scholarly journals Machine-Learned Association of Next-Generation Sequencing-Derived Variants in Thermosensitive Ion Channels Genes with Human Thermal Pain Sensitivity Phenotypes

2020 ◽  
Vol 21 (12) ◽  
pp. 4367 ◽  
Author(s):  
Jörn Lötsch ◽  
Dario Kringel ◽  
Gerd Geisslinger ◽  
Bruno G. Oertel ◽  
Eduard Resch ◽  
...  
Keyword(s):  
Machine Learning ◽  
Next Generation Sequencing ◽  
Ion Channels ◽  
Ion Channel ◽  
Genetic Variants ◽  
Genetic Information ◽  
Pain Thresholds ◽  
Thermal Pain ◽  
Thermal Stimuli ◽  
Generation Sequencing

Genetic association studies have shown their usefulness in assessing the role of ion channels in human thermal pain perception. We used machine learning to construct a complex phenotype from pain thresholds to thermal stimuli and associate it with the genetic information derived from the next-generation sequencing (NGS) of 15 ion channel genes which are involved in thermal perception, including ASIC1, ASIC2, ASIC3, ASIC4, TRPA1, TRPC1, TRPM2, TRPM3, TRPM4, TRPM5, TRPM8, TRPV1, TRPV2, TRPV3, and TRPV4. Phenotypic information was complete in 82 subjects and NGS genotypes were available in 67 subjects. A network of artificial neurons, implemented as emergent self-organizing maps, discovered two clusters characterized by high or low pain thresholds for heat and cold pain. A total of 1071 variants were discovered in the 15 ion channel genes. After feature selection, 80 genetic variants were retained for an association analysis based on machine learning. The measured performance of machine learning-mediated phenotype assignment based on this genetic information resulted in an area under the receiver operating characteristic curve of 77.2%, justifying a phenotype classification based on the genetic information. A further item categorization finally resulted in 38 genetic variants that contributed most to the phenotype assignment. Most of them (10) belonged to the TRPV3 gene, followed by TRPM3 (6). Therefore, the analysis successfully identified the particular importance of TRPV3 and TRPM3 for an average pain phenotype defined by the sensitivity to moderate thermal stimuli.

Measuring Pharmacogene Variant Function at Scale Using Multiplexed Assays

2021 ◽  
Vol 62 (1) ◽  
Author(s):  
Renee C. Geck ◽  
Gabriel Boyle ◽  
Clara J. Amorosi ◽  
Douglas M. Fowler ◽  
Maitreya J. Dunham
Keyword(s):  
Next Generation Sequencing ◽  
Functional Data ◽  
Genetic Variants ◽  
Genetic Information ◽  
Annual Review ◽  
Publication Date ◽  
Drug Selection ◽  
Functional Consequences ◽  
Pharmacology And Toxicology ◽  
Generation Sequencing

As costs of next-generation sequencing decrease, identification of genetic variants has far outpaced our ability to understand their functional consequences. This lack of understanding is a central challenge to a key promise of pharmacogenomics: using genetic information to guide drug selection and dosing. Recently developed multiplexed assays of variant effect enable experimental measurement of the function of thousands of variants simultaneously. Here, we describe multiplexed assays that have been performed on nearly 25,000 variants in eight key pharmacogenes ( ADRB2, CYP2C9, CYP2C19, NUDT15, SLCO1B1, TMPT, VKORC1, and the LDLR promoter), discuss advances in experimental design, and explore key challenges that must be overcome to maximize the utility of multiplexed functional data. Expected final online publication date for the Annual Review of Pharmacology and Toxicology, Volume 62 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals ForestQC: quality control on genetic variants from next-generation sequencing data using random forest

