Query Sequence Research Articles

Curated multiple sequence alignment for the Adenomatous Polyposis Coli (APC) gene and accuracy of in silico pathogenicity predictions.

Computational algorithms are often used to assess pathogenicity of Variants of Uncertain Significance (VUS) that are found in disease-associated genes. Most computational methods include analysis of protein multiple sequence alignments (PMSA), assessing interspecies variation. Careful validation of PMSA-based methods has been done for relatively few genes, partially because creation of curated PMSAs is labor-intensive. We assessed how PMSA-based computational tools predict the effects of the missense changes in the APC gene, in which pathogenic variants cause Familial Adenomatous Polyposis. Most Pathogenic or Likely Pathogenic APC variants are protein-truncating changes. However, public databases now contain thousands of variants reported as missense. We created a curated APC PMSA that contained >3 substitutions/site, which is large enough for statistically robust in silico analysis. The creation of the PMSA was not easily automated, requiring significant querying and computational analysis of protein and genome sequences. Of 1924 missense APC variants in the NCBI ClinVar database, 1800 (93.5%) are reported as VUS. All but two missense variants listed as P/LP occur at canonical splice or Exonic Splice Enhancer sites. Pathogenicity predictions by five computational tools (Align-GVGD, SIFT, PolyPhen2, MAPP, REVEL) differed widely in their predictions of Pathogenic/Likely Pathogenic (range 17.5-75.0%) and Benign/Likely Benign (range 25.0-82.5%) for APC missense variants in ClinVar. When applied to 21 missense variants reported in ClinVar and securely classified as Benign, the five methods ranged in accuracy from 76.2-100%. Computational PMSA-based methods can be an excellent classifier for variants of some hereditary cancer genes. However, there may be characteristics of the APC gene and protein that confound the results of in silico algorithms. A systematic study of these features could greatly improve the automation of alignment-based techniques and the use of predictive algorithms in hereditary cancer genes.

BIBSQLQC: Brown infomax boosted SQL query clustering algorithm to detect anti-patterns in the query log

Discovery of antipatterns from arbitrary SQL query log depends on the static code analysis used to enhance the quality and performance of software applications. The existence of antipatterns reduces the quality and leads to redundant SQL statements. SQL log includes a large load on the database and it is difficult for an analyst to extract large patterns in a minimal time. Existing techniques which discover antipatterns in SQL query face a lot of innumerable challenges to discover the normal sequences of queries within the log. In order to discover the antipatterns in the log, an efficient technique called Brown infomax boosted SQL query clustering (BIBSQLQC) technique is introduced. Initially, the number of patterns (i.e. queries) are extracted from the SQL query log. After extracting the patterns, the ensemble clustering process is carried out to find out the antipatterns from the given query log. The Brown infomax boost clustering is an ensemble learning method for grouping the patterns by constructing several weak learners. The Brown clustering is used as a weak learner for partitioning the patterns into 'k' number of clusters based on the Euclidean distance measure. Then the weak learner merges the two clusters with maximum information gained to minimize the time complexity. The clustering results of weak learners are combined into strong results with minimal error rate (ER). By this way, the antipattern in the SQL query log is detected with a higher accuracy. Experimental evaluation is conducted with different parameters namely detection accuracy (DA), false positive rate (FPR) and time complexity (TC) using the two SQL query log data-sets (DS). The experimental result shows that, the BIBSQLQC technique achieves higher DA with lower TC and FPR than the conventional methods.

Dataset for homologous proteins in Drosophila melanogaster for SARS-CoV-2/human interactome.

Animal modelling for infectious diseases is critical to understand the biology of the pathogens including viruses and to develop therapeutic strategies against it. Herein, we present the sequence homology and expression data analysis of proteins found in Drosophila melanogaster that are orthologous to human proteins, reported as components of SARS-CoV-2/Human interactome. The dataset enlists sequence homology, query coverage, domain conservation, OrthoMCL and Ensembl Genome Browser support of 326 proteins in D.melanogaster that are potentially orthologous to 417 human proteins reported for their direct physical interactions with 28 proteins encoded by SARS-CoV-2 genome. Expression of these D.melanogaster orthologous genes in 26 anatomical positions are also plotted as heat maps in 27 sets, corresponding to the potential protein interactors for each viral protein. The data could be used to direct experiments and potentially predict their phenotypic and molecular outcome in order to dissect the biological roles and molecular functionality of SARS-CoV-2 proteins in a convenient animal model system like D.melanogaster.

