Back

Efficient High-Quality Metagenome Assembly from Long Accurate Reads using Minimizer-space de Bruijn Graphs

Benoit, G.; Raguideau, S.; James, R.; Phillippy, A. M.; Chikhi, R.; Quince, C.

2023-07-08 bioinformatics Community evaluation
10.1101/2023.07.07.548136 bioRxiv
Show abstract

We introduce a novel metagenomics assembler for high-accuracy long reads. Our approach, implemented as metaMDBG, combines highly efficient de Bruijn graph assembly in minimizer space, with both a multi-k' approach for dealing with variations in genome coverage depth and an abundance-based filtering strategy for simplifying strain complexity. The resulting algorithm is more efficient than the state-of-the-art but with better assembly results. metaMDBG was 1.5 to 12 times faster than competing assemblers and requires between one-tenth and one-thirtieth of the memory across a range of data sets. We obtained up to twice as many high-quality circularised prokaryotic metagenome assembled genomes (MAGs) on the most complex communities, and a better recovery of viruses and plasmids. metaMDBG performs particularly well for abundant organisms whilst being robust to the presence of strain diversity. The result is that for the first time it is possible to efficiently reconstruct the majority of complex communities by abundance as nearcomplete MAGs.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
Genome Biology
○ 637 papers in training set
Top 0.4%
17.7%
Similar papers in this journal
2
Microbiome
○ 154 papers in training set
Top 0.2%
11.4%
Similar papers in this journal
3
Nature Communications
○ 5641 papers in training set
Top 17%
11.4%
Similar papers in this journal
4
Bioinformatics
◐ 1204 papers in training set
Top 2%
10.1%
Similar papers in this journal
50% of probability mass above
5
Nature Biotechnology
○ 172 papers in training set
Top 0.3%
7.5%
Similar papers in this journal
6
GigaScience
◐ 212 papers in training set
Top 0.6%
5.3%
7
Nature Methods
○ 385 papers in training set
Top 2%
5.2%
8
Nucleic Acids Research
◐ 1281 papers in training set
Top 6%
3.1%
9
Bioinformatics Advances
◐ 203 papers in training set
Top 2%
2.5%
10
BMC Bioinformatics
○ 457 papers in training set
Top 3%
2.5%
11
PLOS Computational Biology
● 1863 papers in training set
Top 11%
2.5%
12
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 2%
2.3%
13
Genome Research
● 468 papers in training set
Top 3%
2.3%
14
Briefings in Bioinformatics
◐ 354 papers in training set
Top 4%
2.0%
15
Microbial Genomics
● 225 papers in training set
Top 2%
1.6%
16
iScience
○ 1154 papers in training set
Top 20%
1.6%
17
BMC Genomics
○ 406 papers in training set
Top 6%
1.1%
18
Scientific Reports
○ 3612 papers in training set
Top 69%
1.0%
19
PLOS ONE
● 5266 papers in training set
Top 61%
0.8%
20
mSystems
● 394 papers in training set
Top 6%
0.8%
21
iMeta
○ 10 papers in training set
Top 0.3%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.