scholarly journals DEEPGENTM—A Novel Variant Calling Assay for Low Frequency Variants

Genes ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 507
Author(s):  
Bernd Timo Hermann ◽  
Sebastian Pfeil ◽  
Nicole Groenke ◽  
Samuel Schaible ◽  
Robert Kunze ◽  
...  
Keyword(s):  
Cancer Detection ◽  
Genetic Variants ◽  
Liquid Biopsy ◽  
Hot Spot ◽  
Treatment Success ◽  
Low Frequency ◽  
Variant Calling ◽  
Subsequent Treatment ◽  
Precision Oncology ◽  
Orthogonal Comparison

Detection of genetic variants in clinically relevant genomic hot-spot regions has become a promising application of next-generation sequencing technology in precision oncology. Effective personalized diagnostics requires the detection of variants with often very low frequencies. This can be achieved by targeted, short-read sequencing that provides high sequencing depths. However, rare genetic variants can contain crucial information for early cancer detection and subsequent treatment success, an inevitable level of background noise usually limits the accuracy of low frequency variant calling assays. To address this challenge, we developed DEEPGENTM, a variant calling assay intended for the detection of low frequency variants within liquid biopsy samples. We processed reference samples with validated mutations of known frequencies (0%–0.5%) to determine DEEPGENTM’s performance and minimal input requirements. Our findings confirm DEEPGENTM’s effectiveness in discriminating between signal and noise down to 0.09% variant allele frequency and an LOD(90) at 0.18%. A superior sensitivity was also confirmed by orthogonal comparison to a commercially available liquid biopsy-based assay for cancer detection.

scholarly journals Variomes: a high recall search engine to support the curation of genomic variants

2021 ◽  
Author(s):  
Emilie Pasche ◽  
Anaïs Mottaz ◽  
Deborah Caucheteur ◽  
Julien Gobeill ◽  
Pierre-André Michel ◽  
...  
Keyword(s):  
Search Engine ◽  
Genetic Variants ◽  
Variant Calling ◽  
Tumor Board ◽  
Precision Oncology ◽  
Large Set ◽  
Experimental Setting ◽  
Competitive System ◽  
Retrieval Task ◽  
Actionable Variants

Precision oncology relies on the use of treatments targeting specific genetic variants. However, identifying clinically actionable variants as well as relevant information likely to be used to treat a patient with a given cancer is a labor-intensive task, which includes searching the literature for a large set of variants. The lack of universally adopted standard nomenclature for variants requires the development of variant-specific literature search engines. We develop a system to perform triage of publications relevant to support an evidence-based decision. Together with providing a ranked list of articles for a given variant, the system is also able to prioritize variants, as found in a Variant Calling Format, assuming that the clinical actionability of a genetic variant is correlated with the volume of literature published about the variant. Our system searches within three pre-annotated document collections: MEDLINE abstracts, PubMed Central full-text articles and ClinicalTrials.gov clinical trials. A variant synonym generator is used to increase the comprehensiveness of the set of retrieved documents. We then apply different strategies to rank the publications. We assess the search effectiveness of the system using different experimental settings. Experimental setting 1: The literature retrieval task is tuned and evaluated using the TREC Precision Medicine 2018 and 2019 benchmarks consisting respectively in 50 and 40 topics. Almost two thirds (62%) of the publications returned in the top-5 are relevant for clinical decision-support. Experimental setting 2: The evaluation of the variant prioritization task is based on a manually-created benchmark composed of eight patients for a total of 756 variants. For each patient, we used both their complete set of variants and tumor board reports. Our approach enabled identifying 81.8% of clinically actionable variants in the top-3. Experimental setting 3: A comparison of Variomes with LitVar, a well-known search engine for genetic variants is performed. Variomes was able to retrieve on average 90.8% of the content, while LitVar retrieved on average 58.6%. Out of the 9.2% articles, which are "missed" by Variomes, a per error analysis suggests that they are artefacts. To conclude, we are proposing here a competitive system to facilitate the curation of variants for personalized medicine.