2018 ◽  
Author(s):  
Jiajin Li ◽  
Brandon Jew ◽  
Lingyu Zhan ◽  
Sungoo Hwang ◽  
Giovanni Coppola ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Control ◽  
Next Generation Sequencing ◽  
Genetic Variants ◽  
Next Generation ◽  
Sequencing Data ◽  
Machine Learning Approach ◽  
Sequencing Quality ◽  
Generation Sequencing ◽  
Filtering Approach

ABSTRACTNext-generation sequencing technology (NGS) enables discovery of nearly all genetic variants present in a genome. A subset of these variants, however, may have poor sequencing quality due to limitations in sequencing technology or in variant calling algorithms. In genetic studies that analyze a large number of sequenced individuals, it is critical to detect and remove those variants with poor quality as they may cause spurious findings. In this paper, we present a statistical approach for performing quality control on variants identified from NGS data by combining a traditional filtering approach and a machine learning approach. Our method uses information on sequencing quality such as sequencing depth, genotyping quality, and GC contents to predict whether a certain variant is likely to contain errors. To evaluate our method, we applied it to two whole-genome sequencing datasets where one dataset consists of related individuals from families while the other consists of unrelated individuals. Results indicate that our method outperforms widely used methods for performing quality control on variants such as VQSR of GATK by considerably improving the quality of variants to be included in the analysis. Our approach is also very efficient, and hence can be applied to large sequencing datasets. We conclude that combining a machine learning algorithm trained with sequencing quality information and the filtering approach is an effective approach to perform quality control on genetic variants from sequencing data.Author SummaryGenetic disorders can be caused by many types of genetic mutations, including common and rare single nucleotide variants, structural variants, insertions and deletions. Nowadays, next generation sequencing (NGS) technology allows us to identify various genetic variants that are associated with diseases. However, variants detected by NGS might have poor sequencing quality due to biases and errors in sequencing technologies and analysis tools. Therefore, it is critical to remove variants with low quality, which could cause spurious findings in follow-up analyses. Previously, people applied either hard filters or machine learning models for variant quality control (QC), which failed to filter out those variants accurately. Here, we developed a statistical tool, ForestQC, for variant QC by combining a filtering approach and a machine learning approach. We applied ForestQC to one family-based whole genome sequencing (WGS) dataset and one general case-control WGS dataset, to evaluate our method. Results show that ForestQC outperforms widely used methods for variant QC by considerably improving the quality of variants. Also, ForestQC is very efficient and scalable to large-scale sequencing datasets. Our study indicates that combining filtering approaches and machine learning approaches enables effective variant QC.

scholarly journals TACAN is an essential component of the mechanosensitive ion channel responsible for pain sensing

2018 ◽  
Author(s):  
L. Beaulieu-Laroche ◽  
M. Christin ◽  
AM Donoghue ◽  
F. Agosti ◽  
N. Yousefpour ◽  
...  
Keyword(s):  
Ion Channels ◽  
Heterologous Expression ◽  
Ion Channel ◽  
Behavioral Responses ◽  
Mechanical Stimuli ◽  
Thermal Pain ◽  
Fundamental Process ◽  
Mechanosensitive Ion Channel ◽  
Pain Stimuli ◽  
Essential Subunit

SummaryMechanotransduction, the conversion of mechanical stimuli into electrical signals, is a fundamental process underlying several physiological functions such as touch and pain sensing, hearing and proprioception. This process is carried out by specialized mechanosensitive ion channels whose identities have been discovered for most functions except pain sensing. Here we report the identification of TACAN (Tmem120A), an essential subunit of the mechanosensitive ion channel responsible for sensing mechanical pain. TACAN is expressed in a subset of nociceptors, and its heterologous expression increases mechanically-evoked currents in cell lines. Purification and reconstitution of TACAN in synthetic lipids generates a functional ion channel. Finally, knocking down TACAN decreases the mechanosensitivity of nociceptors and reduces behavioral responses to mechanical but not to thermal pain stimuli, without affecting the sensitivity to touch stimuli. We propose that TACAN is a pore-forming subunit of the mechanosensitive ion channel responsible for sensing mechanical pain.

