scholarly journals Gene Prediction in Metagenomic Fragments with Deep Learning

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Shao-Wu Zhang ◽  
Xiang-Yang Jin ◽  
Teng Zhang
Keyword(s):  
Deep Learning ◽  
Next Generation Sequencing ◽  
Gene Prediction ◽  
Learning Model ◽  
Next Generation ◽  
Sequencing Technologies ◽  
Novel Method ◽  
Z Curve ◽  
Length Coverage ◽  
Generation Sequencing

Next generation sequencing technologies used in metagenomics yield numerous sequencing fragments which come from thousands of different species. Accurately identifying genes from metagenomics fragments is one of the most fundamental issues in metagenomics. In this article, by fusing multifeatures (i.e., monocodon usage, monoamino acid usage, ORF length coverage, and Z-curve features) and using deep stacking networks learning model, we present a novel method (called Meta-MFDL) to predict the metagenomic genes. The results with 10 CV and independent tests show that Meta-MFDL is a powerful tool for identifying genes from metagenomic fragments.

scholarly journals A deep learning model for predicting next-generation sequencing depth from DNA sequence

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jinny X. Zhang ◽  
Boyan Yordanov ◽  
Alexander Gaunt ◽  
Michael X. Wang ◽  
Peng Dai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Next Generation Sequencing ◽  
Learning Model ◽  
Sequencing Depth ◽  
Information Storage ◽  
Nucleotide Polymorphisms ◽  
Next Generation ◽  
Snp Panel ◽  
Deep Learning Model ◽  
Generation Sequencing

AbstractTargeted high-throughput DNA sequencing is a primary approach for genomics and molecular diagnostics, and more recently as a readout for DNA information storage. Oligonucleotide probes used to enrich gene loci of interest have different hybridization kinetics, resulting in non-uniform coverage that increases sequencing costs and decreases sequencing sensitivities. Here, we present a deep learning model (DLM) for predicting Next-Generation Sequencing (NGS) depth from DNA probe sequences. Our DLM includes a bidirectional recurrent neural network that takes as input both DNA nucleotide identities as well as the calculated probability of the nucleotide being unpaired. We apply our DLM to three different NGS panels: a 39,145-plex panel for human single nucleotide polymorphisms (SNP), a 2000-plex panel for human long non-coding RNA (lncRNA), and a 7373-plex panel targeting non-human sequences for DNA information storage. In cross-validation, our DLM predicts sequencing depth to within a factor of 3 with 93% accuracy for the SNP panel, and 99% accuracy for the non-human panel. In independent testing, the DLM predicts the lncRNA panel with 89% accuracy when trained on the SNP panel. The same model is also effective at predicting the measured single-plex kinetic rate constants of DNA hybridization and strand displacement.

scholarly journals Description and pilot results from a novel method for evaluating return of incidental findings from next-generation sequencing technologies

2013 ◽  
Vol 15 (9) ◽  
pp. 721-728 ◽  
Author(s):  
Katrina A.B. Goddard ◽  
Evelyn P. Whitlock ◽  
Jonathan S. Berg ◽  
Marc S. Williams ◽  
Elizabeth M. Webber ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Incidental Findings ◽  
Next Generation ◽  
Sequencing Technologies ◽  
Novel Method ◽  
Generation Sequencing

scholarly journals Rapid whole-genome mutational profiling using next-generation sequencing technologies

2008 ◽  
Vol 18 (10) ◽  
pp. 1638-1642 ◽  
Author(s):  
D. R. Smith ◽  
A. R. Quinlan ◽  
H. E. Peckham ◽  
K. Makowsky ◽  
W. Tao ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Whole Genome ◽  
Next Generation ◽  
Sequencing Technologies ◽  
Mutational Profiling ◽  
Generation Sequencing

The application of next-generation sequencing technologies to drug discovery and development

2011 ◽  
Vol 16 (11-12) ◽  
pp. 512-519 ◽  
Author(s):  
Peter M. Woollard ◽  
Nalini A.L. Mehta ◽  
Jessica J. Vamathevan ◽  
Stephanie Van Horn ◽  
Bhushan K. Bonde ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Drug Discovery ◽  
Next Generation ◽  
Drug Discovery And Development ◽  
Sequencing Technologies ◽  
Generation Sequencing

scholarly journals Profiling DNA Methylation Based on Next-Generation Sequencing Approaches: New Insights and Clinical Applications

Genes ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 429 ◽  
Author(s):  
Daniela Barros-Silva ◽  
C. Marques ◽  
Rui Henrique ◽  
Carmen Jerónimo
Keyword(s):  
Dna Methylation ◽  
Next Generation Sequencing ◽  
High Throughput Sequencing ◽  
Epigenetic Modification ◽  
Response To Therapy ◽  
Next Generation ◽  
Sequencing Technologies ◽  
Prognosis And Prediction ◽  
Novel Biomarkers ◽  
Generation Sequencing

