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Quantitative Biology

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Quant. Biol.    2013, Vol. 1 Issue (1) : 71-90    https://doi.org/10.1007/s40484-013-0005-3
REVIEW
Personal genomes, quantitative dynamic omics and personalized medicine
George I. Mias, Michael Snyder()
Department of Genetics, Stanford University School of Medicine, Stanford University, Stanford, CA 94305, USA
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Abstract

The rapid technological developments following the Human Genome Project have made possible the availability of personalized genomes. As the focus now shifts from characterizing genomes to making personalized disease associations, in combination with the availability of other omics technologies, the next big push will be not only to obtain a personalized genome, but to quantitatively follow other omics. This will include transcriptomes, proteomes, metabolomes, antibodyomes, and new emerging technologies, enabling the profiling of thousands of molecular components in individuals. Furthermore, omics profiling performed longitudinally can probe the temporal patterns associated with both molecular changes and associated physiological health and disease states. Such data necessitates the development of computational methodology to not only handle and descriptively assess such data, but also construct quantitative biological models. Here we describe the availability of personal genomes and developing omics technologies that can be brought together for personalized implementations and how these novel integrated approaches may effectively provide a precise personalized medicine that focuses on not only characterization and treatment but ultimately the prevention of disease.
Corresponding Author(s): Snyder Michael,Email:mpsnyder@stanford.edu   
Issue Date: 05 March 2013
 Cite this article:   
George I. Mias,Michael Snyder. Personal genomes, quantitative dynamic omics and personalized medicine[J]. Quant. Biol., 2013, 1(1): 71-90.
 URL:  
https://academic.hep.com.cn/qb/EN/10.1007/s40484-013-0005-3
https://academic.hep.com.cn/qb/EN/Y2013/V1/I1/71
Fig.1  Genomic variants.
(A) Variation in the human genome. The personal genomic code can differ from the published reference genome. Basic examples variation are shown on a single or few base variants (e.g., point mutations, insertions and deletions), or a larger scale for structural variants (>1000 bp, e.g., large insertions, deletions, translocation, tandem repeats, translocations).
(B) Sample variant analysis workflow. In a genomic variant analysis, for example, after sample preparation and sequencing the raw files can be passed through quality control (e.g., using FastQC (http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/) and removing PCR artifacts using tools as Picard (http://picard.sourceforge.net)). Reads are mapped to the genome and variants are assessed, e.g., mapping with several algorithms, including ELAND II (Illumina), SOAP [], MAQ and Burrows-Wheeler Aligner (BWA) [] and Novoalign by ? Novocraft Technologies (http://www.novocraft.com). Read re-alignment can be performed, e.g., using Genome Analysis Toolkit (GATK) [], or HugeSeq [], to call variants, including implementations with Sequence Alignment Map format Tools (SAMtools) [], annotation using Annovar [], SIFT [] and Polyphen [] for determining variant effects on proteomic translation []. Furthermore, using a variety of methods the structural variants can be determined. For example the method considers how paired-end reads mapped to the reference to assign deletions and insertions from reads whose mapped span is longer or shorter than the average span; inversions from position and relative orientations of the ends of reads [,]. The method allows the possibility to identify the proportional genomic copy number variation. In the approach of Abyzov et al. [] the read depth considered as an image is analyzed image processing techniques, viz. mean-shift-theory []. Programs such as Pindel [] and BreakSeq [] consider analysis to determine breakpoints of insertions and deletions. DELLY [] by Rausch et al. takes into account paired-end and split-read methods for determining structural variants. Many packages for analysis are available through the Bioconductor [] project as implemented in the freely available R statistical analysis platform (http://www.R-project.org).
Fig.1  Genomic variants.
(A) Variation in the human genome. The personal genomic code can differ from the published reference genome. Basic examples variation are shown on a single or few base variants (e.g., point mutations, insertions and deletions), or a larger scale for structural variants (>1000 bp, e.g., large insertions, deletions, translocation, tandem repeats, translocations).
