Sequence Alignment Techniques Research Articles

Genetic protein sequence analysis based on sequence alignment techniques for time series data

This study aims to explore the application and effectiveness of sequence comparison techniques in dealing with missing and outliers in time series data. First, the data are pre-processed by convolutional neural network (CNN) and recurrent neural networks (RNN) to remove noise and outliers. Then, time series data at different time points are compared and analysed using the comparison loss function to identify changes and differences in the data. Finally, the prediction performance of different models is evaluated using a variety of assessment metrics, and the results are compared and analysed to verify the effectiveness of the sequence comparison technique in dealing with missing and outliers. The experimental results show that the sequence comparison technique can effectively deal with missing and outliers in time series data, providing important insights for further research on the application and development of the sequence comparison technique. Future research can explore the application of sequence comparison techniques in more fields to optimize model performance and improve accuracy and stability.

EncoMPASS: An encyclopedia of membrane proteins analyzed by structure and symmetry

Protein structure determination and prediction, active site detection, and protein sequence alignment techniques all exploit information about protein structure and structural relationships. For membrane proteins, however, there is limited agreement among available online tools for highlighting and mapping such structural similarities. Moreover, no available resource provides a systematic overview of quaternary and internal symmetries, and their orientation relative to the membrane, despite the fact that these properties can provide key insights into membrane protein function and evolution. Here, we describe the Encyclopedia of Membrane Proteins Analyzed by Structure and Symmetry (EncoMPASS), a database for relating integral membrane proteins of known structure from the points of view of sequence, structure, and symmetry. EncoMPASS is accessible through a web interface, and its contents can be easily downloaded. This allows the user not only to focus on specific proteins, but also to study general properties of the structure and evolution of membrane proteins.

Salivary protein homology between humans and dogs: Mass spectrometry-based proteomics analysis

ObjectiveThis benchmark study aimed to investigate sex-related differences based on the identification and characterization of the salivary proteome of healthy male and female dogs using mass spectrometry (MS) technique and a homology-driven approach to analyze salivary proteins in both human and dog species utilizing protein sequence alignment technique. MethodsUnstimulated whole saliva was collected from 10 healthy Beagles. After processing the samples and determining the total protein content, in-solution protein digestion was performed involving denaturation, reduction of disulfide bonds, alkylation, and removal of interfering compounds. Samples were analyzed using LC-ESI-MS/MS. ResultsLC-ESI-MS/MS analysis identified 327 and 341 unique proteins in male and female dog saliva, respectively, of which 318 (97.25 %) in male dogs and 326 (95.60 %) in female dogs were characterized. Abundant shared proteins included albumin, BPI fold-containing family A member 2, and VWFD domain-containing protein. A notable uncharacterized protein, VWFD domain-containing protein, was among the most abundant in both sexes. Comparative analysis of 69 abundant shared proteins indicated an upregulation of CES5A, EFHD, GC, IGHM, LOC100653049, KRT10, LCP1, PGD, TPI1 in male dogs, while LOC100855593 was upregulated in female dogs. In total, 84 % (n = 229/274) and 86 % (n = 235/275) salivary proteins identified in male and female dogs, respectively, were homologous to human proteins, with an overall homology of 86 % (n = 364/423), including 15 with 100 % homology. ConclusionThe study revealed clear differences in the salivary proteomics profile of healthy male and female dogs. However, most of the salivary proteins in both male and female dogs showed homology with human salivary proteins. Clinical relevanceThe identification of unique salivary proteome profiles in male and female dogs, coupled with substantial homology to human proteins, provides promising biomarkers for health assessment, highlighting its clinical significance for diagnostics and therapeutic exploration not only in veterinary and human dentistry, but across mammalian species.

