Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen

Stewart, Robert D.; Auffret, Marc; Warr, Amanda; Wiser, Andrew H.; Press, Maximilian O.; Langford, Kyle W.; Liachko, Ivan; Snelling, Timothy J.; Dewhurst, Richard J.; Walker, Alan W.; Roehe, R.; Watson, Michael

doi:10.1038/s41467-018-03317-6

Cited by 458 publications

(541 citation statements)

References 80 publications

Supporting

Mentioning

480

Contrasting

Unclassified

Order By: Relevance

“…Improving our understanding of the rumen microbiota provides opportunities for knowledgebased strategies aiming at enhancing efficacy in ruminant production while minimizing its negative effect on the environment. Great advances on microbiota functions in the rumen has been obtained by extensive genome sequencing of cultured rumen bacteria and archaea (Hungate1000 project) [5] and by assembling of draft genomes from metagenomic data [6,7].…”

Section: Abstract: Rumen; Metagenome; Herbivory; Carbohydrate-activementioning

confidence: 99%

“…For the bacterial MAGs, 39% could be annotated to the order level but only 2.5% (8 MAGs) to the genus level; all belonging to Prevotella. These rumen MAGs were compared to the Hungate1000 genomes [5] and to the 913 MAGs reported from Scottish cattle [6] Ninety-six (30%) out of 324…”

Section: Construction Of a Bovine Rumen Prokaryotic Gene Catalogmentioning

confidence: 99%

“…The 324 MAGs were present in all four diet groups from our validation cohort; about 10% of reads from the 77 cattle dataset mapped to these MAGs. For genomes from the Hungate1000 project [5,9], which are representative of the diversity of cultured rumen bacteria and archaea, the mapping rate was 5.4%, whereas the mapping rate for the largest MAGs collection of Stewart et al [6] was around 13%. In contrast, only 0.1% of reads mapped to the 15 metagenomic species described by Hess et al [14] ( Figure 1c).…”

Section: Construction Of a Bovine Rumen Prokaryotic Gene Catalogmentioning

confidence: 99%

See 2 more Smart Citations

A catalog of microbial genes from the bovine rumen unveils a specialized and diverse biomass-degrading environment

Huang

Ramayo-Caldas

et al. 2018

Preprint

View full text Add to dashboard Cite

BackgroundThe rumen microbiota provides essential services to its host and, through its role in ruminant production, contributes to human nutrition and food security. A thorough knowledge of the genetic potential of rumen microbes will provide opportunities for improving the sustainability of ruminant production systems. The availability of gene reference catalogs from gut microbiomes has advanced the understanding of the role of the microbiota in health and disease in humans and other mammals. In this work, we established a catalog of reference prokaryote genes from the bovine rumen. ResultsUsing deep metagenome sequencing we identified 13,825,880 non-redundant prokaryote genes from the bovine rumen. Compared to human, pig and mouse gut metagenome catalogs, the rumen is larger and richer in functions and microbial species associated with the degradation of plant cell wall material and production of methane. Genes encoding enzymes catalyzing the breakdown of plant polysaccharides showed a particularly high richness that is otherwise impossible to infer from available genomes or shallow metagenomics sequencing.The catalog expands by several folds the dataset of carbohydrate-degrading enzymes described in the rumen. Using an independent dataset from a group of 77 cattle fed 4 common dietary regimes, we found that only <0.1% of genes were shared by all animals, which contrast with a large overlap for functions, i.e. 63% for KEGG functions. Different diets induced differences in the relative abundance rather than the presence or absence of genes explaining the great adaptability of cattle to rapidly adjust to dietary changes. Conclusions

show abstract

Section: Abstract: Rumen; Metagenome; Herbivory; Carbohydrate-activementioning

confidence: 99%

Section: Construction Of a Bovine Rumen Prokaryotic Gene Catalogmentioning

confidence: 99%

Section: Construction Of a Bovine Rumen Prokaryotic Gene Catalogmentioning

confidence: 99%

See 1 more Smart Citation

A catalog of microbial genes from the bovine rumen unveils a specialized and diverse biomass-degrading environment

Huang

Ramayo-Caldas

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…As databases of assembled genomes continue to grow, databases of reference sequences used for metagenomics studies will also grow 19,20 . We presented Kraken 2, an extremely memory-efficient metagenomics classification tool that replaces Kraken 1's k-mer database with a probabilistic data structure that is substantially smaller, allowing 6-7 times as much reference data compared to Kraken 1.…”

