemented by a variety of clinical parameters and survival outcomes. Sometimes, additional results are available from alternative technologies/platforms. The data can be downloaded at different abstraction levels, from raw (Level 1, publicly available for some platforms) via normalized (Level 2) to processed (Level 3), also facilitating integration and participation by non-domain experts. Typical questions of interest include the investigation to what degree the integration of such large heterogeneous repositories can improve our understanding of complex biomedical questions. For the 2012 contest, a subset of the Japanese Toxicogenomics Project focusing on liver proved the most popular contest data set, containing over 21,000 arrays for rats treated with mainly human drugs and profiled using the Affymetrix RAE230_2.0 GeneChip. Expression profiles (raw and processed data) were complemented by drug information and pathology data that had been compiled by Weida Tong of the US FDA. Typical questions of interest include whether a better prediction of liver-toxicity from animal experiments can be achieved, and to what degree animal studies could be replaced by in-vitro assays. In fact, the data set sparked such interesting discussions at the conference, that it has been offered again also in the following years. As in past years, the CAMDA c","PeriodicalId":90057,"journal":{"name":"Systems biomedicine (Austin, Tex.)","volume":"1 1","pages":"75 - 75"},"PeriodicalIF":0.0,"publicationDate":"2013-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4161/SYSB.28947","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70655682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jean-François Pessiot, P. Wong, T. Maruyama, R. Morioka, S. Aburatani, Michihiro Tanaka, W. Fujibuchi
In this work, we focus on two fundamental problems of toxicogenomics using the data provided by the Japanese toxicogenomics project. First, we analyze to what extent animal studies can be replaced by in in vitro assays. We show that the probeset-level representation achieves poor agreement between in vivo and in vitro data. We present a data collapsing approach to resolve poor data agreement between in vivo and in vitro data, as measured by GSEA analysis and AUC scores. Second, we address the difficult problem of predicting DILI using available microarray data. Using a binary classification framework, our results suggest that rat in vivo data are more informative than human in vitro data to predict DILI.
{"title":"The impact of collapsing data on microarray analysis and DILI prediction","authors":"Jean-François Pessiot, P. Wong, T. Maruyama, R. Morioka, S. Aburatani, Michihiro Tanaka, W. Fujibuchi","doi":"10.4161/sysb.24255","DOIUrl":"https://doi.org/10.4161/sysb.24255","url":null,"abstract":"In this work, we focus on two fundamental problems of toxicogenomics using the data provided by the Japanese toxicogenomics project. First, we analyze to what extent animal studies can be replaced by in in vitro assays. We show that the probeset-level representation achieves poor agreement between in vivo and in vitro data. We present a data collapsing approach to resolve poor data agreement between in vivo and in vitro data, as measured by GSEA analysis and AUC scores. Second, we address the difficult problem of predicting DILI using available microarray data. Using a binary classification framework, our results suggest that rat in vivo data are more informative than human in vitro data to predict DILI.","PeriodicalId":90057,"journal":{"name":"Systems biomedicine (Austin, Tex.)","volume":"1 1","pages":"137 - 143"},"PeriodicalIF":0.0,"publicationDate":"2013-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4161/sysb.24255","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70654221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aberrant inflammation appears to be a pathogenic factor in autoimmune diseases and other noxious inflammatory conditions in which the inflammatory process is misapplied, exaggerated, recurrent or chronic. The protein molecules involved in pathogenic inflammation—disease-associated proteins (DAP)—which include chemokines, cytokines, and growth factors and their receptors, appear normal; their networks of interaction are at fault. Here we demonstrate a new approach to network regulation of inflammation based on peptide sequence motifs shared by the second extra-cellular loop (ECL2) of different chemokine receptors; previously known chemokine receptor binding sites have not involved the ECL2 loop. These motifs of 9 amino acids, which we detected by sequence alignment, manifest very low E-values compared with slightly modified sequence variations, indicating that they were not likely to have evolved by chance. To test whether this shared sequence network (SSN) might serve a regulatory function, we synthesized 9-amino acid SSN peptides from the ECL2 loops of three different chemokine receptors. We administered these peptides to rats during the induction of a model of autoimmune arthritis. Two of the peptides significantly downregulated the arthritis; one of the peptides synergized with non-specific anti-inflammatory treatment with dexamethasone. These findings suggest that the SSN peptide motif reported here is likely to have adaptive value in controlling inflammation. Moreover, detection of SSN motif peptides could provide a network-based approach to immune modulation.