Generation of Regular Expression from Aligned Sequences of Text Snippets

Introduction: Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Vitae sapien pellentesque habitant morbi tristique senectus et netus. Dignissim cras tincidunt lobortis feugiat vivamus at augue eget arcu. At risus viverra adipiscing at in. Cras semper auctor neque vitae tempus quam. Sed cras ornare arcu dui. Turpis massa sed elementum tempus. Risus commodo viverra maecenas accumsan lacus vel facilisis volutpat est.
 Objectives: Effective data management has become an absolute necessity in today's world as a direct result of the widespread adoption of electronic medical records for patients. For medicine and healthcare domains, particularly effective web healthcare providers, computer-assisted categorization of such information into functional classifications such as medical issues or illnesses may save time and efforts. However, a substantial volume of data in the healthcare domain is still unstructured.
 Methods: The utilization of unstructured text datasets is a challenge in comparison with structured text datasets. Many researchers in various domains have proposed to convert unstructured text into structured text. The text needs to be built in a fixed and aligned pattern for easy comparison, matching, and classification. In the text classification process, accuracy is one of the challenges when text is unstructured. The text with a fixed pattern is easy to classify. The regular expressions are sequences of characters with fixed patterns of text. This strength of regular expression is used in the proposed text classification. Finding patterns in unstructured text is possible with the use of sequence alignment. Basically, sequence alignment is a concept of biomedical research, but the same concept is used to align text snippets that are not perfectly similar to each other.
 Results: The sequence alignment technique detects the similarity score assigned to each potential alignment in order to choose among the numerous local alignments of sequences. The proposed local pairwise alignment method is used to get sequence alignments, which are useful to generate regular expressions.
 Conclusions: A regular expression is a string of characters used to describe a text pattern. The regular expressions are typically created manually by the domain experts. The proposed work is the automatic generation of regular expressions from aligned sequences with a bottom up approach. The generated regular expressions are used as a dataset on which various machine learning algorithms are applied for text classification.

Seedability: optimizing alignment parameters for sensitive sequence comparison.

Most sequence alignment techniques make use of exact k-mer hits, called seeds, as anchors to optimize alignment speed. A large number of bioinformatics tools employing seed-based alignment techniques, such as , use a single value of k per sequencing technology, without a strong guarantee that this is the best possible value. Given the ubiquity of sequence alignment, identifying values of k that lead to more sensitive alignments is thus an important task. To aid this, we present , a seed-based alignment framework designed for estimating an optimal seed k-mer length (as well as a minimal number of shared seeds) based on a given alignment identity threshold. In particular, we were motivated to make more sensitive in the pairwise alignment of short sequences. The experimental results herein show improved alignments of short and divergent sequences when using the parameter values determined by in comparison to the default values of . We also show several cases of pairs of real divergent sequences, where the default parameter values of yield no output alignments, but the values output by produce plausible alignments. https://github.com/lorrainea/Seedability (distributed under GPL v3.0).

Identification of Human miRNA Biomarkers Targeting the SARS-CoV-2 Genome.

SARS-CoV-2 poses a great challenge toward mankind, majorly due to its evolution and frequently occurring variants. On the other hand, in human hosts, microRNA (miRNA) plays a vital role in replication and propagation during a viral infection and can control the biological processes. This may be essential for the progression of viral infection. Moreover, human miRNAs can play a therapeutic role in treatment of different viral diseases by binding to the target sites of the virus genome, thereby hindering the essential functioning of the virus. Motivated by this fact, we have hypothesized a new approach in order to identify human miRNAs that can target the mRNA (genome) of SARS-CoV-2 to degrade their protein synthesis. In this regard, the multiple sequence alignment technique Clustal Omega is used to align a complement of 2656 human miRNAs with the SARS-CoV-2 reference genome (mRNA). Thereafter, ranking of these aligned human miRNAs is performed with the help of a new scoring function that takes into account the (a) total number of nucleotide matches between the human miRNA and the SARS-CoV-2 genome, (b) number of consecutive nucleotide matches between the human miRNA and the SARS-CoV-2 genome, (c) number of nucleotide mismatches between the human miRNA and the SARS-CoV-2 genome, and (d) the difference in length before and after alignment of the human miRNA. As a result, from the 2656 ranked miRNAs, the top 20 human miRNAs are reported, which are targeting different coding and non-coding regions of the SARS-CoV-2 genome. Moreover, molecular docking of such human miRNAs with virus mRNA is performed to verify the efficacy of the interactions. Furthermore, 4 miRNAs out of the top 20 miRNAs are identified to have the seed region. In order to inhibit the virus, the key human targets of the seed regions may be targeted. Repurposable drugs like carfilzomib, bortezomib, hydralazine, and paclitaxel are identified for such purpose.

