Discover and read the best of Twitter Threads about #Hifi

Most recents (3)

1/7🧵
@PythNetwork is a new next-generation oracle solution being developed for #Hifi to #DeFi data transfer.

#Pyth #defi #finance #crypto #Pyth #blockchain Image
2/7🧵
It is created by the biggest names in the field of traditional finance and #DeFi with the goal of providing infrastructure for #DeFi to support the significant growth of the market
3/7🧵
One of the main advantages of the #Pyth network is its ability to provide legitimate access to unique data sets that are not available to most users. This allows you to use more accurate and reliable data, which can improve the performance and reliability of #DeFi products
Read 7 tweets
Our updated study is published! We evaluated methods for taxonomic profiling and classification with long-read #metagenomic datasets. This included @PacBio and @nanopore data, and was a group effort with @saltyscientist and @ctitusbrown: bmcbioinformatics.biomedcentral.com/articles/10.11… 🧵1/8
Top performers include sourmash, @bugseq, and DIAMOND + MEGAN-LR, which displayed overall high precision and solid recall. We recommend trying all of these methods, and each has their own specific advantages. 2/8
Sourmash was magical with HiFi data, having the best precision/recall tradeoff with detection down to 0.001% relative abundance (other methods hit 0.05%). Sourmash works best with highly accurate reads - regardless of read length (!), including @PacBio and @illumina data 🤔. 3/8 Image
Read 8 tweets
What is the best method for taxonomic profiling in long-read shotgun #metagenomic datasets? We put several to the test in a new study, using @PacBio #HiFi and @nanopore data!!! Awesome collaboration with @ctitusbrown and @saltyscientist: biorxiv.org/content/10.110…. 🧵1/8
Top performers are long read methods including @bugseq and MEGAN-LR (using DIAMOND to NCBI nr), which had very high precision and solid recall (with no filtering necessary!). MMseqs2 and MetaMaps required some filtering to reduce false positives, but they also performed well. 2/8
Using methods designed for short reads (Kraken2, Bracken, Centrifuge) produced LOTS of false positives. This required heavy filtering to improve precision (simultaneously dropping recall), but they also produced inaccurate abundance estimates. 3/8
Read 8 tweets

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