scholarly journals Increased yields of duplex sequencing data by a series of quality control tools

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Gundula Povysil ◽  
Monika Heinzl ◽  
Renato Salazar ◽  
Nicholas Stoler ◽  
Anton Nekrutenko ◽  
...  
Keyword(s):  
Low Frequency ◽  
Variant Calling ◽  
Data Loss ◽  
Sequencing Data ◽  
Bioinformatics Pipeline ◽  
Consensus Sequences ◽  
Sequencing Errors ◽  
Data Output ◽  
Reverse Strand ◽  
Duplex Sequencing

Abstract Duplex sequencing is currently the most reliable method to identify ultra-low frequency DNA variants by grouping sequence reads derived from the same DNA molecule into families with information on the forward and reverse strand. However, only a small proportion of reads are assembled into duplex consensus sequences (DCS), and reads with potentially valuable information are discarded at different steps of the bioinformatics pipeline, especially reads without a family. We developed a bioinformatics toolset that analyses the tag and family composition with the purpose to understand data loss and implement modifications to maximize the data output for the variant calling. Specifically, our tools show that tags contain polymerase chain reaction and sequencing errors that contribute to data loss and lower DCS yields. Our tools also identified chimeras, which likely reflect barcode collisions. Finally, we also developed a tool that re-examines variant calls from raw reads and provides different summary data that categorizes the confidence level of a variant call by a tier-based system. With this tool, we can include reads without a family and check the reliability of the call, that increases substantially the sequencing depth for variant calling, a particular important advantage for low-input samples or low-coverage regions.

scholarly journals scSNV: accurate dscRNA-seq SNV co-expression analysis using duplicate tag collapsing

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Gavin W. Wilson ◽  
Mathieu Derouet ◽  
Gail E. Darling ◽  
Jonathan C. Yeung
Keyword(s):  
Genetic Variants ◽  
False Positive ◽  
Variant Calling ◽  
Call Rate ◽  
Rna Seq ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Variant Call ◽  
Two Samples ◽  
Co Detection

AbstractIdentifying single nucleotide variants has become common practice for droplet-based single-cell RNA-seq experiments; however, presently, a pipeline does not exist to maximize variant calling accuracy. Furthermore, molecular duplicates generated in these experiments have not been utilized to optimally detect variant co-expression. Herein, we introduce scSNV designed from the ground up to “collapse” molecular duplicates and accurately identify variants and their co-expression. We demonstrate that scSNV is fast, with a reduced false-positive variant call rate, and enables the co-detection of genetic variants and A>G RNA edits across twenty-two samples.

scholarly journals Machine Learning Protocols in Early Cancer Detection Based on Liquid Biopsy: A Survey

Life ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 638
Author(s):  
Linjing Liu ◽  
Xingjian Chen ◽  
Olutomilayo Olayemi Petinrin ◽  
Weitong Zhang ◽  
Saifur Rahaman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cancer Detection ◽  
Liquid Biopsy ◽  
Cancer Diagnostics ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Cancer Subtype ◽  
Tumor Origin ◽  
Complex Relationships ◽  
Learning Protocols

With the advances of liquid biopsy technology, there is increasing evidence that body fluid such as blood, urine, and saliva could harbor the potential biomarkers associated with tumor origin. Traditional correlation analysis methods are no longer sufficient to capture the high-resolution complex relationships between biomarkers and cancer subtype heterogeneity. To address the challenge, researchers proposed machine learning techniques with liquid biopsy data to explore the essence of tumor origin together. In this survey, we review the machine learning protocols and provide corresponding code demos for the approaches mentioned. We discuss algorithmic principles and frameworks extensively developed to reveal cancer mechanisms and consider the future prospects in biomarker exploration and cancer diagnostics.

