scholarly journals CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 344
Author(s):  
Sandeep Kasaragod ◽  
Varshasnata Mohanty ◽  
Ankur Tyagi ◽  
Santosh Kumar Behera ◽  
Arun H. Patil ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Cancer Cell ◽  
Protein Level ◽  
Genomic Data ◽  
Cell Surfaces ◽  
Protein Database ◽  
Proteomic Data ◽  
Coding Variants ◽  
Variant Protein ◽  
Generation Sequencing

Cancer genome sequencing studies have revealed a number of variants in coding regions of several genes. Some of these coding variants play an important role in activating specific pathways that drive proliferation. Coding variants present on cancer cell surfaces by the major histocompatibility complex serve as neo-antigens and result in immune activation. The success of immune therapy in patients is attributed to neo-antigen load on cancer cell surfaces. However, which coding variants are expressed at the protein level can’t be predicted based on genomic data. Complementing genomic data with proteomic data can potentially reveal coding variants that are expressed at the protein level. However, identification of variant peptides using mass spectrometry data is still a challenging task due to the lack of an appropriate tool that integrates genomic and proteomic data analysis pipelines. To overcome this problem, and for the ease of the biologists, we have developed a graphical user interface (GUI)-based tool called CusVarDB. We integrated variant calling pipeline to generate sample-specific variant protein database from next-generation sequencing datasets. We validated the tool with triple negative breast cancer cell line datasets and identified 423, 408, 386 and 361 variant peptides from BT474, MDMAB157, MFM223 and HCC38 datasets, respectively.

scholarly journals CusVarDB: A tool for building customized sample-specific variant protein database from next-generation sequencing datasets

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 344
Author(s):  
Sandeep Kasaragod ◽  
Varshasnata Mohanty ◽  
Ankur Tyagi ◽  
Santosh Kumar Behera ◽  
Arun H. Patil ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Cancer Cell ◽  
Protein Level ◽  
Genomic Data ◽  
Cell Surfaces ◽  
Protein Database ◽  
Proteomic Data ◽  
Coding Variants ◽  
Variant Protein ◽  
Generation Sequencing

Cancer genome sequencing studies have revealed a number of variants in coding regions of several genes. Some of these coding variants play an important role in activating specific pathways that drive proliferation. Coding variants present on cancer cell surfaces by the major histocompatibility complex serve as neo-antigens and result in immune activation. The success of immune therapy in patients is attributed to neo-antigen load on cancer cell surfaces. However, which coding variants are expressed at the protein level can’t be predicted based on genomic data. Complementing genomic data with proteomic data can potentially reveal coding variants that are expressed at the protein level. However, identification of variant peptides using mass spectrometry data is still a challenging task due to the lack of an appropriate tool that integrates genomic and proteomic data analysis pipelines. To overcome this problem, and for the ease of the biologists, we have developed a graphical user interface (GUI)-based tool called CusVarDB. We integrated variant calling pipeline to generate sample-specific variant protein database from next-generation sequencing datasets. We validated the tool with triple negative breast cancer cell line datasets and identified 423, 408, 386 and 361 variant peptides from BT474, MDMAB157, MFM223 and HCC38 datasets, respectively.

scholarly journals Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data

2019 ◽  
Author(s):  
Kate Chkhaidze ◽  
Timon Heide ◽  
Benjamin Werner ◽  
Marc J. Williams ◽  
Weini Huang ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Tumour Growth ◽  
Evolutionary Dynamics ◽  
Clonal Selection ◽  
Genomic Data ◽  
Confounding Factors ◽  
Data Generation ◽  
Next Generation ◽  
Sequencing Data ◽  
Generation Sequencing

AbstractQuantification of the effect of spatial tumour sampling on the patterns of mutations detected in next-generation sequencing data is largely lacking. Here we use a spatial stochastic cellular automaton model of tumour growth that accounts for somatic mutations, selection, drift and spatial constrains, to simulate multi-region sequencing data derived from spatial sampling of a neoplasm. We show that the spatial structure of a solid cancer has a major impact on the detection of clonal selection and genetic drift from bulk sequencing data and single-cell sequencing data. Our results indicate that spatial constrains can introduce significant sampling biases when performing multi-region bulk sampling and that such bias becomes a major confounding factor for the measurement of the evolutionary dynamics of human tumours. We present a statistical inference framework that takes into account the spatial effects of a growing tumour and allows inferring the evolutionary dynamics from patient genomic data. Our analysis shows that measuring cancer evolution using next-generation sequencing while accounting for the numerous confounding factors requires a mechanistic model-based approach that captures the sources of noise in the data.SummarySequencing the DNA of cancer cells from human tumours has become one of the main tools to study cancer biology. However, sequencing data are complex and often difficult to interpret. In particular, the way in which the tissue is sampled and the data are collected, impact the interpretation of the results significantly. We argue that understanding cancer genomic data requires mathematical models and computer simulations that tell us what we expect the data to look like, with the aim of understanding the impact of confounding factors and biases in the data generation step. In this study, we develop a spatial simulation of tumour growth that also simulates the data generation process, and demonstrate that biases in the sampling step and current technological limitations severely impact the interpretation of the results. We then provide a statistical framework that can be used to overcome these biases and more robustly measure aspects of the biology of tumours from the data.

