Scale-Dependent Statistics of the Numbers of Transcripts and Protein Sequences Encoded in the Genome

Kuznetsov, Vladimir A.

doi:10.1007/0-387-26288-1_10

Cited by 4 publications

(6 citation statements)

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“…Parameter J of the truncated GDP function reflects the maximal observed number of BEs of the GDP model. This parameter positively correlates with the sample size M [9,28]. These results agree with previous findings in ChIP-PET TF-DNA binding assay [9,12,13].…”

Section: Discussionsupporting

confidence: 91%

Statistics of protein-DNA binding and the total number of binding sites for a transcription factor in the mammalian genome

2010

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BackgroundTranscription factor (TF)-DNA binding loci are explored by analyzing massive datasets generated with application of Chromatin Immuno-Precipitation (ChIP)-based high-throughput sequencing technologies. These datasets suffer from a bias in the information about binding loci availability, sample incompleteness and diverse sources of technical and biological noises. Therefore adequate mathematical models of ChIP-based high-throughput assay(s) and statistical tools are required for a robust identification of specific and reliable TF binding sites (TFBS), a precise characterization of TFBS avidity distribution and a plausible estimation the total number of specific TFBS for a given TF in the genome for a given cell type.ResultsWe developed an exploratory mixture probabilistic model for a specific and non-specific transcription factor-DNA (TF-DNA) binding. Within ChiP-seq data sets, the statistics of specific and non-specific DNA-protein binding is defined by a mixture of sample size-dependent skewed functions described by Kolmogorov-Waring (K-W) function (Kuznetsov, 2003) and exponential function, respectively. Using available Chip-seq data for eleven TFs, essential for self-maintenance and differentiation of mouse embryonic stem cells (SC) (Nanog, Oct4, sox2, KLf4, STAT3, E2F1, Tcfcp211, ZFX, n-Myc, c-Myc and Essrb) reported in Chen et al (2008), we estimated (i) the specificity and the sensitivity of the ChiP-seq binding assays and (ii) the number of specific but not identified in the current experiments binding sites (BSs) in the genome of mouse embryonic stem cells. Motif finding analysis applied to the identified c-Myc TFBSs supports our results and allowed us to predict many novel c-Myc target genes.ConclusionWe provide a novel methodology of estimating the specificity and the sensitivity of TF-DNA binding in massively paralleled ChIP sequencing (ChIP-seq) binding assay. Goodness-of fit analysis of K-W functions suggests that a large fraction of low- and moderate- avidity TFBSs cannot be identified by the ChIP-based methods. Thus the task to identify the binding sensitivity of a TF cannot be technically resolved yet by current ChIP-seq, compared to former experimental techniques. Considering our improvement in measuring the sensitivity and the specificity of the TFs obtained from the ChIP-seq data, the models of transcriptional regulatory networks in embryonic cells and other cell types derived from the given ChIp-seq data should be carefully revised.

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Section: Discussionsupporting

confidence: 91%

Statistics of protein-DNA binding and the total number of binding sites for a transcription factor in the mammalian genome

2010

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“…More precisely, the observed frequencies have the following characteristics in common: there are few frequent, and many rare events (clusters, interactions, co-occurrence etc). Such skewed functions are often observed in many natural and technological processes (the birth-death processes, biological evolution, interaction events in genome, transcriptome and proteome scales, artificial complex systems, physics phenomena, biological and social networks, industry incidences [ 25 , 26 ]. Sampling from such populations could be commonly fitted by the Pareto-like frequency distribution function, which is sample-size and context-dependent [ 25 , 26 ].…”

Section: Resultsmentioning

confidence: 99%

“…Such skewed functions are often observed in many natural and technological processes (the birth-death processes, biological evolution, interaction events in genome, transcriptome and proteome scales, artificial complex systems, physics phenomena, biological and social networks, industry incidences [ 25 , 26 ]. Sampling from such populations could be commonly fitted by the Pareto-like frequency distribution function, which is sample-size and context-dependent [ 25 , 26 ]. In practical applications, the left side of the observed skewed distribution could be enriched with 'admixture' events which consist of 'null' or 'background' additive noise events due to error measurements.…”

Section: Resultsmentioning

confidence: 99%

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Multiple signatures of a disease in potential biomarker space: Getting the signatures consensus and identification of novel biomarkers

