Whole human exome capture for high-throughput sequencing

Genome ◽  
2010 ◽  
Vol 53 (7) ◽  
pp. 568-574 ◽  
Author(s):  
Dae-Won Kim ◽  
Seong-Hyeuk Nam ◽  
Ryong Nam Kim ◽  
Sang-Haeng Choi ◽  
Hong-Seog Park
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Exome Capture ◽  
Microarray Design ◽  
Genome Wide ◽  
Human Genes ◽  
Practical Usefulness ◽  
A Genome ◽  
Wide Scale ◽  
Total Base

We captured the whole human exome by hybridization using synthesized oligonucleotides, based on a high-density microarray design, and we sequenced those captured human exons using high-throughput sequencing on a Genome Sequencer FLX instrument. Of the uniquely mapped reads, 71% fell within target regions, and these corresponded to coverage of 94% of human genes, 87% of exons, and 70% of the total base-pair length of the CCDS set. Our study provides strong evidence for the practical usefulness of this method on a genome-wide scale, showing the resequenced whole human exome database with 501 microRNAs and 307 novel SNPs.

scholarly journals Genome-wide prediction and prioritization of human aging genes by data fusion: a machine learning approach

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Masoud Arabfard ◽  
Mina Ohadi ◽  
Vahid Rezaei Tabar ◽  
Ahmad Delbari ◽  
Kaveh Kavousi
Keyword(s):  
Machine Learning ◽  
Process Comparison ◽  
Age Related ◽  
Genome Wide ◽  
Human Genes ◽  
Disease Candidate Gene ◽  
A Genome ◽  
Machine Learning Approach ◽  
Wide Scale ◽  
Biological Aspects

Abstract Background Machine learning can effectively nominate novel genes for various research purposes in the laboratory. On a genome-wide scale, we implemented multiple databases and algorithms to predict and prioritize the human aging genes (PPHAGE). Results We fused data from 11 databases, and used Naïve Bayes classifier and positive unlabeled learning (PUL) methods, NB, Spy, and Rocchio-SVM, to rank human genes in respect with their implication in aging. The PUL methods enabled us to identify a list of negative (non-aging) genes to use alongside the seed (known age-related) genes in the ranking process. Comparison of the PUL algorithms revealed that none of the methods for identifying a negative sample were advantageous over other methods, and their simultaneous use in a form of fusion was critical for obtaining optimal results (PPHAGE is publicly available at https://cbb.ut.ac.ir/pphage). Conclusion We predict and prioritize over 3,000 candidate age-related genes in human, based on significant ranking scores. The identified candidate genes are associated with pathways, ontologies, and diseases that are linked to aging, such as cancer and diabetes. Our data offer a platform for future experimental research on the genetic and biological aspects of aging. Additionally, we demonstrate that fusion of PUL methods and data sources can be successfully used for aging and disease candidate gene prioritization.

scholarly journals Alternative Splicing: Expanding the Landscape of Cancer Biomarkers and Therapeutics

2020 ◽  
Vol 21 (23) ◽  
pp. 9032
Author(s):  
Cláudia Bessa ◽  
Paulo Matos ◽  
Peter Jordan ◽  
Vânia Gonçalves
Keyword(s):  
Alternative Splicing ◽  
Messenger Rna ◽  
Human Cancer ◽  
Genetic Alterations ◽  
Altered Expression ◽  
Genome Wide ◽  
Human Genes ◽  
Transcriptional Regulatory Mechanism ◽  
A Genome ◽  
Wide Scale

Alternative splicing (AS) is a critical post-transcriptional regulatory mechanism used by more than 95% of transcribed human genes and responsible for structural transcript variation and proteome diversity. In the past decade, genome-wide transcriptome sequencing has revealed that AS is tightly regulated in a tissue- and developmental stage-specific manner, and also frequently dysregulated in multiple human cancer types. It is currently recognized that splicing defects, including genetic alterations in the spliced gene, altered expression of both core components or regulators of the precursor messenger RNA (pre-mRNA) splicing machinery, or both, are major drivers of tumorigenesis. Hence, in this review we provide an overview of our current understanding of splicing alterations in cancer, and emphasize the need to further explore the cancer-specific splicing programs in order to obtain new insights in oncology. Furthermore, we also discuss the recent advances in the identification of dysregulated splicing signatures on a genome-wide scale and their potential use as biomarkers. Finally, we highlight the therapeutic opportunities arising from dysregulated splicing and summarize the current approaches to therapeutically target AS in cancer.

