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ChimSim Summary
ChimSim takes a reference set of sequences, e.g. transcript library or cDNA library, and generates a second corresponding set of references sequences in which a user specified portion of the input sequences are chimeric according to three categories of chimerism.

Publication
1. Linheiro R, Archer J. Quantification of the effects of chimerism on read mapping, differential expression and annotation following short-read de novo assembly. F1000Research 2022 11120. 2022;11: 120. (view)

Abstract
Background: De novo assembly is often required for analysing short-read RNA sequencing data. An under-characterized aspect of the contigs produced is chimerism, the extent to which affects mapping, differential expression analysis and annotation. Despite long-read sequencing negating this issue, short-reads remain in use through on-going research and archived datasets created during the last two decades. Consequently, there is still a need to quantify chimerism and its effects. Methods: Effects on mapping were quantified by simulating reads off the Drosophila melanogaster cDNA library and mapping these to related reference sets containing increasing levels of chimerism. Next, ten read datasets were simulated and divided into two conditions where, within one, reads representing 1000 randomly selected transcripts were over-represented across replicates. Differential expression analysis was performed iteratively with increasing chimerism within the reference set. Finally, an expectation of r-squared values describing the relationship between alignment and transcript lengths for matches involving cDNA library transcripts and those within sets containing incrementing chimerism was created. Similar values calculated for contigs produced by three graph-based assemblers, relative to the cDNA library from which input reads were simulated, or sequenced (relative to the species represented), were compared. Results: At 5% and 95% chimerism within reference sets, 100% and 77% of reads still mapped, making mapping success a poor indicator of chimerism. At 5% chimerism, of the 1000 transcripts selected for over-representation, 953 were identified during differential expression analysis; at 10% 936 were identified, while at 95% it was 510. This indicates that despite mapping success, per-transcript counts are unpredictably altered. R-squared values obtained for the three assemblers suggest that between 5-15% of contigs are chimeric. Conclusions: Although not evident based on mapping, chimerism had a significant impact on differential expression analysis and megablast identification. This will have consequences for past and present experiments involving short-reads.

Conference(s)
A poster utilizing ChimSim, presented at Genomes of Animals& Plants (GAP2022), is available here.

Getting Started
See the [quick start] guide for a description on how to use the tool as well as the various the parameter options

Funding
This work was funded by National Funds through Portuguese Foundation for Science and Technology (FCT - Fundação para a Ciência e a Tecnologia) and FEDER through the Operational Programme for Competitiveness Factors (COMPETE) under the references POCI-01-0145-FEDER-029115 and PTDC/BIA-EVL/29115/2017.

General detail on this project are available here.

Related Software
1. CStone
2. CSReadGen
3. CView
4. ChimSim < (current location)
5. TVScript

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| linheiro . web . orcid |---| archer . web . orcid |