Kuznetsov

2015

BMC Genomics

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BackgroundThe lack of consensus among reported gene signature subsets (GSSs) in multi-gene biomarker discovery studies is often a concern for researchers and clinicians. Subsequently, it discourages larger scale prospective studies, prevents the translation of such knowledge into a practical clinical setting and ultimately hinders the progress of the field of biomarker-based disease classification, prognosis and prediction.MethodsWe define all "gene identificators" (gIDs) as constituents of the entire potential disease biomarker space. For each gID in a GSS of interest ("tested GSS"/tGSS), our method counts the empirical frequency of gID co-occurrences/overlaps in other reference GSSs (rGSSs) and compares it with the expected frequency generated via implementation of a randomized sampling procedure. Comparison of the empirical frequency distribution (EFD) with the expected background frequency distribution (BFD) allows dichotomization of statistically novel (SN) and common (SC) gIDs within the tGSS.ResultsWe identify SN or SC biomarkers for tGSSs obtained from previous studies of high-grade serous ovarian cancer (HG-SOC) and breast cancer (BC). For each tGSS, the EFD of gID co-occurrences/overlaps with other rGSSs is characterized by scale and context-dependent Pareto-like frequency distribution function. Our results indicate that while independently there is little overlap between our tGSS with individual rGSSs, comparison of the EFD with BFD suggests that beyond a confidence threshold, tested gIDs become more common in rGSSs than expected. This validates the use of our tGSS as individual or combined prognostic factors. Our method identifies SN and SC genes of a 36-gene prognostic signature that stratify HG-SOC patients into subgroups with low, intermediate or high-risk of the disease outcome. Using 70 BC rGSSs, the method also predicted SN and SC BC prognostic genes from the tested obesity and IGF1 pathway GSSs.ConclusionsOur method provides a strategy that identify/predict within a tGSS of interest, gID subsets that are either SN or SC when compared to other rGSSs. Practically, our results suggest that there is a stronger association of the IGF1 signature genes with the 70 BC rGSSs, than for the obesity-associated signature. Furthermore, both SC and SN genes, in both signatures could be considered as perspective prognostic biomarkers of BCs that stratify the patients onto low or high risks of cancer development.

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“…The appropriate statistical-based predictive models in this case, can lead to unbiased variable selection of highly informative, robust and reproducible components of classifiers and survival predictors [10, 25, 44–46]. It was demonstrated that statistical-based optimization of dichotomous threshold of the continuous variables can be quite accurate, even with highly correlated data [10, 25, 30, 44–47]. …”

Section: Discussionmentioning

confidence: 99%

Big data and computational biology strategy for personalized prognosis

Tang

Kuznetsov

2016

Oncotarget

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The era of big data and precision medicine has led to accumulation of massive datasets of gene expression data and clinical information of patients. For a new patient, we propose that identification of a highly similar reference patient from an existing patient database via similarity matching of both clinical and expression data could be useful for predicting the prognostic risk or therapeutic efficacy.Here, we propose a novel methodology to predict disease/treatment outcome via analysis of the similarity between any pair of patients who are each characterized by a certain set of pre-defined biological variables (biomarkers or clinical features) represented initially as a prognostic binary variable vector (PBVV) and subsequently transformed to a prognostic signature vector (PSV). Our analyses revealed that Euclidean distance rather correlation distance measure was effective in defining an unbiased similarity measure calculated between two PSVs.We implemented our methods to high-grade serous ovarian cancer (HGSC) based on a 36-mRNA predictor that was previously shown to stratify patients into 3 distinct prognostic subgroups. We studied and revealed that patient's age, when converted into binary variable, was positively correlated with the overall risk of succumbing to the disease. When applied to an independent testing dataset, the inclusion of age into the molecular predictor provided more robust personalized prognosis of overall survival correlated with the therapeutic response of HGSC and provided benefit for treatment targeting of the tumors in HGSC patients.Finally, our method can be generalized and implemented in many other diseases to accurately predict personalized patients’ outcomes.

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Scale-Dependent Statistics of the Numbers of Transcripts and Protein Sequences Encoded in the Genome

Cited by 4 publications

References 71 publications

Statistics of protein-DNA binding and the total number of binding sites for a transcription factor in the mammalian genome

Statistics of protein-DNA binding and the total number of binding sites for a transcription factor in the mammalian genome

Multiple signatures of a disease in potential biomarker space: Getting the signatures consensus and identification of novel biomarkers

Big data and computational biology strategy for personalized prognosis

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