mineer 0.4.2

Automated sequence truncation algorithm

minEER - A sensible sequence trimming algorithm

For a given quality annotated read, the minEER algorithm identifies the subsequence which minimizes the expected error rate (EER; the mean q-score) while maximizing the subsequence length according to user defined EER and minimum sequence length thresholds. This procedure mimics the manual exercise of choosing truncation positions by glancing at a quality profile distribution, but ensures consistent results and is fast (can run on 20,000 files in under 15s). The minEER algorithm offers an improvement to current heuristic methods (sliding windows, quality score drop offs, etc.) which can be sensitive to noise and miss the opportunity for a deterministic solution based on reasonable criteria. The algorithm itself can be seen here.

The minEER pipeline (see documentation with mineer -h after installing) operates on an entire set of files from a single project. Assuming that all reads from a given direction (forward or reverse for paired reads) share a "similar" quality profile, the minEER pipeline runs the algorithm on a subsample of reads and to determine global truncation positions (where to start and end each read). All reads are then truncated according to these global positions and reads that fail to meet the user defined EER and minimum sequence lenght thresholds.

Install

Install with pip install mineer

Run mineer -h to view the following documentation:

usage: mineer [-h] -i FILEPATHS -f FWD_FORMAT [-r REV_FORMAT] [--mal MAL] [--mae MAE] [-m {mean,median}] [-n NREADS] [--filter {any,both,no}] [-o OUTDIR] [-v VIZ_OUTDIR]

minEER pipeline

Github: https://github.com/michaelsilverstein/minEER
AUTHOR: MICHAEL SILVERSTEIN
EMAIL: [e-mail]

optional arguments:
-h, --help            show this help message and exit
-i FILEPATHS          Path to directory containing all (unzipped!) fastq files for a single project
-f FWD_FORMAT         Forward read filename suffix for paired fastqs or single read filename suffix
                                Ex. sample1_1.fastq
                                    -f _1.fastq
                                If extension contains a '-', like sample1-r1.fastq:
                                    -f="-r1.fastq"
                            
-r REV_FORMAT         Reverse read filename suffix (leave blank for single end)
--mal MAL             Minimum acceptable length. Default: 100
--mae MAE             Maximum acceptable error. Deafult: 0.010000
-m {mean,median}      Aggregation method for computing truncation. Default: "median"
-n NREADS             Number of reads to subsample per direction for computing truncation position. Default: 5000
--filter {any,both,no}
                        How to filter out truncated reads
                                --filter any: Filter out read pairs where any read has EER > mae
                                --filter both [Default]: Only filter read pairs where both reads have EER > mae
                                --filter no: Do not filter read pairs based on EER
                                * In all cases, reads that fall within truncation positions will be filtered
-o OUTDIR             Output directory. Default: current working directory
-v VIZ_OUTDIR         Provide output directory to generate and visualizations

Tutorial

After installing mineer, run the following:

# Download some fastq files to `sample_files/`
mineer-test-files
# Run the pipeline with default parameters (minimal acceptable error=.01)
mineer -i sample_files -f _1.fastq -r _2.fastq -n 5000 -o test_out -v sample_figs

Once you run the pipeline, a report of each step will appear as they execute. Files containing truncated reads will appear in the directory specified with -o. Providing the -v flag will produce visualizations like the following of quality profiles of untrimmed reads and the distribution of truncation positions identified by minEER:

Pipeline

Method:

1. Ingest files and recognize pairs based on file names
2. Run minEER on subset of reads
3. Determine global truncation positions
4. Truncate all reads to global positions and filter out read pairs that don't pass QC (currently requires longest length)
5. Save truncated sequences
6. Produce visualizations, if visualization output directory provided

Contributing

Run tests with python -m unittest or pytest

Here is a longer list of SRRs to test on:

SRR9660346
SRR9660368
SRR9660375
SRR9660380
SRR9660372
SRR9660321
SRR9660322
SRR9660307
SRR9660387
SRR9660385