Discover and read the best of Twitter Threads about #metagenomic
Most recents (5)
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
Have you ever wished to sequence SPECIFIC bacteria but NOT the vast background in #microbiome? Today, we introduce *mEnrich-seq*: #methylation-guided enrichment sequencing of bacterial taxa of interest from microbiome. 4yr project led by @clannabel7! biorxiv.org/content/10.110…🧵1/10 
#Metagenomic sequencing throughput is largely consumed by abundant microbes while those with relatively lower abundance cannot be sequenced in a cost-effective manner, due to the highly skewed abundance distribution of diverse bacteria and host DNA. 2/10
Although several methods have been developed (pls see Introduction in preprint) to exclude host DNA and/or the highly abundant microbes, there are still key challenges in achieving high enrichment efficiency and differentiating between bacterial taxa w/ very similar genomes. 3/10
Need to identify and analyze each organism in a sample (isolate or polymicrobial)? We built a highly-accurate method to tease apart the organisms in a #metagenomic assembly, enabling organism-specific AMR, abundance estimation, strain-typing & more: doi.org/hhqr 🧵1/7
We show that aligning contigs against a nucleotide reference database enables far more precise classification of assembled sequences, surpassing amino acid alignments. 2/7
For example, on an isolate of Bacillus anthracis, BugSplit identified the organism to species, whereas protein-based tools (MMSeqs2, DIAMOND+MEGAN-LR) identified the organism to genus (Bacillus). 3/7
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
This team successfully identified key factors driving a complex multifactorial disease tied to persistent #infection! (necrotizing colitis)👉 They used a combo of genome-resolved #metagenomics + machine learning approaches to identify #microbial features predictive of the illness
Features considered included genes, #bacterial strain types, eukaryotes, bacteriophages, plasmids + growth rates 👉 ...which allowed the team to “uncover #biomarkers that could lead to early detection of necrotizing enterocolitis and targets for #microbiome-based therapeutics.”
Important consideration from study👉 “Bioinformatics techniques can also infer in situ bacterial replication rates directly from #metagenomic data, an important metric, as some #microbiome-related diseases have a signal related to bacterial replication but not relative abundance”
