Sentieon Vision
01.Sentieon Vision
Sentieon is dedicated to solving the bottleneck of speed and accuracy in bioinformatics data analysis, through the deep optimization of algorithms and enterprise-level software engineering, to significantly improve the efficiency, accuracy and reliability of NGS data processing.
Sentieon provides software solutions to partners and research institutions in...
Sentieon software is a comprehensive, pure software solution for secondary analysis of genetic variant detection. Its analysis pipeline fully adheres to the mathematical models of gold standards such as BWA, GATK, MuTect2, STAR, Minimap2, Fgbio, and Picard. While matching the results of open-source pipeline analysis, it significantly improves the analysis efficiency and detection accuracy of...
Background
All modules in the Sentieon suite are several times to dozens of times faster than their corresponding open-source software. While using these modules, users sometimes hope that the Sentieon team can help accelerate their custom-developed software. To help these users enjoy the speed of Sentieon modules in their own software, we have developed a Python API system to meet the needs...
Introduction
This document describes germline variant calling for PacBio® HiFi data using Sentieon® DNAscope. PacBio® HiFi technology produces high-quality long reads with quality scores above Q20 and average lengths between 10-25kb. These accurate long reads enable precise variant detection in genomic repeat regions that are challenging for short-read and noisy long-read methods....
Introduction
This document describes the recommended usage of the RGID field when using Sentieon® Genomics software to minimize potential issues. This document will help you determine the best practices for setting the different fields of the RG tags in the bam files you use.
Detailed description of RG fields and their usage
Detailed description of RG fields
The SAM format...
Using Machine Learning Models in TNscope®
Objectives of Machine Learning Models in TNscope®
TNscope® allows you to use machine learning models for variant filtering to improve the accuracy of results. The machine learning model approach is described in https://www.biorxiv.org/content/early/2018/01/19/250647, and TNscope® uses a series of sensitive settings to detect more...
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