MetaPont 0.0.6

MetaPont - A tool to bridge the gap between the output of metagenomic tools and the analysis of the data

MetaPont

MetaPont - A tool to bridge the gap between the output of metagenomic tools and the analysis of the data

Features - These are the current aims of this project - Still under development

• Targeted Functional Analysis: Search for specific functional IDs (e.g., GO terms) within the _Final_Contig.tsv files provided by the HuwsLab Metagenome Workflow (https://github.com/TheHuwsLab/Metagenome_Workflow) .
• Taxonomic Breakdown: Extract genus-level taxonomy information and calculate their proportions in the dataset.
• Batch Processing: Analyse all _Final_Contig.tsv files in a specified directory.
• Customisable Output: Save results in a format suitable for downstream analysis.

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Installation

Prerequisites

Ensure you have the following installed:

• Python ~3.6 or later

Installation via pip

MetaPont is provided as a pip distribution.

pip install MetaPont 

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Usage

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Extract-By-Function Command-line Arguments

Extract-By-Function -h

usage: Extract_By_Function.py [-h] -d DIRECTORY -f FUNCTION_ID -o OUTPUT
                              [-m MIN_PROPORTION] [-top TOP_TAXA]

MetaPont v0.0.6: Extract-By-Function - Identify taxa contributing to a
specific function.

options:
  -h, --help            show this help message and exit
  -d DIRECTORY, --directory DIRECTORY
                        Directory containing TSV files to analyse.
  -f FUNCTION_ID, --function_id FUNCTION_ID
                        Specific function ID to search for (e.g.,
                        'GO:0016597').
  -o OUTPUT, --output OUTPUT
                        Output file to save results.
  -m MIN_PROPORTION, --min_proportion MIN_PROPORTION
                        Minimum proportion threshold for taxa to be included
                        in the output.
  -top TOP_TAXA, --top_taxa TOP_TAXA
                        Top n taxa to be included in the output.

The Extract-By-Function tool provides several command-line options:
Note: Either -m or -top is required.

Option	Description	Required	Default
-d, --directory	Directory containing _Final_Contig.tsv files to analyse.	Yes	None
-f, --function_id	Functional ID to search for (e.g., GO:0016597).	Yes	None
-m, --min_proportion	Minimum proportion needed for reporting.	Yes/No	None
-top, --top_taxa	Number of taxa to report.	Yes/No	None
-o, --output	Output file name to save results.	Yes	None

Example

To search for the functional ID GO:0016597 in all _Final_Contig.tsv files within the test_data/ directory:

Extract=By-Function -d .../test_data/Final_contig/ -f GO:0016597 -top 3 -o .../test_data/Final_Contig/Extract_By_Function_Out/results.tsv

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Output

The tool generates a tab-delimited output file with the following columns:

1. Sample: Name of the processed Sample.
2. Taxa: Genus-level taxonomic assignment extracted from the Lineage column.
3. Reads Assigned (Function): Number of reads assigned to contigs with the given functional ID.
4. Proportion: Proportion of reads assigned to contigs of stated Taxa with the given functional ID within the sample.
5. Proportion (Total Reads): Proportion of reads assigned to contigs of stated Taxa with the given functional ID within the total reads of the sample.

Example output:

Function ID: GO:0016597
Sample	Taxa	Reads Assigned (Function)	Proportion (Function)	Proportion (Total Reads)
PN0536_0001_S1_Final_Contig.tsv	Lactobacillus	111963	0.602	0.004
PN0536_0003_S83_Final_Contig.tsv	Lactobacillus	20072	0.457	0.001
PN0536_0002_S2_Final_Contig.tsv	Acutalibacter	145222	0.795	0.005
PN0536_0004_S3_Final_Contig.tsv	Lactobacillus	40076	0.404	0.002

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Workflow - unfinished

1. The script reads _Final_Contig.tsv files from the specified directory.
2. For each file, it searches for occurrences of the given functional ID within specific columns.
3. Matches are associated with genus-level taxonomic information extracted from the Lineage column.
4. Taxa proportions are calculated and saved to the output file.

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Extract-By-Taxa Command-line Arguments

Extract-By-Taxa -h

usage: Extract_By_Taxa.py [-h] -d DIRECTORY -t TAXON -o OUTPUT -func
                          FUNCTIONAL_CLASSES [-top TOP_FUNCTIONS]

MetaPont: Extract Top Functions by Taxon

options:
  -h, --help            show this help message and exit
  -d DIRECTORY, --directory DIRECTORY
                        Directory containing TSV files to analyse.
  -t TAXON, --taxon TAXON
                        Target taxon to search for (e.g., 'g__Bacillus').
  -o OUTPUT, --output OUTPUT
                        Output file to save results.
  -func FUNCTIONAL_CLASSES, --functional_classes FUNCTIONAL_CLASSES
                        Which functional classes to report (e.g. GO,EC,KEGG
                        etc).
  -top TOP_FUNCTIONS, --top_functions TOP_FUNCTIONS
                        Top n functions to include in the output for each
                        sample (default: 3).

The Extract-By-Taxa tool provides several command-line options:

Option	Description	Required	Default
-d, --directory	Directory containing _Fincal_Contig.tsv files to analyse.	Yes	None
-t, --taxon	Taxa to search for (e.g., g__Bacillus).	Yes	None
-func, --functional_classes	Functional classes to report (e.g. GO,EC,KEGG etc).	Yes	None
-top, --top_taxa	Number of functions to report (default 3).	No	None
-o, --output	Output file name to save results.	Yes	None

Example

To search for the top reported functions for taxon g__Bacillus in all _Final_Contig.tsv files within the test_data/ directory:

Extract-By-Taxa -d .../test_data/Final_Contig -t g__Bacillus -o .../test_data/Final_Contig/Extract_By_Taxa/results.tsv  -func GO

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Output

The tool generates a tab-delimited output file with the following columns:

1. Sample: Name of the processed Sample.
2. Function: Reported 'top' function.
3. Num of Assignments (Functions): Number of times the function has been assigned across all contigs reported as chosen Taxon.

Example output:

Selected Taxon: g__Bacillus
Sample	Function	Num of Assignments
PN0536_0001_S1	GO:0008150	296
PN0536_0001_S1	GO:0003674	285
PN0536_0001_S1	GO:0005575	254
PN0536_0003_S83	GO:0005575	45
PN0536_0003_S83	GO:0008150	44
PN0536_0003_S83	GO:0003674	43
PN0536_0002_S2	GO:0005575	5
PN0536_0002_S2	GO:0008150	5
PN0536_0002_S2	GO:0005623	4
PN0536_0004_S3	GO:0008150	4
PN0536_0004_S3	GO:0003674	3
PN0536_0004_S3	GO:0005488	3

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Large File Handling (Might be a failure point)

The script uses csv.field_size_limit to handle exceptionally large .tsv files.

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