Decontaminating eukaryotic genome assemblies with machine learning

Abstract

Background: High-throughput sequencing has made it theoretically possible to obtain high-quality de novo assembled genome sequences but in practice DNA extracts are often contaminated with sequences from other organisms. Currently, there are few existing methods for rigorously decontaminating eukaryotic assemblies. Those that do exist filter sequences based on nucleotide similarity to contaminants and risk eliminating sequences from the target organism.

Results: We introduce a novel application of an established machine learning method, a decision tree, that can rigorously classify sequences. The major strength of the decision tree is that it can take any measured feature as input and does not require a priori identification of significant descriptors. We use the decision tree to classify de novo assembled sequences and compare the method to published protocols.

Conclusions: A decision tree performs better than existing methods when classifying sequences in eukaryotic de novo assemblies. It is efficient, readily implemented, and accurately identifies target and contaminant sequences. Importantly, a decision tree can be used to classify sequences according to measured descriptors and has potentially many uses in distilling biological datasets.

Keywords: Contamination; DNA sequencing; Genome assembly; High-throughput; Sequence filtering.

Fig. 1

The workflow from raw DNA sequence reads to assembled genome sequence for Anvi’o with CONCOCT binning, Busybee, Blobology, Kraken, and the decision tree. Both Blobology and Kraken required pre-assembly, filtering for target and contaminant reads, and final assembly. The decision tree, Anvi’o and Busybee filtered for target and contaminant scaffolds by constructing models and classifying contiguous sequences after assembly

Fig. 2

The top 20 organisms identified in BLAST analysis of the empirical genome sequences for (a) C. remanei (b) C. latens (c) A. vaga. For C. remanei the most common BLAST hit was C. remanei, followed by two likely contaminants and scaffolds that could not be assigned origin with BLAST. For C. latens the most common BLAST hit was the microbial contaminant S. matophilia followed by C. remanei, a second contaminant P. protegens, and scaffolds that could not be assigned origin. For A. vaga the majority of scaffolds could not be assigned origin with BLAST, likely due to the low number of rotifer sequences in public databases

Fig. 3

Accuracy, sensitivity and specificity for (a) decision tree and (b) bagging decision tree models. Decision tree models achieved high accuracy, sensitivity and specificity but were influenced by variation in the training dataset. The bagging decision tree model achieves high accuracy, sensitivity and specificity with lower variance between models constructed with different training datasets. For the decision tree models accuracy, sensitivity and specificity plateau with >25% of the data used in training while the performance of the bagging model plateaus with >40% of the data used in training

Fig. 4

Accuracy, sensitivity and specificity for (a) random forest and (b) boosted decision tree models. Both random forest and boosted decision tree models resulted in high accuracy, sensitivity and specificity but showed non-monotonic responses to the training datasets

Fig. 5

GC content and the average per-base sequencing coverage for individual scaffolds in the empirical datasets (a) C. remanei training; (b) C. remanei full dataset; (c) C. latens training; (d) C. latens full dataset; (e) A. vaga training; and (f) A. vaga full dataset. Training datasets with BLAST-identified origins are shown on the left and decision tree bagging model predictions for full datasets are shown on the right with model error

Fig. 6

Accuracy, sensitivity and specificity for (a) the decision tree bagging model constructed with 2-8 predictors and (b) Anvi’o with CONCOCT binning and Busybee. Acccuracy and sensitivity for the decision tree bagging model plateau with 4 predictors but small increases in specificity resulted from additional predictors. Anvi’o had the highest specificity compared to the decision tree bagging model or Busybee while Busybee had the highest sensitivity

Fig. 7

Busybee bin 4 (a) contained primarily scaffolds of Caenorhabditis or unknown origin with few microbial contaminants while Busybee bin 3 (b) was a hetereogeneous mix of sequences with different origins. The scaffolds in Busybee bin 3 separated by taxonomic origin when visualized by scaffold GC content and sequencing coverage

Fig. 8

GC content and average per-base sequencing coverage for the simulated datasets contaminated with microbial DNA. Training datasets are shown on the left and bagging decision tree predictions are shown on the right for a-b) A. thaliana; c-d) C. elegans; e-f) D. melanogaster; and g-h) T. rubripes. The microbial genomes were GC-rich relative to the target organisms and a simple decision tree based on GC content and sequencing coverage predicted scaffold origin with low error for each dataset

Fig. 9

GC content and average per-base sequencing coverage for the simulated datasets contaminated with C. albicans DNA. Training datasets and bagging decision tree predictions are shown for a-b) A. thaliana; c-d) C. elegans; e-f) D. melanogaster; and g-h) T. rubripes. C. albicans and the target organisms had similar GC contents and the bagging decision tree predictions were based on a complex relationship that included multiple predictors and mRNA data