HAlign 3: Fast Multiple Alignment of Ultra-Large Numbers of Similar DNA/RNA Sequences

Tang, Furong; Chao, Jiannan; Wei, Yanming; Yang, Fenglong; Zhai, Yixiao; Xu, Lei; Zou, Quan

doi:10.1093/molbev/msac166

Cited by 18 publications

(7 citation statements)

References 13 publications

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“…As shown in Fig. 2 , to explore the relationship between sequence similarity and performance in terms of runtime, memory usage, and alignment quality, a mitochondrial-like dataset comprising 100 sequences with different similarities ranging from 90% to 99% is simulated using INDELible v1.03 ( Fletcher and Yang 2009 ) provided by Tang et al (2022) . As demonstrated in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Parallel alignment—FMAlign2 integrates with MAFFT ( Katoh et al 2002 ), HAlign2 ( Zou et al 2015 ), and HAlign3 ( Tang et al 2022 ) for aligning segments in parallel. While these tools were chosen mainly for benchmarking against FMAlign, it’s worth noting that with the appropriate setup, FMAlign2 can collaborate with most MSA software.…”

Section: Methodsmentioning

confidence: 99%

“…The identified similar substrings, combined with MEMs, form the partial chains used for subsequent sequence segmentation to generate segments. External tools such as MAFFT ( Katoh et al 2002 ) and HAlign ( Zou et al 2015 , Tang et al 2022 ) align these segments in parallel. FMAlign2 leverages sequence-profile alignment based on FFT/K-Band ( Wei et al 2022 ) to incorporate fragments into the backbone.…”

Section: Introductionmentioning

confidence: 99%

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FMAlign2: a novel fast multiple nucleotide sequence alignment method for ultralong datasets

Zhang,

Liu,

Wei

et al. 2024

Bioinformatics

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Motivation In bioinformatics, multiple sequence alignment (MSA) is a crucial task. However, conventional methods often struggle with aligning ultralong sequences. To address this issue, researchers have designed MSA methods rooted in a vertical division strategy, which segments sequence data for parallel alignment. A prime example of this approach is FMAlign, which utilizes the FM-index to extract common seeds and segment the sequences accordingly. Results FMAlign2 leverages the suffix array to identify maximal exact matches, redefining the approach of FMAlign from searching for global chains to partial chains. By employing a vertical division strategy, large-scale problem is deconstructed into manageable tasks, enabling parallel execution of subMSA. Furthermore, sequence-profile alignment and refinement are incorporated to concatenate subsets, yielding the final result seamlessly. Compared to FMAlign, FMAlign2 markedly augments the segmentation of sequences and significantly reduces the time while maintaining accuracy, especially on ultralong datasets. Importantly, FMAlign2 enhances existing MSA methods by conferring the capability to handle sequences reaching billions in length within an acceptable time frame. Availability Source code and datasets are available at https://github.com/malabz/FMAlign2 and https://zenodo.org/records/10435770. Contact pingluzhang@outlook.com Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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FMAlign2: a novel fast multiple nucleotide sequence alignment method for ultralong datasets

Zhang,

Liu,

Wei

et al. 2024

Bioinformatics

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“…Furthermore, the experiment involved initial alignments obtained from eleven MSA tools: ClustalW2 [ 21 ], Dialign-TX, HAlign3 [ 22 ], Kalign3 [ 23 ], MAFFT, MUSCLE3, MUSCLE5 [ 24 ], PCMA [ 25 ], POA[ 26 ], T-Coffee, and WMSA2[ 27 ]. The identical software versions and running commands were used for obtaining initial alignments by TPMA and M-Coffee, with detailed information provided in S10 Table .…”

Section: Methodsmentioning

confidence: 99%

TPMA: A two pointers meta-alignment tool to ensemble different multiple nucleic acid sequence alignments

