TKSM: highly modular, user-customizable, and scalable transcriptomic sequencing long-read simulator

Author:

Karaoğlanoğlu Fatih1ORCID,Orabi Baraa2ORCID,Flannigan Ryan34ORCID,Chauve Cedric5ORCID,Hach Faraz234ORCID

Affiliation:

1. Computing Science Department, Simon Fraser University , Burnaby, BC V5A 1S6, Canada

2. Department of Computer Science, the University of British Columbia , Vancouver, BC V6T 1Z4, Canada

3. Department of Urologic Sciences, the University of British Columbia , Vancouver, BC V5Z 1M9, Canada

4. Vancouver Prostate Centre , Vancouver, BC V6H 3Z6, Canada

5. Department of Mathematics, Simon Fraser University , Burnaby, BC V5A 1S6, Canada

Abstract

Abstract Motivation Transcriptomic long-read (LR) sequencing is an increasingly cost-effective technology for probing various RNA features. Numerous tools have been developed to tackle various transcriptomic sequencing tasks (e.g. isoform and gene fusion detection). However, the lack of abundant gold-standard datasets hinders the benchmarking of such tools. Therefore, the simulation of LR sequencing is an important and practical alternative. While the existing LR simulators aim to imitate the sequencing machine noise and to target specific library protocols, they lack some important library preparation steps (e.g. PCR) and are difficult to modify to new and changing library preparation techniques (e.g. single-cell LRs). Results We present TKSM, a modular and scalable LR simulator, designed so that each RNA modification step is targeted explicitly by a specific module. This allows the user to assemble a simulation pipeline as a combination of TKSM modules to emulate a specific sequencing design. Additionally, the input/output of all the core modules of TKSM follows the same simple format (Molecule Description Format) allowing the user to easily extend TKSM with new modules targeting new library preparation steps. Availability and implementation TKSM is available as an open source software at https://github.com/vpc-ccg/tksm.

Funder

National Science and Engineering Council of Canada

Publisher

Oxford University Press (OUP)

Reference23 articles.

1. Opportunities and challenges in long-read sequencing data analysis;Amarasinghe;Genome Biol,2020

2. long-read-tools.org: an interactive catalogue of analysis methods for long-read sequencing data;Amarasinghe;GigaScience,2021

3. A systematic benchmark of nanopore long read RNA sequencing for transcript level analysis in human cell lines;Chen;bioRxiv,2021

4. Fast and accurate matching of cellular barcodes across short-reads and long-reads of single-cell RNA-seq experiments;Ebrahimi;iScience,2022

5. Single-cell isoform RNA sequencing characterizes isoforms in thousands of cerebellar cells;Gupta;Nat Biotechnol,2018

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