Sequence Alignment Methods Research Articles

Automated evaluation of multiple sequence alignment methods to handle third generation sequencing errors

Automated evaluation of multiple sequence alignment methods to handle third generation sequencing errors

Exploring geometry of genome space via Grassmann manifolds

It is important to understand the geometry of genome space in biology. After transforming genome sequences into frequency matrices of the chaos game representation (FCGR), we regard a genome sequence as a point in a suitable Grassmann manifold by analyzing the column space of the corresponding FCGR. To assess the sequence similarity, we employ the generalized Grassmannian distance, an intrinsic geometric distance that differs from the traditional Euclidean distance used in the classical k-mer frequency-based methods. With this method, we constructed phylogenetic trees for various genome datasets, including influenza A virus hemagglutinin gene, Orthocoronavirinae genome, and SARS-CoV-2 complete genome sequences. Our comparative analysis with multiple sequence alignment and alignment-free methods for large-scale sequences revealed that our method, which employs the subspace distance between the column spaces of different FCGRs (FCGR-SD), outperformed its competitors in terms of both speed and accuracy. In addition, we used low-dimensional visualization of the SARS-CoV-2 genome sequences and spike protein nucleotide sequences with our methods, resulting in some intriguing findings. We not only propose a novel and efficient algorithm for comparing genome sequences but also demonstrate that genome data have some intrinsic manifold structures, providing a new geometric perspective for molecular biology studies.

Implementasi Algoritma Needleman-Wunsch dalam Pengujian Tingkat Kemiripan DNA Babi Sulawesi Utara

Data is a collection generated from various media used by humans. DNA data is a form of data, which is data in the form of genetic code text to store biological identities and characteristics of living things. This study used DNA to test the similarity level of North Sulawesi pigs. The method used is the Pairwise Sequence Alignment method. With sequences of different DNA lengths, the researcher implemented the Needleman-Wunsch algorithm for alignment. Matching is used using the BioPython module, which is in pairwise2. And this study produces a model and proportion of the similarity level of North Sulawesi Pigs that can be used as future references for biological research on molecular biology or for the field of informatics in implementing Needleman-Wunsch algorithm in Python, or references and additional knowledge for research in Bioinformatics.

Auto-phylo v2 and auto-phylo-pipeliner: building advanced, flexible, and reusable pipelines for phylogenetic inferences, estimation of variability levels and identification of positively selected amino acid sites.

The vast amount of genome sequence data that is available, and that is predicted to drastically increase in the near future, can only be efficiently dealt with by building automated pipelines. Indeed, the Earth Biogenome Project will produce high-quality reference genome sequences for all 1.8 million named living eukaryote species, providing unprecedented insight into the evolution of genes and gene families, and thus on biological issues. Here, new modules for gene annotation, further BLAST search algorithms, further multiple sequence alignment methods, the adding of reference sequences, further tree rooting methods, the estimation of rates of synonymous and nonsynonymous substitutions, and the identification of positively selected amino acid sites, have been added to auto-phylo (version 2), a recently developed software to address biological problems using phylogenetic inferences. Additionally, we present auto-phylo-pipeliner, a graphical user interface application that further facilitates the creation and running of auto-phylo pipelines. Inferences on S-RNase specificity, are critical for both cross-based breeding and for the establishment of pollination requirements. Therefore, as a test case, we develop an auto-phylo pipeline to identify amino acid sites under positive selection, that are, in principle, those determining S-RNase specificity, starting from both non-annotated Prunus genomes and sequences available in public databases.

Optimization and Performance Analysis of CAT Method for DNA Sequence Similarity Searching and Alignment.

