HERRO is a highly-accurate, haplotype-aware, deep-learning tool for error correction of Nanopore R10.4.1 or R9.4.1 reads (read length of >= 10 kbps is recommended).
I am using herro since last weekend when ONT included this into their dorado 0.7 basecaller (i am running through dorado). I have 10.4.1 ONT data of bees (some of them are male, i.e. haploid)
first I had an error about CUDA out of Memory some 618m into the analysis (time /opt/dorado-0.7.0-linux-x64/bin/dorado correct --verbose --threads 40 --device 'cuda:0' -b 56 --infer-threads 2 -m /opt/dorado-0.7.0-linux-x64/bin/herro-v1 $INPUTFOLDER/$INPUT.gz > $SPECIES.dorado.sup430.2kbQ90.herro-v1.fa)
setting the PYTORCH_CUDA_ALLOC_CONF and batchsize to AUTO it worked (but took 2days, basically as long as basecalling, using 19GB out of 24 on my RTX3090)
export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:4096time /opt/dorado-0.7.0-linux-x64/bin/dorado correct --verbose --threads 60 --device 'cuda:0' -b 0 --infer-threads 2 -m /opt/dorado-0.7.0-linux-x64/bin/herro-v1 $INPUTFOLDER/$INPUT.gz > $SPECIES.dorado.sup430.2kbQ90.herro-v1.fa
The resulting assemblies (various flye, hifiasm) look very promising, including some chromosomes T2T, where previously it wasn't T2T. The assemblies are longer, but at similar very good N50 etc stats, i.e. i am quite happy with this thus far.
Just for our data processing we had a question regarding reads less than 10kb.
Should we remove these entirely? We usually have alot of reads around that length, i.e. many that are 2-10kb, while only a certain fraction is above 10kb. The amount of 10kb+ reads sometimes is very good, thus filtering is no problem.
Is it better to run herro on a filtered dataset (10kb+) than all reads (incl the small ones?
If the small reads are included, are they also corrected or remain unmodified?
is it detrimental to leave shorter reads in?
I'm just trying to understand the potential risks/biases that are possible by not pruning the datasets (sometimes the input datasets are not ideally distributed, i.e. too short on average, too few long reads)
Hi
I am using herro since last weekend when ONT included this into their dorado 0.7 basecaller (i am running through dorado). I have 10.4.1 ONT data of bees (some of them are male, i.e. haploid)
first I had an error about CUDA out of Memory some 618m into the analysis (
time /opt/dorado-0.7.0-linux-x64/bin/dorado correct --verbose --threads 40 --device 'cuda:0' -b 56 --infer-threads 2 -m /opt/dorado-0.7.0-linux-x64/bin/herro-v1 $INPUTFOLDER/$INPUT.gz > $SPECIES.dorado.sup430.2kbQ90.herro-v1.fa)setting the PYTORCH_CUDA_ALLOC_CONF and batchsize to AUTO it worked (but took 2days, basically as long as basecalling, using 19GB out of 24 on my RTX3090)
export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:4096time /opt/dorado-0.7.0-linux-x64/bin/dorado correct --verbose --threads 60 --device 'cuda:0' -b 0 --infer-threads 2 -m /opt/dorado-0.7.0-linux-x64/bin/herro-v1 $INPUTFOLDER/$INPUT.gz > $SPECIES.dorado.sup430.2kbQ90.herro-v1.faThe resulting assemblies (various flye, hifiasm) look very promising, including some chromosomes T2T, where previously it wasn't T2T. The assemblies are longer, but at similar very good N50 etc stats, i.e. i am quite happy with this thus far.
Just for our data processing we had a question regarding reads less than 10kb.
Should we remove these entirely? We usually have alot of reads around that length, i.e. many that are 2-10kb, while only a certain fraction is above 10kb. The amount of 10kb+ reads sometimes is very good, thus filtering is no problem.
I'm just trying to understand the potential risks/biases that are possible by not pruning the datasets (sometimes the input datasets are not ideally distributed, i.e. too short on average, too few long reads)
Thanks for your recommendation