Cloudy, With a Chance of (Data) Showers
09/05/12 13:08 Filed in: GenomeWeb Daily Scan
Submitted by S. Pelech - Kinexus on Wed, 05/09/2012 - 14:18.
Adina Mangubat is correct that the interpretation of genomics information is partly limited by the availability of computational power for its analysis, but the development of improved bioinformatics software that operates better in a cloud environment is only part of the solution. Greater emphasis should be directed towards the collection and consolidation of large data sets from high throughput genomics, proteomics and metabolomics measurements of clinical specimens, and the development of predictive biochemistry algorithms that can efficiently interrogate this meta-data. This would be an ideal problem for synthetic intelligence research and development as humans are ill equipped to quickly recognize important correlations in such large and diverse data sets.
Our knowledge of the activities and interactions of the metabolites and the macromolecules that are encoded by the genome remains extremely rudimentary and deficient even after a decade of completion of the sequencing of the human genome. For about 40% of the human proteins, we do not know what they do, never mind how they are regulated. While the challenge of mapping molecular interactions is already formidable, the added complexity of understanding the consequences of individual nucleotide and amino acid changes in genes and proteins is overwhelming. Yet, the point of sequencing the genomes of individuals is to uncover these genetic differences to gain insight into their contributions to the aetiology of diseases.
It is possible that the successful recognition of patterns of mutations in individual genomes may be sufficient for disease prediction and diagnosis. However, with some 60 million single nucleotide polymorphisms apparently existing in the genomes of healthy people, and the more likely scenario of multiple, complementary mutations required for the vast majority of common diseases, simple pattern recognition will be insufficient in most cases. What is needed is a true understanding of the composition and architecture of the metabolic and signalling protein networks that support healthy cells and how pathogenic genetic mutations and environmental toxins (including those from viruses and bacteria) compromise their operations.
While tremendous advancements have been achieved in genomics research with strong public and private support through governments and industry, the situation for proteomics and metabolomics research is quite different. The costs of genomics analyses have plummeted in the last 20 years, because this is where most of the basic research support has been placed. When societies finally recognize the importance of providing comparable support to studies of proteins and metabolites, it is likely that better technologies can be developed and applied for their analyses.
It is intriguing that one of the biggest concerns of government and industry is that despite the doubling of biomedical research support over the last decade, the actual number of new disease diagnostic tests and therapeutic drugs approved annually has markedly declined. This is better known as the "translational research gap," which has been widening. However, disease phenotype correlates better with the specific levels of active proteins than total protein, total mRNA or individually mutated genes. mRNA sequences are "translated" into protein sequences by ribosomes. Maybe the real "translational gap" is too much emphasis in research on genes rather than the proteins that they encode.
Link to the original blog post
Adina Mangubat is correct that the interpretation of genomics information is partly limited by the availability of computational power for its analysis, but the development of improved bioinformatics software that operates better in a cloud environment is only part of the solution. Greater emphasis should be directed towards the collection and consolidation of large data sets from high throughput genomics, proteomics and metabolomics measurements of clinical specimens, and the development of predictive biochemistry algorithms that can efficiently interrogate this meta-data. This would be an ideal problem for synthetic intelligence research and development as humans are ill equipped to quickly recognize important correlations in such large and diverse data sets.
Our knowledge of the activities and interactions of the metabolites and the macromolecules that are encoded by the genome remains extremely rudimentary and deficient even after a decade of completion of the sequencing of the human genome. For about 40% of the human proteins, we do not know what they do, never mind how they are regulated. While the challenge of mapping molecular interactions is already formidable, the added complexity of understanding the consequences of individual nucleotide and amino acid changes in genes and proteins is overwhelming. Yet, the point of sequencing the genomes of individuals is to uncover these genetic differences to gain insight into their contributions to the aetiology of diseases.
It is possible that the successful recognition of patterns of mutations in individual genomes may be sufficient for disease prediction and diagnosis. However, with some 60 million single nucleotide polymorphisms apparently existing in the genomes of healthy people, and the more likely scenario of multiple, complementary mutations required for the vast majority of common diseases, simple pattern recognition will be insufficient in most cases. What is needed is a true understanding of the composition and architecture of the metabolic and signalling protein networks that support healthy cells and how pathogenic genetic mutations and environmental toxins (including those from viruses and bacteria) compromise their operations.
While tremendous advancements have been achieved in genomics research with strong public and private support through governments and industry, the situation for proteomics and metabolomics research is quite different. The costs of genomics analyses have plummeted in the last 20 years, because this is where most of the basic research support has been placed. When societies finally recognize the importance of providing comparable support to studies of proteins and metabolites, it is likely that better technologies can be developed and applied for their analyses.
It is intriguing that one of the biggest concerns of government and industry is that despite the doubling of biomedical research support over the last decade, the actual number of new disease diagnostic tests and therapeutic drugs approved annually has markedly declined. This is better known as the "translational research gap," which has been widening. However, disease phenotype correlates better with the specific levels of active proteins than total protein, total mRNA or individually mutated genes. mRNA sequences are "translated" into protein sequences by ribosomes. Maybe the real "translational gap" is too much emphasis in research on genes rather than the proteins that they encode.
Link to the original blog post