Prevalence and Molecular Mapping of Multidrug-resistant Mycobacterium tuberculosis in Sokoto, North-Western Nigeria

Background: The emergence of multidrug-resistant tuberculosis tuberculosis (MDR-TB) poses a significant danger to Nigeria’s TB control efforts. Nigeria records 570000 new TB cases each year, and it is one of the world’s greatest TB-burden countries as well as one of the top ten countries with MDR-TB. This study aimed to determine the MDR prevalence, phylogenetic analysis, and molecular mapping as well as the link between MDR prevalence and demographic data. Methods: The study comprised 100 TB patients recruited consecutively. The proportion method on Lowenstein-Jensen (LJ) medium was used for drug susceptibility testing. Conventional polymerase chain reaction (PCR) was used, and the rrs genes were amplified and sequenced. Multiple sequence alignment techniques were used to compare the PCR product sequences to reference sequences retrieved from GenBank. Results: Rifampicin (RIF) resistance was found in 29% (22.75), ethambutol resistance was found in 20% (15.75), and isoniazid (INH) resistance was found in 28% (21.75). Further, RIF and INH resistance were found in 20% (15.75) of the samples. At nucleotide position 892, four isolates (31%) contained a G-A transition, and the most prevalent mutation found in the rrs gene was S531L (80%, 12.15). The phylogenetic analysis indicated that three of the Sokoto isolates are closely linked to reference isolates from Iran, Germany, China, and Sudan in terms of geographical relatedness. Conclusions: The obtained data revealed that acquired resistance is a major factor in the establishment of MDR-TB in Sokoto, which could be owing to poor adherence to medication or poor treatment of TB patients.

Proposal of Smith-Waterman algorithm on FPGA to accelerate the forward and backtracking steps.

In bioinformatics, alignment is an essential technique for finding similarities between biological sequences. Usually, the alignment is performed with the Smith-Waterman (SW) algorithm, a well-known sequence alignment technique of high-level precision based on dynamic programming. However, given the massive data volume in biological databases and their continuous exponential increase, high-speed data processing is necessary. Therefore, this work proposes a parallel hardware design for the SW algorithm with a systolic array structure to accelerate the forward and backtracking steps. For this purpose, the architecture calculates and stores the paths in the forward stage for pre-organizing the alignment, which reduces the complexity of the backtracking stage. The backtracking starts from the maximum score position in the matrix and generates the optimal SW sequence alignment path. The architecture was validated on Field-Programmable Gate Array (FPGA), and synthesis analyses have shown that the proposed design reaches up to 79.5 Giga Cell Updates per Second (GCPUS).

Human miRNAs to Identify Potential Regions of SARS-CoV-2.

It is two years now but the world is still struggling against COVID-19 due to the havoc created by the SARS-CoV-2 virus and its multiple variants. Considering this perspective, in this work, we have hypothesized a new approach in order to identify potential regions in SARS-CoV-2 similar to the human miRNAs. Thus, they may have similar consequences as caused by the human miRNAs in human body. Therefore, the same way by which human miRNAs are inhibited can be applied for such potential regions of virus as well by administering drugs to the interacting human proteins. In this regard, the multiple sequence alignment technique Clustal Omega is used to align 2656 human miRNAs with the SARS-CoV-2 reference genome to identify the potential regions within the virus reference genome which have high similarities with the human miRNAs. The potential regions in virus genome are identified based on the highest number of nucleotide match, greater than or equal to 5 at a genomic position, for the aligned miRNAs. As a result, 38 potential SARS-CoV-2 regions are identified consisting of 249 human miRNAs. Among these 38 potential regions, some top regions belong to nucleocapsid, RdRp, helicase, and ORF8. To understand the biological significance of these potential regions, the targets of the human miRNAs are considered for KEGG pathways and protein–protein and drug–protein interaction analysis as the human miRNAs are similar to the potential regions of SARS-CoV-2. Significant pathways are found which lead to comorbidities. Subsequently, drugs like emodin, bicalutamide, vorinostat, etc. are identified that may be used for clinical trials.

Combining user behavioural information at the feature level to enhance continuous authentication systems

The scientific and business communities are proposing new authentication methods more robust than traditional solutions relying on a single security point such as passwords (i.e. “something you know”). User and Entity Behavior Analysis (UEBA) has postulated as an excellent solution to improve authentication systems by performing continuous authentication to extend the authentication process over time. UEBA is based on detecting anomalies in the intrinsic behaviour of each user or entity (i.e. it is based on “something you are/do”). This paper presents a method for performing continuous authentication using UEBA techniques that allows combining information from multiple sources at the feature level. This combination is achieved through a novel Symbolic Aggregate approximation (SAX) using Random Trees Embeddings for each information source, producing a sequence of symbols. Then, these sequences of symbols are combined into a single sequence using temporal information. The resulting sequence of symbols feeds a density-based clustering model that uses a distance based on DNA sequence alignment techniques to extract behavioural cores. Finally, new samples are compared against these cores to detect anomalies using a risk model that evaluates if a behaviour is anomalous (suspected user impersonation). The model has been extensively tested and evaluated against well-known state-of-the-art datasets.

