# Change log

## 2.17

### Skani The tool Skani claims to be better and faster than the combination of mash + FastANI as used by dRep I implemented the skin for species clustering. We now do the species clustering in the atlas run binning step. So you get information about the number of dereplicated species in the binning report. This allows you to run different binners before choosing the one to use for the genome annotation. Also, the file storage was improved all important files are in Binning/{binner}/

My custom species clustering does the following steps:

  1. Pre-cluster genomes with single-linkage at 92.5 ANI.
  2. Re-calibrate checkm2 results.
  • If a minority of genomes from a pre-cluster use a different translation table they are removed
  • If some genomes of a pre-cluster don’t use the specialed completeness model we re-calibrate completeness to the minimum value.

This ensures that not a bad genome evaluated on the general model is preferred over a better genome evaluated on the specific model.

See also https://silask.github.io/post/better_genomes/ Section 2. - Drop genomes that don’t correspond to the filter criteria after re-calibration 3. Cluster genomes with ANI threshold default 95% 4. Select the best genome as representative based on the Quality score Completeness - 5x Contamination

### New Contributors * @jotech made their first contribution in https://github.com/metagenome-atlas/atlas/pull/667

## 2.16

  • gtdb08

## 2.15

## 2.14

Thank you @trickovicmatija for your help.

Full Changelog: https://github.com/metagenome-atlas/atlas/compare/v2.13.1…v2.14.0 ## 2.13

The filter function is defined in the config file: ` genome_filter_criteria: "(Completeness-5*Contamination >50 ) & (Length_scaffolds >=50000) & (Ambigious_bases <1e6) & (N50 > 5*1e3) & (N_scaffolds < 1e3)" ` The genome filtering is similar as other publications in the field, e.g. GTDB. What is maybe a bit different is that genomes with completeness around 50% and contamination around 10% are excluded where as using the default parameters dRep would include those.

We saw better performances using drep. This scales also now to ~1K samples * Use new Dram version 1.4 by in https://github.com/metagenome-atlas/atlas/pull/564

Full Changelog: https://github.com/metagenome-atlas/atlas/compare/v2.12.0…v2.13.0

## 2.12

  • GTDB-tk requires rule extract_gtdb to run first by @Waschina in https://github.com/metagenome-atlas/atlas/pull/551
  • use Galah instead of Drep
  • use bbsplit for mapping to genomes (maybe move to minimap in future)
  • faster gene catalogs quantification using minimap.
  • Compatible with snakemake v7.15

### New Contributors * @Waschina made their first contribution in https://github.com/metagenome-atlas/atlas/pull/551

Full Changelog: https://github.com/metagenome-atlas/atlas/compare/v2.11.1…v2.12.0

## 2.11 * Make atlas handle large gene catalogs using parquet and pyfastx (Fix #515)

parquet files can be opened in python with ``` import pandas as pd coverage = pd.read_parquet(“working_dir/Genecatalog/counts/median_coverage.parquet”) coverage.set_index(“GeneNr”, inplace=True)

```

and in R it should be something like:

``` arrow::read_parquet(“working_dir/Genecatalog/counts/median_coverage.parquet”)

```

Full Changelog: https://github.com/metagenome-atlas/atlas/compare/v2.10.0…v2.11.0

## [2.10](https://github.com/metagenome-atlas/atlas/compare/v2.9.1…v2.10.0)

### Features * GTDB version 207 * Low memory taxonomic annotation

## [2.9](https://github.com/metagenome-atlas/atlas/compare/v2.8.2…v2.9.0)

### Features * ✨ Start an atlas project from public data in SRA [Docs](https://metagenome-atlas.readthedocs.io/en/latest/usage/getting_started.html#start-a-new-project-with-public-data) * Make atlas ready for python 3.10 https://github.com/metagenome-atlas/atlas/pull/498 * Add strain profiling using inStrain You can run atlas run genomes strains

### New Contributors * @alienzj made their first contribution to fix config when run DRAM annotate in https://github.com/metagenome-atlas/atlas/pull/495

## 2.8 This is a major update of metagenome-atlas. It was developed for the [3-day course in Finnland](https://silask.github.io/talk/3-day-course-on-metagenome-atlas/), that’s also why it has a finish release name.

### New binners It integrates bleeding-edge binners Vamb and SemiBin that use Co-binning based on co-abundance. Thank you @yanhui09 and @psj1997 for helping with this. The first results show better results using these binners over the default.

[See more](https://metagenome-atlas.readthedocs.io/en/v2.8.0/usage/output.html#binning)

### Pathway annotations The command atlas run genomes produces genome-level functional annotation and Kegg pathways respective modules. It uses DRAM from @shafferm with a hack to produce all available Kegg modules.

[See more](https://metagenome-atlas.readthedocs.io/en/v2.8.0/usage/output.html#annotations)

### Genecatalog The command atlas run genecatalog now produces directly the abundance of the different genes. See more in #276

> In future this part of the pipeline will include protein assembly to better tackle complicated metagenomes.

### Minor updates

#### Reports are back See for example the [QC report](https://metagenome-atlas.readthedocs.io/en/v2.8.0/_static/QC_report.html)

#### Update of all underlying tools All tools use in atlas are now up to date. From assebler to GTDB. The one exception is, BBmap which contains a [bug](https://sourceforge.net/p/bbmap/tickets/48/) and ignores the minidenty parameter.

#### Atlas init Atlas init correctly parses fastq files even if they are in subfolders and if paired-ends are named simply Sample_1/Sample_2. @Sofie8 will be happy about this. Atlas log uses nice colors.

#### Default clustering of Subspecies

The default ANI threshold for genome-dereplication was set to 97.5% to include more sub-species diversity.

[See more](https://metagenome-atlas.readthedocs.io/en/v2.8.0/usage/output.html#genomes)