{"title":"A network approach to controlling pathogenic inflammation","authors":"C. Ezerzer, R. Margalit, I. Cohen","doi":"10.4161/sysb.21734","DOIUrl":"https://doi.org/10.4161/sysb.21734","url":null,"abstract":"Aberrant inflammation appears to be a pathogenic factor in autoimmune diseases and other noxious inflammatory conditions in which the inflammatory process is misapplied, exaggerated, recurrent or chronic. The protein molecules involved in pathogenic inflammation—disease-associated proteins (DAP)—which include chemokines, cytokines, and growth factors and their receptors, appear normal; their networks of interaction are at fault. Here we demonstrate a new approach to network regulation of inflammation based on peptide sequence motifs shared by the second extra-cellular loop (ECL2) of different chemokine receptors; previously known chemokine receptor binding sites have not involved the ECL2 loop. These motifs of 9 amino acids, which we detected by sequence alignment, manifest very low E-values compared with slightly modified sequence variations, indicating that they were not likely to have evolved by chance. To test whether this shared sequence network (SSN) might serve a regulatory function, we synthesized 9-amino acid SSN peptides from the ECL2 loops of three different chemokine receptors. We administered these peptides to rats during the induction of a model of autoimmune arthritis. Two of the peptides significantly downregulated the arthritis; one of the peptides synergized with non-specific anti-inflammatory treatment with dexamethasone. These findings suggest that the SSN peptide motif reported here is likely to have adaptive value in controlling inflammation. Moreover, detection of SSN motif peptides could provide a network-based approach to immune modulation.","PeriodicalId":90057,"journal":{"name":"Systems biomedicine (Austin, Tex.)","volume":"1 1","pages":"35 - 46"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4161/sysb.21734","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70654304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ye Tian, Li Chen, Bai Zhang, Zhen Zhang, Guoqiang Yu, R. Clarke, J. Xuan, I. Shih, Yue Wang
Characterizing the origin of high-grade serous ovarian cancer has significant practical importance for advancing biological knowledge and improving clinical treatments. Rapid advances in molecular profiling technologies and machine learning based data analytics provide new opportunities to investigate this important question using data-driven approaches at the molecular and network levels. We now report novel analytic results in assessing the origin of high-grade serous ovarian carcinoma. Using genome-wide gene expression data and effective machine learning approaches, we design proper statistical significance tests and perform both genomic and network analyses to discriminate among three possible origins. The experimental results are consistent with recent scientific hypothesis and independent findings.
{"title":"Genomic and network analysis to study the origin of ovarian cancer","authors":"Ye Tian, Li Chen, Bai Zhang, Zhen Zhang, Guoqiang Yu, R. Clarke, J. Xuan, I. Shih, Yue Wang","doi":"10.4161/sysb.25313","DOIUrl":"https://doi.org/10.4161/sysb.25313","url":null,"abstract":"Characterizing the origin of high-grade serous ovarian cancer has significant practical importance for advancing biological knowledge and improving clinical treatments. Rapid advances in molecular profiling technologies and machine learning based data analytics provide new opportunities to investigate this important question using data-driven approaches at the molecular and network levels. We now report novel analytic results in assessing the origin of high-grade serous ovarian carcinoma. Using genome-wide gene expression data and effective machine learning approaches, we design proper statistical significance tests and perform both genomic and network analyses to discriminate among three possible origins. The experimental results are consistent with recent scientific hypothesis and independent findings.","PeriodicalId":90057,"journal":{"name":"Systems biomedicine (Austin, Tex.)","volume":"1 1","pages":"55 - 64"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4161/sysb.25313","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70654406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
It has been twelve years since Katie Couric, the American journalist, underwent, on the “Today” show, live colonoscopy. It has been said that this single gusty act (no pun intended), has saved more lives than the entire campaign for the awareness of the importance of colonoscopy. More recently, Angelina Jolie’s mastectomy may have provided a similar effect for the awareness of women to what their genome might tell them about their future health. Jolie’s decision has been the result of awareness on her side, due to her familial history, combined with an ability to affiliate a specific genome sequence with this familial history. The decline in cost for obtaining knowledge about one’s genome, and the computational ability to make sense of these data, have recently seemed to synergize in a manner that would soon allow many people to gain details about their genomes that would eventually lead to life-changing decisions, not unlike the decision Ms Jolie had to make. Dr Dudley and Mr Karczewski’s book, Exploring Personal Genomics, is an inspiring exploration-of-the-possible with today’s personal genomics.