Semi Global Pairwise Sequence Alignment Using New Chromosome Structure Genetic Algorithm

Biological sequence alignment is a prominent and eminent task in the analysis of biological data. This paper proposes a pair wise semi global sequence alignment technique using New Chromosome Structure based Genetic algorithm (NCSGA) for aligning sequences by automatically detecting optimal number of gaps and their positions to explore the optimal score for DNA or protein sequences. The experimental results are conducted using simulated real datasets from NCBI. The proposed method can be tested on real data sets of nucleotide sequence pairs. The computational results show that NCSGA produces the near optimal solutions for semi global alignment compared to other existing approaches.

Study of Glutathione-s-transferase and Reduced Glutathione Receiving Chemotherapy

Cervical cancer is the third most prevalent cancer in women worldwide, and the fourth leading cause of death from cancer in women. Recent advances, such as the availability of broad scale genome data, articulated gene tag (EST) data bases, innovative sequence alignment techniques, and X-ray crystallography determination of three-dimensional structures, have significantly expanded our understanding of structure–function relationships in this important enzyme superfamily. Total 36 histologically confirmed patients, locally advanced FIGO stage IIB–IIIB cervical cancer were enrolled. Based on the findings of our research, it can be concluded that improvements in GSH concentration during the treatment of locally advanced cervical cancer can have a major impact on the treatment response. In comparison to the lack of concentration changes in the blood serum of patients who have had no reaction to medication or who have had a reported relapse following treatment, GSH tends to be an effective indicator.

Immunogenicity and antigenicity based T-cell and B-cell epitopes identification from conserved regions of 10664 SARS-CoV-2 genomes.

The surge of SARS-CoV-2 has created a wave of pandemic around the globe due to its high transmission rate. To contain this virus, researchers are working around the clock for a solution in the form of vaccine. Due to the impact of this pandemic, the economy and healthcare have immensely suffered around the globe. Thus, an efficient vaccine design is the need of the hour. Moreover, to have a generalised vaccine for heterogeneous human population, the virus genomes from different countries should be considered. Thus, in this work, we have performed genome-wide analysis of 10,664 SARS-CoV-2 genomes of 73 countries around the globe in order to identify the potential conserved regions for the development of peptide based synthetic vaccine viz. epitopes with high immunogenic and antigenic scores. In this regard, multiple sequence alignment technique viz. Clustal Omega is used to align the 10,664 SARS-CoV-2 virus genomes. Thereafter, entropy is computed for each genomic coordinate of the aligned genomes. The entropy values are then used to find the conserved regions. These conserved regions are refined based on the criteria that their lengths should be greater than or equal to 60 nt and their corresponding protein sequences are without any stop codons. Furthermore, Nucleotide BLAST is used to verify the specificity of the conserved regions. As a result, we have obtained 17 conserved regions that belong to NSP3, NSP4, NSP6, NSP8, RdRp, Helicase, endoRNAse, 2’-O-RMT, Spike glycoprotein, ORF3a protein, Membrane glycoprotein and Nucleocapsid protein. Finally, these conserved regions are used to identify the T-cell and B-cell epitopes with their corresponding immunogenic and antigenic scores. Based on these scores, the most immunogenic and antigenic epitopes are then selected for each of these 17 conserved regions. Hence, we have obtained 30 MHC-I and 24 MHC-II restricted T-cell epitopes with 14 and 13 unique HLA alleles and 21 B-cell epitopes for the 17 conserved regions. Moreover, for validating the relevance of these epitopes, the binding conformation of the MHC-I and MHC-II restricted T-cell epitopes are shown with respect to HLA alleles. Also, the physico-chemical properties of the epitopes are reported along with Ramchandran plots and Z-Scores and the population coverage is shown as well. Overall, the analysis shows that the identified epitopes can be considered as potential candidates for vaccine design.

