| 1. |
- Lang, Daniel, et al.
(författare)
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The Physcomitrella patens chromosome-scale assembly reveals moss genome structure and evolution
- 2018
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Ingår i: The Plant Journal. - : Wiley. - 0960-7412 .- 1365-313X. ; 93:3, s. 515-533
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Tidskriftsartikel (refereegranskat)abstract
- The draft genome of the moss model, Physcomitrella patens, comprised approximately 2000 unordered scaffolds. In order to enable analyses of genome structure and evolution we generated a chromosome-scale genome assembly using genetic linkage as well as (end) sequencing of long DNA fragments. We find that 57% of the genome comprises transposable elements (TEs), some of which may be actively transposing during the life cycle. Unlike in flowering plant genomes, gene-and TE-rich regions show an overall even distribution along the chromosomes. However, the chromosomes are mono-centric with peaks of a class of Copia elements potentially coinciding with centromeres. Gene body methylation is evident in 5.7% of the protein-coding genes, typically coinciding with low GC and low expression. Some giant virus insertions are transcriptionally active and might protect gametes from viral infection via siRNA mediated silencing. Structure-based detection methods show that the genome evolved via two rounds of whole genome duplications (WGDs), apparently common in mosses but not in liverworts and hornworts. Several hundred genes are present in colinear regions conserved since the last common ancestor of plants. These syntenic regions are enriched for functions related to plant-specific cell growth and tissue organization. The P. patens genome lacks the TE-rich pericentromeric and gene-rich distal regions typical for most flowering plant genomes. More non-seed plant genomes are needed to unravel how plant genomes evolve, and to understand whether the P. patens genome structure is typical for mosses or bryophytes.
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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
- 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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| 3. |
- 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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