scholarly journals Kronos: a workflow assembler for genome analytics and informatics

2016 ◽  
Author(s):  
M Jafar Taghiyar ◽  
Jamie Rosner ◽  
Diljot Grewal ◽  
Bruno Grande ◽  
Radhouane Aniba ◽  
...  
Keyword(s):  
Feature Detection ◽  
Automated Analysis ◽  
Configuration File ◽  
Detection Methods ◽  
Reproducible Research ◽  
Robust Implementation ◽  
Free Open Source ◽  
Standard Framework ◽  
Python Package ◽  
Generation Sequencing

The field of next generation sequencing informatics has matured to a point where algorithmic advances in sequence alignment and individual feature detection methods have stabilized. Practical and robust implementation of complex analytical workflows (where such tools are structured into "best practices" for automated analysis of NGS datasets) still requires significant programming investment and expertise. We present Kronos, a software platform for automating the development and execution of reproducible, auditable and distributable bioinformatics workflows. Kronos obviates the need for explicit coding of workflows by compiling a text configuration file into executable Python applications. The framework of each workflow includes a run manager to execute the encoded workflows locally (or on a cluster or cloud), parallelize tasks, and log all runtime events. Resulting workflows are highly modular and configurable by construction, facilitating flexible and extensible meta-applications which can be modified easily through configuration file editing. The workflows are fully encoded for ease of distribution and can be instantiated on external systems, promoting and facilitating reproducible research and comparative analyses. We introduce a framework for building Kronos components which function as shareable, modular nodes in Kronos workflows. The Kronos platform provides a standard framework for developers to implement custom tools, reuse existing tools, and contribute to the community at large. Kronos is shipped with both Docker and Amazon AWS machine images. It is free, open source and available through PyPI (Python Package Index) and https://github.com/jtaghiyar/kronos. Keywords: genomics; workflow; pipeline; reproducibility

IsoDetect: Detection of splice isoforms from third generation long reads based on short feature sequences

2020 ◽  
Vol 15 ◽  
Author(s):  
Hongdong Li ◽  
Wenjing Zhang ◽  
Yuwen Luo ◽  
Jianxin Wang
Keyword(s):  
Sequence Similarity ◽  
Detection Methods ◽  
Sequence Information ◽  
Third Generation ◽  
Sequencing Data ◽  
Splice Isoforms ◽  
Third Generation Sequencing ◽  
Long Reads ◽  
Feature Sequence ◽  
Generation Sequencing

Aims: Accurately detect isoforms from third generation sequencing data. Background: Transcriptome annotation is the basis for the analysis of gene expression and regulation. The transcriptome annotation of many organisms such as humans is far from incomplete, due partly to the challenge in the identification of isoforms that are produced from the same gene through alternative splicing. Third generation sequencing (TGS) reads provide unprecedented opportunity for detecting isoforms due to their long length that exceeds the length of most isoforms. One limitation of current TGS reads-based isoform detection methods is that they are exclusively based on sequence reads, without incorporating the sequence information of known isoforms. Objective: Develop an efficient method for isoform detection. Method: Based on annotated isoforms, we propose a splice isoform detection method called IsoDetect. First, the sequence at exon-exon junction is extracted from annotated isoforms as the “short feature sequence”, which is used to distinguish different splice isoforms. Second, we aligned these feature sequences to long reads and divided long reads into groups that contain the same set of feature sequences, thereby avoiding the pair-wise comparison among the large number of long reads. Third, clustering and consensus generation are carried out based on sequence similarity. For the long reads that do not contain any short feature sequence, clustering analysis based on sequence similarity is performed to identify isoforms. Result: Tested on two datasets from Calypte Anna and Zebra Finch, IsoDetect showed higher speed and compelling accuracy compared with four existing methods. Conclusion: IsoDetect is a promising method for isoform detection. Other: This paper was accepted by the CBC2019 conference.

scholarly journals Augmented Reality and Machine Learning Incorporation Using YOLOv3 and ARKit

2021 ◽  
Vol 11 (13) ◽  
pp. 6006
Author(s):  
Huy Le ◽  
Minh Nguyen ◽  
Wei Qi Yan ◽  
Hoa Nguyen
Keyword(s):  
Machine Learning ◽  
Augmented Reality ◽  
Object Detection ◽  
Feature Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Data Annotation ◽  
Machine Learning Model ◽  
Potential Benefits ◽  
Feature Detection And Tracking

Augmented reality is one of the fastest growing fields, receiving increased funding for the last few years as people realise the potential benefits of rendering virtual information in the real world. Most of today’s augmented reality marker-based applications use local feature detection and tracking techniques. The disadvantage of applying these techniques is that the markers must be modified to match the unique classified algorithms or they suffer from low detection accuracy. Machine learning is an ideal solution to overcome the current drawbacks of image processing in augmented reality applications. However, traditional data annotation requires extensive time and labour, as it is usually done manually. This study incorporates machine learning to detect and track augmented reality marker targets in an application using deep neural networks. We firstly implement the auto-generated dataset tool, which is used for the machine learning dataset preparation. The final iOS prototype application incorporates object detection, object tracking and augmented reality. The machine learning model is trained to recognise the differences between targets using one of YOLO’s most well-known object detection methods. The final product makes use of a valuable toolkit for developing augmented reality applications called ARKit.

