| 1. |
- Olson, Nathan D., et al.
(författare)
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precisionFDA Truth Challenge V2: Calling variants from short- and long-reads in difficult-to-map regions
- 2020
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- The precisionFDA Truth Challenge V2 aimed to assess the state-of-the-art of variant calling in difficult-to-map regions and the Major Histocompatibility Complex (MHC). Starting with FASTQ files, 20 challenge participants applied their variant calling pipelines and submitted 64 variant callsets for one or more sequencing technologies (~35X Illumina, ~35X PacBio HiFi, and ~50X Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with the new GIAB benchmark sets and genome stratifications. Challenge submissions included a number of innovative methods for all three technologies, with graph-based and machine-learning methods scoring best for short-read and long-read datasets, respectively. New methods out-performed the 2016 Truth Challenge winners, and new machine-learning approaches combining multiple sequencing technologies performed particularly well. Recent developments in sequencing and variant calling have enabled benchmarking variants in challenging genomic regions, paving the way for the identification of previously unknown clinically relevant variants.
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| 2. |
- Olson, Nathan D., et al.
(författare)
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PrecisionFDA Truth Challenge V2: Calling variants from short and long reads in difficult-to-map regions
- 2022
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Ingår i: Cell Genomics. - : Elsevier BV. - 2666-979X. ; 2:5, s. 1-12
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Tidskriftsartikel (refereegranskat)abstract
- The precisionFDA Truth Challenge V2 aimed to assess the state of the art of variant calling in challenging genomic regions. Starting with FASTQs, 20 challenge participants applied their variant-calling pipelines and submitted 64 variant call sets for one or more sequencing technologies (Illumina, PacBio HiFi, and Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with updated Genome in a Bottle benchmark sets and genome stratifications. Challenge submissions included numerous innovative methods, with graph-based and machine learning methods scoring best for short-read and long-read datasets, respectively. With machine learning approaches, combining multiple sequencing technologies performed particularly well. Recent developments in sequencing and variant calling have enabled benchmarking variants in challenging genomic regions, paving the way for the identification of previously unknown clinically relevant variants.
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| 3. |
- Shang, Xiaobo, et al.
(författare)
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Emerging materials for circularly polarized light detection
- 2022
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Ingår i: Journal of Materials Chemistry C. - : ROYAL SOC CHEMISTRY. - 2050-7526 .- 2050-7534. ; 10:7, s. 2400-2410
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Forskningsöversikt (refereegranskat)abstract
- Detecting circularly polarized light (CPL) signals is the key technique in many advanced sensing technologies. Over recent decades, many efforts have been devoted to both the material design and the device engineering of CPL photodetectors. CPL detectors with different sensing wavelengths have distinct applications in bio-imaging, drug discovery, and information encryption. In this review, we first introduce the working principle of state-of-the-art CPL photodetectors followed by a general material design strategy. We then systematically summarize the recent progress on the chiral materials developed for CPL detection, including inorganic metamaterials, organics, hybridized materials, etc. We compare and analyse the photocurrent dissymmetry factors of these systems and provide perspectives on strategies to improve the dissymmetry factors and extend the detection wavelength. We believe that the information we include in this review would attract broader interest from researchers working on different aspects of organic and hybridized semiconductor materials and devices.
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| 4. |
- Yoo, Seungyeul, et al.
(författare)
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A community effort to identify and correct mislabeled samples in proteogenomic studies
- 2021
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Ingår i: Patterns. - : Elsevier BV. - 2666-3899. ; 2:5
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Tidskriftsartikel (refereegranskat)abstract
- Sample mislabeling or misannotation has been a long-standing problem in scientific research, particularly prevalent in large-scale, multi-omic studies due to the complexity of multi-omic workflows. There exists an urgent need for implementing quality controls to automatically screen for and correct sample mislabels or misannotations in multi-omic studies. Here, we describe a crowdsourced precisionFDA NCI-CPTAC Multi-omics Enabled Sample Mislabeling Correction Challenge, which provides a framework for systematic benchmarking and evaluation of mislabel identification and correction methods for integrative proteogenomic studies. The challenge received a large number of submissions from domestic and international data scientists, with highly variable performance observed across the submitted methods. Post-challenge collaboration between the top-performing teams and the challenge organizers has created an open-source software, COSMO, with demonstrated high accuracy and robustness in mislabeling identification and correction in simulated and real multi-omic datasets.
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