scholarly journals Artificial Intelligence, Machine Learning and Deep Learning in Ion Channel Bioinformatics

Membranes ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 672
Author(s):  
Md. Ashrafuzzaman
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Ion Channels ◽  
Ion Channel ◽  
Research Trend ◽  
Cellular Processes ◽  
Research Areas ◽  
Huge Data ◽  
Functional Aspects

Ion channels are linked to important cellular processes. For more than half a century, we have been learning various structural and functional aspects of ion channels using biological, physiological, biochemical, and biophysical principles and techniques. In recent days, bioinformaticians and biophysicists having the necessary expertise and interests in computer science techniques including versatile algorithms have started covering a multitude of physiological aspects including especially evolution, mutations, and genomics of functional channels and channel subunits. In these focused research areas, the use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms and associated models have been found very popular. With the help of available articles and information, this review provide an introduction to this novel research trend. Ion channel understanding is usually made considering the structural and functional perspectives, gating mechanisms, transport properties, channel protein mutations, etc. Focused research on ion channels and related findings over many decades accumulated huge data which may be utilized in a specialized scientific manner to fast conclude pinpointed aspects of channels. AI, ML, and DL techniques and models may appear as helping tools. This review aims at explaining the ways we may use the bioinformatics techniques and thus draw a few lines across the avenue to let the ion channel features appear clearer.

scholarly journals Assay of frequency and spectrum of genetic variants in TTN in healthy russian population

2021 ◽  
Vol 8 (5) ◽  
pp. 29-37
Author(s):  
Yu. A. Vakhrushev ◽  
A. A. Kozyreva ◽  
S. V. Zhuk ◽  
O. P. Rotar ◽  
A. A. Kostareva
Keyword(s):  
Next Generation Sequencing ◽  
Genetic Variants ◽  
Sanger Sequencing ◽  
Heart Diseases ◽  
Genetic Research ◽  
Russian Population ◽  
Next Generation ◽  
Data Bases ◽  
International Data ◽  
Generation Sequencing

Background. Gene TTN associated with all types of cardiomyopathy, however its large size (294 b.p.) warrants a lot of individual unique genetic variants or variants with low frequency, that aggravates their interpretation. Besides that nowadays there is no data about spectrum of variants in this gene in healthy Russian population. Recognition frequency and spectrum of variants in gene TTN in healthy Russian population will allow us to use it for interpretation results of molecular genetic research for patients with different heart pathology, and define prognosis for different heart diseases.Objective. Recognize frequency and spectrum of single nucleotide and truncating variants in gene TTN in healthy Russian population and compare it with international data bases, and evaluate level of pathogenicity these variants and their distributing across titin structure.Design and methods. 192 men in age 55,8±6,6 years were tested with next-generation sequencing. Identified genetic variants were confirmed by Sanger sequencing. Results. Allele missense variant frequency (with frequency less than 0.1%) in TTN in healthy Russian population amount to 15.1 %, and truncating variants — 0.52 %. 37,9 % of them were variants of unknown significance, 62 % — likely-benign and 0.1 % — benign. There was no pathological and likely-pathological variants. Identified genetic variants distributed throughout the titin structure.Conclusion. Received result is congruent с international data bases and researches. Expended laboratory method (Next generation sequencing and confirmation with Sanger sequencing) can be used both in clinical practice, and in creating data bases of genetic variants in healthy Russian population.

scholarly journals Clinical results and importance of next-generation sequencing (NGS) in detecting targeted mutations in the treatment of metastatic Lung Cancer: Single center initial results

2019 ◽  
Vol 6 (12) ◽  
pp. 327-332
Author(s):  
Cem Mirili ◽  
Çiğdem Kahraman ◽  
Ali Yılmaz ◽  
Mehmet Bilici ◽  
Salim Başol Tekin ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Next Generation Sequencing ◽  
Genetic Variants ◽  
Genetic Variant ◽  
Clinical Results ◽  
Clinical Effects ◽  
Next Generation ◽  
Variant Analysis ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

