Using HaMMLET for Bayesian Segmentation of WGS Read-Depth Data

2018 ◽  
pp. 83-93
Author(s):  
John Wiedenhoeft ◽  
Alexander Schliep
Keyword(s):  
Read Depth ◽  
Depth Data ◽  
Bayesian Segmentation

scholarly journals Robust foreground detection in somatic copy number data

2019 ◽  
Author(s):  
Aditya Deshpande ◽  
Trent Walradt ◽  
Ya Hu ◽  
Amnon Koren ◽  
Marcin Imielinski
Keyword(s):  
Copy Number ◽  
Replication Timing ◽  
Read Depth ◽  
R Package ◽  
Foreground Detection ◽  
Signal Processing Algorithm ◽  
Biologically Relevant ◽  
Depth Data ◽  
Data Application ◽  
Sequencing Platforms

Sensitive detection of somatic copy number alterations (SCNA) in cancer genomes is confounded by “waviness” in read depth data. We present dryclean, a signal processing algorithm to optimize SCNA detection in whole genome (WGS) and targeted sequencing platforms through foreground detection and background subtraction of read depth data. Application of dryclean to WGS demonstrates that WGS waviness is driven by replication timing. Re-analysis of thousands of tumor profiles reveals that dryclean provides superior detection of biologically relevant SCNAs relative to state-of-the-art algorithms. Applied to in silico tumor dilutions, dryclean improves the sensitivity of relapse detection 10-fold relative to current standards. dryclean is available as an R package in the GitHub repository https://github.com/mskilab/dryclean

scholarly journals Efficient storage and analysis of quantitative genomics data with the Dense Depth Data Dump (D4) format and d4tools

2020 ◽  
Author(s):  
Hao Hou ◽  
Brent Pedersen ◽  
Aaron Quinlan
Keyword(s):  
Random Access ◽  
Data Access ◽  
Read Depth ◽  
Whole Genome ◽  
Rna Seq ◽  
File Size ◽  
Depth Data ◽  
Efficient Storage ◽  
Optimal Encoding ◽  
Quantitative Genomics

AbstractModern DNA sequencing is used as a readout for diverse assays, with the count of aligned sequences, or “read depth”, serving as the quantitative signal for many underlying cellular phenomena. Despite wide use and thousands of datasets, existing formats used for the storage and analysis of read depths are limited with respect to both file size and analysis speed. For example, it is faster to recalculate sequencing depth from an alignment file than it is to analyze the text output from that calculation. We sought to improve on existing formats such as BigWig and compressed BED files by creating the Dense Depth Data Dump (D4) format and tool suite. The D4 format is adaptive in that it profiles a random sample of aligned sequence depth from the input BAM or CRAM file to determine an optimal encoding that often affords reductions in file size, while also enabling fast data access. We show that D4 uses less storage for both RNA-Seq and whole-genome sequencing and offers 3 to 440-fold speed improvements over existing formats for random access, aggregation and summarization. This performance enables scalable downstream analyses that would be otherwise difficult. The D4 tool suite (d4tools) is freely available under an MIT license at: https://github.com/38/d4-format.

scholarly journals Iranian kinect face database (IKFDB): a color-depth based face database collected by kinect v.2 sensor

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Seyed Muhammad Hossein Mousavi ◽  
S. Younes Mirinezhad
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Depth Image ◽  
Sensor Technology ◽  
Support Vector ◽  
Expression Recognition ◽  
Face Database ◽  
Depth Data ◽  
Color Depth

AbstractThis study presents a new color-depth based face database gathered from different genders and age ranges from Iranian subjects. Using suitable databases, it is possible to validate and assess available methods in different research fields. This database has application in different fields such as face recognition, age estimation and Facial Expression Recognition and Facial Micro Expressions Recognition. Image databases based on their size and resolution are mostly large. Color images usually consist of three channels namely Red, Green and Blue. But in the last decade, another aspect of image type has emerged, named “depth image”. Depth images are used in calculating range and distance between objects and the sensor. Depending on the depth sensor technology, it is possible to acquire range data differently. Kinect sensor version 2 is capable of acquiring color and depth data simultaneously. Facial expression recognition is an important field in image processing, which has multiple uses from animation to psychology. Currently, there is a few numbers of color-depth (RGB-D) facial micro expressions recognition databases existing. With adding depth data to color data, the accuracy of final recognition will be increased. Due to the shortage of color-depth based facial expression databases and some weakness in available ones, a new and almost perfect RGB-D face database is presented in this paper, covering Middle-Eastern face type. In the validation section, the database will be compared with some famous benchmark face databases. For evaluation, Histogram Oriented Gradients features are extracted, and classification algorithms such as Support Vector Machine, Multi-Layer Neural Network and a deep learning method, called Convolutional Neural Network or are employed. The results are so promising.