A systematic data-mining-based methodology for product family design and product configuration

Product family design and product configuration based on data mining technology is identified as an intelligent and automated means to improve the efficiency of product development. However, few of previous literatures have proposed systematic product family design method based on data mining technology. To make up for this deficiency, this research put forward a systematic data-mining-based method for product family design and product configuration. First, the customer requirement information and product engineering information in the historical order are formatted into structural data. Second, principal component analysis is performed on historical orders to extract the customers' differentiated needs. Third, association rule algorithm is introduced to mine the rules between differentiated needs and module instances in the historical orders, thus obtained the configuration knowledge between customer needs and product engineer. Forth, the mined rules are used to construct association rule-based classifier (CBA) that is employed to sort out the best product configuration schemes as popular product variants. Fifth, sequence alignment technique is employed to identify modules for popular product variants, so that the module instances are divided into optional, common and special module, respectively, thereby the product platform is generated based on common modules. Finally, according to new customer needs, the CBA classifier is used to recommend the best configuration schemes, and then popular product variants are configured based on the product platform. The feasibility of the proposed method is demonstrated by the product family design example of desktop computer hosts.

Using Sequence Analysis to Determine the Well-Functioning of Small Groups in Large Online Courses

Collaborative learning in small groups can enrich and enhance the learning experience in large online courses by facilitating interaction and collaborative knowledge building between peers. on the work reported here addresses scenarios based on asynchronous communication and exchange. As compared to synchronous and face-to-face settings, these scenarios require higher explicit coordination efforts and specific support mechanisms to reach out to inactive group members. This can be achieved by human tutors as well as through automatic interventions by the system in the organization and facilitation of group work. Our approach is based on characterizing the quality or well-functioning of collaboration using sequence alignment techniques that were originally developed in bioinformatics for the comparison of DNA sequences. Sequence alignment provides a similarity measure between pairs of action logs originating from the group work. The ensuing similarity matrix allows for clustering the working groups characterized by the similarity of their complete action logs. Notably, these clusters differ clearly in terms of quality measures related to the activity distribution and group output. This allows for determining quality indicators that can trigger feedback and adaptive scaffolding. Our data stem from inter-university online courses in which regular (weekly or bi-weekly) assignments had to be completed in small groups supported by an online forum for coordination and a separate tool for the actual collaborative writing. The basic actions are interpreted/coded as different contribution types, namely coordination, monitoring, minor/major contributions. Longer periods of inactivity are coded as another type of action (“gap”). These are the building blocks for the sequence alignment. One consequence of our analysis is a revision of the intuitive assumption that inactivity is a main indicator of inefficient group work: We found that inactivity can be compensated by early coordination to the extent that coordination is even the most important factor for successful group work. These insights are now being used to design alert mechanisms to signal inefficient group work and to generate timely interventions.

Genome-wide analysis of Indian SARS-CoV-2 genomes to identify T-cell and B-cell epitopes from conserved regions based on immunogenicity and antigenicity

SARS-CoV-2 has a high transmission rate and shows frequent mutations, thus making vaccine development an arduous task. However, researchers around the globe are working hard to find a solution e.g. synthetic vaccine. Here, we have performed genome-wide analysis of 566 Indian SARS-CoV-2 genomes to extract the potential conserved regions for identifying peptide based synthetic vaccines, viz. epitopes with high immunogenicity and antigenicity. In this regard, different multiple sequence alignment techniques are used to align the SARS-CoV-2 genomes separately. Subsequently, consensus conserved regions are identified after finding the conserved regions from each aligned result of alignment techniques. Further, the consensus conserved regions are refined considering that their lengths are greater than or equal to 60nt and their corresponding proteins are devoid of any stop codons. Subsequently, their specificity as query coverage are verified using Nucleotide BLAST. Finally, with these consensus conserved regions, T-cell and B-cell epitopes are identified based on their immunogenic and antigenic scores which are then used to rank the conserved regions. As a result, we have ranked 23 consensus conserved regions that are associated with different proteins. This ranking also resulted in 34 MHC-I and 37 MHC-II restricted T-cell epitopes with 16 and 19 unique HLA alleles and 29 B-cell epitopes. After ranking, the consensus conserved region from NSP3 gene is obtained that is highly immunogenic and antigenic. In order to judge the relevance of the identified epitopes, the physico-chemical properties and binding conformation of the MHC-I and MHC-II restricted T-cell epitopes are shown with respect to HLA alleles.

