_configuration:

Configure Atlas¶

_contaminants:

Remove reads from Host¶

One of the most important steps in the Quality control is to remove host genome. You can add any number of genomes to be removed.

We recommend you to use genomes where repetitive sequences are masked. See here for more details human genome.

Co-abundance Binning¶

While binning each sample individually is faster, using co-abundance for binning is recommended. Quantifying the coverage of contigs across multiple samples provides valuable insights about contig co-variation.

There are two primary strategies for co-abundance binning:

Cross mapping: Map the reads from multiple samples to each sample’s contigs.
Co-binning: Concatenate contigs from multiple samples and map all the reads to these combined contigs.

final_binner: metabat2 is used for cross-mapping, while vamb or SemiBin is used for co-binning.

The samples to be binned together are specified using the BinGroup in the sample.tsv file. The size of the BinGroup should be selected based on the binner and the co-binning strategy in use.

Cross mapping complexity scales quadratically with the size of the BinGroup since each sample’s reads are mapped to each other. This might yield better results for complex metagenomes, although no definitive benchmark is known. On the other hand, co-binning is more efficient, as it maps a sample’s reads only once to a potentially large assembly.

Default Behavior¶

Starting with version 2.18, Atlas places every sample in a single BinGroup and defaults to vamb as the binner unless there are very few samples. For fewer than 8 samples, metabat is the default binner.

Note

This represents a departure from previous versions, where each sample had its own BinGroup. Running vamb in those versions would consider all samples, regardless of their BinGroup. This change might cause errors if using a sample.tsv file from an older Atlas version. Typically, you can resolve this by assigning a unique BinGroup to each sample.

The mapping threshold has been adjusted to 95% identity (single sample binning is 97%) to allow reads from different strains — but not other species — to map to contigs from a different sample.

If you’re co-binning more than 150-200 samples or cross-mapping more than 50 samples, Atlas will issue a warning regarding excessive samples in a BinGroup. Although VAMB’s official publication suggests it can handle up to 1000 samples, this demands substantial resources.

Therefore, splitting your samples into multiple BinGroups is recommended. Ideally, related samples, or those where the same species are anticipated, should belong to the same BinGroup.

Single-sample Binning¶

To employ single-sample binning, simply assign each sample to its own BinGroup and select metabat or DASTool as the final_binner.

Although it’s not recommended, it’s feasible to use DASTool and feed it inputs from metabat and other co-abundance-based binners.

Add the following lines to your config.yaml:

final_binner: DASTool

binner:
  - metabat
  - maxbin
  - vamb

Long reads¶

Limitation: Hybrid assembly of long and short reads is supported with spades and metaSpades. However metaSpades needs a paired-end short-read library.

The path of the (preprocessed) long reads should be added manually to the the sample table under a new column heading ‘longreads’.

In addition the type of the long reads should be defined in the config file: longread_type one of [“pacbio”, “nanopore”, “sanger”, “trusted-contigs”, “untrusted-contigs”]

Example config file¶

include:: ../../workflow/../config/template_config.yaml

code:

Detailed configuration¶

toctree::

maxdepth:	1

../advanced/qc ../advanced/assembly