Bioinformatics is a rapidly developing field enabling scientific experiments via computer models and simulations. In recent years, there has been an extraordinary growth in biological databases. Therefore, it is extremely important to propose effective methods and algorithms for the fast and accurate processing of biological data. Sequence comparisons are the best way to investigate and understand the biological functions and evolutionary relationships between genes on the basis of the alignment of two or more DNA sequences in order to maximize the identity level and degree of similarity. This paper presents a new version of the pairwise DNA sequences alignment algorithm, based on a new method called CAT, where a dependency with a previous match and the closest neighbor are taken into consideration to increase the uniqueness of the CAT profile and to reduce possible collisions, i.e., two or more sequence with the same CAT profiles. This makes the proposed algorithm suitable for finding the exact match of a concrete DNA sequence in a large set of DNA data faster. In order to enable the usage of the profiles as sequence metadata, CAT profiles are generated once prior to data uploading to the database. The proposed algorithm consists of two main stages: CAT profile calculation depending on the chosen benchmark sequences and sequence comparison by using the calculated CAT profiles. Improvements in the generation of the CAT profiles are detailed and described in this paper. Block schemes, pseudo code tables, and figures were updated according to the proposed new version and experimental results. Experiments were carried out using the new version of the CAT method for DNA sequence alignment and different datasets. New experimental results regarding collisions, speed, and efficiency of the suggested new implementation are presented. Experiments related to the performance comparison with Needleman-Wunsch were re-executed with the new version of the algorithm to confirm that we have the same performance. A performance analysis of the proposed algorithm based on the CAT method against the Knuth-Morris-Pratt algorithm, which has a complexity of O(n) and is widely used for biological data searching, was performed. The impact of prior matching dependencies on uniqueness for generated CAT profiles is investigated. The experimental results from sequence alignment demonstrate that the proposed CAT method-based algorithm exhibits minimal deviation, which can be deemed negligible if such deviation is considered permissible in favor of enhanced performance. It should be noted that the performance of the CAT algorithm in terms of execution time remains stable, unaffected by the length of the analyzed sequences. Hence, the primary benefit of the suggested approach lies in its rapid processing capabilities in large-scale sequence alignment, a task that traditional exact algorithms would require significantly more time to perform.

Computational Analysis of B-Cell Receptor (BCR) Immune Repertoires with Abalign.

The widespread application of high-throughput sequencing technology has generated massive sequences of B-cell receptor (BCR) immune repertoires. Computational analysis of these data has gained significant attention due to the increasing importance of immunotherapy and precision medicine. It not only reveals the diversity and dynamic changes in immune responses, contributing to the study of associated diseases, but also provides valuable information for immunodiagnostics and drug development. Recently, we introduced a BCR-specific multiple sequence alignment (MSA) method along with a comprehensive platform software called Abalign, which stands out as an excellent choice for analyzing BCR immune repertoires due to its unique high-throughput processing capability. It offers ultra-fast MSA functionality and a wide range of analytical features, including BCR/antibody extraction, clonal grouping, lineage tree construction, mutation profiling, diversity statistics, VJ gene assignment, antibody humanization, and more. Importantly, users can perform these analyses using the graphical user interface without any programming skills or scripts. In this article, we present a series of protocols that integrate Abalign's analysis modules into a cohesive workflow. This step-by-step workflow provides detailed instructions for software installation, data preparation, and comprehensive analysis of BCR immune repertoires. This workflow facilitates the efficient acquisition of comprehensive results in profiling BCR immune repertoires, offering insights into the impacts of infectious diseases, allergies, autoimmune disorders, tumor immunology, and antibody drugs. Abalign is freely available at http://cao.labshare.cn/abalign/. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Resource preparation Basic Protocol 2: Analyzing BCR immune repertoires Support Protocol 1: Aiding antibody humanization Support Protocol 2: Constructing B-cell lineage trees Alternate Protocol: Running with Linux command line Basic Protocol 3: Comparing BCR immune repertoires.

Identification of pathogenic-specific open reading frames in staphylococci species

Background Bacteria within the Staphylococcus genus are notorious for causing a wide range of infections, and they possess genes that play a pivotal role in determining their pathogenicity. In this study, we characterized open reading frames (ORFs), which represent potential functional gene sequences, from selected staphylococcal genomes. Methods Our study involved the extraction, categorization, and annotation of ORFs using diverse analytical methods. This approach unveiled distinct ORFs in both pathogenic and non-pathogenic species, with some commonalities. To assess the conservation of these ORFs and their relevance to pathogenicity, we employed tblastn and Clustal Omega-Multiple Sequence Alignment (MSA) methods. Results Remarkably, we identified 23 ORFs that displayed high conservation among pathogenic staphylococci, with five of them extending beyond the Staphylococcus genus. These particular ORFs may encode products associated with RNA catabolism and could potentially function as regulatory small open reading frames (smORFs). Of particular interest, we found a single smORF situated within a conserved locus of the 50S ribosomal protein L1, present in 200 genomes, including 102 pathogenic strains. Conclusions Our findings highlight the existence of ORFs with highly conserved elements, proposing the existence of 23 novel smORFs that may play a role in the pathogenicity of Staphylococcus species.

