scholarly journals Strelka2: Fast and accurate variant calling for clinical sequencing applications

2017 ◽  
Author(s):  
Sangtae Kim ◽  
Konrad Scheffler ◽  
Aaron L Halpern ◽  
Mitchell A Bekritsky ◽  
Eunho Noh ◽  
...  
Keyword(s):  
Open Source ◽  
Mixture Model ◽  
Variant Calling ◽  
Normal Sample ◽  
Clinical Sequencing ◽  
Model Based ◽  
Link Type ◽  
Sample Contamination ◽  
Modeling Strategy

We describe Strelka2 (https://github.com/Illumina/strelka), an open-source small variant calling method for clinical germline and somatic sequencing applications. Strelka2 introduces a novel mixture-model based estimation of indel error parameters from each sample, an efficient tiered haplotype modeling strategy and a normal sample contamination model to improve liquid tumor analysis. For both germline and somatic calling, Strelka2 substantially outperforms current leading tools on both variant calling accuracy and compute cost.

scholarly journals A RESTful API to serve BAM file with OAuth2 compatible authorization

2017 ◽  
Author(s):  
Julien Delafontaine ◽  
Sylvain Pradervand
Keyword(s):  
Open Source ◽  
Source Code ◽  
Variant Calling ◽  
Use Case ◽  
Web Interface ◽  
Sensitive Data ◽  
Link Type ◽  
Restful Service

AbstractSummaryBam-server is an open-source RESTful service to query slices of BAM files securely and manage their user accesses. A typical use case is the visualization of local read alignments in a web interface for variant calling diagnostic, without exposing sensitive data to unauthorized users through the network, and without moving the original - heavy - file. Bam-server follows the standard implementation of a protected resource server in the context of a typical token-based authorization protocol, supporting HMAC- and RSA-hashed signatures from an authorization server of choice.AvailabilityThe source code is available at https://github.com/chuv-ssrc/bam-server-scala, and a complete documentation can be found at http://bam-server-scala.readthedocs.io/en/latest/[email protected]

scholarly journals CopyMix: Mixture Model Based Single-Cell Clustering and Copy Number Profiling using Variational Inference

2020 ◽  
Author(s):  
Negar Safinianaini ◽  
Camila P. E. de Souza ◽  
Jens Lagergren
Keyword(s):  
Single Cell ◽  
Mixture Model ◽  
Copy Number ◽  
Tumor Heterogeneity ◽  
Variational Inference ◽  
Cell Subpopulation ◽  
Model Based ◽  
Link Type ◽  
Cell Clustering ◽  
Cancer Tumor

AbstractMotivationSingle-cell sequencing technologies are becoming increasingly more established, in particular, in the study of tumor heterogeneity, i.e., the cell subpopulations that a cancer tumor typically comprises. Investigating tumor heterogeneity is imperative to better understand how tumors evolve since each of cell subpopulation harbors a unique set of genomic features that yields a unique phenotype, an issue that is bound to have clinical relevance. Clustering of cells based on copy number data, obtained from single-cell DNA sequencing, provides an opportunity to assess different tumor cell sub-populations. Accordingly, computational methods have emerged for detecting single-cell copy number variations (copy number profiling) as well as clustering; however, these two tasks have up to now been handled sequentially with various ad-hoc preprocessing steps lacking an automated, generalized and fully probabilistic framework.ResultsWe propose CopyMix, a novel probabilistic mixture model based method for single-cell clustering and copy number profiling using Variational Inference, to simultaneously cluster cells and infer copy number profiles corresponding to the clusters. CopyMix is evaluated using simulated data as well as published biological data from metastatic colorectal cancer. The results reveal high V-measures for clustering and low errors in copy number inference. These favorable results indicate a considerable potential to obtain clinical impact by using CopyMix in studies of cancer tumor heterogeneity.AvailabilityThe software is available at: https://github.com/negar7918/CopyMix and the previously published biological dataset is available from the NCBI Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra) under accession number SRP074289)

scholarly journals State-of-the-art structural variant calling: What went conceptually wrong and how to fix it?