(B) Sample variant analysis workflow. In a genomic variant analysis, for example, after sample preparation and sequencing the raw files can be passed through quality control (e.g., using FastQC (http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/) and removing PCR artifacts using tools as Picard (http://picard.sourceforge.net)). Reads are mapped to the genome and variants are assessed, e.g., mapping with several algorithms, including ELAND II (Illumina), SOAP [], MAQ and Burrows-Wheeler Aligner (BWA) [] and Novoalign by ? Novocraft Technologies (http://www.novocraft.com). Read re-alignment can be performed, e.g., using Genome Analysis Toolkit (GATK) [], or HugeSeq [], to call variants, including implementations with Sequence Alignment Map format Tools (SAMtools) [], annotation using Annovar [], SIFT [] and Polyphen [] for determining variant effects on proteomic translation []. Furthermore, using a variety of methods the structural variants can be determined. For example the method considers how paired-end reads mapped to the reference to assign deletions and insertions from reads whose mapped span is longer or shorter than the average span; inversions from position and relative orientations of the ends of reads [,]. The method allows the possibility to identify the proportional genomic copy number variation. In the approach of Abyzov et al. [] the read depth considered as an image is analyzed image processing techniques, viz. mean-shift-theory []. Programs such as Pindel [] and BreakSeq [] consider analysis to determine breakpoints of insertions and deletions. DELLY [] by Rausch et al. takes into account paired-end and split-read methods for determining structural variants. Many packages for analysis are available through the Bioconductor [] project as implemented in the freely available R statistical analysis platform (http://www.R-project.org).
Fig.2  RNA-Seq analysis.
In RNA-Seq analysis, short reads can be assembled and then mapped to the reference genome (with tools such as Illumina’s ELAND, MAQ and BWA [], Bowtie [-], SOAP [], and others). A recent protocol by Trapnell et al. [] describes in detail the use of dedicated RNA-Seq programs from the Tuxedo suite, such as TopHat [], Cufflinks [,] and an R implementation called CummeRBund as a Bioconductor package (an alternative is to run these directly or using GenePattern [,], which also includes possible reconstruction by Scripture []). Other programs such as DESeq, another package in Bioconductor, can also help test for differential expression []. The numerous analyses availabilities are now publically discussed online, in a forum that (http://SEQanswers.com/) discusses many other examples and all aspects of the mapping process [].
Fig.2  RNA-Seq analysis.
In RNA-Seq analysis, short reads can be assembled and then mapped to the reference genome (with tools such as Illumina’s ELAND, MAQ and BWA [], Bowtie [-], SOAP [], and others). A recent protocol by Trapnell et al. [] describes in detail the use of dedicated RNA-Seq programs from the Tuxedo suite, such as TopHat [], Cufflinks [,] and an R implementation called CummeRBund as a Bioconductor package (an alternative is to run these directly or using GenePattern [,], which also includes possible reconstruction by Scripture []). Other programs such as DESeq, another package in Bioconductor, can also help test for differential expression []. The numerous analyses availabilities are now publically discussed online, in a forum that (http://SEQanswers.com/) discusses many other examples and all aspects of the mapping process [].
Fig.3  Proteome analysis.
In quantitative proteomics using mass spectrometry typical methods employ trypsin digestion coupled with tagging methods - non label-free methods include use of isotopic labeling (SILAC) or isobaric tagging (iTRAQ, TMT). One typical bottom-up-approach setup uses a combination of high affinity liquid chromatography coupled with two rounds of mass spectrometry (LC-MS/MS) to fractionate peptides for identification and obtain their mass spectra. Raw files may be analyzed using vendor software or converted to open formats (such as .mzxml, .mzdata or the current standard .mzml [-], e.g., using MSConvert []). The mass spectra can be mapped to known protein using a protein library, or less frequently assembled, using an array of programs (e.g., X!Tandem [], SEQUEST [], Mascot [], Open Mass Spectrometry Search Algorithm (OMSSA) [], Proteome Discoverer by Thermo Scientific, or MassHunter Workstation by Agilent). Quality control includes estimation of false discovery rates (FDR), often using a reverse database search [,255,]. Quantitation can be carried out to estimate relative levels of proteins in different samples (employing standardization and normalization of average sample ratios to a unit mean). Finally annotation is made using databases such as UniProt or NCBI. Some of the analysis can be performed using suites and programs, such as PEAKS [], the Trans-Proteomic Pipeline (TPP) [-] multiple tools from ProteoWizard [], OpenMS [–] or vendor complete solutions Proteome Discoverer and MassHunter Workstation mentioned above. Multiple other programs for mass spectrometry are available (e.g., see http://www.msutils.org).
Fig.3  Proteome analysis.
