scholarly journals Computational genomics of brain tumors: identification and characterization of glioma candidate biomarkers through multi-omics integrative molecular profiling

2019 ◽  
Author(s):  
Lin Liu ◽  
Guangyu Wang ◽  
Liguo Wang ◽  
Chunlei Yu ◽  
Mengwei Li ◽  
...  
Keyword(s):  
Data Mining ◽  
Cerebrospinal Fluid ◽  
Brain Tumors ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Molecular Profiling ◽  
Good Survival ◽  
Specific Gene ◽  
Sequencing Technologies ◽  
The World

AbstractGlioma is one of the most common malignant brain tumors and exhibits low resection rate and high recurrence risk. Although a large number of glioma studies powered by high-throughput sequencing technologies have led to massive multi-omics datasets, there lacks of comprehensive integration of glioma datasets for uncovering candidate biomarker genes. In this study, we collected a large-scale assemble of multi-omics multi-cohort datasets from worldwide public resources, involving a total of 16,939 samples across 19 independent studies. Through comprehensive multi-omics molecular profiling across different datasets, we revealed that PRKCG (Protein Kinase C Gamma), a brain-specific gene detectable in cerebrospinal fluid, is closely associated with glioma. Specifically, it presents lower expression and higher methylation in glioma samples compared with normal samples. PRKCG expression/methylation change from high to low is indicative of glioma progression from low-grade to high-grade and high RNA expression is suggestive of good survival. Importantly, PRKCG in combination with MGMT is effective to predict survival outcomes after TMZ chemotherapy in a more precise manner. Collectively, PRKCG bears the great potential for glioma diagnosis, prognosis and therapy, and PRKCG-like genes may represent a set of important genes associated with different molecular mechanisms in glioma tumorigenesis. Accordingly, our study indicates the importance of computational integrative multi-omics data analysis and represents a data-driven scheme toward precision tumor subtyping and accurate personalized healthcare.Author SummaryGlioma is a type of brain tumors that represents one of the most lethal human malignancies with little chance for recovery. Nowadays, more and more studies have adopted high-throughput sequencing technologies to decode the molecular profiles of glioma from different omics levels, accordingly resulting in massive glioma datasets generated from different projects and laboratories throughout the world. Therefore, it has become crucially important on how to make full use of these valuable datasets for computational identification of glioma candidate biomarker genes in aid of precision tumor subtyping and accurate personalized treatment. In this study, we comprehensively integrated glioma datasets from all over the world and performed multi-omics molecular data mining. We revealed that PRKCG, a brain-specific gene abundantly expressed in cerebrospinal fluid, bears the great potential for glioma diagnosis, prognosis and treatment prediction, which has been consistently observed on multiple independent datasets. In the era of big data, our study highlights the significance of computational integrative data mining toward precision medicine in cancer research.

scholarly journals Computational identification and characterization of glioma candidate biomarkers through multi-omics integrative profiling

2020 ◽  
Author(s):  
Lin Liu ◽  
Guangyu Wang ◽  
Liguo Wang ◽  
Chunlei Yu ◽  
Mengwei Li ◽  
...  
Keyword(s):  
Large Scale ◽  
Molecular Mechanisms ◽  
High Throughput Sequencing ◽  
Recurrence Risk ◽  
Good Survival ◽  
Specific Gene ◽  
Low Grade ◽  
Sequencing Technologies ◽  
Protein Kinase C Gamma ◽  
Public Resources

Abstract Background: Glioma is one of the most common malignant brain tumors and exhibits low resection rate and high recurrence risk. Although a large number of glioma studies powered by high-throughput sequencing technologies have led to massive multi-omics datasets, there lacks of comprehensive integration of glioma datasets for uncovering candidate biomarker genes.Results: In this study, we collected a large-scale assemble of multi-omics multi-cohort datasets from worldwide public resources, involving a total of 16,939 samples across 19 independent studies. Through comprehensive molecular profiling across different datasets, we revealed that PRKCG (Protein Kinase C Gamma), a brain-specific gene detectable in cerebrospinal fluid, is closely associated with glioma. Specifically, it presents lower expression and higher methylation in glioma samples compared with normal samples. PRKCG expression/methylation change from high to low is indicative of glioma progression from low-grade to high-grade and high RNA expression is suggestive of good survival. Importantly, PRKCG in combination with MGMT is effective to predict survival outcomes in a more precise manner.Conclusions: PRKCG bears the great potential for glioma diagnosis, prognosis and therapy, and PRKCG-like genes may represent a set of important genes associated with different molecular mechanisms in glioma tumorigenesis. Our study indicates the importance of computational integrative multi-omics data analysis and represents a data-driven scheme toward precision tumor subtyping and accurate personalized healthcare.