In the twilight zone of protein sequence homology: do protein language models learn protein structure?

Protein language models based on the transformer architecture are increasingly improving performance on protein prediction tasks, including secondary structure, subcellular localization, and more. Despite being trained only on protein sequences, protein language models appear to implicitly learn protein structure. This paper investigates whether sequence representations learned by protein language models encode structural information and to what extent. We address this by evaluating protein language models on remote homology prediction, where identifying remote homologs from sequence information alone requires structural knowledge, especially in the "twilight zone" of very low sequence identity. Through rigorous testing at progressively lower sequence identities, we profile the performance of protein language models ranging from millions to billions of parameters in a zero-shot setting. Our findings indicate that while transformer-based protein language models outperform traditional sequence alignment methods, they still struggle in the twilight zone. This suggests that current protein language models have not sufficiently learned protein structure to address remote homology prediction when sequence signals are weak. We believe this opens the way for further research both on remote homology prediction and on the broader goal of learning sequence- and structure-rich representations of protein molecules. All code, data, and models are made publicly available.

Gene sequence analysis model construction based on k-mer statistics.

With the rapid development of biotechnology, gene sequencing methods are gradually improved. The structure of gene sequences is also more complex. However, the traditional sequence alignment method is difficult to deal with the complex gene sequence alignment work. In order to improve the efficiency of gene sequence analysis, D2 series method of k-mer statistics is selected to build the model of gene sequence alignment analysis. According to the structure of the foreground sequence, the sequence to be aligned can be cut by different lengths and divided into multiple subsequences. Finally, according to the selected subsequences, the maximum dissimilarity in the alignment results is determined as the statistical result. At the same time, the research also designed an application system for the sequence alignment analysis of the model. The experimental results showed that the statistical power of the sequence alignment analysis model was directly proportional to the sequence coverage and cutting length, and inversely proportional to the K value and module length. At the same time, the model was applied to the system designed in this paper. The maximum storage capacity of the system was 71 GB, the maximum disk capacity was 135 GB, and the running time was less than 2.0s. Therefore, the k-mer statistic sequence alignment model and system proposed in this study have considerable application value in gene alignment analysis.

New alignment method for remote protein sequences by the direct use of pairwise sequence correlations and substitutions.

Understanding protein sequences and how they relate to the functions of proteins is extremely important. One of the most basic operations in bioinformatics is sequence alignment and usually the first things learned from these are which positions are the most conserved and often these are critical parts of the structure, such as enzyme active site residues. In addition, the contact pairs in a protein usually correspond closely to the correlations between residue positions in the multiple sequence alignment, and these usually change in a systematic and coordinated way, if one position changes then the other member of the pair also changes to compensate. In the present work, these correlated pairs are taken as anchor points for a new type of sequence alignment. The main advantage of the method here is its combining the remote homolog detection from our method PROST with pairwise sequence substitutions in the rigorous method from Kleinjung et al. We show a few examples of some resulting sequence alignments, and how they can lead to improvements in alignments for function, even for a disordered protein.

Unveiling the architecture variability of coding regions in Prunus persica: Identification and characterization of key gene families impacting plant development

This study aimed to investigate gene families related to Prunus persica (peach) and their evolutionary relationships. Gene identification and sequence alignment methods were used to characterize Homeobox, MADS-box, NAC, and Receptor-kinase-like gene families. Gene expression data analysis was conducted to assess gene activity and regulatory mechanisms in peach plant development. Gene identification involved a local BLASTp analysis using protein sequences from Arabidopsis thaliana gene families as queries against the peach proteome. Multiple sequence alignment and phylogenetic tree construction were performed using ClustalW and iToL, respectively. Gene expression analysis utilized the GEO2R online tool and focused on two datasets, GSE71561 and GSE71460. Gene identification revealed 593 genes in total, with varying gene counts and sequence lengths among the different families. Phylogenetic analysis demonstrated distinct clusters within each gene family. Protein-protein interaction analysis showed significant interconnectivity among Homeobox transcription factors, while MADS-box and NAC gene families displayed lower connectivity. Enrichment analysis highlighted developmental processes associated with these gene families. Gene expression analysis provided insights into peach fruit development, ripening, and postharvest changes, revealing differential gene expression patterns and regulatory mechanisms. This study provides valuable insights into the gene families related to Prunus persica, their evolutionary relationships, and regulatory mechanisms. The identified genes and their functional characteristics contribute to a better understanding of peach development and fruit phenotypes. Further research is required to elucidate the implications of gene variations and interactions in the studied system