TreeSAPP: the Tree-based Sensitive and Accurate Phylogenetic Profiler.

MotivationMicrobial communities drive matter and energy transformations integral to global biogeochemical cycles, yet many taxonomic groups facilitating these processes remain poorly represented in biological sequence databases. Due to this missing information, taxonomic assignment of sequences from environmental genomes remains inaccurate.ResultsWe present the Tree-based Sensitive and Accurate Phylogenetic Profiler (TreeSAPP) software for functionally and taxonomically classifying genes, reactions and pathways from genomes of cultivated and uncultivated microorganisms using reference packages representing coding sequences mediating multiple globally relevant biogeochemical cycles. TreeSAPP uses linear regression of evolutionary distance on taxonomic rank to improve classifications, assigning both closely related and divergent query sequences at the appropriate taxonomic rank. TreeSAPP is able to provide quantitative functional and taxonomic classifications for both assembled and unassembled sequences and files supporting interactive tree of life visualizations.Availability and implementationTreeSAPP was developed in Python 3 as an open-source Python package and is available on GitHub at https://github.com/hallamlab/TreeSAPP.Supplementary information Supplementary data are available at Bioinformatics online.

Constructing dummy query sequences to protect location privacy and query privacy in location-based services

Location-based services (LBS) have become an important part of people’s daily life. However, while providing great convenience for mobile users, LBS result in a serious problem on personal privacy, i.e., location privacy and query privacy. However, existing privacy methods for LBS generally take into consideration only location privacy or query privacy, without considering the problem of protecting both of them simultaneously. In this paper, we propose to construct a group of dummy query sequences, to cover up the query locations and query attributes of mobile users and thus protect users’ privacy in LBS. First, we present a client-based framework for user privacy protection in LBS, which requires not only no change to the existing LBS algorithm on the server-side, but also no compromise to the accuracy of a LBS query. Second, based on the framework, we introduce a privacy model to formulate the constraints that ideal dummy query sequences should satisfy: (1) the similarity of feature distribution, which measures the effectiveness of the dummy query sequences to hide a true user query sequence; and (2) the exposure degree of user privacy, which measures the effectiveness of the dummy query sequences to cover up the location privacy and query privacy of a mobile user. Finally, we present an implementation algorithm to well meet the privacy model. Besides, both theoretical analysis and experimental evaluation demonstrate the effectiveness of our proposed approach, which show that the location privacy and attribute privacy behind LBS queries can be effectively protected by the dummy queries generated by our approach.

Identification of ligand-binding residues using protein sequence profile alignment and query-specific support vector machine model.

Information embedded in ligand-binding residues (LBRs) of proteins is important for understanding protein functions. How to accurately identify the potential ligand-binding residues is still a challenging problem, especially only protein sequence is given. In this paper, we establish a new query-specific computational method, named I-LBR, for the identification of LBRs without directly using the information of protein 3D structure. I-LBR includes two modes, named as I-LBRGP and I-LBRLS, for the general-purpose and ligand-specific LBR identification. For both modes, I-LBR first construct the specific training subset based on the query sequence information; then use support vector machine (SVM) algorithm to learn the LBR identification model; finally, predict the probability of each residue in query protein belongs to the class of LBR. Experimental results on four testing dataset demonstrate that I-LBRLS is the better choice against I-LBRGP, when the ligand type/types of the query protein binds is/are known. Comparing to other state-of-the-art LBR identification methods, I-LBR can achieve a better or comparable performance. The web-server of I-LBR and dataset used in this study are freely available for academic use at https://jun-csbio.github.io/I-LBR.

DenvInD: dengue virus inhibitors database for clinical and molecular research.