scholarly journals Cracking the genetic code of our cities: researchers around the world aim to map the urban genome

2016 ◽  
Vol 37 (4) ◽  
pp. 194
Author(s):  
Sofia Ahsanuddin ◽  
Ebrahim Afshinnekoo ◽  
Christopher E. Mason
Keyword(s):  
Genetic Code ◽  
High Throughput ◽  
Light Microscope ◽  
Scientific Revolution ◽  
High Throughput Sequencing ◽  
Cost Effective ◽  
The Past ◽  
Sequencing Technologies ◽  
The World ◽  
The One

It is rare to say that one has lived through a revolution, but we are all living through one right now. High-throughput sequencing technologies have become cheaper and more cost-effective over the past decade, moving even faster than Moore’s Law for computer power (doubling every 18 months). Because sequencers are modern-day 'molecular microscopes', scientists believe that we are currently experiencing a scientific revolution similar to the one sparked by Antonie van Leeuwenhoek’s invention of the world’s first light microscope in the 17th century.

scholarly journals The Generations of Sequencing Technologies: An Overview

2020 ◽  
Author(s):  
Ayobami Adesiyan ◽  
Emmanuel Kade ◽  
Iyebeye Ifeakachukwu ◽  
Kafayat Oladimeji ◽  
Kehinde Sowunmi ◽  
...  
Keyword(s):  
Drug Discovery ◽  
High Throughput ◽  
Sanger Sequencing ◽  
High Throughput Sequencing ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Sequencing Technologies ◽  
The World ◽  
Sequencing By Synthesis ◽  
Oligonucleotide Detection

The world has now entered into a replacement era of genomics due to the continued advancements within the next generation high throughput sequencing technologies, which incorporates sequencing by synthesis-fluorescent in place sequencing (FISSEQ), pyrosequencing, sequencing by ligation using polony amplification, supported oligonucleotide detection (SOLiD), sequencing by hybridization alongside sequencing by ligation, and nanopore technology. Great impacts of those methods are often seen for solving the genome related problems of plant and Animalia which will open the door of a replacement era of genomics. This might ultimately overcome the Sanger sequencing that ruled for 30 years. NGS is predicted to advance and make the drug discovery process more rapid.

scholarly journals Assessing genotyping errors in mammalian museum study skins using high-throughput genotyping-by-sequencing

2021 ◽  
Author(s):  
Stella C. Yuan ◽  
Eric Malekos ◽  
Melissa T. R. Hawkins
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Massively Parallel Sequencing ◽  
Massively Parallel ◽  
Museum Specimens ◽  
Museum Specimen ◽  
Genotyping Errors ◽  
Allelic Dropout ◽  
Parallel Sequencing ◽  
Sequencing Technologies

AbstractThe use of museum specimens held in natural history repositories for population and conservation genetic research is increasing in tandem with the use of massively parallel sequencing technologies. Short Tandem Repeats (STRs), or microsatellite loci, are commonly used genetic markers in wildlife and population genetic studies. However, they traditionally suffered from a host of issues including length homoplasy, high costs, low throughput, and difficulties in reproducibility across laboratories. Massively parallel sequencing technologies can address these problems, but the incorporation of museum specimen derived DNA suffers from significant fragmentation and exogenous DNA contamination. Combatting these issues requires extra measures of stringency in the lab and during data analysis, yet there have not been any high-throughput sequencing studies evaluating microsatellite allelic dropout from museum specimen extracted DNA. In this study, we evaluate genotyping errors derived from mammalian museum skin DNA extracts for previously characterized microsatellites across PCR replicates utilizing high-throughput sequencing. We found it useful to classify samples based on DNA concentration, which determined the rate by which genotypes were accurately recovered. Longer microsatellites performed worse in all museum specimens. Allelic dropout rates across loci were dependent on sample quantity, with high concentration museum specimens performing as well and recovering quality metrics nearly as high as the frozen tissue sample. Based on our results, we provide a set of best practices for quality assurance and incorporation of reliable genotypes from museum specimens.