Protein remote homology detection and structural alignment using deep learning

Exploiting sequence–structure–function relationships in biotechnology requires improved methods for aligning proteins that have low sequence similarity to previously annotated proteins. We develop two deep learning methods to address this gap, TM-Vec and DeepBLAST. TM-Vec allows searching for structure–structure similarities in large sequence databases. It is trained to accurately predict TM-scores as a metric of structural similarity directly from sequence pairs without the need for intermediate computation or solution of structures. Once structurally similar proteins have been identified, DeepBLAST can structurally align proteins using only sequence information by identifying structurally homologous regions between proteins. It outperforms traditional sequence alignment methods and performs similarly to structure-based alignment methods. We show the merits of TM-Vec and DeepBLAST on a variety of datasets, including better identification of remotely homologous proteins compared with state-of-the-art sequence alignment and structure prediction methods.

Classification of Promoter Sequences from Human Genome.

We have developed a new method for promoter sequence classification based on a genetic algorithm and the MAHDS sequence alignment method. We have created four classes of human promoters, combining 17,310 sequences out of the 29,598 present in the EPD database. We searched the human genome for potential promoter sequences (PPSs) using dynamic programming and position weight matrices representing each of the promoter sequence classes. A total of 3,065,317 potential promoter sequences were found. Only 1,241,206 of them were located in unannotated parts of the human genome. Every other PPS found intersected with either true promoters, transposable elements, or interspersed repeats. We found a strong intersection between PPSs and Alu elements as well as transcript start sites. The number of false positive PPSs is estimated to be 3 × 10-8 per nucleotide, which is several orders of magnitude lower than for any other promoter prediction method. The developed method can be used to search for PPSs in various eukaryotic genomes.

Performance Analysis of Parallel Implementations of Global Sequence Alignment Methods for DNA Sequencing

Performance Analysis of Parallel Implementations of Global Sequence Alignment Methods for DNA Sequencing

Efficiency of Method for Biological Sequence Alignment

Efficiency of Method for Biological Sequence Alignment

Classification of group A rotavirus VP7 and VP4 genotypes using random forest.

Introduction: Group A rotaviruses are major pathogens in causing severe diarrhea in young children and neonates of many different species of animals worldwide and group A rotavirus sequence data are becoming increasingly available over time. Different methods exist that allow for rotavirus genotyping, but machine learning methods have yet to be explored. Usage of machine learning algorithms such as random forest alongside alignment-based methodology may allow for both efficient and accurate classification of circulating rotavirus genotypes through the dual classification system. Methods: Random forest models were trained on positional features obtained from pairwise and multiple sequence alignment and cross-validated using methods of repeated 10-fold cross-validation thrice and leave one- out cross validation. Models were then validated on unseen data from the testing datasets to observe real-world performance. Results: All models were found to perform strongly in classification of VP7 and VP4 genotypes with high overall accuracy and kappa values during model training (0.975-0.992, 0.970-0.989) and during model testing (0.972-0.996, 0.969-0.996), respectively. Models trained on multiple sequence alignment generally had slightly higher overall accuracy and kappa values than models trained on pairwise sequence alignment method. In contrast, pairwise sequence alignment models were found to be generally faster than multiple sequence alignment models in computational speed when models do not need to be retrained. Models that used repeated 10-fold cross-validation thrice were also found to be much faster in model computational speed than models that used leave-one-out cross validation, with no noticeable difference in overall accuracy and kappa values between the cross-validation methods. Discussion: Overall, random forest models showed strong performance in the classification of both group A rotavirus VP7 and VP4 genotypes. Application of these models as classifiers will allow for rapid and accurate classification of the increasing amounts of rotavirus sequence data that are becoming available.

