Research Article: Outlier detection in BLAST hits

Date Published: March 22, 2018

Publisher: BioMed Central

Author(s): Nidhi Shah, Stephen F. Altschul, Mihai Pop.


An important task in a metagenomic analysis is the assignment of taxonomic labels to sequences in a sample. Most widely used methods for taxonomy assignment compare a sequence in the sample to a database of known sequences. Many approaches use the best BLAST hit(s) to assign the taxonomic label. However, it is known that the best BLAST hit may not always correspond to the best taxonomic match. An alternative approach involves phylogenetic methods, which take into account alignments and a model of evolution in order to more accurately define the taxonomic origin of sequences. Similarity-search based methods typically run faster than phylogenetic methods and work well when the organisms in the sample are well represented in the database. In contrast, phylogenetic methods have the capability to identify new organisms in a sample but are computationally quite expensive.

We propose a two-step approach for metagenomic taxon identification; i.e., use a rapid method that accurately classifies sequences using a reference database (this is a filtering step) and then use a more complex phylogenetic method for the sequences that were unclassified in the previous step. In this work, we explore whether and when using top BLAST hit(s) yields a correct taxonomic label. We develop a method to detect outliers among BLAST hits in order to separate the phylogenetically most closely related matches from matches to sequences from more distantly related organisms. We used modified BILD (Bayesian Integral Log-Odds) scores, a multiple-alignment scoring function, to define the outliers within a subset of top BLAST hits and assign taxonomic labels. We compared the accuracy of our method to the RDP classifier and show that our method yields fewer misclassifications while properly classifying organisms that are not present in the database. Finally, we evaluated the use of our method as a pre-processing step before more expensive phylogenetic analyses (in our case TIPP) in the context of real 16S rRNA datasets.

Our experiments make a good case for using a two-step approach for accurate taxonomic assignment. We show that our method can be used as a filtering step before using phylogenetic methods and provides a way to interpret BLAST results using more information than provided by E-values and bit-scores alone.

Partial Text

One of the goals of metagenomic analyses is to characterize the biological diversity of microbial communities. This is usually achieved by targeted amplicon sequencing of the 16S rRNA gene, either as a whole gene or focused on a hypervariable region within the gene [1]. The 16S rRNA gene is commonly used for this purpose because it is universally found in bacteria and contains a combination of highly conserved and highly variable regions. Advances in sequencing technology, targeted to a specific gene, have generated millions to hundreds of millions of reads per study [2]. Assigning accurate taxonomic labels to these reads is one of the critical steps for downstream analyses.

Broadly, our approach constructs a multiple alignment from all the top hits obtained by comparing a query sequence to a database. We use BILD scores to determine whether the multiple alignment can be split into two groups that model the data better than does a single group. In essence, we find a subset of the sequences that are more closely related to one another and to the query than to the rest of the sequences in the multiple alignment. When there is no such subset i.e. when the single alignment models the data better, we leave the query unclassified and such a query sequence is then classified in the second step by a phylogenetic method.

We propose a two-step approach for taxonomic assignment, in which we gain as much information as we reliably can from BLAST output before using computationally expensive phylogenetic-tree based methods on sequences that are difficult to classify. In this paper, we developed an outlier detection method for taxonomy assignment using BLAST hits that separates phylogenetically correct matches from matches to sequences from similar but phylogenetically more distant organisms. This method can thus be used for step one of a two-step approach, to identify sequences that can be assigned accurate labels using just a BLAST search of a reference database.




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