Inferring the genetic variability in Indian SARS-CoV-2 genomes using consensus of multiple sequence alignment techniques.

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is a threat to the human population and has created a worldwide pandemic. Daily thousands of people are getting affected by the SARS-CoV-2 virus; India being no exception. In this situation, there is no doubt that vaccine is the primary prevention strategy to contain the wave of COVID-19 pandemic. In this regard, genome-wide analysis of SARS-CoV-2 is important to understand its genetic variability. This has motivated us to analyse 566 Indian SARS-CoV-2 sequences using multiple sequence alignment techniques viz. ClustalW, MUSCLE, ClustalO and MAFFT to align and subsequently identify the lists of mutations as substitution, deletion, insertion and SNP. Thereafter, a consensus of these results, called as Consensus Multiple Sequence Alignment (CMSA), is prepared to have the final list of mutations so that the advantages of all four alignment techniques can be preserved. The analysis shows 767, 2025 and 54 unique substitutions, deletions and SNPs in Indian SARS-CoV-2 genomes. More precisely, out of 54 SNPs, 4 SNPs are present close to the 60% of the virus population. The results of this experiment can be useful for virus classification, designing and defining the dose of vaccine for the Indian population.

Computation of Implementation Shortfall for Algorithmic Trading by Sequence Alignment

Implementation shortfall measures the difference in performance between paper portfolio and real portfolio. It is decomposed as the sum of execution cost and opportunity cost. The authors show that the original framework is not directly applicable to algorithmic trading and propose a new framework to compute implementation shortfall and its decomposition. They use an efficient algorithm inspired by DNA sequence alignment techniques to align the trade records from both portfolios and then compute the implementation shortfall with a breakdown of execution cost and opportunity cost for diagnosis. Their framework is simple, objective, and computationally efficient—the complexity only grows linearly with respect to the numbers of trades of paper and real portfolios. Thus, the framework proposed in this article is applicable to high-frequency trading data. <b>TOPICS:</b>Portfolio construction, big data, performance measurement

LegumeDB: Development of Legume Medicinal Plant Database and Comparative Molecular Evolutionary Analysis of matK Proteins of Legumes and Mangroves

Background: Legume plants are known for their rich medicinal and nutritional values. Large amount of medicinal information of various legume plants have been dispersed in the form of text. Objective: It is essential to design and construct a legume medicinal plants database, which integrate respective classes of legumes and include knowledge regarding medicinal applications along with their protein/enzyme sequences. Methods: The design and development of Legume Medicinal Plants Database (LegumeDB) has been done by using Microsoft Structure Query Language Server 2017. DBMS was used as back end and ASP.Net was used to lay out front end operations. VB.Net was used as arranged program for coding. Multiple sequence alignment, phylogenetic analysis and homology modeling techniques were also used. Results: This database includes information of 50 Legume medicinal species, which might be helpful to explore the information for researchers. Further, maturase K (matK) protein sequences of legumes and mangroves were retrieved from NCBI for multiple sequence alignment and phylogenetic analysis to understand evolutionary lineage between legumes and mangroves. Homology modeling technique was used to determine three-dimensional structure of matK from Legume species i.e. Vigna unguiculata using matK of mangrove species, Thespesia populnea as a template. The matK sequence analysis results indicate the conserved residues among legume and mangrove species. Conclusion: Phylogenetic analysis revealed closeness between legume species Vigna unguiculata and mangrove species Thespesia populnea to each other, indicating their similarity and origin from common ancestor. Thus, these studies might be helpful to understand evolutionary relationship between legumes and mangroves. : LegumeDB availability: http://legumedatabase.co.in

Modelling melodic variation and extracting melodic templates from flamenco singing performances

A key concept in flamenco singing is the constant re-interpretation of orally transmitted melodic templates, which undergo variation and ornamentation during spontaneous performances. We propose a novel framework which allows us to model a set of interpretations of the same melody and analyse differences among them. We apply a multiple progressive sequence alignment technique to obtain a graphical model holding the frequency of occurrence of characteristic notes and note transitions, from which the underlying melodic template can be approximated. In a case study, we showcase how visualisations of the derived model yield insights into local and global melodic stability and inter-style contour similarities. We furthermore show how the extracted templates can used to identify instances of a particular melody in a large corpus of automatic transcriptions.