scholarly journals Assimilation of D-InSAR snow depth data by an ensemble Kalman filter

2021 ◽  
Vol 14 (6) ◽  
Author(s):  
Jinming Yang ◽  
Chengzhi Li
Keyword(s):  
Remote Sensing ◽  
Kalman Filter ◽  
Water Resource ◽  
Ensemble Kalman Filter ◽  
Snow Depth ◽  
Resource Assessment ◽  
Measured Data ◽  
Hydrological Process ◽  
Depth Data ◽  
Water Resource Assessment

AbstractSnow depth mirrors regional climate change and is a vital parameter for medium- and long-term numerical climate prediction, numerical simulation of land-surface hydrological process, and water resource assessment. However, the quality of the available snow depth products retrieved from remote sensing is inevitably affected by cloud and mountain shadow, and the spatiotemporal resolution of the snow depth data cannot meet the need of hydrological research and decision-making assistance. Therefore, a method to enhance the accuracy of snow depth data is urgently required. In the present study, three kinds of snow depth data which included the D-InSAR data retrieved from the remote sensing images of Sentinel-1 synthetic aperture radar, the automatically measured data using ultrasonic snow depth detectors, and the manually measured data were assimilated based on ensemble Kalman filter. The assimilated snow depth data were spatiotemporally consecutive and integrated. Under the constraint of the measured data, the accuracy of the assimilated snow depth data was higher and met the need of subsequent research. The development of ultrasonic snow depth detector and the application of D-InSAR technology in snow depth inversion had greatly alleviated the insufficiency of snow depth data in types and quantity. At the same time, the assimilation of multi-source snow depth data by ensemble Kalman filter also provides high-precision data to support remote sensing hydrological research, water resource assessment, and snow disaster prevention and control program.

scholarly journals Performance of Cell-Free DNA Screening for Fetal Common Aneuploidies and Sex Chromosomal Abnormalities: A Prospective Study from a Less Developed Autonomous Region in Mainland China

Genes ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 478
Author(s):  
Yunli Lai ◽  
Xiaofan Zhu ◽  
Sheng He ◽  
Zirui Dong ◽  
Yanqing Tang ◽  
...  
Keyword(s):  
Sex Chromosome ◽  
Chromosomal Abnormalities ◽  
False Negative ◽  
Prenatal Testing ◽  
Read Depth ◽  
Trisomy 13 ◽  
Prognostic Information ◽  
Aneuploidy Screening ◽  
Predictive Values ◽  
Risk Result

To evaluate the performance of noninvasive prenatal screening (NIPS) in the detection of common aneuploidies in a population-based study, a total of 86,262 single pregnancies referred for NIPS were prospectively recruited. Among 86,193 pregnancies with reportable results, follow-up was successfully conducted in 1160 fetuses reported with a high-risk result by NIPS and 82,511 cases (95.7%) with a low-risk result. The screen-positive rate (SPR) of common aneuploidies and sex chromosome abnormalities (SCAs) provided by NIPS were 0.7% (586/83,671) and 0.6% (505/83,671), respectively. The positive predictive values (PPVs) for Trisomy 21, Trisomy 18, Trisomy 13 and SCAs were calculated as 89.7%, 84.0%, 52.6% and 38.0%, respectively. In addition, less rare chromosomal abnormalities, including copy number variants (CNVs), were detected, compared with those reported by NIPS with higher read-depth. Among these rare abnormalities, only 23.2% (13/56) were confirmed by prenatal diagnosis. In total, four common trisomy cases were found to be false negative, resulting in a rate of 0.48/10,000 (4/83,671). In summary, this study conducted in an underdeveloped region with limited support for the new technology development and lack of cost-effective prenatal testing demonstrates the importance of implementing routine aneuploidy screening in the public sector for providing early detection and precise prognostic information.

scholarly journals Deep learning-based single image face depth data enhancement

2021 ◽  
pp. 103247
Author(s):  
Torsten Schlett ◽  
Christian Rathgeb ◽  
Christoph Busch
Keyword(s):  
Deep Learning ◽  
Single Image ◽  
Depth Data
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