If the Q1 and Q3 are quite tight, then the quantification results are quite reliable. If the reciprocal labeling gives similar results, such as SR45, then the quantification should be reliable, even if the total peptides from this protein are only a few. We recommend at least three to four biological experiments be done for quantification, including at least one reciprocal labeling experiment . After the quantification is done, the users can evaluate the quality of the quantification of each protein and peptide of interest. Protein Prospector provides interactive feedback during the quantification process to allow for manual validation of the quantification results or visual assessment of what went wrong in case the ratio is incorrect. For protein quantification, a tight range between Q1 and Q3 often indicates the quantification is reliable. In cases where the range is big and the protein itself is of interest, then users can use the Cosine Similarity score to determine the quality of the matches or manually check them.Once the peptide sequence is identified, the elemental composition of the peptide is generated based on the peptide sequence. The CS score, similar to the Isotope Dot Product “idotp” product used in Skyline , automatically measures the similarity between the experimentally measured isotope pattern and the calculated pattern using the M, M+1, M+2 peaks, thus reducing manual checking time by auto-flagging the contaminated peaks .
The CS score ranges between 1.0 and 0.0 and can be determined by measured peak intensity or area. Figure 6A shows the pair have both good CS scores and the L/H ratio of this peptide is close to the median number of the protein. Figure 6B shows one peptide in the pair of another peptide from the same protein gives a lower CS score ,grow bucket and thus the L/H ratio of this peptide will produce an outlier ratio. Importantly, Protein Prospector takes account of labeling efficiency when calculating the CS score, as low labeling efficiency changes the isotope pattern quite dramatically. It should be noted that the CS score will be less accurate when the peak intensity of the peptide is very low. Users can create a cache file when submitting the quantification in Search Compare. This stores the data required to regenerate the Search Compare report in a JSON file. The cache function is quite useful for various reasons: when the user needs to retrieve the data or manually check the data, there is no need to re-calculate the quantification, which can take many hours for a large dataset. With the cache file, the reports come up quickly for a few seconds rather than hours; Often it is hard to display an HTML peptide report when many proteins or peptides are quantified. The cache function can allow visualization of such reports easily. This workflow allows users to report quantification of thousands of proteins and is applicable to the quantification of the total proteomes, sub-proteomes, and immuno precipitated samples . During the extraction of elution profile of every peptide identified, Protein Prospector averages together scans over a time window but doesn’t fit the peak shape to a Gaussian function. Therefore, each identified peptide/protein will be quantified and none gets discarded due to failing the scoring threshold for fitting the Gaussian function. To get high quality data, we recommend to get 97% or above labeling efficiency to achieve higher ID rates in 15N samples, so more proteins will be reproducibly identified and quantified between different replicates. Data acquired on high resolution and high accuracy instruments will also improve the quality of the dataset. A systematic normalization is normally required before comparing results between different experiments , as the samples are rarely mixed at exactly 1:1.
One choice is to use the median number of all the quantified proteins, or median number of top one hundred abundant proteins . Alternatively, users can use housekeeping proteins that are assumed to not change for normalization. Statistical analysis of quantification data on three or more replicates is advised. Users need to determine how to apply statistical analysis on the data using a separate tool. Benjamini-Hochberg multiple hypothesis test has been used to determine significant regulated PTM peptide groups in 15N metabolic labeled samples . A standardized statistic pipeline for protein quantification is still lacking, particularly a pipeline that can leverage quantification ratios of each peptide from a protein. Our current workflow uses a median value, which takes advantage of the quantification ratios of each peptide but is less affected by outliers than using the mean. However, the statistical power utilizing quantifications from these multiple peptides from single protein has not been explored and awaits development in the future. A targeted quantification strategy is recommended for further analysis of proteins of interest because this provides more accurate quantification and is less likely to have missing values, particularly in the 15N labeled samples . In addition to targeted analysis, data-independent acquisition can also be utilized, which can be done in label free samples or combined with 15N metabolic labeling in the future. DIA benefits from having few missing values, but more efforts will be needed to deconvolve the mixed MS2 spectra in DIA datasets. 15N metabolic labeling has been utilized in studies of analyzing protein synthesis and degradation . These studies are based on incomplete and often low incorporation rates which result in very broad satellite peak distributions and cause 15N labeled peptide isotope clusters to overlap with 14N labeled peptide clusters. As Protein Prospector doesn’t deconvolve the 15N distribution from the 14N distributions , the presented workflow will not provide accurate quantification in this type of study. On the other hand, for chase studies that analyze the assembly kinetics in vitro , the presented workflow can be applied because the proteins involved have a very high labeling efficiency.
