PLoT-ME 0.9.1

Description:

PLoTME 0.9.1

PLoT-ME
Pre-classification of Long-reads for Memory Efficient Taxonomic assignment
Sylvain Riondet, K. Križanović, J. Marić and M, Šikić, Niranjan Nagarajan
NUS/SoC, Biopolis/GIS, Singapore
Tool in active development, any feedback or bug report is welcome, either through github or on twitter
Description
Pre-Processing

Segmentation of NCBI RefSeq into clusters
Building of taxonomic classifiers' indexes for each cluster

Classification
Taxonomic classification of mock communities / metagenomic fastq files

Assignment of long DNA reads (Nanopore/PacBio) to each cluster
Classification by the classifier with a subset of RefSeq
Merging of reports

Kraken2 (Derrick E. Wood et al. 2019) and Centrifuge (D.Kim et al. 2016) are currently automated, and any classifier able to build its index on a set of .fna files with a provided taxid should work.
Take-aways


High reduction in memory needs, defined by the number of clusters *
Compatible and enhancing existing taxonomic classifiers
Slight over-head of the pre-classification (currently ~3-5x in time, improvements for future releases)

* Mini Batch K-Means, Web-Scale K-Means Clustering D. Sculley 2010
Requirements

Database of Genomes, in .fna / .fasta format, with an associated taxonomy id.
Tested with NCBI RefSeq (ftp server)
Taxonomic classifier, must be installed and added to PATH. Currently supported:

Kraken2
Centrifuge
(feel free to request support for more)


Linux (tested on Ubuntu 18.04)
Python >= 3.7




Package
Version




biopython
>= 1.72


ete3
>= 3.1.1


numpy
>= 1.17.3


pandas
>= 0.23


scikit-learn
>= 0.18


tqdm
>= 4.24.0



Installation
Create a Python 3 environment with conda
or pyenv.
Installation is then done with pip:
python3 -m pip install plot-me
This will create 2 commands, plot-me.preprocess and plot-me.classify detailed in the 'Usage'.

It is also possible to clone PLoT-ME's repo,
and launching commands directly with python path/to/PLoT-ME/parse_DB.py or classify.py
Usage
Pre-Processing
For the full help: plot-me.preprocess -h
Typical usage:
plot-me.preprocess <path/NCBI/refseq> <folder/for/clusters> <path/taxonomy> -k 4 -w 10000 -n 10 -o <OmitFoldersContainingString>
Pre-classification + classification
For the full help: plot-me.classify -h
Typical usage:
plot-me.classify <folder/with/clusters> <folder/reports> -i <fastq files to preclassify>
Example
/mnt/data
|-- mock_files
| |-- mock_community_1.fastq
| | \-- minikm_b10_k3_s10000_oplant-vertebrate (one tmp file per cluster, generated by PLoT-ME)
| \-- mock_community_2.fastq
|-- PLoT-ME
| |-- k3_s10000
| | | -- kmer_counts
| | | |-- counts.k3_s10000 (same tree as RefSeq, with <sequencing_name>.3mer_count.pd)
| | | \-- all-counts.k3_s10000_oplant-vertebrate.csv
| | | -- minikm_b10_k3_s10000_oplant-vertebrate <*>
| | | |-- centrifuge (10 folders with indexes)
| | | |-- kraken2 (10 folders with indexes)
| | | |-- RefSeq_binned (10 folders with fna files)
| | | |-- model.minikm_b10_k3_s10000_oplant-vertebrate.pkl
| | | \-- segments-clustered.minikm_b10_k3_s10000_oplant-vertebrate.pd
| | \ -- minikm_b20_k3_s10000_oplant-vertebrate
| | \-- (same structure)
| |-- k4_s10000
| | ` -- (same structure)
| \-- no-binning
| |-- oAllRefSeq
| \-- oplant-vertebrate
| |-- centrifuge
| \-- kraken2
|-- NCBI
| \-- refseq
|-- reports
| \-- mock_community_1 (one report per cluster)
\-- taxonomy

This <*> can be generated with:
plot-me.preprocess /mnt/data/NCBI/refseq /mnt/data/PLoT-ME /mnt/data/taxonomy -k 3 -w 10000 -n 10 -o plant vertebrate
And can be used with:
plot-me.classify /mnt/data/PLoT-ME/k3_s10000/minikm_b10_k3_s10000_oplant-vertebrate /mnt/data/reports -i /mnt/data/mock_files/mock_community_1.fastq
Technical details
Python 3 is the main programming language, with extensive use of libraries.
Dependencies are resolved using PIP
Intermediate Data
Data is saved as pickle .pkl or Pandas DataFrame .pd

Kmer counts Pandas DataFrames are saved under .../kmer_counts/counts.<param> and have the following columns:
taxon category start end name description fna_path AAAA ... TTTT
Cluster assignments segments-clustered.\<param\>.pd trade the nucleotides columns to a cluster column.
RefSeq_binned is the clustering made by PLoT-ME, and holds one folder per cluster, with concatenated segments of genomes (one .fna file per taxa)
Libraries generated by classifier, depends on each of them.

Final files
The model*.pkl and the folder kraken2 or centrifuge are needed for PLoT-ME to work. Folder tree needs to remain intact.
Work in progress
April 2021

Implementation of Cython version of the kmer counter
Adding reverse complement to forward strand

July 2020:

pre-process Using large k (5+) and small s (10000-) yield very large kmer counts, costing
high amounts of RAM (esp. when combining all kmer counts together,
RAM needs to reach ~30GB or more).
classify Merging of reports
pre-process Cleaning of pre-processing files --clean

Future work

classify Cleaning of pre-classification tmp files
classify Multi cores
classify/pre-process Speed up kmer counting
pre-process Even sized bins
pre-process Overlapping clusters or tricks for higher accuracy

Contact
Author: Sylvain Riondet, PhD student at the National University of Singapore, School of Computing
Email: [email protected]
Lab: Genome Institute of Singapore / National University of Singapore
Supervisors: Niranjan Nagarajan & Martin Henz
Thanks
Thanks for your support and supervision all along my PhD and this project: Martin Henz, Chenhao Li, Rafael Peres, D. Bertrand and the whole MTMS lab