DeepNGS automates the workflow of gene analyses and can assist in diagnostic procedures.
In Next Generation Sequencing, the analysis of genomics data requires a standardised pipeline that will help clinicians and researchers obtain explicit and comprehensive information.
Achieve a higher level of accuracy by incorporating all the patient's genetic data in a secure, agile and easy-to-use platform. Capable of digesting information to ensure the accuracy of the final diagnosis and the adequacy of treatment recommendations.
Single Nucleotide
Variants and INDELs
(small insertions and deletions)
Large Structural
Variants
Copy Number
Variants
Genomic Fusions and
Rearrangements *
* Soon available
DeepNGS makes it possible to integrate the entire pipeline in a single cloud software. Key benefits:
In our aim for continuous improvement, we are progressively incorporating ML algorithms to optimize the analysis process and achieve even better and more reliable results. This will also help avoid errors at critical stages of the process.
Machine Learning improves variant calling and variant annotation, and helps increase the success of diagnostics and treatment.
DeepNGS implements the following Intelligent Algorithms:
* Soon available
Contact us!
Standard pipeline of detection and interpretation of genetic variants
Detection of somatic variants, typical of tumour cells
Detection and basic analysis of structural variants
Basic annotations from main public information repositories
Links to external public databases with further information
Basic filtering tool to select values in multiple fields of the detected variant table
Export (PDF format) of the results obtained
Automated and secure data storage in Amazon AWS
Basic Machine Learning models to optimize specific stages of the pipeline
Already deployed
Population frequencies based on massive sequencing experiments
Prediction of pathogenicity through standardized computational methods: SIFT, DANN, MetaSVM, etc.
Scores of conservation of the genomic position in which the nucleotide change occurs
Gene-drug association analysis powered by PanDrugs tool
Advanced algorithmic filters
Export of variant table in Excel format
Intuitive help page and videotutorial
Clinical module with quick insights about the data
Already deployed
Analysis of genomic samples from different sequencing techniques (Illumina, ONT)
Links to external private databases, such as COSMIC or ICGC
Multi-sample analysis
Advanced analysis of structural variants and CNVs, with specific algorithms and charts
Add comments to tables and reports, with your own personal conclusions to the results
Prediction of pathogenicity through own computational methods and IA/ML algorithms
Duo/trio analysis for the diagnosis of complex diseases in familiar cases
Analysis of complex samples: cfDNA, circulating free DNA, liquid biopsies, FFPE, etc.
Quality graphical metrics of SAM/BAM alignment files
Implementation of advanced algorithms and machine learning models to improve the results
Accurate detection of de novo variants
Expected mid 2022
Intelligent end-to-end genomic analysis solution for local on premises architecture available soon:
Contact us: info@deepngs.com
Number of analysis
1
Total Samples Size
5 GB
Results Storage
None
Max Results Downloads
1
Number of analysis
No limit
Total Samples Size
20 GB
Results Storage
2 GB (during 6 months)
Max Results Downloads
20
Number of analysis
No limit
Total Samples Size
100 GB
Results Storage
5 GB (during 6 months)
Max Results Downloads
100
Number of analysis
No limit
Total Samples Size
1000 GB
Results Storage
50 GB (during 6 months)
Max Results Downloads
1000
DeepNGS is developed by specialists in Genetics and Artificial Intelligence with more than 20 years of experience.
DeepNGS is a tool developed by DCSC in collaboration with AiR Institute and BISITE (Bioinformatics, Intelligent Systems and Educational Technology).
DCSC is a member of the IoT Digital Innovation Hub.
BISITE is a Research Group of the University of Salamanca and member of the Institute for Biomedical Research of Salamanca (IBSAL).