2021 ◽  
Author(s):  
Markus Schmidt ◽  
Arne Kutzner
Keyword(s):  
Open Source ◽  
State Of The Art ◽  
Graph Model ◽  
Variant Calling ◽  
Genomic Rearrangements ◽  
Prototype Implementation ◽  
Structural Variant ◽  
Link Type ◽  
Adjacency Matrices ◽  
Yeast Genomes

AbstractStructural variant (SV) calling belongs to the standard tools of modern bioinformatics for identifying and describing alterations in genomes. Initially, this work presents several complex genomic rearrangements that reveal conceptual ambiguities inherent to the SV representations of state-of-the-art SV callers. We contextualize these ambiguities theoretically as well as practically and propose a graph-based approach for resolving them. Our graph model unifies both genomic strands by using the concept of skew-symmetry; it supports graph genomes in general and pan genomes in specific. Instances of our model are inferred directly from seeds instead of the commonly used alignments that conflict with various types of SV as reported here. For yeast genomes, we practically compute adjacency matrices of our graph model and demonstrate that they provide highly accurate descriptions of one genome in terms of another. An open-source prototype implementation of our approach is available under the MIT license at https://github.com/ITBE-Lab/MA.

scholarly journals Open source tools for geographic analysis in transport planning

2021 ◽  
Author(s):  
Robin Lovelace
Keyword(s):  
User Interface ◽  
Open Source ◽  
Citizen Participation ◽  
Simulation Software ◽  
First Century ◽  
Transport Planning ◽  
Geographic Analysis ◽  
Link Type ◽  
Twenty First Century ◽  
Interactive Map

AbstractGeographic analysis has long supported transport plans that are appropriate to local contexts. Many incumbent ‘tools of the trade’ are proprietary and were developed to support growth in motor traffic, limiting their utility for transport planners who have been tasked with twenty-first century objectives such as enabling citizen participation, reducing pollution, and increasing levels of physical activity by getting more people walking and cycling. Geographic techniques—such as route analysis, network editing, localised impact assessment and interactive map visualisation—have great potential to support modern transport planning priorities. The aim of this paper is to explore emerging open source tools for geographic analysis in transport planning, with reference to the literature and a review of open source tools that are already being used. A key finding is that a growing number of options exist, challenging the current landscape of proprietary tools. These can be classified as command-line interface, graphical user interface or web-based user interface tools and by the framework in which they were implemented, with numerous tools released as R, Python and JavaScript packages, and QGIS plugins. The review found a diverse and rapidly evolving ‘ecosystem’ tools, with 25 tools that were designed for geographic analysis to support transport planning outlined in terms of their popularity and functionality based on online documentation. They ranged in size from single-purpose tools such as the QGIS plugin AwaP to sophisticated stand-alone multi-modal traffic simulation software such as MATSim, SUMO and Veins. Building on their ability to re-use the most effective components from other open source projects, developers of open source transport planning tools can avoid ‘reinventing the wheel’ and focus on innovation, the ‘gamified’ A/B Street https://github.com/dabreegster/abstreet/#abstreet simulation software, based on OpenStreetMap, a case in point. The paper, the source code of which can be found at https://github.com/robinlovelace/open-gat, concludes that, although many of the tools reviewed are still evolving and further research is needed to understand their relative strengths and barriers to uptake, open source tools for geographic analysis in transport planning already hold great potential to help generate the strategic visions of change and evidence that is needed by transport planners in the twenty-first century.

A Gaussian mixture model based virtual sample generation approach for small datasets in industrial processes

2021 ◽  
Vol 581 ◽  
pp. 262-277
Author(s):  
Ling Li ◽  
Seshu Kumar Damarla ◽  
Yalin Wang ◽  
Biao Huang
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Industrial Processes ◽  
Virtual Sample ◽  
Model Based

Bayesian estimation of generalized Gamma mixture model based on variational EM algorithm

2019 ◽  
Vol 87 ◽  
pp. 269-284 ◽  
Author(s):  
Chi Liu ◽  
Heng-Chao Li ◽  
Kun Fu ◽  
Fan Zhang ◽  
Mihai Datcu ◽  
...  
Keyword(s):  
Em Algorithm ◽  
Bayesian Estimation ◽  
Mixture Model ◽  
Model Based ◽  
Generalized Gamma ◽  
Variational Em ◽  
Variational Em Algorithm

A mixture model-based cluster analysis of DNA microarray gene expression data on Brahman and Brahman composite steers fed high-, medium-, and low-quality diets1

2003 ◽  
Vol 81 (8) ◽  
pp. 1900-1910 ◽  
Author(s):  
A. Reverter ◽  
K. A. Byrne ◽  
H. L. Bruce ◽  
Y. H. Wang ◽  
B. P. Dalrymple ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cluster Analysis ◽  
Mixture Model ◽  
Dna Microarray ◽  
Gene Expression Data ◽  
Microarray Gene Expression Data ◽  
Expression Data ◽  
Microarray Gene Expression ◽  
Model Based ◽  
Microarray Gene
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