ngs-analysis 0.0.4

Analyze deep sequencing of complex libraries

ngs-analysis

Convenient analysis of sequencing reads that span multiple DNA or protein parts. For instance, given a library of protein variants linked to DNA barcodes, this tool can answer questions like:

• How accurate are the variant sequences, at the DNA or protein level?
• How frequently is the same barcode linked to two different variants?
• Which reads contain parts required for function (e.g., a kozak start sequence, or a fused protein tag)?

This kind of analysis often involves parsing raw sequencing reads for DNA and/or protein sub-sequences (parts), then mapping the parts to a reference of anticipated part combinations. This package offers a simple workflow:

1. Define how to parse reads into parts using plain text expressions (no code)
2. Run the parser on your anticipated DNA sequences to generate a reference
3. Parse a batch of sequencing samples
4. Map the parts found in each read to the reference

It’s been tested with Illumina paired-end reads and Oxford Nanopore long reads. Under the hood it uses NGmerge to merge paired reads and MMseqs2 for sequencing mapping. It is moderately performant: 1 million paired-end reads can be mapped to a reference of 100,000 variant-barcode pairs in ~1 minute.

Workflow

A cartoon example with two reference sequences, each consisting of a variant linked to a barcode:

Here's the analysis workflow and outputs:

Note that in the last two columns, the parsed variant is mapped to a reference variant defined by the barcode present in the same read, rather than all possible reference variants. Check out the example notebook for paired end reads for details.

TL;DR

Run ngs-analysis --help to see available commands.

1. Make an empty directory, add config.yaml and samples.csv based on the example.
2. Add reference_dna.csv with anticipated DNA sequences (including adapters).
3. Run ngs-analysis setup. Add --clean to start the analysis from scratch.
4. Check that designs.csv is accurate; if not, fix config.yaml.
5. • If you have paired-end data, put it in 0_paired_reads/ and run ngs-analysis merge_read_pairs <sample>.
   • If you have single-end data (e.g., nanopore), put it in 1_reads/.
6. Run ngs-analysis parse_reads <sample>. Check that 2_parsed/<sample>.parsed.pq looks alright (with pandas, use pd.read_parquet)
7. Run ngs-analysis map_parsed_reads <sample>. Results are in 3_mapped/<sample>.mapped.csv

Install

pip install ngs-analysis

Make sure that the mmseqs and NGmerge executables are available (NGmerge is only needed for paired reads).

On Linux and Intel-based MacOS, you can use conda install -c bioconda -c conda-forge mmseqs2 ngmerge. On Apple Silicon mmseqs can be installed via Homebrew with brew install mmseqs2, and NGmerge can be installed from source, or via brew install brewsci/bio/ngmerge.

Tested on Linux and MacOS (Apple Silicon).