mentioning

confidence: 99%

Improved metagenomic analysis with Kraken 2

Wood

Langmead

2019

Preprint

712

887

View full text Add to dashboard Cite

Although Kraken's k-mer-based approach provides fast taxonomic classification of metagenomic sequence data, its large memory requirements can be limiting for some applications. Kraken 2 improves upon Kraken 1 by reducing memory usage by 85%, allowing greater amounts of reference genomic data to be used, while maintaining high accuracy and increasing speed five-fold. Kraken 2 also introduces a translated search mode, providing increased sensitivity in viral metagenomics analysis.Assigning taxonomic labels to sequencing reads is an important part of many computational genomics pipelines for metagenomics projects. Recent years have seen several approaches to accomplish this task in a time-efficient manner 1-3 . Kraken 4 used a memory-intensive algorithm that associates short genomic substrings (k-mers) with lowest common ancestor (LCA) taxa. Kraken and related tools like KrakenUniq 5 have proven highly efficient and accurate in other tool comparisons 6,7 . But Kraken's high memory requirements force many researchers to either use a reduced-sensitivity MiniKraken database 8,9 , or to build and use many indexes over subsets of the reference sequences 10,11 . Its memory requirements can easily exceed 100 GB 7 , especially when the reference data includes large eukaryotic genomes 12,13 . Here we introduce Kraken 2, which provides a major reduction in memory usage as well as faster classification, a spaced-seed searching scheme, a translated search mode for matching in amino acid space, and continued compatibility with the Bracken 14 species-level quantification algorithm.Kraken 2 addresses the issue of large memory requirements through two changes to Kraken 1's data structures and algorithms. While Kraken 1 used a sorted list of k-mer/LCA pairs indexed by minimizers 15 , Kraken 2 introduces a probabilistic, compact hash table to map minimizers to LCAs. This table uses one-third of the memory of a standard hash table, at the cost of some specificity and accuracy. Additionally, Kraken 2 only stores minimizers (of length ℓ, ℓ ≤ k) from the reference sequence library in the data structure, whereas Kraken 1's stored all k-mers. Kraken 2's index for a reference database consisting of 9.1 Gbp of genomic sequence uses 10.6 gigabytes of memory at classification time. Kraken 1's index for the same reference set uses 72.4 gigabytes of memory for classification (Figure 1a, Supplementary Table S1). In general, a Kraken 2 database is about 15% as large as a Kraken 1 database over the same references (Supplementary Figure S1).Kraken 2's approach is faster than Kraken 1's because only the distinct minimizers from the query (read) trigger accesses to the hash table. A similar minimizer-based approach has proven useful in accelerating read alignment 16 . Kraken 2 additionally provides a hash-based filtering approach that subsamples the set of minimizer/LCA pairs included in the table, allowing the user to specify a target hash table size; smaller hash tables yield lower memory footprint and higher classification throughput at the expens...

show abstract

“…While initially, the reconstruction of MAGs was only achievable in lower-diversity or highly uneven communities (1), in the past five years reports on the reconstruction of hundreds to thousands of MAGs have become routine (2)(3)(4)(5). In the past year, highly automated assembly and binning pipelines have accelerated this trend (6,7).…”

mentioning

confidence: 99%

To dereplicate or not to dereplicate?

Evans

Denef

2019

Preprint

View full text Add to dashboard Cite

Our ability to reconstruct genomes from metagenomic datasets has rapidly evolved over the past decade, leading to publications presenting 1,000s, and even more than 100,000 metagenome-assembled genomes (MAGs) from 1,000s of samples. While this wealth of genomic data is critical to expand our understanding of microbial diversity, evolution, and ecology, various issues have been observed in some of these datasets that risk obfuscating scientific inquiry. In this perspective we focus on the issue of identical or highly similar genomes assembled from independent datasets. While obtaining multiple genomic representatives for a species is highly valuable, multiple copies of the same or highly similar genomes complicates downstream analysis. We analyzed data from recent studies to show the levels of redundancy within these datasets, the highly variable performance of commonly used dereplication tools, and to point to existing approaches to account and leverage repeated sampling of the same/similar populations.While initially, the reconstruction of MAGs was only achievable in lower-diversity or highly uneven communities (1), in the past five years reports on the reconstruction of hundreds to thousands of MAGs have become routine (2-5). In the past year, highly automated assembly and binning pipelines have accelerated this trend (6, 7). While these advances open up exciting prospects for addressing questions regarding the physiology, ecology, and evolution of microbial life, MAGs are inherently less reliable than isolate genomes due to their assembly and binning from DNA sequences originating from a mixed community. Various reports have highlighted issues associated with MAGs, including how misassemblies and/or incorrect binning can lead to composite genomes (8,9) and how fragmented assembly due to strain variation can lead to incomplete genomes that lead to wrong conclusions (10, 11). The latter is a reason why independent assembly of each individual sample is often preferable to avoid assembly fragmentation due to genomic variation between conspecific populations in different samples.However, this often leads to highly similar or identical MAGs being generated across the sample dataset. Multiple tools have been developed to remove redundant MAGs, mainly based on average nucleotide identity between MAGs after sequence alignment using blastn (e.g., pyANI (12)), or faster algorithms combining Mash (13) and gANI (14) or ANIm (15) (e.g., as implemented in dRep (16)).Why dereplicate? Dereplication is the reduction of a set of genomes, typically assembled from metagenomic data, based on high sequence similarity between these genomes. The main reason to do so is that when redundancy in a database of genomes is maintained, the subsequent step of mapping sequencing reads back to this database of genomes leads to sequencing reads having multiple high quality alignments which, depending on the software used and parameters chosen, leads to reads being randomly distributed across the redundant genomes with one random alignment report...

show abstract

Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen

Cited by 458 publications

References 80 publications

A catalog of microbial genes from the bovine rumen unveils a specialized and diverse biomass-degrading environment

A catalog of microbial genes from the bovine rumen unveils a specialized and diverse biomass-degrading environment

Improved metagenomic analysis with Kraken 2

To dereplicate or not to dereplicate?

Contact Info

Product

Resources

About