Dengue virus (DENV) researchers often face challenges with the highly time-consuming process of collecting and curating information on known inhibitors during the standard drug discovery process. To this end, however, required collective information is not yet available on a single platform. Hence, we have developed the DenvInD database for experimentally validated DENV inhibitors against its known targets presently hosted at https://webs.iiitd.edu.in/raghava/denvind/. This database provides comprehensive information, i.e. PubChem IDs, SMILES, IC50, EC50, CC50, and wherever available Ki values of the 484 compounds in vitro validated as inhibitors against respective drug targets of DENV. Also, the DenvInD database has been linked to the user-friendly web-based interface and accessibility features, such as simple search, advanced search and data browsing. All the required data curation was conducted manually from the reported scientific literature and PubChem. The collected information was then organized into the DenvInD database using sequence query language under user interface by hypertext markup language. DenvInD is the first useful repository of its kind which would augment the DENV drug discovery research by providing essential information on known DENV inhibitors for molecular docking, computational screening, pharmacophore modeling and quantitative structure-activity relationship modeling.

Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review.

Coronaviruses (CoVs) are a large family of viruses that are common in many animal species, including camels, cattle, cats and bats. Animal CoVs, such as Middle East respiratory syndrome-CoV, severe acute respiratory syndrome (SARS)-CoV, and the new virus named SARS-CoV-2, rarely infect and spread among humans. On January 30, 2020, the International Health Regulations Emergency Committee of the World Health Organisation declared the outbreak of the resulting disease from this new CoV called ‘COVID-19’, as a ‘public health emergency of international concern’. This global pandemic has affected almost the whole planet and caused the death of more than 315,131 patients as of the date of this article. In this context, publishers, journals and researchers are urged to research different domains and stop the spread of this deadly virus. The increasing interest in developing artificial intelligence (AI) applications has addressed several medical problems. However, such applications remain insufficient given the high potential threat posed by this virus to global public health. This systematic review addresses automated AI applications based on data mining and machine learning (ML) algorithms for detecting and diagnosing COVID-19. We aimed to obtain an overview of this critical virus, address the limitations of utilising data mining and ML algorithms, and provide the health sector with the benefits of this technique. We used five databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus and performed three sequences of search queries between 2010 and 2020. Accurate exclusion criteria and selection strategy were applied to screen the obtained 1305 articles. Only eight articles were fully evaluated and included in this review, and this number only emphasised the insufficiency of research in this important area. After analysing all included studies, the results were distributed following the year of publication and the commonly used data mining and ML algorithms. The results found in all papers were discussed to find the gaps in all reviewed papers. Characteristics, such as motivations, challenges, limitations, recommendations, case studies, and features and classes used, were analysed in detail. This study reviewed the state-of-the-art techniques for CoV prediction algorithms based on data mining and ML assessment. The reliability and acceptability of extracted information and datasets from implemented technologies in the literature were considered. Findings showed that researchers must proceed with insights they gain, focus on identifying solutions for CoV problems, and introduce new improvements. The growing emphasis on data mining and ML techniques in medical fields can provide the right environment for change and improvement.

PlaToLoCo: the first web meta-server for visualization and annotation of low complexity regions in proteins.

Low complexity regions (LCRs) in protein sequences are characterized by a less diverse amino acid composition compared to typically observed sequence diversity. Recent studies have shown that LCRs may co-occur with intrinsically disordered regions, are highly conserved in many organisms, and often play important roles in protein functions and in diseases. In previous decades, several methods have been developed to identify regions with LCRs or amino acid bias, but most of them as stand-alone applications and currently there is no web-based tool which allows users to explore LCRs in protein sequences with additional functional annotations. We aim to fill this gap by providing PlaToLoCo - PLAtform of TOols for LOw COmplexity—a meta-server that integrates and collects the output of five different state-of-the-art tools for discovering LCRs and provides functional annotations such as domain detection, transmembrane segment prediction, and calculation of amino acid frequencies. In addition, the union or intersection of the results of the search on a query sequence can be obtained. By developing the PlaToLoCo meta-server, we provide the community with a fast and easily accessible tool for the analysis of LCRs with additional information included to aid the interpretation of the results. The PlaToLoCo platform is available at: http://platoloco.aei.polsl.pl/.

VitisGDB: The Multifunctional Database for Grapevine Breeding and Genetics

VitisGDB: The Multifunctional Database for Grapevine Breeding and Genetics

Conserved unique peptide patterns (CUPP) online platform: peptide-based functional annotation of carbohydrate active enzymes.

