nose-pipeline 1.0.0

NOSE — Novel Species Identification Pipeline (Computational Biology Lab, IIT Madras)

NOvel SpEcies Identification Pipeline

A Modular Snakemake Toolkit for Novelty Identification and Characterization

Documentation Site  ·  Wiki  ·  Setup Guide  ·  Report an Issue

NOSE is a scalable bioinformatics pipeline designed to identify and characterize novel microbial species. By integrating quality assessment, phylogenomics, and functional mapping into a unified Snakemake workflow, NOSE ensures research is reproducible, automated, and ready for HPC environments.

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Table of Contents

• Overview
• Pipeline Structure
• Installation
• Modules
  • Module 1: Genome Summary & Classification
  • Module 2: Overall Genome-Relatedness (OGRI)
  • Module 3: Phylogenetic Tree Workflow
  • Module 4: Metagenome Mapping Workflow
  • Module 5: Functional Characterization
  • Module 6: Metabolic Modeling Workflow
• Authors
• License

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Overview

NOSE takes a directory of genome FASTA files and returns — with full scientific evidence — whether each genome belongs to an already-described species or represents a candidate novel organism. Each module is an independent Snakemake workflow with its own conda environments, config file, and output directory.

FASTA Files  →  M1: Quality Check  →  M2: Novelty Screen  →  Novel / Known
                                                                    ↓
                                                     · M3: Phylogenetic Tree Workflow
                                                     · M4: Metagenome Mapping
                                                     · M5: Functional Characterization
                                                     · M6: Metabolic Modeling

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Pipeline Structure

Module	Name	Trigger	Key Output
M1	Genome Summary & Classification	All input genomes	genome_summary.csv
M2	Overall Genome-Relatedness (OGRI)	M1 output	compiled_results.csv · potential_novel.csv
M3	Phylogenetic ML Tree	ANI < 95% (novel)	ML tree + iTOL annotation
M4	Metagenome Mapping	ANI < 95% (novel)	final_report.csv
M5	Functional Characterization	ANI < 95% (novel)	7 CSV outputs
M6	Metabolic Modeling	ANI < 95% (novel)	SBML models + model_summary.csv

How to read the figure: FASTA files enter at the top. M1 and M2 run on all genomes. The ANI < 95% gate separates known species (right branch) from novel candidates (green path). All four characterization modules (M3–M6) run only on novel candidates.

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Installation

Follow the guide below to install Anaconda and configure a dedicated Snakemake environment. This ensures a stable, reproducible workspace with all required bioinformatics channels pre-configured.

📖 Anaconda and Snakemake Setup & Installation

Quick setup:

# 1. Install conda and activate
source ~/anaconda3/bin/activate && conda init

# 2. Add bioinformatics channels
conda config --add channels conda-forge
conda config --add channels bioconda
conda config --set channel_priority strict

# 3. Create the Snakemake environment
conda create -n snakemake snakemake -y
conda activate snakemake

# 4. Clone the full NOSE repository
git clone https://github.com/RamanLab/NOSE.git
cd NOSE

# 5. Enter any module, edit config.yaml, then run
cd Module1
# Edit config.yaml with your paths, then:
bash Nose_Module1.sh

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Modules

Module 1: Genome Summary & Classification

📁 Module 1  |  Script: Nose_Module1.sh

Automated Snakemake workflow for assembly quality assessment and full taxonomic classification. A single is_euk flag in config.yaml selects the prokaryotic or eukaryotic path. Genomes passing the HQ filter are written to genome_summary.csv for downstream use.

Tools:

Path	Tools
Prokaryotic	QUAST · CheckM2 · GTDB-Tk
Eukaryotic	QUAST · EukCC · CAT

Quality thresholds: Completeness ≥ 95% · Contamination ≤ 5% · Classified to genus level minimum

Outputs:

• genome_summary.csv — genomes passing QC thresholds → input for Module 2
• unqualified_genome_summary.csv — genomes that did not meet thresholds

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Module 2: Overall Genome-Relatedness (OGRI)

📁 Module 2  |  Script: Nose_Module2.sh

Computes three complementary genomic distance metrics against all RefSeq type strains in the genome's genus, downloaded automatically via the NCBI Datasets API. Supports both WGS and 16S-only input modes. Genomes with ANI < 95% are flagged as candidate novel species.