This workflow can be also applied to the quantification of post-translational modification with a slight modification. The users will include related PTM search parameters into data search. Instead of reporting median number at protein level, ratios from each peptide are reported and then compared across different replicates. Trait analysis, especially genome-wide trait analysis, is centered on how genetic variation gives rise to phenotypic variation. This type of analysis relies on statistical methods and tools to perform association mapping between causal genetic variants and resulting phenotypes, which can determine the heritability of a trait at a subset of genetic variants and delineate regions of the genome that control the trait, thereby providing markers that can be utilized to accelerate breeding by marker-assisted selection. Because of the great success of genome-wide association studies , hundreds of SNPs conferring genetic variation of complex traits have been identified and reported. However, the genetic structures of most traits remain unexplained, as associated SNPs detected from GWAS explain only a small fraction of heritability and a much smaller percentage of the total phenotypic variance. This is mainly because a number of these studies employed only additive models that fail to account for epistasis,blueberries container or the interaction between multiple loci and the environment. Xu et al. proposed a new linear mixed model for mapping quantitative loci by incorporating multiple polygenic covariance structures. Based on this model, a pipeline for estimating epistatic effects was developed to com prehensively estimate additive effects, dominance effects, and interaction effects between multiple genetic loci. PEPIS allows analysis of genome-wide genetic architectures, including genotype interaction effects , and can thereby explain more than 80% of phenotypic variance. Compared with standard GWAS tools that consider only additive effects, the PEPIS pipeline is equipped with a more complex polygenic linear model that can explain more phenotypic variance. However, neither of these methods can explain nearly 100% of phenotypic variance, as neither considers the interaction between genotypes and environments . Today, the predominant thinking in biology is that the orchestrated expression of many genes in different environmental conditions affects the transcriptome, proteome, and metabolome to produce a final observable phenotype. Recent work in Saccharomyces cerevisiae suggests that GxE can occur at the individual locus level and the group level for multiple loci, leading to environment dependent epistatic interactions. Although Muir et al.conceptualized the partitioning of GxE into two possible inter action types, our mathematical understanding of the genetic and molecular mechanisms by which GxE collectively gives rise to phenotypes is still incomplete. The central dogma of biology is that the genome, tran scriptome, proteome, and metabolome are cascading and connected to the end phenome. The development of life science technologies enables transcriptomic, proteomic, and metabolomic events to be analyzed in detail within the same biological system, allowing the systematic study of a complete biological system.
Out of all the omic data from the same biological system, genomic data generally remain constant across environments, although the same genotype subjected to different environments can produce a wide range of phenotypes by triggering the expressions of different genes, downstream enzymes, and metabolites. Most current association methods and analysis tools perform associa tion mapping based on fundamental relationships between DNA sequence variation and phenotypic variation without addressing environmental variation. GxE can be understood by observing and measuring the expression of genes or metabolites. Harper et al.developed an associative transcriptomic approach to study complex traits in the polyploidy crop species Brassica napus by correlating trait variation with the quantitative expression of genes and sequence variation of transcripts, with the consistent physical positions of the two kinds of associative markers allowing the identification of high-confidence transcription factor candidates. However, their method is based on a pure additive model only, and they make no mention of interaction effects between biomarkers or their contribution to phenotypic variation. To overcome the limitation of standard GWAS that fails to consider the GxG and GxE effects, we extend associative geno mics and transcriptomics into a broader associative omics by systematically integrating all available omic data into one analytical model. Here we propose a new LMM and describe the development of a pipeline for analyzing traits through ome-wide association studies to implement the model. The proposed LMM considers not only the additive effects of each biological marker but also the interaction effect of each marker pair. The marker pairs’ interaction effect introduced here corresponds to two-dimensional association mapping, which is complementary to one-dimensional association mapping in regular GWAS. Consequently, the proposed model and PATO WAS pipeline are not limited to GWAS for genotype-to phenotype mapping ; instead, they are capable of per forming multiple types of ome-wide association studies, such as transcriptome-wide association studies for transcript-to phenotype mapping and metabolome-wide association studies for metabolite-to-phenotype mapping . We submit a rice recombinant inbred line dataset with three omics markers and two agronomic traits to PATOWAS for comprehensive analyses of associative omics. The results demonstrate that our proposed LMM and the pipeline PATO WAS can effectively address the GxG effect and the GxE effect, perform multiple-level associative omics in one platform, and innovatively provide a systems biology view into the traits analyzed.We aimed to systematically integrate multiple associative omic results to provide more biological insights into the phenotypic traits to be analyzed. We first collected a dataset of 210 rice RILs geno typed with 1619 marker bins, profiled with 22,584 transcripts and 1000 metabolites, and phenotyped with two agronomic traits . The phenotypic traits were yield and thousand grain weight , and the omic quantitative markers were bin based genotype data, Affymetrix RNA microarray-based gene expression data, and mass spectrometry-based profiling of metabolite abundance data. We presumed that expressed tran scripts, proteins, and metabolites are prone to vary when sub jected to the environments, while the genetic variants are considerably stable. Therefore, compared with genome-wide genotypic data, we further presumed that measured gene expression and metabolite abundance contain both gene and environment information and expect that associative transcriptomics or metabolomics could explain more phenotypic variance . Motivated by our consideration of genetic epistasis and our desire to explain more phenotypic variance, we next proposed astatistical LMM that considers not only the additive effects of the two components, the lower the residual component and the each marker variant but also the interaction effects of each more phenotypic variance can be explained by the model.