scholarly journals Identification and characterization of salt-tolerance relative miRNAs in Procambarus clarkii by high-throughput sequencing

ExRNA ◽  
2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Fangfang Jin ◽  
Yuling Sun
Keyword(s):  
Salt Tolerance ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Procambarus Clarkii ◽  
Economic Losses ◽  
Srna Library ◽  
Genome Wide ◽  
A Genome

Abstract Procambarus clarkii is one of the important economic species in China and has been served as tasty food in recent years after being introduced to Nanjing. Significant problems of environment factors, such as salinity, pH and temperature, especially salinity, has the potential to result in significant economic losses in many crayfish-producing farms in China. miRNAs are a kind of ~ 22 nucleotide small non coding RNAs which were encoded by plants, animals and some viruses with functions in RNA silencing or post-transcription regulation. We constructed four sRNA library of P. clarkia from different tissues and treatments by using high-throughput sequencing technology. A total of 101 conserved miRNAs and two novel pre-miRNAs were identified and RT-qPCR were further performed to confirm existence of part of identified miRNAs. A genome wide expression profile of salt-tolerance miRNAs was proved and three miRNAs were further validated by RT-qPCR with dynamic response to different salinity stages. The study of miRNAs in P. clarkia can help us better understanding the role of miRNAs in salt-tolerance in P. clarkia.

Computational methods for annotation of plant regulatory non-coding RNAs using RNA-seq

2020 ◽  
Author(s):  
A T Vivek ◽  
Shailesh Kumar
Keyword(s):  
Computational Methods ◽  
High Throughput ◽  
Rna Seq ◽  
Physiological Mechanisms ◽  
Software Packages ◽  
Genome Wide ◽  
A Genome ◽  
Wide Scale ◽  
Plant Transcriptome ◽  
Non Coding Rnas

Abstract Plant transcriptome encompasses numerous endogenous, regulatory non-coding RNAs (ncRNAs) that play a major biological role in regulating key physiological mechanisms. While studies have shown that ncRNAs are extremely diverse and ubiquitous, the functions of the vast majority of ncRNAs are still unknown. With ever-increasing ncRNAs under study, it is essential to identify, categorize and annotate these ncRNAs on a genome-wide scale. The use of high-throughput RNA sequencing (RNA-seq) technologies provides a broader picture of the non-coding component of transcriptome, enabling the comprehensive identification and annotation of all major ncRNAs across samples. However, the detection of known and emerging class of ncRNAs from RNA-seq data demands complex computational methods owing to their unique as well as similar characteristics. Here, we discuss major plant endogenous, regulatory ncRNAs in an RNA sample followed by computational strategies applied to discover each class of ncRNAs using RNA-seq. We also provide a collection of relevant software packages and databases to present a comprehensive bioinformatics toolbox for plant ncRNA researchers. We assume that the discussions in this review will provide a rationale for the discovery of all major categories of plant ncRNAs.

scholarly journals CAM: a quality control pipeline for MNase-seq data

2017 ◽  
Author(s):  
Sheng’en Hu ◽  
Xiaolan Chen ◽  
Ji Liao ◽  
Yiqing Chen ◽  
Chengchen Zhao ◽  
...  
Keyword(s):  
Quality Control ◽  
High Throughput Sequencing ◽  
Micrococcal Nuclease ◽  
Functional Regions ◽  
Nucleosome Organization ◽  
Genome Wide ◽  
Micrococcal Nuclease Digestion ◽  
A Genome ◽  
Nuclease Digestion ◽  
Wide Scale

AbstractNucleosome organization affects the accessibility of cis-elements to trans-acting factors. Micrococcal nuclease digestion followed by high-throughput sequencing (MNase-seq) is the most popular technology used to profile nucleosome organization on a genome-wide scale. Evaluating the data quality of MNase-seq data remains challenging, especially in mammalian. There is a strong need for a convenient and comprehensive approach to obtain dedicated quality control (QC) for MNase-seq data analysis. Here we developed CAM, which is a comprehensive QC pipeline for MNase-seq data. The CAM pipeline provides multiple informative QC measurements and nucleosome organization profiles on different potentially functional regions for given MNase-seq data. CAM also includes 268 historical MNase-seq datasets from human and mouse as a reference atlas for unbiased assessment. CAM is freely available at: http://www.tongji.edu.cn/~zhanglab/CAM

scholarly journals Functional Genomics–Linking Genotype with Phenotype on Genome-wide Scale

