Optimized splitting of mixed-species RNA sequencing data.
paper
Primary
Public
- Authors
- Song, Xuan; Gao, Hai Yun; Herrup, Karl; Hart, Ronald P
- Year
- 2022
- Journal
- Journal of bioinformatics and computational biology
- PMID
- 34991436
- DOI
- 10.1142/S0219720022500019
- PMCID
- PMC9081140
Gene expression studies using xenograft transplants or co-culture systems, usually with mixed human and mouse cells, have proven to be valuable to uncover cellular dynamics during development or in disease models. However, the mRNA sequence similarities among species presents a challenge for accurate transcript quantification. To identify optimal strategies for analyzing mixed-species RNA sequencing data, we evaluate both alignment-dependent and alignment-independent methods. Alignment of reads to a pooled reference index is effective, particularly if optimal alignments are used to classify sequencing reads by species, which are re-aligned with individual genomes, generating [Formula: see text] accuracy across a range of species ratios. Alignment-independent methods, such as convolutional neural networks, which extract the conserved patterns of sequences from two species, classify RNA sequencing reads with over 85% accuracy. Importantly, both methods perform well with different ratios of human and mouse reads. While non-alignment strategies successfully partitioned reads by species, a more traditional approach of mixed-genome alignment followed by optimized separation of reads proved to be the more successful with lower error rates.
Comparison of alignment methods. A. Percentage of reads aligned using each method. Dots indicate the proportion of the two genomes. B. The error rate is calculated as the difference between fraction of reads aligned and the expected fraction, summed for each genome. C. Accuracy was assessed for each method and genome proportion by comparing the count of human read pairs correctly aligned to the human genome to the number of human read pairs in the input file.
Performance of Hidden Markov Models. A. Separation of human and mouse reads compared with randomly generated sequence with same length using third order Markov Model. B. Separation of human and mouse reads with 10th order of Markov Models. C. ROC plot showing the false positive rate and true positive rate of different orders of Markov models.
Performance of Convolutional Neural Networks. A. Accuracy of classification with different ratios of human reads in the training datasets. B. Accuracy of classification with different ratios of human reads in the testing datasets. Accuracy was calculated as the percentage of correctly classified reads in the input data.
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