The CUPP platform includes a web server for functional annotation and sub-grouping of carbohydrate active enzymes (CAZymes) based on a novel peptide-based similarity assessment algorithm, i.e. protein grouping according to Conserved Unique Peptide Patterns (CUPP). This online platform is open to all users and there is no login requirement. The web server allows the user to perform genome-based annotation of carbohydrate active enzymes to CAZy families, CAZy subfamilies, CUPP groups and EC numbers (function) via assessment of peptide-motifs by CUPP. The web server is intended for functional annotation assessment of the CAZy inventory of prokaryotic and eukaryotic organisms from genomic DNA (up to 30MB compressed) or directly from amino acid sequences (up to 10MB compressed). The custom query sequences are assessed using the CUPP annotation algorithm, and the outcome is displayed in interactive summary result pages of CAZymes. The results displayed allow for inspection of members of the individual CUPP groups and include information about experimentally characterized members. The web server and the other resources on the CUPP platform can be accessed from https://cupp.info.

Identification, characterization and epitope mapping of proteins encoded by putative allergenic napin genes from Brassica rapa.

Brassica rapeseed crops contain high concentrations of oil in the seed. The remaining meal, following oil extraction, has a high protein content, but is of low value due to the presence of high amounts of napin seed storage proteins. These 2S albumin-like proteins are difficult to digest and have been identified as major allergens in humans. To comprehensively characterize the napin gene (NG) family in Brassica rapa and to gain an understanding of the structural basis of allergenicity of the expressed proteins. To identify candidate napin genes in B rapa, 2S albumin-like napin genes of Arabidopsis thaliana were used as query sequences to search for similarity against the B rapa var. pekinensis Chiifu-401 v2 and the var. trilocularis R-o-18 v1.5 genomes. Multiple sequence alignment (MSA) and epitope modelling was carried out to determine structural and evolutionary relationships of NGs and their potential allergenicity. Fourcandidate napin genes in R-o-18 and ten in Chiifu-401 were identified with high sequence similarity to A thaliana napin genes. Multiple sequence alignment revealed strong conservation among the candidate genes. An epitope survey indicated high conservation of allergenic epitope motifs with known 2S albumin-like allergens. Napin is thought to be responsible for a high prevalence of foodallergies. Characterization of the napin gene family in B rapa will give important insight into the protein structure, and epitope modelling will help to advance studies into allergenicity including the development of precise diagnostic screenings and therapies for this potential foodallergy as well as the possible manipulation of napin levels in the seed by gene editing technology.

A Location Privacy-Preserving System Based on Query Range Cover-Up or Location-Based Services

Location-based service (LBS) has been widely used in various fields of industry, and become a vital part of people's daily life. However, while providing great convenience for users, LBS results in a serious threat on users’ location privacy, due to its more and more untrusted server-side. In this article, we propose a location privacy-preserving system for LBS by constructing “cover-up ranges” to protect the query ranges associated with a location query sequence. Firstly, we present a client-based system framework for location privacy protection in LBS, which requires no compromise to the accuracy and usability of LBS. Secondly, based on the framework, we introduce a location privacy model to formulate the constraints that ideal cover-up ranges should satisfy, so as to improve the efficiency of location services and the security of location privacy. Finally, we describe an implementation algorithm to well meet the location privacy model. Both theoretical analysis and experimental evaluation demonstrate the effectiveness of our system, which can improve the security of users’ location privacy on the untrusted server-side, without compromising the accuracy and usability of LBS.

BOLD and GenBank revisited - Do identification errors arise in the lab or in the sequence libraries?

Applications of biological knowledge, such as forensics, often require the determination of biological materials to a species level. As such, DNA-based approaches to identification, particularly DNA barcoding, are attracting increased interest. The capacity of DNA barcodes to assign newly encountered specimens to a species relies upon access to informatics platforms, such as BOLD and GenBank, which host libraries of reference sequences and support the comparison of new sequences to them. As parameterization of these libraries expands, DNA barcoding has the potential to make valuable contributions in diverse applied contexts. However, a recent publication called for caution after finding that both platforms performed poorly in identifying specimens of 17 common insect species. This study follows up on this concern by asking if the misidentifications reflected problems in the reference libraries or in the query sequences used to test them. Because this reanalysis revealed that missteps in acquiring and analyzing the query sequences were responsible for most misidentifications, a workflow is described to minimize such errors in future investigations. The present study also revealed the limitations imposed by the lack of a polished species-level taxonomy for many groups. In such cases, applications can be strengthened by mapping the geographic distributions of sequence-based species proxies rather than waiting for the maturation of formal taxonomic systems based on morphology.

