Taxonomic Assignment
What you need for taxonomic assignment
Taxonomy classification workflow
Compare observed sequences to annotated reference sequences to make taxonomic assignments
NOTE: This process is the same regardless of which algorithm you choose.
The goal
Taxonomy classification workflow
Your query
FASTA file -- your output from QC step --enclose in the rep-seqs_16S.qza file
Taxonomy classification workflow
Taxonomic assignment of observed sequences
Reference sequences
Taxonomy classification workflow
Taxonomic assignment of observed sequences
Reference sequences
Map taxonomy
Taxonomy classification workflow
Taxonomic assignment of observed sequences
The output --> taxonomy.qza
Feature
Taxon
Confidence
RESCRIPt
REference Sequence annotation and CuRatIon Pipeline RESCRIPt is a QIIME 2 plugin to support a variety of operations for managing and curating reference sequence databases, DNA/RNA sequence data, and taxonomic data.
https://library.qiime2.org/plugins/rescript/27/
The algorithms
BLAST -Local Alignment
BLAST is an acronym for Basic Local Alignment Search Tool. BLAST represents both an algorithm and a suite of tools that implement said algorithm. The primary purpose of BLAST is to search a collection of target sequences for alignments that match a query sequence.
BLAST can be run as a web interface from NCBI and a standalone downloadable tool.
BLAST -Local Alignment
Local alignments are used when we need to find the region of maximal similarity between two sequences. When performing local alignments, the algorithms look for the highest-scoring (partial) interval between the two sequences
BLAST search results are sequences that have similarities to the query sequence. As the name says, the results of a BLAST search are local alignments; thus, a blast result is generally a partial match of the query sequence to a target sequence in the database.
BLAST -Local Alignment
E-values were designed to bring some level of confidence to the search results and are defined as the number of hits one can “expect” to see by chance when searching a database of a particular size.
A smaller the e-value is “better”.
Problem using BLAST
BLAST is not an optimal aligner. This means that it may not find all alignments and there are limits for how short and how long sequences may be for BLAST to operate correctly. Also, BLAST is tuned for performance and efficiency; its primary purpose is to search a large body of known information for similarities (hits).
Against Local Database
Against NCBI
Vsearch - Global Alignment
In global alignments, the bases of both sequences are arranged next to one another over their entire length. Each base of the first sequence is matched to another base or a “gap” of the second sequence.
THISLINE - ISALIGNED
Change gap penality
Underperforms when the amount of data is very high
Sklearn - Classify reads by taxon using a fitted classifier�
Where does the machine learning classifier come from?
Using the reference database (our examples) with train a classifier (create), like said:
“if you see a sequence like this one, you should be outputting a taxonomy string like this one”
Sklearn - Classify reads by taxon using a fitted classifier�
Sklearn - Classify reads by taxon using a fitted classifier�
Imagine a bag fill of k-mer put in a machine learning classifier
7-mer
Sklearn - Classify reads by taxon using a fitted classifier�
Naive Bayes (posterior probability) algorithm for multinomial distributed data is used to create a train set (learn from a bag of k-mers to taxonomic string)
It is a classification technique based on Bayes’ Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.
For example, a fruit may be considered an apple if it is red, round, and about 3 inches in diameter. Even if these features depend on each other or upon the existence of the other features, all of these properties independently contribute to the probability that this fruit is an apple, which is why it is known as ‘Naive’.
Reference Databases
SILVA
The SILVA database is a large project developed and maintained by the Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures in Bremen, Germany, in cooperation with a company.
SILVA is an interdisciplinary project of biologists and computer scientists to provide:
SILVA
SILVA
GTDB
The Genome Taxonomy Database (GTDB) is an initiative to establish a standardized microbial taxonomy based on phylogenomics.
https://gtdb.ecogenomic.org/about
UNITE
Among the databases dedicated to ITS sequences for the identification of fungi, a central place is taken by UNITE (User-friendly Nordic ITS Ectomycorrhiza Database).
UNITE makes available the collection of only high-quality fungal ITS sequences stored in the International Nucleotide Sequence Databases (INSDC) and well - curated taxonomic and ecological annotations from a third-party distributed effort.
In particular, the taxonomic annotation is improved following the Index Fungorum (http://www.indexfungorum.org/) classification scheme.
UNITE
UNITE
PR2
The Protist Ribosomal Reference Database 's aim is to provide a reference database of carefully annotated 18S rRNA gene sequences using nine unique taxonomic fields (from domain to species).
At present, it contains over 220,000 sequences. Although it focuses on protists, it also contains sequences from metazoa, fungi, and plants and a limited set of 16S sequences from plastids and bacteria.
Several metadata fields are available for many sequences, including geo-localization, whether it originates from a culture or a natural sample, host type, etc. … Experts from each taxonomic group annotate PR2.
PR2
https://pr2-database.org/
PR2