Metagenomics Profiling

Given a subset of ENA project identifiers related to metagenomics can we explore the sample attributes, profile different types and curate? All code is written in python 3. Note that you also require samples.csv. This is a large file that exceeds the 100MB limit on git. This file can be generated using the BioSamples API with make_input.py (see https://github.com/EBIBioSamples/curami for more details). Otherwise please get in touch and I can send you the latest dump (hewgreen@ebi.ac.uk).

Getting Started

1. Have a look at the attribute counts in metagenome_profile.csv. This is for the whole subset of 38120 samples.
2. Look at the coocurence raw data in samples_subset_coocurences.csv or weighted coexistencesProb.csv
3. Download gephi and look at session_file.gephi or coexistences.gexf

Data Processing (ID_converter.py)

input.txt contains a list of ENA project identifiers. ID_converter.py expands these project IDs into individual sample IDS using the ENA API and XML parsing. Then we convert these into BiopSamples IDs again from the ENA API with XML parsing. The script makes three files, expanded_ENAIDs.json, missing_BioSampleIDs.json and fetched_BioSampleIDs.json. The latter is required for the next script.

Attribute Analysis

profiler.py requires fetched_BioSampleIDs.json and samples.csv. It returns the following:

profile_dict.json - counts of attributes in the samples metagenome_profile.csv - counts of attributes in the samples samples_subset.csv - each sample and the attributes associated with them (extracted from samples.csv) samples_subset_coocurences.json - nested dictionary of coocurences counted samples_subset_coocurences.csv - coocurences counted in a 4 column csv (index, attribute 1, attribute 2 and count respectively) coexistencesProb.csv - weighted coocurences taking popularity into account when considering coocurrence. This is used as the edge weight in gephi. coexistences.gexf - a file readable by gephi to explore the coocurences (a free graph layout too https://gephi.org). There is also a session file in the repo that has been colored and laid out.

Future

• Dimension reduction of the samples and clustering to define different subsets
• Building slicing capability into curation app curami so these attributes can be refined