DNA methylation is an epigenetic modification that plays a pivotal role in regulating gene expression and, consequently, influences a wide variety of biological processes and diseases. The advances in next-generation sequencing technologies allow for genome-wide profiling of methyl marks both at a single-nucleotide and at a single-cell resolution. These profiling approaches vary in many aspects, such as DNA input, resolution, coverage, and bioinformatics analysis. Thus, the selection of the most feasible method according with the project’s purpose requires in-depth knowledge of those techniques. Currently, high-throughput sequencing techniques are intensively used in epigenomics profiling, which ultimately aims to find novel biomarkers for detection, diagnosis prognosis, and prediction of response to therapy, as well as to discover new targets for personalized treatments. Here, we present, in brief, a portrayal of next-generation sequencing methodologies’ evolution for profiling DNA methylation, highlighting its potential for translational medicine and presenting significant findings in several diseases.

scholarly journals Immortalization and Functional Screening of Natively Paired Human T Cell Receptor Repertoires

2021 ◽  
Author(s):  
Ahmed S Fahad ◽  
Cheng Yu Chung ◽  
Sheila N. Lopez Acevedo ◽  
Nicoleen Boyle ◽  
Bharat Madan ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
T Cell ◽  
T Cell Receptor ◽  
Cell Receptor ◽  
Functional Screening ◽  
Next Generation ◽  
Sequencing Technologies ◽  
Human T Cell ◽  
Screening Platform ◽  
Generation Sequencing

Functional analyses of the T cell receptor (TCR) landscape can reveal critical information about protection from disease and molecular responses to vaccines. However, it has proven difficult to combine advanced next-generation sequencing technologies with methods to decode the peptide-major histocompatibility complex (pMHC) specificity of individual TCRs. Here we developed a new high-throughput approach to enable repertoire-scale functional evaluations of natively paired TCRs. In particular, we leveraged the immortalized nature of physically linked TCRα:β amplicon libraries to analyze binding against multiple recombinant pMHCs on a repertoire scale. To exemplify the utility of this approach, we also performed affinity-based functional mapping in conjunction with quantitative next-generation sequencing to track antigen- specific TCRs. These data successfully validated a new immortalization and screening platform to facilitate detailed molecular analyses of human TCRs against diverse antigen targets associated with health, vaccination, or disease.

scholarly journals Investigation of methods for extraction of bacterial DNA metagenome from goat rumen

2018 ◽  
Vol 15 (2) ◽  
pp. 367-372
Author(s):  
Lê Ngọc Giang ◽  
Lưu Hàn Ly ◽  
Nguyễn Mai Phương ◽  
Lê Tùng Lâm ◽  
Đỗ Thị Huyền ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Rumen Fluid ◽  
Next Generation ◽  
Taq Polymerase ◽  
Bead Beating ◽  
Sequencing Technologies ◽  
Polymerase Inhibitor ◽  
Stool Dna ◽  
Generation Sequencing ◽  
Goat Rumen

Microorganisms, particularly bacteria, in the ruminant's rumen are valuable genetic resources that many scientists interested in. In recent years, the application of next-generation sequencing technologies allows direct decoding an extracted DNA metagenome in each ecological community without culture, increasing the efficiency of exploiting interested genes. Notably, the quantity and quality of extracted DNA play an important role in getting a reliable metagenome database. In this study, DNA metagenome from goat rumen fluid was extracted by five different methods RBB (repeated bead beating plus column), RBBC (repeated bead beating), PSP1, PSP2 (PSP®Spin Stool DNA Kit, protocol 1, 2, Germany) và QIA (QIAamp® DNA Stool Mini Kit, Germany). The results showed that DNA metagenome obtained by all methods had A260/280 greater than 1.8. DNA extracted by the RBB method had high DNA concentration but low A260/230 values (less than 1.4) and still contained Taq polymerase inhibitor. After purifying by QIA column, A260/230 values of RBB-extracted DNA significantly increased up to 2.0 and Taq polymerase inhibitor in samples were removed. However, the concentrations decreased by 57% that nearly equivalent to concentration of DNA metagenome obtained by QIA. The method using PSP®Spin Stool DNA kit produced the highest DNA concentrations (from 149.7 to 195.5 ng/µl) with A260/280 ratios of 1.9 and A260/230 ratios of 1.8 to 1.9. Morever, this method was able to remove polymerase inhibitor and be performed on short time. Therefore, the PSP®Spin Stool DNA kit is a suitable method for DNA metagenome extraction of bacteria from goat rumen. DNA obtained by this method fulfilled all criteria about quality and concentration for sequencing by next-generation sequencing Illumina.

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