Systematic Review: Long-Read Sequencing in Algal Studies

TL;DR

This paper reviews how long-read sequencing has improved algal research in areas like genomics and microbiome studies, and discusses future directions including AI.

Contribution

A systematic review of long-read sequencing applications in algal research, highlighting advancements and future AI integration.

Findings

Long-read sequencing improves algal genomic studies compared to short-read methods.

AI is identified as a potential future tool to enhance algal biology and ecology research.

Only 51 studies met the criteria for inclusion in the review.

Abstract

Long-read sequencing (LRS) has transformed life science research by introducing third-generation sequencing (TGS) platforms applicable across various research fields, including environmental sciences. In the past decade, LRS platforms have been utilized to extensively study algal systems by improving genomic approaches such as metabarcoding, chromosome-level genome and pangenome assemblies, as well as providing new insights into algae-associated microbiomes and host–symbiont interactions. This review aims to discuss recent advancements in LRS in algal research. To achieve this aim, a systematic review was conducted according to the PRISMA 2020 guidelines and across three electronic databases (Web of Science, Scopus, and Google Scholar), with additional citation searching for relevant studies in four key algal research areas: metabarcoding, genomics, pangenomics, and host–symbionts interactions. Following the inclusion and exclusion criteria, only 51 studies were selected for this review. Throughout the review, we summarize the challenges of short-read sequencing (SRS) and discuss how LRS platforms address these challenges in algal studies. Furthermore, we discuss the future of LRS and explore how artificial intelligence (AI) can advance research on algal biology and ecology.

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