⚠️ An NCBI API key must be set in config.yaml. Without it, reference genome downloads will be rate-limited. 🔗 How to get an NCBI API key: NCBI API Integration Guide

Tools: FastANI · AAI (aai.rb) · POCP (pocp.sh) · Barrnap · BLASTn · NCBI Datasets API

Three OGRI metrics:

Metric	Boundary	Significance
ANI	< 95% = novel	Primary species-level boundary (IJSEM standard)
AAI	—	Broader evolutionary distances at proteome level
POCP	< 50% = new genus	Genus-level delineation

Outputs:

• compiled_results.csv — all genomes with ANI / AAI / POCP / 16S identity
• potential_novel.csv — ANI < 95% candidates, staged for M3–M6

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Module 3: Phylogenetic Tree Workflow

📁 Module 3  |  Script: Nose_Module3.sh

Validates taxonomic novelty via genus-specific Maximum Likelihood (ML) trees built from concatenated Single-Copy Genes (SCGs). GToTree identifies SCGs using HMMs; IQ-TREE infers the ML tree with 1000 ultrafast bootstrap replicates; tree_annotation.py generates iTOL-ready annotation files.

⚠️ Requires manual addition of Outroup (GCF ID) and HMM columns to genome_summary_mod.csv before running.

Tools: GToTree · IQ-TREE · iTOL · tree_annotation.py

IQ-TREE command:

iqtree \
  -s Aligned_SCGs.faa \   # concatenated SCG alignment
  -spp Partitions.txt \   # per-gene partition model
  -m MFP \                # ModelFinder Plus
  -bb 1000 \              # ultrafast bootstraps
  -nt 4 \                 # CPU threads
  -pre {genus}_iqtree_out

Outputs: Per-genus ML tree files + iTOL annotation CSV files

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Module 4: Metagenome Mapping Workflow

📁 Module 4  |  Script: Nose_Module4.sh

Quantifies isolate prevalence and relative abundance across metagenomic datasets using sylph k-mer containment estimation. No BAM files or read alignment required. Species-level threshold (c=100) with minimum 5 k-mers enforced to suppress false positives.

Tools: sylph · pandas · merge_results.py

Containment thresholds:

Threshold (c)	Resolution
100	Species-level (default)
95	Genus-level
90	Family-level

Output: final_report.csv — Sample_ID · Genome · Containment · ANI · Reads_Queried · Reads_Matching

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Module 5: Functional Characterization

📁 Module 5  |  Script: Nose_Module5.sh

Multi-modal pipeline integrating structural annotation, COG functional classification, biosynthetic gene cluster detection, resistance and virulence screening, and mobile element identification.

Tools: Prokka · COGclassifier · antiSMASH · ABRICATE · geNomad

Workflow steps:

Step	Tool	Output
Gene annotation	Prokka	.gff · .faa · .ffn · .gbk
Functional classification	COGclassifier	merged_classifier_count.csv
BGC detection	antiSMASH	AntiSMASH_results.csv
Resistance & virulence	ABRICATE (CARD · VFDB · BacMet · NCBI)	Per-database tabular reports
Phage & plasmid detection	geNomad	virus_summary.csv · plasmid_summary.csv

💡 geNomad tells you whether resistance or virulence genes reside on mobile elements — meaning they can spread horizontally to other organisms.

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Module 6: Metabolic Modeling Workflow

📁 Module 6  |  Script: Nose_Module6.sh

Genome-scale metabolic reconstruction using CarveMe (top-down approach). Each GEM is validated with COBRApy unconstrained growth tests and benchmarked with MEMOTE for stoichiometric consistency. Models are exported in SBML/FBC format compatible with COBRA Toolbox, cobrapy, and OptFlux.

Tools: CarveMe · COBRApy · MEMOTE · generate_model_stats.py · compile_model_summary.py

⚠️ Always use the --prodigal flag with CarveMe. Default gene prediction produces incomplete GEM reconstruction for certain isolates.

Output: model_summary.csv — sample · n_reactions · n_metabolites · n_genes · growth · memote_score

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Authors

Developed at Computational Biology Lab, IIT Madras

Role	Name
Principal Investigator	Prof. Karthik Raman
Pipeline Authors	Prithvi · Harippriya · Enos · Pratyay Sengupta

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License

This project is licensed under the MIT License — see the LICENSE file for details.

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_{© 2026 NOSE Project Team · Computational Biology Lab · IIT Madras · Built for Novel Species Identification}