{"title":"Personal understanding","authors":"S. Efroni","doi":"10.4161/SYSB.25866","DOIUrl":"https://doi.org/10.4161/SYSB.25866","url":null,"abstract":"It has been twelve years since Katie Couric, the American journalist, underwent, on the “Today” show, live colonoscopy. It has been said that this single gusty act (no pun intended), has saved more lives than the entire campaign for the awareness of the importance of colonoscopy. More recently, Angelina Jolie’s mastectomy may have provided a similar effect for the awareness of women to what their genome might tell them about their future health. Jolie’s decision has been the result of awareness on her side, due to her familial history, combined with an ability to affiliate a specific genome sequence with this familial history. The decline in cost for obtaining knowledge about one’s genome, and the computational ability to make sense of these data, have recently seemed to synergize in a manner that would soon allow many people to gain details about their genomes that would eventually lead to life-changing decisions, not unlike the decision Ms Jolie had to make. Dr Dudley and Mr Karczewski’s book, Exploring Personal Genomics, is an inspiring exploration-of-the-possible with today’s personal genomics.","PeriodicalId":90057,"journal":{"name":"Systems biomedicine (Austin, Tex.)","volume":"1 1","pages":"65 - 66"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4161/SYSB.25866","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70654443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Ward, Robert Schmieder, Gareth Highnam, D. Mittelman
The advent of high-throughput sequencing, coupled with advances in computational methods, has enabled genome-wide dissection of genetics, evolution, and disease, with nucleotide resolution. The discoveries derived from genomics promise benefits to basic research, biotechnology, and medicine; however, the speed and affordability of sequencing has resulted in a flood of “big data” in the life sciences. In addition, the current heterogeneity of sequencing platforms and diversity of applications complicate the development of tools for analysis, and this has slowed widespread adoption of the technology. Making sense of the data and delivering actionable insight requires improved computational infrastructure, new methods for interpreting the data, and unique collaborative approaches. Here we review the role of big data in genomics, its impact on the development of tools for collaborative analysis of genomes, and successes and ongoing challenges in coping with big data.
{"title":"Big data challenges and opportunities in high-throughput sequencing","authors":"R. Ward, Robert Schmieder, Gareth Highnam, D. Mittelman","doi":"10.4161/sysb.24470","DOIUrl":"https://doi.org/10.4161/sysb.24470","url":null,"abstract":"The advent of high-throughput sequencing, coupled with advances in computational methods, has enabled genome-wide dissection of genetics, evolution, and disease, with nucleotide resolution. The discoveries derived from genomics promise benefits to basic research, biotechnology, and medicine; however, the speed and affordability of sequencing has resulted in a flood of “big data” in the life sciences. In addition, the current heterogeneity of sequencing platforms and diversity of applications complicate the development of tools for analysis, and this has slowed widespread adoption of the technology. Making sense of the data and delivering actionable insight requires improved computational infrastructure, new methods for interpreting the data, and unique collaborative approaches. Here we review the role of big data in genomics, its impact on the development of tools for collaborative analysis of genomes, and successes and ongoing challenges in coping with big data.","PeriodicalId":90057,"journal":{"name":"Systems biomedicine (Austin, Tex.)","volume":"50 1","pages":"29 - 34"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4161/sysb.24470","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70654762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
F. Emmert-Streib, S. Tripathi, R. D. Simoes, A. Hawwa, M. Dehmer
In this paper, we review the construction, the application, the meaning and the interpretation of the Diseasome network, which enables a systematic connection between the molecular and the phenotype level, and derived models like the human disease network. Further, we are surveying recent conceptual and methodological enhancements that integrate data from diverse sources, e.g., from protein databases or genome-wide association studies. For our review, we assume a “data-centric” view that allows to distinguish different approaches based on the types of data used in a model. In addition, we discuss the need for network-based approaches in medicine.