Comparison of three human papillomavirus DNA detection methods: Next generation sequencing, multiplex-PCR and nested-PCR followed by Sanger based sequencing

2015 ◽  
Vol 88 (5) ◽  
pp. 888-894 ◽  
Author(s):  
Allex Jardim da Fonseca ◽  
Renata Silva Galvão ◽  
Angelica Espinosa Miranda ◽  
Luiz Carlos de Lima Ferreira ◽  
Zigui Chen
Keyword(s):  
Human Papillomavirus ◽  
Next Generation Sequencing ◽  
Multiplex Pcr ◽  
Nested Pcr ◽  
Dna Detection ◽  
Detection Methods ◽  
Next Generation ◽  
Papillomavirus Dna ◽  
Generation Sequencing

Objective 3D atmospheric front detection in high-resolution numerical weather prediction data

2021 ◽  
Author(s):  
Andreas Beckert ◽  
Lea Eisenstein ◽  
Tim Hewson ◽  
George C. Craig ◽  
Marc Rautenhaus
Keyword(s):  
High Resolution ◽  
Numerical Weather Prediction ◽  
Feature Detection ◽  
3D Visualization ◽  
Weather Prediction ◽  
Three Dimensional ◽  
Detection Methods ◽  
Numerical Weather ◽  
Atmospheric Fronts ◽  
Front Detection

<p><span>Atmospheric fronts, a widely used conceptual model in meteorology, describe sharp boundaries between two air masses of different thermal properties. In the mid-latitudes, these sharp boundaries are commonly associated with extratropical cyclones. The passage of a frontal system is accompanied by significant weather changes, and therefore fronts are of particular interest in weather forecasting. Over the past decades, several two-dimensional, horizontal feature detection methods to objectively identify atmospheric fronts in numerical weather prediction (NWP) data were proposed in the literature (e.g. Hewson, Met.Apps. 1998). In addition, recent research (Kern et al., IEEE Trans. Visual. Comput. Graphics, 2019) has shown the feasibility of detecting atmospheric fronts as three-dimensional surfaces representing the full 3D frontal structure. In our work, we build on the studies by Hewson (1998) and Kern et al. (2019) to make front detection usable for forecasting purposes in an interactive 3D visualization environment. We consider the following aspects: (a) As NWP models evolved in recent years to resolve atmospheric processes on scales far smaller than the scale of midlatitude-cyclone- fronts, we evaluate whether previously developed detection methods are still capable to detect fronts in current high-resolution NWP data. (b) We present integration of our implementation into the open-source “Met.3D” software (http://met3d.wavestoweather.de) and analyze two- and three-dimensional frontal structures in selected cases of European winter storms, comparing different models and model resolution. (c) The considered front detection methods rely on threshold parameters, which mostly refer to the magnitude of the thermal gradient within the adjacent frontal zone - the frontal strength. If the frontal strength exceeds the threshold, a so-called feature candidate is classified as a front, while others are discarded. If a single, fixed, threshold is used, unwanted “holes” can be observed in the detected fronts. Hence, we use transparency mapping with fuzzy thresholds to generate continuous frontal features. We pay particular attention to the adjustment of filter thresholds and evaluate the dependence of thresholds and resolution of the underlying data.</span></p>

scholarly journals It’s the Little Things (in Viral RNA)

mBio ◽  
2020 ◽  
Vol 11 (5) ◽  
Author(s):  
Jiří František Potužník ◽  
Hana Cahová
Keyword(s):  
Mass Spectrometry ◽  
Viral Rna ◽  
Detection Methods ◽  
Scientific Methodology ◽  
Rna Modifications ◽  
Chemical Modifications ◽  
Viral Protein Synthesis ◽  
Antibody Assays ◽  
Rna Export ◽  
Generation Sequencing