EVALUATION OF CYTOTOXIC ACTIVITY OF ETHYL ACETATE EXTRACT OF PIGMENT FROM PSEUDOMONAS AERUGINOSA

Objective: Bacterial pigments have promising applications in food, cosmetics, textile, and therapeutics. Pigments from microbial origin are stable, safer, cost effective, easy production, and extraction and thus preferred over other natural sources. Under this backdrop, isolation and characterization of pigment-producing bacteria and analysis of bioactivity of the pigment were the aim of the study. From the literature studies, the pigment production was found to be influenced by various physical factors which directed the study toward optimization of physical parameters for pigment production.
 Methods: Isolation of pigment-producing bacteria from water sample, cultural, and microscopic identification was done as per the standard protocol. Extraction of pigment by solvent extraction was carried out and its antibacterial and cytotoxic activity was assayed.
 Results: Molecular characterization of the bacteria resembled the query sequence of the isolate to 99% with Pseudomonas aeruginosa strain. Extraction of pigment by solvent extraction method resulted in crude pigment extract with antibacterial activity against Gram-negative bacteria (17 mm zone of inhibition) at 100 μg/ml concentration. Pigment showed dose-dependent inhibition on proliferation of HeLa cells at the concentration of 345.83 μg/mL.
 Conclusion: From the above results, it was evident that the pigment extracted from the bacterial isolate Pseudomonas aeruginosa strain JBT18N was therapeutically potential.

Sparse Binary Relation Representations for Genome Graph Annotation.

High-throughput DNA sequencing data are accumulating in public repositories, and efficient approaches for storing and indexing such data are in high demand. In recent research, several graph data structures have been proposed to represent large sets of sequencing data and to allow for efficient querying of sequences. In particular, the concept of labeled de Bruijn graphs has been explored by several groups. Although there has been good progress toward representing the sequence graph in small space, methods for storing a set of labels on top of such graphs are still not sufficiently explored. It is also currently not clear how characteristics of the input data, such as the sparsity and correlations of labels, can help to inform the choice of method to compress the graph labeling. In this study, we present a new compression approach, Multi-binary relation wavelet tree (BRWT), which is adaptive to different kinds of input data. We show an up to 29% improvement in compression performance over the basic BRWT method, and up to a 68% improvement over the current state-of-the-art for de Bruijn graph label compression. To put our results into perspective, we present a systematic analysis of five different state-of-the-art annotation compression schemes, evaluate key metrics on both artificial and real-world data, and discuss how different data characteristics influence the compression performance. We show that the improvements of our new method can be robustly reproduced for different representative real-world data sets.

Geometrically Mappable Image Features

Vision-based localization of an agent in a map is an important problem in robotics and computer vision. In that context, localization by learning matchable image features is gaining popularity due to recent advances in machine learning. Features that uniquely describe the visual contents of images have a wide range of applications, including image retrieval and understanding. In this work, we propose a method that learns image features targeted for image-retrieval-based localization. Retrieval-based localization has several benefits, such as easy maintenance and quick computation. However, the state-of-the-art features only provide visual similarity scores which do not explicitly reveal the geometric distance between query and retrieved images. Knowing this distance is highly desirable for accurate localization, especially when the reference images are sparsely distributed in the scene. Therefore, we propose a novel loss function for learning image features which are both visually representative and geometrically relatable. This is achieved by guiding the learning process such that the feature and geometric distances between images are directly proportional. In our experiments we show that our features not only offer significantly better localization accuracy, but also allow to estimate the trajectory of a query sequence in absence of the reference images.

