Tim Downing
@downingtim.bsky.social
@downingtim@genomic.social - also downingtim.bsky.social - Head of #Genomics at The #PirbrightInstitute (UK). He/him. All posts (etc) are in a personal capacity. I log in regularly, nearly every month. #Andacyclist #genomics #virus #pathogen #evolution
To mitigate mapping compute time vs output, we suggest making PVGs using population structure info.
This means 1 sample from each main lineage for a multi-sample PVG.
This can be combined with SNPs from a 1-sample PVG to avoid coordinate confusion & to allow interpretation by non-experts.
[7/7]
This means 1 sample from each main lineage for a multi-sample PVG.
This can be combined with SNPs from a 1-sample PVG to avoid coordinate confusion & to allow interpretation by non-experts.
[7/7]
November 28, 2025 at 5:32 AM
To mitigate mapping compute time vs output, we suggest making PVGs using population structure info.
This means 1 sample from each main lineage for a multi-sample PVG.
This can be combined with SNPs from a 1-sample PVG to avoid coordinate confusion & to allow interpretation by non-experts.
[7/7]
This means 1 sample from each main lineage for a multi-sample PVG.
This can be combined with SNPs from a 1-sample PVG to avoid coordinate confusion & to allow interpretation by non-experts.
[7/7]
We looked reads (red/blue in image) mapped to our 3-sample PVG (3 genomes shown in black, dark grey, light gray).
We found they mapped better to one PVG path at certain regions.
This illustrates the biases inherent in using a single linear reference genome, which misses ~27% of SNPs.
[6/7]
We found they mapped better to one PVG path at certain regions.
This illustrates the biases inherent in using a single linear reference genome, which misses ~27% of SNPs.
[6/7]
November 28, 2025 at 5:32 AM
We looked reads (red/blue in image) mapped to our 3-sample PVG (3 genomes shown in black, dark grey, light gray).
We found they mapped better to one PVG path at certain regions.
This illustrates the biases inherent in using a single linear reference genome, which misses ~27% of SNPs.
[6/7]
We found they mapped better to one PVG path at certain regions.
This illustrates the biases inherent in using a single linear reference genome, which misses ~27% of SNPs.
[6/7]
For example in the 3-sample PVG, SNPs were detected right across each of the 3 genomes (image).
Giraffe found additional SNPs when mapping compared to Minimap2, even to our 1-sample PVG.
Overall, 27% of 3-sample PVG SNPs could not be projected onto the linear reference genome.
[5/7]
Giraffe found additional SNPs when mapping compared to Minimap2, even to our 1-sample PVG.
Overall, 27% of 3-sample PVG SNPs could not be projected onto the linear reference genome.
[5/7]
November 28, 2025 at 5:32 AM
For example in the 3-sample PVG, SNPs were detected right across each of the 3 genomes (image).
Giraffe found additional SNPs when mapping compared to Minimap2, even to our 1-sample PVG.
Overall, 27% of 3-sample PVG SNPs could not be projected onto the linear reference genome.
[5/7]
Giraffe found additional SNPs when mapping compared to Minimap2, even to our 1-sample PVG.
Overall, 27% of 3-sample PVG SNPs could not be projected onto the linear reference genome.
[5/7]
We mapped all available short read libraries to these LSDV 1-, 3-, 6-, 121-sample PVGs with #VG #Giraffe & compared that to mapping to a linear reference with #Minimap2. Generally, most SNPs were found by both methods (black in image), but Giraffe found more SNPs (blue) and missed few (red).
[4/7]
[4/7]
November 28, 2025 at 5:32 AM
The 3-sample PVG captured 94% of the nodes of the 6-sample one.
Similarly, the 6-sample PVG retained 82% of the 121-sample PVG.
The 3-sample PVG had 1 sample from each of the main clades, so we tested if this was sufficient for short Illumina read mapping (relative to the 121-sample PVG).
[3/7]
Similarly, the 6-sample PVG retained 82% of the 121-sample PVG.
The 3-sample PVG had 1 sample from each of the main clades, so we tested if this was sufficient for short Illumina read mapping (relative to the 121-sample PVG).
[3/7]
November 28, 2025 at 5:32 AM
The 3-sample PVG captured 94% of the nodes of the 6-sample one.
Similarly, the 6-sample PVG retained 82% of the 121-sample PVG.
The 3-sample PVG had 1 sample from each of the main clades, so we tested if this was sufficient for short Illumina read mapping (relative to the 121-sample PVG).
[3/7]
Similarly, the 6-sample PVG retained 82% of the 121-sample PVG.
The 3-sample PVG had 1 sample from each of the main clades, so we tested if this was sufficient for short Illumina read mapping (relative to the 121-sample PVG).
[3/7]
We created bigger & smaller pangenome graphs (1, 3, 6 and 121 genomes) using our tool Panalyze (github.com/downingtim/P...) which is essentially a wrapper for #PGGB, #OGDI, #Bandage and related tools.
This meant we could analyse different PVGs quickly.
[2/7]
This meant we could analyse different PVGs quickly.
[2/7]
November 28, 2025 at 5:32 AM
We created bigger & smaller pangenome graphs (1, 3, 6 and 121 genomes) using our tool Panalyze (github.com/downingtim/P...) which is essentially a wrapper for #PGGB, #OGDI, #Bandage and related tools.
This meant we could analyse different PVGs quickly.
[2/7]
This meant we could analyse different PVGs quickly.
[2/7]
Reposted by Tim Downing
In case you missed it.
www.science.org/content/arti...
www.science.org/content/arti...
The ‘invented persona’ behind a key pandemic database
GISAID offers a safe space to post viral genomes. Peter Bogner, its perplexing creator and overseer, may be jeopardizing its future
www.science.org
November 10, 2025 at 2:46 AM
In case you missed it.
www.science.org/content/arti...
www.science.org/content/arti...
Reposted by Tim Downing
Why then is LP under strong selection only in pastoralists like the Toda (South India) and Gujjar (Pakistan), despite their distinct environments and shared exposure to famine and disease with nearby non-pastoralists? This suggests unique selective pressures linked to pastoralism itself.
November 7, 2025 at 2:33 PM
Why then is LP under strong selection only in pastoralists like the Toda (South India) and Gujjar (Pakistan), despite their distinct environments and shared exposure to famine and disease with nearby non-pastoralists? This suggests unique selective pressures linked to pastoralism itself.