Objective:  In Lung cancer (LC), which is one of the most deadly cancers, longer survival has been achieved with targeted agents. For this reason, it is important to find the patients who are suitable for targeted therapies. Next-generation sequencing (NGS) is a method that allows multiple genetic variants to be detected simultaneously by performing massive parallel DNA sequencing at the same time. We wanted to reveal the clinical effects and benefits of genetic variant analysis with NGS for our patients. Material and Methods: Patients with stage 4 non-squamous and not otherwise specified (NOS) Non-small cell LC who underwent genetic variant analysis with NGS were included in the study, retrospectively. Results: Total of the 51 patients, 41 (80.4%) were male and the median age was 64 (35-85) years. According to TNM, 21 (41.2%) patients were stage 4A, 30 (58.8%) patients were stage 4B and 39 (76.5%) patients had adenocarcinoma and 12 (23.5%) had NOS histology. NGS analyzes were performed in median 14 days (8-43) and determined 24 pathogenic variants in 17 (%25) patients: 9EGFR (%17,6), 6PIKC3A (%11,7), 5KRAS (%9,8), 2PTEN (%3,9), 1BRAF (%1,9), 1MET (%1,6) (7 of them concomitantly). Cytotoxic chemotherapy was recommended in 41, anti-EGFR agents in 8 (afatinib in 4, erlotinib in 4 patients) patients and anti-BRAF+MEK inhibitor agent (dabrafenib+trametinib) in 1 patient. Conclusion: With the NGS, in just two weeks, both target and resistance genetic variants of our patients were detected at the same time and individualized treatments were applied. In this way, both time and cost were saved.

scholarly journals Using Machine Learning to Identify True Somatic Variants from Next-Generation Sequencing

2019 ◽  
Vol 66 (1) ◽  
pp. 239-246 ◽  
Author(s):  
Chao Wu ◽  
Xiaonan Zhao ◽  
Mark Welsh ◽  
Kellianne Costello ◽  
Kajia Cao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Next Generation Sequencing ◽  
Clinical Laboratory ◽  
Next Generation ◽  
Single Nucleotide Variants ◽  
Test Set ◽  
Clinical Laboratories ◽  
Bona Fide ◽  
Validation Set ◽  
Generation Sequencing

Abstract BACKGROUND Molecular profiling has become essential for tumor risk stratification and treatment selection. However, cancer genome complexity and technical artifacts make identification of real variants a challenge. Currently, clinical laboratories rely on manual screening, which is costly, subjective, and not scalable. We present a machine learning–based method to distinguish artifacts from bona fide single-nucleotide variants (SNVs) detected by next-generation sequencing from nonformalin-fixed paraffin-embedded tumor specimens. METHODS A cohort of 11278 SNVs identified through clinical sequencing of tumor specimens was collected and divided into training, validation, and test sets. Each SNV was manually inspected and labeled as either real or artifact as part of clinical laboratory workflow. A 3-class (real, artifact, and uncertain) model was developed on the training set, fine-tuned with the validation set, and then evaluated on the test set. Prediction intervals reflecting the certainty of the classifications were derived during the process to label “uncertain” variants. RESULTS The optimized classifier demonstrated 100% specificity and 97% sensitivity over 5587 SNVs of the test set. Overall, 1252 of 1341 true-positive variants were identified as real, 4143 of 4246 false-positive calls were deemed artifacts, whereas only 192 (3.4%) SNVs were labeled as “uncertain,” with zero misclassification between the true positives and artifacts in the test set. CONCLUSIONS We presented a computational classifier to identify variant artifacts detected from tumor sequencing. Overall, 96.6% of the SNVs received definitive labels and thus were exempt from manual review. This framework could improve quality and efficiency of the variant review process in clinical laboratories.

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