Usage

Input Formats

Binette supports two input formats for bin sets:

1. Contig2bin Tables: You can provide bin sets using contig2bin tables, which establish the relationship between each contig and its corresponding bin. In this format, you need to specify the --contig2bin_tables argument.

For example, consider the following two contig2bin_tables:

• bin_set1.tsv:

  contig_1   binA
  contig_8   binA
  contig_15  binB
  contig_9   binC
  

• bin_set2.tsv:

  contig_1   bin.0
  contig_8   bin.0
  contig_15  bin.1
  contig_9   bin.2
  contig_10  bin.0
  

  The binette command to process this input would be:

  binette --contig2bin_tables bin_set1.tsv bin_set2.tsv --contigs assembly.fasta
  

2. Bin Directories: Alternatively, you can use bin directories, where each bin is represented by a separate FASTA file. For this format, you need to provide the --bin_dirs argument. Here’s an example of two bin directories:

   bin_set1/
   ├── binA.fa: contains sequences of contig_1, contig_8
   ├── binB.fa: contains sequences of contig_15
   └── binC.fa: contains sequences of contig_9
   

   bin_set2/
   ├── binA.fa: contains sequences of contig_1, contig_8, contig_10
   ├── binB.fa: contains sequences of contig_15
   └── binC.fa: contains sequences of contig_9
   

   The binette command to process this input would be:

   binette --bin_dirs bin_set1 bin_set2 --contigs assembly.fasta
   

In both formats, the --contigs argument should specify a FASTA file containing all the contigs found in the bins. Typically, this file would be the assembly FASTA file used to generate the bins. In these examples the assembly.fasta file should contain at least the five contigs mentioned in the contig2bin_tables files or in the bin fasta files: contig_1, contig_8, contig_15, contig_9, and contig_10.

Providing Precomputed Protein Sequences

You can provide protein sequences in FASTA format to Binette using the --proteins argument. The sequence identifiers must follow the Prodigal convention: <contigID>_<GeneID>. This naming format ensures proper mapping of each gene to its contig.

By using this option, the gene prediction step is skipped.

Note

When using precomputed protein sequences, the coding_density column in the output reports will be empty, as this metric requires gene coordinates that are only available when genes are freshly predicted.

Example

If your contig is named contig_A, the gene identifiers should follow this pattern:

• contig_A_1

• contig_A_2

• contig_A_3

Outputs

Binette results are stored in the results directory. You can specify a different directory using the --outdir option.

In this directory you will find:

• final_bins_quality_reports.tsv: This is a TSV (tab-separated values) file containing quality information about the final selected bins.

• final_bins/: This directory stores all the selected bins in fasta format. Can be skipped with --no-write-fasta-bins.

• final_contig_to_bin.tsv: A headerless TSV file mapping each contig to its assigned bin. This format is much lighter than the fasta output to describe the final Binette bins.

• input_bins_quality_reports/: A directory storing quality reports for the input bin sets, with files following the same structure as final_bins_quality_reports.tsv.

• temporary_files/: This directory contains intermediate files. If you choose to use the --resume option, Binette will utilize files in this directory to prevent the recomputation of time-consuming steps.

The final_bins_quality_reports.tsv file contains the following columns:

Column Name	Description
name	The unique name of the bin.
origin	Indicates the source of the bin: either an original bin set (e.g., B) or binette for intermediate bins.
is_original	Boolean flag indicating if the bin is an original bin (True) or an intermediate bin (False).
original_name	The name of the original bin from which this bin was derived.
completeness	The completeness of the bin, determined by CheckM2.
contamination	The contamination of the bin, determined by CheckM2.
checkm2_model	The CheckM2 model used for quality prediction: Gradient Boost (General Model) or Neural Network (Specific Model).
score	Computed score: completeness - contamination * weight. The contamination weight can be customized using the --contamination_weight option.
size	Total size of the bin in nucleotides.
N50	The N50 of the bin, representing the length for which 50% of the total nucleotides are in contigs of that length or longer.
coding_density	The percentage of the bin that codes for proteins (genes length / total bin length × 100). Only computed when genes are freshly identified. Empty when using --proteins or --resume options.
contig_count	Number of contigs contained within the bin.