In quantitative proteomics using mass spectrometry typical methods employ trypsin digestion coupled with tagging methods - non label-free methods include use of isotopic labeling (SILAC) or isobaric tagging (iTRAQ, TMT). One typical bottom-up-approach setup uses a combination of high affinity liquid chromatography coupled with two rounds of mass spectrometry (LC-MS/MS) to fractionate peptides for identification and obtain their mass spectra. Raw files may be analyzed using vendor software or converted to open formats (such as .mzxml, .mzdata or the current standard .mzml [-], e.g., using MSConvert []). The mass spectra can be mapped to known protein using a protein library, or less frequently assembled, using an array of programs (e.g., X!Tandem [], SEQUEST [], Mascot [], Open Mass Spectrometry Search Algorithm (OMSSA) [], Proteome Discoverer by Thermo Scientific, or MassHunter Workstation by Agilent). Quality control includes estimation of false discovery rates (FDR), often using a reverse database search [,255,]. Quantitation can be carried out to estimate relative levels of proteins in different samples (employing standardization and normalization of average sample ratios to a unit mean). Finally annotation is made using databases such as UniProt or NCBI. Some of the analysis can be performed using suites and programs, such as PEAKS [], the Trans-Proteomic Pipeline (TPP) [-] multiple tools from ProteoWizard [], OpenMS [–] or vendor complete solutions Proteome Discoverer and MassHunter Workstation mentioned above. Multiple other programs for mass spectrometry are available (e.g., see http://www.msutils.org).
Fig.4  Metabolome analysis.
In metabolomics analysis chromatography columns are used for purification and preparation of samples coupled to mass spectrometry (gas chromatography (GC) or liquid chromatography (LC)-MS); standards for specific compounds may also be used in parallel for positive identification. Raw files may be analyzed using vendor software or converted to open formats (such as .mzxml, .mzdata or the current standard .mzml [-], e.g., using MSConvert). The spectral data may be aligned for retention time and mass intensity calibration, e.g., using XCMS [-], SIEVE by Thermo Scientific, Matlab toolboxes by MathWorks, MassHunterProfiler by Agilent, MzMine [,]. After quality control and statistical analysis, masses of interest can be annotated using databases, e.g., Metlin [,], KEGG [], MetaCyc [,,], Reactome [-].
Fig.4  Metabolome analysis.
In metabolomics analysis chromatography columns are used for purification and preparation of samples coupled to mass spectrometry (gas chromatography (GC) or liquid chromatography (LC)-MS); standards for specific compounds may also be used in parallel for positive identification. Raw files may be analyzed using vendor software or converted to open formats (such as .mzxml, .mzdata or the current standard .mzml [-], e.g., using MSConvert). The spectral data may be aligned for retention time and mass intensity calibration, e.g., using XCMS [-], SIEVE by Thermo Scientific, Matlab toolboxes by MathWorks, MassHunterProfiler by Agilent, MzMine [,]. After quality control and statistical analysis, masses of interest can be annotated using databases, e.g., Metlin [,], KEGG [], MetaCyc [,,], Reactome [-].
Fig.5  iPOP for personalized medicine.
The framework described in the text employs multi-omics analyses (see above and Figures 1-4) that may be implemented for individuals. In step I) for disease is carried out using a whole genome sequencing to perform variant analysis coupled to medical history, environmental considerations and pharmacogenomics evaluations. In step II) using an array of technologies follows multiple omics longitudinally in a subject as the progress through their different physiological states, including healthy, disease, and recovery states. Thus thousands of molecular components are collected over time for III) , using temporal patterns to obtain matched omics information, correlate and classify responses, compare against pathway databases and visualize components, e.g., current pathway tools include DAVID [,], KEGG [], Reactome [-], Ingenuity Pathway Analysis (IPA); networks can be visualized using Cytoscape [], various R packages through Bioconductor [], Matlab by MathWorks and several others. The future iPOP implementations may be gathered into a curated database of iPOP-disease associations that may help in categorizing an omics dynamic response to a catalogued physiological state and disease onset, with potential diagnostic capabilities.
Fig.5  iPOP for personalized medicine.
The framework described in the text employs multi-omics analyses (see above and Figures 1-4) that may be implemented for individuals. In step I) for disease is carried out using a whole genome sequencing to perform variant analysis coupled to medical history, environmental considerations and pharmacogenomics evaluations. In step II) using an array of technologies follows multiple omics longitudinally in a subject as the progress through their different physiological states, including healthy, disease, and recovery states. Thus thousands of molecular components are collected over time for III) , using temporal patterns to obtain matched omics information, correlate and classify responses, compare against pathway databases and visualize components, e.g., current pathway tools include DAVID [,], KEGG [], Reactome [-], Ingenuity Pathway Analysis (IPA); networks can be visualized using Cytoscape [], various R packages through Bioconductor [], Matlab by MathWorks and several others. The future iPOP implementations may be gathered into a curated database of iPOP-disease associations that may help in categorizing an omics dynamic response to a catalogued physiological state and disease onset, with potential diagnostic capabilities.
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