ABSTRACT Chemical modifications of viral RNA are an integral part of the viral life cycle and are present in most classes of viruses. To date, more than 170 RNA modifications have been discovered in all types of cellular RNA. Only a few, however, have been found in viral RNA, and the function of most of these has yet to be elucidated. Those few we have discovered and whose functions we understand have a varied effect on each virus. They facilitate RNA export from the nucleus, aid in viral protein synthesis, recruit host enzymes, and even interact with the host immune machinery. The most common methods for their study are mass spectrometry and antibody assays linked to next-generation sequencing. However, given that the actual amount of modified RNA can be very small, it is important to pair meticulous scientific methodology with the appropriate detection methods and to interpret the results with a grain of salt. Once discovered, RNA modifications enhance our understanding of viruses and present a potential target in combating them. This review provides a summary of the currently known chemical modifications of viral RNA, the effects they have on viral machinery, and the methods used to detect them.

scholarly journals Emerging approaches for detection of methylation sites in RNA

Open Biology ◽  
2018 ◽  
Vol 8 (9) ◽  
pp. 180121 ◽  
Author(s):  
Anna Ovcharenko ◽  
Andrea Rentmeister
Keyword(s):  
High Throughput Sequencing ◽  
De Novo ◽  
Direct Detection ◽  
False Negative ◽  
Detection Methods ◽  
Rna Modifications ◽  
Negative Results ◽  
Third Generation Sequencing ◽  
False Negative Results ◽  
Generation Sequencing

RNA methylations play a significant regulatory role in diverse biological processes. Although the transcriptome-wide discovery of unknown RNA methylation sites is essential to elucidate their function, the development of a bigger variety of detection approaches is desirable for multiple reasons. Many established detection methods for RNA modifications heavily rely on the specificity of the respective antibodies. Thus, the development of antibody-independent transcriptome-wide methods is beneficial. Even the antibody-independent high-throughput sequencing-based methods are liable to produce false-positive or false-negative results. The development of an independent method for each modification could help validate the detected modification sites. Apart from the transcriptome-wide methods for methylation detection de novo , methods for monitoring the presence of a single methylation at a determined site are also needed. In contrast to the transcriptome-wide detection methods, the techniques used for monitoring purposes need to be cheap, fast and easy to perform. This review considers modern approaches for site-specific detection of methylated nucleotides in RNA. We also discuss the potential of third-generation sequencing methods for direct detection of RNA methylations.

scholarly journals PGcloser: Fast Parallel Gap-Closing Tool Using Long-Reads or Contigs to Fill Gaps in Genomes

2020 ◽  
Vol 16 ◽  
pp. 117693432091385
Author(s):  
Peng Lu ◽  
Jingjing Jin ◽  
Zefeng Li ◽  
Yalong Xu ◽  
Dasha Hu ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Open Source Software ◽  
Next Generation ◽  
Gap Filling ◽  
Long Reads ◽  
Parallel Mode ◽  
Similar Accuracy ◽  
Free Open Source ◽  
Generation Sequencing ◽  
Gap Closing

Assembled draft genomes usually contain many gaps because of the length limit of next-generation sequencing. Although many gap-closing tools have been developed, most of them still attempt to fill gaps on the basis of next-generation sequencing reads (always < 200 bp). Hence, the gap-filling effect is inferior. Several tools that use long-reads to close gaps have recently been created. However, they require extensive runtimes, which may not be suitable for large genomes. We describe a gap-closing tool called PGcloser, which supports parallel mode and adopts long-reads/contigs to fill gaps in genome sequences. Three tests show that PGcloser is faster than other tools but exhibits similar accuracy. PGcloser is free open-source software that is available at http://software.tobaccodb.org/software/pgcloser .

Systematic development and evaluation of composite methods for recognition of three-dimensional subtractive features

2005 ◽  
Vol 219 (12) ◽  
pp. 871-890 ◽  
Author(s):  
O Owodunni ◽  
S Hinduja
Keyword(s):  
Feature Detection ◽  
Three Dimensional ◽  
Detection Methods ◽  
Test Bed ◽  
The Public ◽  
Detection Systems ◽  
Composite Methods ◽  
Composite Feature ◽  
Tree Pruning ◽  
Pruning Technique

This paper describes a systematic procedure for developing composite feature detection systems from six methods for detecting three-dimensional depression features. The six methods, proposed by the authors in earlier papers, correspond to all the possible ways of grouping faces together from the simplest to the most complex grouping. All the possible ways of combining the six feature detection methods are considered and arranged in a tree structure. The possible composites are reduced to 20, using a tree pruning technique based on the criteria that the features detected should be the same (i.e. consistent), irrespective of the ordering of the faces in the B-rep model and that all faces of the component should be detected (i.e. complete coverage). A test bed for these 20 composites has been developed, implemented, and tested using carefully selected components from the public domain. The performance of these 20 composites is evaluated on the basis of suitability of the features as input to a machining application with minimal or no additional geometric reasoning, thus enabling the most promising composites to be identified.

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