2019 ◽  
Vol 4 (01) ◽  
pp. 4-12
Author(s):  
Nida Tabassum Khan ◽  
Namra Jameel ◽  
Maham Jamil Khan
Keyword(s):  
Functional Genomics ◽  
High Throughput ◽  
Genomic Data ◽  
Genome Wide ◽  
A Genome ◽  
Genomic Studies ◽  
Wide Scale

Functional genomics manipulates genomic data to study genes and its expression on a genome wide scale involving high-throughput methods. The keyobjective of Functional genomics is to exploit the data acquired from transcriptomic and genomic studies to explain the functions and interfaces of a genome and its corresponding phenotype.

scholarly journals Epigenetic regulation and epigenomic landscape in rice

2016 ◽  
Vol 3 (3) ◽  
pp. 309-327 ◽  
Author(s):  
Xian Deng ◽  
Xianwei Song ◽  
Liya Wei ◽  
Chunyan Liu ◽  
Xiaofeng Cao
Keyword(s):  
Epigenetic Regulation ◽  
High Throughput Sequencing ◽  
Agronomic Traits ◽  
Food Crop ◽  
Recent Advances ◽  
Genome Wide ◽  
A Genome ◽  
Wide Scale ◽  
Epigenetic Regulators ◽  
Complex Genome

Abstract Epigenetic regulation has been implicated in the control of complex agronomic traits in rice (Oryza sativa), a staple food crop and model monocot plant. Recent advances in high-throughput sequencing and the moderately complex genome of rice have made it possible to study epigenetic regulation in rice on a genome-wide scale. This review discusses recent advances in our understanding of epigenetic regulation in rice, with an emphasis on the roles of key epigenetic regulators, the epigenomic landscape, epigenetic variation, transposon repression, and plant development.

scholarly journals The Fate of Deleterious Variants in a Barley Genomic Prediction Population

Genetics ◽  
2019 ◽  
Vol 213 (4) ◽  
pp. 1531-1544
Author(s):  
Thomas J. Y. Kono ◽  
Chaochih Liu ◽  
Emily E. Vonderharr ◽  
Daniel Koenig ◽  
Justin C. Fay ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Genetic Variants ◽  
Exome Capture ◽  
Novel Approach ◽  
Genome Wide ◽  
A Genome ◽  
Wide Scale ◽  
Cultivated Species ◽  
Whole Genome Resequencing ◽  
Dna Resequencing

Targeted identification and purging of deleterious genetic variants has been proposed as a novel approach to animal and plant breeding. This strategy is motivated, in part, by the observation that demographic events and strong selection associated with cultivated species pose a “cost of domestication.” This includes an increase in the proportion of genetic variants that are likely to reduce fitness. Recent advances in DNA resequencing and sequence constraint-based approaches to predict the functional impact of a mutation permit the identification of putatively deleterious SNPs (dSNPs) on a genome-wide scale. Using exome capture resequencing of 21 barley lines, we identified 3855 dSNPs among 497,754 total SNPs. We generated whole-genome resequencing data of Hordeum murinum ssp. glaucum as a phylogenetic outgroup to polarize SNPs as ancestral vs. derived. We also observed a higher proportion of dSNPs per synonymous SNPs (sSNPs) in low-recombination regions of the genome. Using 5215 progeny from a genomic prediction experiment, we examined the fate of dSNPs over three breeding cycles. Adjusting for initial frequency, derived alleles at dSNPs reduced in frequency or were lost more often than other classes of SNPs. The highest-yielding lines in the experiment, as chosen by standard genomic prediction approaches, carried fewer homozygous dSNPs than randomly sampled lines from the same progeny cycle. In the final cycle of the experiment, progeny selected by genomic prediction had a mean of 5.6% fewer homozygous dSNPs relative to randomly chosen progeny from the same cycle.

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