PATH-40. SPORADIC NF2 WILD-TYPE MULTIPLE MENINGIOMAS HARBOR DISTINCT DRIVER MUTATIONS

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi124-vi124
Author(s):  
Insa Prilop ◽  
Thomas Pinzer ◽  
Daniel Cahill ◽  
Priscilla Brastianos ◽  
Gabriele Schackert ◽  
...  
Keyword(s):  
Hot Spot ◽  
Low Frequency ◽  
Neurofibromatosis Type ◽  
Driver Mutations ◽  
Wild Type ◽  
Driver Genes ◽  
Who Grade ◽  
Genetic Changes ◽  
Histologic Subtype ◽  
Multiple Meningiomas

Abstract OBJECTIVE Multiple meningiomas (MM) are rare and present a unique management challenge. While the mutational landscape of single meningiomas has been extensively studied, understanding the molecular pathogenesis of sporadic MM remains incomplete. The objective of this study is to elucidate the genetic features of sporadic MM. METHODS We identified nine patients with MM (n=19) defined as ≥2 spatially separated synchronous or metachronous meningiomas. We profiled genetic changes in these tumors using next-generation sequencing (NGS) assay that covers a large number of targetable and frequently mutated genes in meningiomas including AKT1, KLF4, NF2, PIK3CA/PIK3R1, POLR2A, SMARCB1, SMO, SUFU, TRAF7, and the TERT promoter. RESULTS Most of MM were WHO grade 1 (n= 16, 84.2%). Within individual patients, no driver mutation was shared between separate tumors. All but two cases harbored different hot spot mutations in known meningioma-driver genes like TRAF7 (n= 5), PIK3CA (n= 4), AKT1 (n= 3), POLR2A (n=1) and SMO (n= 1). Moreover, individual tumors differed in histologic subtype in 8/9 patients. The low frequency of NF2 mutations in our series stands in contrast to previous studies that included hereditary cases arising in the setting of neurofibromatosis type 2 (NF2). CONCLUSIONS Our findings provide evidence for genomic inter-tumor heterogeneity and an independent molecular origin of sporadic NF2 wild-type MM. Furthermore, these findings suggest that genetic characterization of each lesion is warranted in sporadic MM.

scholarly journals PrediCTC, liquid biopsy in precision oncology: a technology transfer experience in the Spanish health system

2017 ◽  
Vol 20 (5) ◽  
pp. 630-638 ◽  
Author(s):  
L. Alonso-Alconada ◽  
J. Barbazan ◽  
S. Candamio ◽  
J. L. Falco ◽  
C. Anton ◽  
...  
Keyword(s):  
Technology Transfer ◽  
Health System ◽  
Liquid Biopsy ◽  
Precision Oncology ◽  
Spanish Health

A New Preprocessing Algorithm of Hand Vein Image

2013 ◽  
Vol 462-463 ◽  
pp. 312-315
Author(s):  
Cai Xia Liu
Keyword(s):  
Wavelet Decomposition ◽  
Feature Recognition ◽  
Hot Spot ◽  
Low Frequency ◽  
Histogram Equalization ◽  
Segmentation Algorithm ◽  
Human Hand ◽  
Security Technology ◽  
Hand Vein ◽  
Vein Recognition

Biometrics technology is an important security technology and the research of it has become a new hot spot for its superior security features. Then hand vein recognition is a new biological feature recognition which has many advantages, such as safety, non-contact. According to the features of human hand vein image, a hand vein preprocessing method based on wavelet transform and windows maximum between-class difference method threshold (OTSU) segmentation algorithm is proposed. In this paper, the hand vein image is enhanced by adaptive histogram equalization in low frequency part of the hand vein image after wavelet decomposition and filtering before feature extraction. Then the windows OTSU threshold segmentation algorithm is used to get the features. The experimental results show that this method is simple and easy to realize and has laid a good foundation for the latter part of the vein recognition.

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