Large-Scale Multiple Sequence Alignment and the Maximum Weight Trace Alignment Merging Problem.

MAGUS is a recent multiple sequence alignment method that provides excellent accuracy on large challenging datasets. MAGUS uses divide-and-conquer: it divides the sequences into disjoint sets, computes alignments on the disjoint sets, and then merges the alignments using a technique it calls the Graph Clustering Method (GCM). To understand why MAGUS is so accurate, we show that GCM is a good heuristic for the NP-hard MWT-AM problem (Maximum Weight Trace, adapted to the Alignment Merging problem). Our study, using both biological and simulated data, establishes that MWT-AM scores correlate very well with alignment accuracy and presents improvements to GCM that are even better heuristics for MWT-AM. This study suggests a new direction for large-scale MSA estimation based on improved divide-and-conquer strategies, with the merging step based on optimizing MWT-AM. MAGUS and its enhanced versions are available at https://github.com/vlasmirnov/MAGUS.

Rhetoric Mining: A New Text-Analytics Approach for Quantifying Persuasion

Rhetoric mining is a novel text-analytics method for quantifying persuasion based on rhetorical analysis theory. Our mixed-methodology approach combines qualitative context analysis with automated tagging and quantification of rhetorical moves. Rhetorical moves are complex discursive patterns and, thus, require a sequence-based text-mining approach, rather than the simpler word-based frequency analyses. We apply a sequence-alignment method to detect semantically equivalent sequences with high precision and efficiency. We illustrate our method by analyzing arguments used to justify stock picks in an online investment community. For these data, we detect and quantify the rhetorical moves of ethos (personal versus cited expertise), hedging (confidence versus uncertainty), and evidence type (product-, company-, or stock-based evidence). We use rhetoric mining to identify argument styles of persuasiveness (pitches that receive community recommendations) and trustworthiness (pitches that are written by successful investors). Rhetoric mining provides a new analytic lens in Information Systems research to analyze the influence of persuasion in consumer decision making. History: W. Nick Street served as the senior editor for this article. Data Ethics & Reproducibility Note: The code capsule is available on Code Ocean at https://codeocean.com/capsule/9373643/tree/v1 and in the e-Companion to this article (available at https://doi.org/10.1287/ijds.2022.0024 ).

Developing New Pairwise Sequence Alignment Method Based on NeedlemanWunsch Algorithm

Developing New Pairwise Sequence Alignment Method Based on NeedlemanWunsch Algorithm

Deep reinforcement learning-based pairwise DNA sequence alignment method compatible with embedded edge devices

Sequence alignment is an essential component of bioinformatics, for identifying regions of similarity that may indicate functional, structural, or evolutionary relationships between the sequences. Genome-based diagnostics relying on DNA sequencing have benefited hugely from the boom in computing power in recent decades, particularly due to cloud-computing and the rise of graphics processing units (GPUs) and other advanced computing platforms for running advanced algorithms. Translating the success of such breakthroughs in diagnostics to affordable solutions for low-cost healthcare requires development of algorithms that can operate on the edge instead of in the cloud, using low-cost and low-power electronic systems such as microcontrollers and field programmable gate arrays (FPGAs). In this work, we present EdgeAlign, a deep reinforcement learning based method for performing pairwise DNA sequence alignment on stand-alone edge devices. EdgeAlign uses deep reinforcement learning to train a deep Q-network (DQN) agent for performing sequence alignment on fixed length sub-sequences, using a sliding window that is scanned over the length of the entire sequence. The hardware resource-consumption for implementing this scheme is thus independent of the lengths of the sequences to be aligned, and is further optimized using a novel AutoML based method for neural network model size reduction. Unlike other algorithms for sequence alignment reported in literature, the model demonstrated in this work is highly compact and deployed on two edge devices (NVIDIA Jetson Nano Developer Kit and Digilent Arty A7-100T, containing Xilinx XC7A35T Artix-7 FPGA) for demonstration of alignment for sequences from the publicly available Influenza sequences at the National Center for Biotechnology Information (NCBI) Virus Data Hub.