Zhai,

Chao,

Wang

et al. 2024

PLoS Comput Biol

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Accurate multiple sequence alignment (MSA) is imperative for the comprehensive analysis of biological sequences. However, a notable challenge arises as no single MSA tool consistently outperforms its counterparts across diverse datasets. Users often have to try multiple MSA tools to achieve optimal alignment results, which can be time-consuming and memory-intensive. While the overall accuracy of certain MSA results may be lower, there could be local regions with the highest alignment scores, prompting researchers to seek a tool capable of merging these locally optimal results from multiple initial alignments into a globally optimal alignment. In this study, we introduce Two Pointers Meta-Alignment (TPMA), a novel tool designed for the integration of nucleic acid sequence alignments. TPMA employs two pointers to partition the initial alignments into blocks containing identical sequence fragments. It selects blocks with the high sum of pairs (SP) scores to concatenate them into an alignment with an overall SP score superior to that of the initial alignments. Through tests on simulated and real datasets, the experimental results consistently demonstrate that TPMA outperforms M-Coffee in terms of aSP, Q, and total column (TC) scores across most datasets. Even in cases where TPMA’s scores are comparable to M-Coffee, TPMA exhibits significantly lower running time and memory consumption. Furthermore, we comprehensively assessed all the MSA tools used in the experiments, considering accuracy, time, and memory consumption. We propose accurate and fast combination strategies for small and large datasets, which streamline the user tool selection process and facilitate large-scale dataset integration. The dataset and source code of TPMA are available on GitHub (https://github.com/malabz/TPMA).

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“…For instance, the evolutionary information frequently utilized by these methods is derived from position-specific scoring matrices (PSSM). These matrices are generated through sequence alignment 33 between the query proteins and extensive protein databases. The process of extracting and integrating various biological features involves substantial manual curation and domain expertise, resulting in high costs and time investments.…”

Section: Introductionmentioning

confidence: 99%

MucLiPred: Multi-Level Contrastive Learning for Predicting Nucleic Acid Binding Residues of Proteins

Zhang,

Wang,

Wei

2024

J. Chem. Inf. Model.

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Protein−molecule interactions play a crucial role in various biological functions, with their accurate prediction being pivotal for drug discovery and design processes. Traditional methods for predicting protein−molecule interactions are limited. Some can only predict interactions with a specific molecule, restricting their applicability, while others target multiple molecule types but fail to efficiently process diverse interaction information, leading to complexity and inefficiency. This study presents a novel deep learning model, MucLiPred, equipped with a dual contrastive learning mechanism aimed at improving the prediction of multiple molecule-protein interactions and the identification of potential molecule-binding residues. The residue-level paradigm focuses on differentiating binding from non-binding residues, illuminating detailed local interactions. The type-level paradigm, meanwhile, analyzes overarching contexts of molecule types, like DNA or RNA, ensuring that representations of identical molecule types gravitate closer in the representational space, bolstering the model's proficiency in discerning interaction motifs. This dual approach enables comprehensive multi-molecule predictions, elucidating the relationships among different molecule types and strengthening precise protein−molecule interaction predictions. Empirical evidence demonstrates MucLiPred's superiority over existing models in robustness and prediction accuracy. The integration of dual contrastive learning techniques amplifies its capability to detect potential molecule-binding residues with precision. Further optimization, separating representational and classification tasks, has markedly improved its performance. MucLiPred thus represents a significant advancement in protein−molecule interaction prediction, setting a new precedent for future research in this field.

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HAlign 3: Fast Multiple Alignment of Ultra-Large Numbers of Similar DNA/RNA Sequences

Cited by 18 publications

References 13 publications

FMAlign2: a novel fast multiple nucleotide sequence alignment method for ultralong datasets

FMAlign2: a novel fast multiple nucleotide sequence alignment method for ultralong datasets

TPMA: A two pointers meta-alignment tool to ensemble different multiple nucleic acid sequence alignments

MucLiPred: Multi-Level Contrastive Learning for Predicting Nucleic Acid Binding Residues of Proteins

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