Key Takeaways & Executive Findings
- •• MARS is a comprehensive RNA sequence database, 20-fold larger than NCBI's nt database and 60-fold larger than RNAcentral, integrating multiple sources including RNAcentral, MG-RAST, GWH, and MGnify. • The new split–search strategy in RNAcmap3 substantially improves homology search accuracy and sensitivity compared to existing state-of-the-art techniques. • MARS-based MSAs are more accurate and sensitive than manually curated Rfam MSAs for the majority of structured RNAs, enhancing structural and functional inference. • MARS and RNAcmap3 are publicly accessible, providing a valuable resource for RNA research and RNA language model development.
Abstract
Recent success of AlphaFold2 in protein structure prediction relied heavily on co-evolutionary information derived from homologous protein sequences found in the huge, integrated database of protein sequences (Big Fantastic Database). In contrast, the existing nucleotide databases were not consolidated to facilitate wider and deeper homology search. Here, we built a comprehensive database by incorporating the non-coding RNA (ncRNA) sequences from RNAcentral, the transcriptome assembly and metagenome assembly from metagenomics RAST (MG-RAST), the genomic sequences from Genome Warehouse (GWH), and the genomic sequences from MGnify, in addition to the nucleotide (nt) database and its subsets in National Center of Biotechnology Information (NCBI). The resulting Master database of All possible RNA sequences (MARS) is 20-fold larger than NCBI's nt database or 60-fold larger than RNAcentral. The new dataset along with a new split–search strategy allows a substantial improvement in homology search over existing state-of-the-art techniques. It also yields more accurate and more sensitive multiple sequence alignments (MSAs) than manually curated MSAs from Rfam for the majority of structured RNAs mapped to Rfam. The results indicate that MARS coupled with the fully automatic homology search tool RNAcmap will be useful for improved structural and functional inference of ncRNAs and RNA language models based on MSAs. MARS is accessible at https://ngdc.cncb.ac.cn/omix/release/OMIX003037, and RNAcmap3 is accessible at http://zhouyq-lab.szbl.ac.cn/download/.
1. Introduction
There are two major categories of RNAs: those coding for proteins [messenger RNAs (mRNAs)] and those not [non-coding RNAs (ncRNAs)]. The first ncRNA discovered was transfer RNA (tRNA) in 1958 [1]. Since then, new types of ncRNAs were constantly uncovered once every a few years [2]. These ncRNAs can have a length ranging from ~20 nt in microRNAs (miRNAs) [3] to more than 100 kb for long ncRNAs (lncRNAs) like antisense Igf2r RNA (Air) [4]. These RNAs can perform functions at the sequence level by simple complementary base-pairing in the case of miRNAs [3], at the secondary structural level in the case of protein-directed RNA switches [5], and at the tertiary structural level in the cases of tRNAs, ribosomal RNAs (rRNAs), ribozymes, and riboswitches [6]. The number of distinct ncRNAs likely exceeds that of distinct proteins [7]. This is exemplified by the fact that our human genome dedicates more than 70% to RNA transcripts, compared with a tiny 1.5% coding for proteins [8]. These ncRNAs actively participate in essentially all biological processes and are implicated in more than 1000 diseases [2,9]. Given the increasing importance of annotated and unannotated RNAs in biology (coding and non-coding), a comprehensive sequence database for all RNAs is necessary.
The most comprehensive database for ncRNAs is perhaps RNAcentral [10], which consolidates 56 expert databases and over 30 million sequences as of Jan 2022 (release 20). Another widely used sequence library is nucleotide (nt) database in National Center of Biotechnology Information (NCBI) [11]. Unlike RNAcentral, NCBI's nt database contains both RNA and DNA sequences. It combines sequences from the databases including GenBank, European Nucleotide Archive (ENA) at the European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), and DNA Data Bank of Japan, amounting to 72.9 million sequences as of Aug 2021. However, neither RNAcentral nor NCBI's nt database is complete for all possible RNA sequences, as many specialized databases and depositories, such as Genome Warehouse (GWH) [12,13] and metagenomics RAST (MG-RAST) [14,15], are not included.
Recently, AlphaFold2 achieved an incredible feat of accurate protein structure prediction for most predicted proteins in the biannual meeting of 14th Critical Assessment of protein Structure Prediction (CASP 14) [16]. This success was in part built on the utilization of homologous sequences to extract evolution and co-evolution information, which contai
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Ke Chen, Thomas Litfin, Jaswinder Singh, Jian Zhan, Yaoqi Zhou (2026). MARS and RNAcmap3: The Master Database of All Possible RNA Sequences Integrated with RNAcmap for RNA Homology Search. Genomics, Proteomics & Bioinformatics. https://doi.org/10.1093/gpbjnl/qzae018
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Frequently Asked Questions
What is MARS?
MARS (Master database of All possible RNA sequences) is a comprehensive RNA sequence database that integrates sequences from RNAcentral, MG-RAST, GWH, MGnify, and NCBI's nt database, making it 20-fold larger than NCBI's nt database and 60-fold larger than RNAcentral.
How does RNAcmap3 improve homology search?
RNAcmap3 employs a new split–search strategy that substantially improves homology search accuracy and sensitivity compared to existing state-of-the-art techniques, leading to more accurate multiple sequence alignments.
What are the benefits of using MARS for RNA research?
MARS provides a more comprehensive set of RNA sequences, enabling better homology search and more accurate MSAs, which are crucial for structural and functional inference of non-coding RNAs and for training RNA language models.
Where can I access MARS and RNAcmap3?
MARS is accessible at https://ngdc.cncb.ac.cn/omix/release/OMIX003037, and RNAcmap3 is accessible at http://zhouyq-lab.szbl.ac.cn/download/.
How does MARS compare to existing databases like RNAcentral?
MARS is significantly larger than RNAcentral, incorporating additional sources such as MG-RAST, GWH, and MGnify, and provides improved performance in homology search and MSA generation.
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