A supervised deep convolutional based bidirectional long short term memory video hashing for large scale video retrieval applications

Recently, large scale video content retrieval has gained more attention due to a large amount of user-generated images and video content available over the internet. Hashing is one of the effective techniques that encode the high dimensional features vectors into compact binary codes. The aim of hashing is to generate the short binary codes and map the similar hash code values to retrieve similar video from the database with minimum distance measure. Deep learning-based hashing networks are employed to learn the representative video feature to estimate the hash functions. However, the existing hashing approaches fail due to the frame-level feature representation and does not well exploits the effective temporal features in visual search. Furthermore, significant loss of features during the dimensionality reduction step causes low accuracy. Hence, it is essential to develop a deep learning-based hashing framework that should exploits both the strong spatial and temporal features for the scalable video search. The main objective is to learn the high dimensional features from the entire video and derive compact binary codes to retrieve the similar videos for the input query sequence. In this paper, we propose a joint network model of supervised stacked heterogeneous convolutional multi-kernel (Stacked HetConv-MK)-bidirectional Long Short Term Memory (BiDLSTM) network model that effectively encodes the rich structural as well as the discriminative features from the video sequence to estimate the compact binary codes. Initially, the video frames are passed to the stacked convolution networks with heterogeneous convolutional kernel size and residual learning to extract the spatial features at different views from the video sequences and to improve the learning efficiency. Then, the bidirectional network computes the sequence in both forward and backward directions and obtains the series of hidden state output. Finally, the fully connected structure with an activation unit performs hashing to learn the multiple codes for each video. Experimental analysis is performed on three datasets and the result shows a better accuracy measure than the other state-of-art approaches.

Characterization of HIV-1 genetic diversity and antiretroviral resistance in the state of Maranhão, Northeast Brazil.

The HIV-1 epidemic in Brazil has been growing in northeast and north regions, particularly an increase in AIDS cases among the younger male population has been observed. This study aims to characterize the HIV-1 genetic diversity and to evaluate its antiretroviral resistance profile among individuals presenting virological failure in the state of Maranhão—Brazil. HIV-1 pol gene sequences from 633 patients on antiretroviral therapy were obtained from the Department of Surveillance, Prevention and Control of Sexually Transmitted Infections, HIV/AIDS and Viral Hepatitis of the Brazilian Ministry of Health. Phylogenetic and recombination analyses were performed to characterize viral genetic diversity. The presence of antiretroviral resistance mutations was assessed using the HIV Drug Resistance Database online platform of Stanford University. A predominance of subtype B (84.5%) was observed, followed by recombinant BF (9.5%), where more than half of the sequences were dispersed in 3 clusters. Antiretroviral resistance was detected in 74.1% of the sequences, and it was significantly higher for nucleoside analogue reverse-transcriptase inhibitors (NRTIs) than for non-nucleoside analogue reverse-transcriptase inhibitors (NNRTIs) and protease inhibitors (PIs). Inference of putative transmissions clusters identified 11 clusters with 22 query sequences (22/633, 3.5%). Thus, we conclude that continuous monitoring of the molecular epidemiology of HIV-1 is essential for prevention strategies, epidemic control, and treatment adequacy.

Understanding online health information consumers' search as a learning process

PurposeThis study considers online searching by health information consumers as a learning process. We focus on search sequences, query reformulation, and conceptual changes.Design/methodology/approachA qualitative user study (30 participants; three health information seeking tasks) investigated mobile searching behavior. Recorded screen activity, questionnaires, and in-depth personal interview data were collected and analyzed.Findings(1) Search platform sequences of health information consumers in search as a learning process were exacted and their features were highlighted. (2) Query sequence and reformulation pattern of health information consumers were exacted and discussed. (3) The types and degree of conceptual changes of health consumers were reflected by their query reformulation behavior and differ from different health information search tasks. (4) Characteristics of health consumers' search as learning process were revealed.Research limitations/implications(1) A novel perspective of consumer health information studies was proposed by exacting search platform sequence, query sequence and linking them with conceptual changes during the search as learning process. (2) Conceptual changes in the searching as a learning process are regarded as a measure of search outcome in this study, in which terms extracted from queries were used to reflect conceptual changes in consumers' mind. (3) Our findings provide evidences that types of health information seeking tasks do have significant influences on the search as a learning process.Practical ImplicationsThe findings of this study can lead to the fit-to-needs of the search platforms, provide advice for information architecture of search list of search platforms, and guide the design of knowledge graph of health information systems.Originality/valuePotential relationships between information-seeking behavior and conceptual changes in search as a learning process relative to health information were revealed.