Research PaperNo Access

Optimized splitting of mixed-species RNA sequencing data

Xuan Song

Department of Neurology, Alzheimer’s Disease Research Center, University of Pittsburgh, Pittsburgh, PA 15213, USA

E-mail Address: [email protected]

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Hai Yun Gao

Department of Cell Biology & Neuroscience, Rutgers University, Piscataway, NJ 08854, USA

E-mail Address: [email protected]

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Karl Herrup

Department of Neurology, Alzheimer’s Disease Research Center, University of Pittsburgh, Pittsburgh, PA 15213, USA

E-mail Address: [email protected]

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, and

Ronald P. Hart

https://orcid.org/0000-0003-4836-8712

Department of Cell Biology & Neuroscience, Rutgers University, Piscataway, NJ 08854, USA

E-mail Address: [email protected]

Corresponding author.

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https://doi.org/10.1142/S0219720022500019Cited by:1 (Source: Crossref)

Abstract

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 $> 97 %$ 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.

Keywords:

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