WBFQC: A new approach for compressing next-generation sequencing data splitting into homogeneous streams

2018 ◽  
Vol 16 (05) ◽  
pp. 1850018 ◽  
Author(s):  
Sanjeev Kumar ◽  
Suneeta Agarwal ◽  
Ranvijay
Keyword(s):  
Next Generation Sequencing ◽  
Genomic Data ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Compression Technique ◽  
Compression Algorithms ◽  
Ngs Data ◽  
And Storage ◽  
Generation Sequencing

Genomic data nowadays is playing a vital role in number of fields such as personalized medicine, forensic, drug discovery, sequence alignment and agriculture, etc. With the advancements and reduction in the cost of next-generation sequencing (NGS) technology, these data are growing exponentially. NGS data are being generated more rapidly than they could be significantly analyzed. Thus, there is much scope for developing novel data compression algorithms to facilitate data analysis along with data transfer and storage directly. An innovative compression technique is proposed here to address the problem of transmission and storage of large NGS data. This paper presents a lossless non-reference-based FastQ file compression approach, segregating the data into three different streams and then applying appropriate and efficient compression algorithms on each. Experiments show that the proposed approach (WBFQC) outperforms other state-of-the-art approaches for compressing NGS data in terms of compression ratio (CR), and compression and decompression time. It also has random access capability over compressed genomic data. An open source FastQ compression tool is also provided here ( http://www.algorithm-skg.com/wbfqc/home.html ).

scholarly journals Next-Generation Sequencing–Based Cancer Panel Data Conversion Using International Standards to Implement a Clinical Next-Generation Sequencing Research System: Single-Institution Study

10.2196/14710 ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. e14710 ◽  
Author(s):  
Phillip Park ◽  
Soo-Yong Shin ◽  
Seog Yun Park ◽  
Jeonghee Yun ◽  
Chulmin Shin ◽  
...  
Keyword(s):  
Clinical Practice ◽  
Next Generation Sequencing ◽  
Clinical Data ◽  
Genomic Data ◽  
International Standards ◽  
Research System ◽  
Next Generation ◽  
Sequencing Data ◽  
Clinical Sequencing ◽  
Generation Sequencing

Background The analytical capacity and speed of next-generation sequencing (NGS) technology have been improved. Many genetic variants associated with various diseases have been discovered using NGS. Therefore, applying NGS to clinical practice results in precision or personalized medicine. However, as clinical sequencing reports in electronic health records (EHRs) are not structured according to recommended standards, clinical decision support systems have not been fully utilized. In addition, integrating genomic data with clinical data for translational research remains a great challenge. Objective To apply international standards to clinical sequencing reports and to develop a clinical research information system to integrate standardized genomic data with clinical data. Methods We applied the recently published ISO/TS 20428 standard to 367 clinical sequencing reports generated by panel (91 genes) sequencing in EHRs and implemented a clinical NGS research system by extending the clinical data warehouse to integrate the necessary clinical data for each patient. We also developed a user interface with a clinical research portal and an NGS result viewer. Results A single clinical sequencing report with 28 items was restructured into four database tables and 49 entities. As a result, 367 patients’ clinical sequencing data were connected with clinical data in EHRs, such as diagnosis, surgery, and death information. This system can support the development of cohort or case-control datasets as well. Conclusions The standardized clinical sequencing data are not only for clinical practice and could be further applied to translational research.

scholarly journals Canine Genetics and Genomics

2021 ◽  
Author(s):  
Edo D’Agaro ◽  
Andrea Favaro ◽  
Davide Rosa
Keyword(s):  
Heart Disease ◽  
Next Generation Sequencing ◽  
Genomic Data ◽  
Behavioral Traits ◽  
Canine Genetics ◽  
Genome Wide ◽  
Tremendous Progress ◽  
Dog Breed ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

In the past fifteen years, tremendous progress has been made in dog genomics. Several genetic aspects of cancer, heart disease, hip dysplasia, vision and hearing problems in dogs have been investigated and studied in detail. Genome-wide associative studies have made it possible to identify several genes associated with diseases, morphological and behavioral traits. The dog genome contains an extraordinary amount of genetic variability that distinguishes the different dog breeds. As a consequence of the selective programs, applied using stringent breed standards, each dog breed represents, today, a population isolated from the others. The availability of modern next generation sequencing (NGS) techniques and the identification of millions of single functional mutations (SNPs) has enabled us to obtain new and unknown detailed genomic data of the different breeds.

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