scholarly journals Detection of novel allelic variations in soybean mutant population using Tilling by Sequencing

2019 ◽  
Author(s):  
Reneth Millas ◽  
Mary Espina ◽  
CM Sabbir Ahmed ◽  
Angelina Bernardini ◽  
Ekundayo Adeleke ◽  
...  
Keyword(s):  
Fatty Acid ◽  
High Throughput ◽  
Reverse Genetics ◽  
High Throughput Sequencing ◽  
Fatty Acid Biosynthesis ◽  
Induced Mutations ◽  
Mutant Population ◽  
Sequencing Technologies ◽  
Allelic Variations ◽  
Tilling By Sequencing

ABSTRACTOne of the most important tools in genetic improvement is mutagenesis, which is a useful tool to induce genetic and phenotypic variation for trait improvement and discovery of novel genes. JTN-5203 (MG V) mutant population was generated using an induced ethyl methane sulfonate (EMS) mutagenesis and was used for detection of induced mutations in FAD2-1A and FAD2-1B genes using reverse genetics approach. Optimum concentration of EMS was used to treat 15,000 bulk JTN-5203 seeds producing 1,820 M2 population. DNA was extracted, normalized, and pooled from these individuals. Specific primers were designed from FAD2-1A and FAD2-1B genes that are involved in the fatty acid biosynthesis pathway for further analysis using next-generation sequencing. High throughput mutation discovery through TILLING-by-Sequencing approach was used to detect novel allelic variations in this population. Several mutations and allelic variations with high impacts were detected for FAD2-1A and FAD2-1B. This includes GC to AT transition mutations in FAD2-1A (20%) and FAD2-1B (69%). Mutation density for this population is estimated to be about 1/136kb. Through mutagenesis and high-throughput sequencing technologies, novel alleles underlying the mutations observed in mutants with reduced polyunsaturated fatty acids will be identified, and these mutants can be further used in breeding soybean lines with improved fatty acid profile, thereby developing heart-healthy-soybeans.

scholarly journals Adenosine-to-inosine RNA editing may be implicated in human pathogenesis

2019 ◽  
pp. 22-25
Author(s):  
AA Kliuchnikova ◽  
SA Moshkovskii
Keyword(s):  
Immune Responses ◽  
Rna Editing ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Common Mechanism ◽  
Adenosine Deaminases ◽  
Human Transcriptome ◽  
Sequencing Technologies

Adenosine-to-inosine (A-to-I) RNA editing is a common mechanism of post-transcriptional modification in many metazoans including vertebrates; the process is catalyzed by adenosine deaminases acting on RNA (ADARs). Using high-throughput sequencing technologies resulted in finding thousands of RNA editing sites throughout the human transcriptome however, their functions are still poorly understood. The aim of this brief review is to draw attention of clinicians and biomedical researchers to ADAR-mediated RNA editing phenomenon and its possible implication in development of neuropathologies, antiviral immune responses and cancer.

Minirmd: accurate and fast duplicate removal tool for short reads via multiple minimizers

2020 ◽  
Author(s):  
Yuansheng Liu ◽  
Xiaocai Zhang ◽  
Quan Zou ◽  
Xiangxiang Zeng
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
De Novo ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Complementary Strand ◽  
Short Reads ◽  
Sequencing Technologies ◽  
Computational Resources

Abstract Summary Removing duplicate and near-duplicate reads, generated by high-throughput sequencing technologies, is able to reduce computational resources in downstream applications. Here we develop minirmd, a de novo tool to remove duplicate reads via multiple rounds of clustering using different length of minimizer. Experiments demonstrate that minirmd removes more near-duplicate reads than existing clustering approaches and is faster than existing multi-core tools. To the best of our knowledge, minirmd is the first tool to remove near-duplicates on reverse-complementary strand. Availability and implementation https://github.com/yuansliu/minirmd. Supplementary information Supplementary data are available at Bioinformatics online.

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