2-AIN-505, 2-AIN-251: Seminar in Bioinformatics (1), (3)
Winter 2022
Abstrakt

Arun Das, Michael C. Schatz. Sketching and sampling approaches for fast and accurate long read classification. BMC bioinformatics, 23(1):452. 2022.

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Download from publisher: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-05014-0 PubMed

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Bibliography entry: BibTeX

Abstract:

BACKGROUND: In modern sequencing experiments, quickly and accurately identifying 
the sources of the reads is a crucial need. In metagenomics, where each read 
comes from one of potentially many members of a community, it can be important to 
identify the exact species the read is from. In other settings, it is important 
to distinguish which reads are from the targeted sample and which are from 
potential contaminants. In both cases, identification of the correct source of a 
read enables further investigation of relevant reads, while minimizing wasted 
work. This task is particularly challenging for long reads, which can have a 
substantial error rate that obscures the origins of each read. RESULTS: Existing 
tools for the read classification problem are often alignment or index-based, but 
such methods can have large time and/or space overheads. In this work, we 
investigate the effectiveness of several sampling and sketching-based approaches 
for read classification. In these approaches, a chosen sampling or sketching 
algorithm is used to generate a reduced representation (a \"screen\") of potential 
source genomes for a query readset before reads are streamed in and compared 
against this screen. Using a query read's similarity to the elements of the 
screen, the methods predict the source of the read. Such an approach requires 
limited pre-processing, stores and works with only a subset of the input data, 
and is able to perform classification with a high degree of accuracy. 
CONCLUSIONS: The sampling and sketching approaches investigated include uniform 
sampling, methods based on MinHash and its weighted and order variants, a 
minimizer-based technique, and a novel clustering-based sketching approach. We 
demonstrate the effectiveness of these techniques both in identifying the source 
microbial genomes for reads from a metagenomic long read sequencing experiment, 
and in distinguishing between long reads from organisms of interest and potential 
contaminant reads. We then compare these approaches to existing alignment, index 
and sketching-based tools for read classification, and demonstrate how such a 
method is a viable alternative for determining the source of query reads. 
Finally, we present a reference implementation of these approaches at 
https://github.com/arun96/sketching .