{"title":"The human disease network","authors":"F. Emmert-Streib, S. Tripathi, R. D. Simoes, A. Hawwa, M. Dehmer","doi":"10.4161/sysb.22816","DOIUrl":"https://doi.org/10.4161/sysb.22816","url":null,"abstract":"In this paper, we review the construction, the application, the meaning and the interpretation of the Diseasome network, which enables a systematic connection between the molecular and the phenotype level, and derived models like the human disease network. Further, we are surveying recent conceptual and methodological enhancements that integrate data from diverse sources, e.g., from protein databases or genome-wide association studies. For our review, we assume a “data-centric” view that allows to distinguish different approaches based on the types of data used in a model. In addition, we discuss the need for network-based approaches in medicine.","PeriodicalId":90057,"journal":{"name":"Systems biomedicine (Austin, Tex.)","volume":"1 1","pages":"20 - 28"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4161/sysb.22816","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70653917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Genetic networks integrate the reported interactions between genes into a global view of the transcription regulation. These networks contain, beyond each specific interaction, the information flow between genes and groups of genes that determine the cellular response to different stimuli. The flow of information in such networks is based on the structure of the directed interactions paths, and is not obviously decipherable from the number of paths between genes in the network, which grows exponentially with the number of nodes. We show here that the directional large scale information flow in genetic networks can be understood by combining the cycle (closed walk in graph theory terms) length and distance distributions. These properties are highly sensitive to the effect of flipping the direction of a small number of random edges. Here we focus on cycles composed of back and forth minimal paths between a pair of nodes that we further denote as loops. Intra-cellular networks contain a surprisingly large number of long directed loops that can carry information through multiple components of the network, and in parallel a surprisingly small number of short loops. The direction of practically every edge affects the network’s loop length distribution and the flow of information in the network. Swapping the direction of even 2.5% of the edges in regulatory genetic networks from their target to their source drastically reduces the number of long directed loops. All other properties tested here, such as the clustering coefficient or the degree distributions, are practically not affected by a swap of even 50% of edges. We propose a model of information flow to explain this hyper-sensitivity of the loop length distribution to the direction of edges.
{"title":"Long loops of information flow in genetic networks highlight an inherent directionality","authors":"Royi Itzhack, Lea Tsaban, Y. Louzoun","doi":"10.4161/sysb.24471","DOIUrl":"https://doi.org/10.4161/sysb.24471","url":null,"abstract":"Genetic networks integrate the reported interactions between genes into a global view of the transcription regulation. These networks contain, beyond each specific interaction, the information flow between genes and groups of genes that determine the cellular response to different stimuli. The flow of information in such networks is based on the structure of the directed interactions paths, and is not obviously decipherable from the number of paths between genes in the network, which grows exponentially with the number of nodes. We show here that the directional large scale information flow in genetic networks can be understood by combining the cycle (closed walk in graph theory terms) length and distance distributions. These properties are highly sensitive to the effect of flipping the direction of a small number of random edges. Here we focus on cycles composed of back and forth minimal paths between a pair of nodes that we further denote as loops. Intra-cellular networks contain a surprisingly large number of long directed loops that can carry information through multiple components of the network, and in parallel a surprisingly small number of short loops. The direction of practically every edge affects the network’s loop length distribution and the flow of information in the network. Swapping the direction of even 2.5% of the edges in regulatory genetic networks from their target to their source drastically reduces the number of long directed loops. All other properties tested here, such as the clustering coefficient or the degree distributions, are practically not affected by a swap of even 50% of edges. We propose a model of information flow to explain this hyper-sensitivity of the loop length distribution to the direction of edges.","PeriodicalId":90057,"journal":{"name":"Systems biomedicine (Austin, Tex.)","volume":"1 1","pages":"47 - 54"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4161/sysb.24471","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70654505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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