1. |
- Olson, Nathan D., et al.
(författare)
-
precisionFDA Truth Challenge V2: Calling variants from short- and long-reads in difficult-to-map regions
- 2020
-
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.
|
|
2. |
- Olson, Nathan D., et al.
(författare)
-
PrecisionFDA Truth Challenge V2: Calling variants from short and long reads in difficult-to-map regions
- 2022
-
Ingår i: Cell Genomics. - : Elsevier BV. - 2666-979X. ; 2:5, s. 1-12
-
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.
|
|
3. |
- Velasquez, L. N., et al.
(författare)
-
Targeting Mycobacterium tuberculosis Antigens to Dendritic Cells via the DC-Specific-ICAM3-Grabbing-Nonintegrin Receptor Induces Strong T-Helper 1 Immune Responses
- 2018
-
Ingår i: Frontiers in Immunology. - : Frontiers Media SA. - 1664-3224. ; 9
-
Tidskriftsartikel (refereegranskat)abstract
- Tuberculosis remains a major global health problem and efforts to develop a more effective vaccine have been unsuccessful so far. Targeting antigens (Ags) to dendritic cells (DCs) in vivo has emerged as a new promising vaccine strategy. In this approach, Ags are delivered directly to DCs via antibodies that bind to endocytic cell-surface receptors. Here, we explored DC-specific-ICAM3-grabbing-nonintegrin (DC-SIGN) targeting as a potential vaccine against tuberculosis. For this, we made use of the hSIGN mouse model that expresses human DC-SIGN under the control of the murine CD11c promoter. We show that in vitro and in vivo delivery of anti-DC-SIGN antibodies conjugated to Ag85B and peptide 25 of Ag85B in combination with anti-CD40, the fungal cell wall component zymosan, and the cholera toxin-derived fusion protein CTA1-DD induces strong Ag-specific CD4(+) T-cell responses. Improved anti-mycobacterial immunity was accompanied by increased frequencies of Ag-specific IFN-gamma(+) IL-2(+) TNF-alpha(+) polyfunctional CD4(+) T cells in vaccinated mice compared with controls. Taken together, in this study we provide the proof of concept that the human DC-SIGN receptor can be efficiently exploited for vaccine purposes to promote immunity against mycobacterial infections.
|
|