Yet Another 16S rRNA database

ya16sdb is a pipeline for downloading, curating, and annotating a database of bacterial 16S rRNA sequences. This repository also implements a web application (https://ya16sdb.labmed.uw.edu/) that can be used to visualize the distance-based relationships among sequences for a given species.

The purpose of the project is to provide a high quality source of bacterial 16S rRNA sequences that is up to date with NCBI, in a format that is useful as an input for various bioinformatics pipleines such as blast searching, phylogenetic reference set creation, sequence-based taxonomic assignment, etc.

Project information

This project is a product of ongoing research interests of Noah Hoffman (https://faculty.washington.edu/ngh2/home/pages/software.html) at the University of Washington in the Department of Laboratory Medicine.

Christopher Rosenthal is the primary author of the pipeline.

The pipeline heavily relies on taxtastic (https://github.com/fhcrc/taxtastic) and deenurp (https://github.com/fhcrc/deenurp), both of which began as collaborations with Erick Matsen at The Fred Hutchinson Cancer Research Center in Seattle, WA.

Please cite this project as

Rosenthal C and Hoffman NG. 2019. ya16sdb: a pipeline for creating a collection of high-quality bacterial 16S rRNA sequences from NCBI. Version 0.6.1. University of Washington. https://github.com/nhoffman/ya16sdb

Overview

At a high level, this pipeline does the following:

• Downloads annotation for all available sequence records from the NCBI matching search terms for 16S rRNA.
• Retrieves sequence records for corresponding full length (or near full-length) 16S rRNA genes; this involves extracting subsequences from genome sequences or contigs.
• Ensures that all records are 16S rRNA genes
• Ensures that sequences are in a consistent orientation.
• Identifies the taxonomic lineage of each record.
• Annotates records as a "type strain" (according to NCBI's definition of type strain), "published" (annotation has an accompanying PubMed ID), "refseq" (belonging to the Genbank refseq collection), or "direct" (direct submissions).
• Discards records likely to be mis-annotated using deenurp filter-outliers.
• Provides various subsets of annotated sequences. Each record subset provides sequence metadata, sequences, taxonomic lineages, and a blast database. For example:
  • only records with taxonomic name consistent with species-level classifications
  • type strains only
  • outliers removed
  • downsampled to a subset of sequences for each species, prioritizing type strains and "published" records.
• Stores record annotations in a single database table feather file

Database feather file

Record annotations are stored in a single table database feather file with the following columns and datatypes:

extract_genbank.py

seqname	string
version	string
accession	string
name	string
description	string
tax_id	string
modified_date	datetime
download_date	datetime
version_num	string
source	string
keywords	string
organism	string
length	int
ambig_count	int
strain	string
mol_type	string
isolate	string
isolation_source	string
seq_start	int
seq_stop	int
16s_start	int
16s_stop	int
master	string
locus_tag	string
old_locus_tag	string

taxonomy.py

species	string
genus	string
species_name	string
genus_name	string

is_type.py

is_type

bool

is_published.py

is_published

bool

is_refseq.py

is_refseq

bool

is_valid.py

is_valid

bool

confidence.py

confidence

string

ani.py

assembly_genbank	string
assembly_refseq	string
declared-type-ANI	string
declared-type-qcoverage.	string
best-match-type-assembly	string
best-match-species-taxid	string
best-match-species-name	string
best-match-type-category	string
best-match-type-ANI	string
best-match-type-qcoverage	string
taxonomy-check-status	string

filter_outliers.py

seqhash	string
centroid	string
dist	float
is_out	bool
cluster	float
x	float
y	float
filter_outliers	bool
dist_pct	float
rank_order	float

Docker

Docker image can be built with the following:

docker build --tag ya16sdb:latest .

Once a Docker image has been built a Singularity image can be built using the docker daemon:

singularity build ya16sdb.img docker-daemon://ya16sdb:latest

A Singularity image can also be built using a Singularity Docker container:

docker run --volume /var/run/:/var/run/ --volume $(pwd):$(pwd) --workdir $(pwd) singularity:latest build ya16sdb.img docker-daemon://ya16sdb:latest

Pipeline execution

The virtual containers have a predefined entry point to the SConstruct pipeline file.

To execute using Docker just a settings.conf file is required and can be run as follows:

docker run --volume $(pwd):$(pwd) --workdir $(pwd) ya16sdb:latest

And with Singularity

singularity run --bind $(pwd) --pwd $(pwd) ya16sdb.img