A statistical approach for tracking clonal dynamics in cancer using longitudinal next-generation sequencing data

A statistical approach for tracking clonal dynamics in cancer using longitudinal next-generation sequencing data

Motivation: The tumor is composed of different cancer cell population (clones), which continues to adapt to their local microenvironment. the standard method for deconvolution clonal mutation group seeks to identify and estimate the prevalence of each group in the tumor, while taking into account the purity and copy number profile. This method has been applied to a cross-sectional and longitudinal data after removing information about the time of sample collection.

Two key question is how we can combine the information in our analyzes and there are benefits in doing so?
Results: We developed clonal deconvolution method, which combines explicit temporal distance longitudinal tumor samples. By combining the Dirichlet Process Mixture Model Gaussian Process priors and used as an input sequence is rarely collected several samples, our method can reconstruct the temporal profile mutation abundance of each cluster is supported by data as a continuous function of time.

We refer to our method on the whole genome, whole exome and sequencing data targeted from patients with chronic lymphocytic leukemia, the data biopsy fluid of patients with melanoma and synthetic data and we found that combining information about a collection of networks improve the performance of the model, as long as the data volume and complexity is available for the free estimate model parameters. Thus, our approach is very useful when collecting a relatively long sequences of tumor samples feasible, such as in liquid cancers (eg, leukemia) and liquid biopsy.


Bactrian camel population size smaller than the dromedary, and they are distributed in the cold and mountain regions and also at risk of extinction in some countries such as Iran. To identify and investigate genome-wide variation, whole-genome sequencing of two Bactrian camels Iran do with the scope 37.4- and 42.6-fold for the first time. Along with Iran Bactrian camel, sequencing data from two domestic and two Mongolian wild Bactrian camel is stored in the NCBI reanalyzed.

 A statistical approach for tracking clonal dynamics in cancer using longitudinal next-generation sequencing data
A statistical approach for tracking clonal dynamics in cancer using longitudinal next-generation sequencing data

scPADGRN: An asphalt ADMM approach to reconstruct gene regulatory networks dynamically using RNA sequencing data-single cell

development and cell differentiation disease both involve dynamic changes; Therefore, reconstruction of dynamic gene regulatory network (DGRNs) is an important issue but it is difficult in biological systems. With recent advances in single-cell technical RNA sequencing (scRNA-seq), a large volume of data scRNA-seq is being obtained for the various processes. However, current methods of concluding DGRNs of the bulk sample may not be suitable for data-seq scRNA.

In this work, we present scPADGRN, inference DGRN new method using “time-series” scRNA-seq data. scPADGRN combining the method of alternating directions asphalt multiplier by grouping cells for reconstruction DGRN. It shows the advantages in accuracy, robustness and fast convergence. In addition, so-called quantitative index Differentiation Gene Interactions Enrichment (DGIE) served to measure the interaction enrichment of genes related to differentiation.

From the score DGIE of the relevant subnetwork, we conclude that the function of embryonic stem (ES) cells are most active initially and may gradually fade over time. Communication power of genes contribute to note that facilitate the differentiation of cells increased from ES cells differentiated into severe cells.

1L SequaGel UreaGel-6

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ESEQ1200-SYS ea
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We also identified some of the genes responsible for the change in the score DGIE occur during cell differentiation is based on three single-cell dataset real. Our results show that single-cell analysis is based on inference network coupled with quantitative calculations can reveal key transcriptional regulators involved in cell differentiation and disease progression.

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