Query Sequence Research Articles

Alignment-free Sequence Searching over Whole Genomes Using 3D random plot of Query DNA Sequences

Most genomic data studies are based on sequence comparisons and searches, and comparison models based on alignment algorithms are most commonly used. This method is very accurate, but it is useful when the query is short in kilobytes, because it requires the quadratic time and space complexity, O(n2) where n is the length of target and query sequences. With the development of Next Generation Sequencing techniques, researches on whole genome sequence data of megabyte size are being actively studied, and new comparison and search methods for large-scale sequence data are needed. We propose a new alignment-free sequence comparison and search method to overcome the limitations of the alignment-based model. In this graphical model, the sequence searching problem in DNA strings can be reduced to find some parts of geometric object within a relatively small-scale geometric space. When comparing similarity by modifying sequences of similar length, we can confirm that the comparison model is appropriate by accurately reflecting the degree of similarity. When searching the query sequence comparison model based on 200MB sized whole genome sequence, using the compressed coordinate information, it was able to search the 10MB sequences in 22s, which is a very reduced time compared to alignment. Although it is not possible to find the exact position of the base pair unit as in the alignment result, it is a model that can be used as a preprocessing process to quickly search a whole genome sequence of several hundred megabytes-size.

A Relational Framework for Classifier Engineering

In the design of analytical procedures and machine learning solutions, a critical and time-consuming task is that of feature engineering, for which various recipes and tooling approaches have been developed. In this article, we embark on the establishment of database foundations for feature engineering. We propose a formal framework for classification in the context of a relational database. The goal of this framework is to open the way to research and techniques to assist developers with the task of feature engineering by utilizing the database’s modeling and understanding of data and queries and by deploying the well-studied principles of database management. As a first step, we demonstrate the usefulness of this framework by formally defining three key algorithmic challenges. The first challenge is that of separability, which is the problem of determining the existence of feature queries that agree with the training examples. The second is that of evaluating the VC dimension of the model class with respect to a given sequence of feature queries. The third challenge is identifiability, which is the task of testing for a property of independence among features that are represented as database queries. We give preliminary results on these challenges for the case where features are defined by means of conjunctive queries, and, in particular, we study the implication of various traditional syntactic restrictions on the inherent computational complexity.

SIG-DB: Leveraging homomorphic encryption to securely interrogate privately held genomic databases.

Genomic data are becoming increasingly valuable as we develop methods to utilize the information at scale and gain a greater understanding of how genetic information relates to biological function. Advances in synthetic biology and the decreased cost of sequencing are increasing the amount of privately held genomic data. As the quantity and value of private genomic data grows, so does the incentive to acquire and protect such data, which creates a need to store and process these data securely. We present an algorithm for the Secure Interrogation of Genomic DataBases (SIG-DB). The SIG-DB algorithm enables databases of genomic sequences to be searched with an encrypted query sequence without revealing the query sequence to the Database Owner or any of the database sequences to the Querier. SIG-DB is the first application of its kind to take advantage of locality-sensitive hashing and homomorphic encryption to allow generalized sequence-to-sequence comparisons of genomic data.

Creating improvisations on chord progressions using suffix trees

Technology nowadays takes an increasing part of “creativity” in live music software such as the OMax-ImproteK-Djazz improvisation environment. Specifically, Djazz implements techniques for indexing and creating improvisations using a given chord progression. It relies on a database that we call a dictionary, storing musical sequences (audio or MIDI) associated with known chord changes. We define an improvisation on a given chord progression x as a sequence obtained by concatenating musical phrases identified with factors of x that can be found in the dictionary. Given a query sequence x and a dictionary D, we present an algorithm that finds an improvisation on x with respect to D. We use the suffix tree data structure to index D and then produce a graph formulation of the problem in order to find a suitable improvisation in linear time, with respect to the length of x.

USING DNA BARCODE TO IMPROVE THE IDENTIFICATION OF MARINE FISH LARVAE, CASE STUDY COASTAL WATER NEAR JAKARTA AND BANDA SEA, INDONESIA

The sustainability of the exploitation of the Indonesian fishes depends heavily on many of fish’s basic information including both larvae distribution and dispersal. However, the identification of fish larvae and juvenile to species is very limited. In this study, we employed DNA barcoding techniques to identify marine fish larvae to a species level in Jakarta Bay and Banda Sea by comparing the querries with sequences from adult stage as reference library to contribute on biodiversity information on that particular area. The result revealed that in Jakarta Bay, the molecular marker of a 471 bp region of the mitchondrial cyctochrome c oxidase I gene (COI) has been successfully found to be species-specific, genetic distance within species (0.0 - 1.30 percent). There are total of 8 families, 5 genera and 5 species from a total 15 successful PCR that could be used to calculate the accuracy of larval fish identification in three taxonomic categories. Meanwhile in Banda Sea, for the adult specimens, after some of PCR experiment, we have successfully amplified 27 individuals, only 8 sequences available. There are a total 326 eggs and larvae which been collected from 19 stations, of the 28 successfully amplified PCR samples, 11 sequences were available for DNA analysis and at least 10 species used Banda Sea and surrounding area as their spawning ground. We prove the ability of COI barcodes to identify species level resolution from query sequences and to classify species from distinct geographical origins and determine of how the data retrieved give important information for proposing plans for conserving and managing of fisheries in the sea waters.

Automatic assessment of interactive OLAP explorations

Interactive Database Exploration (IDE) is the process of exploring a database by means of a sequence of queries aiming at answering an often imprecise user information need. In this paper, we are interested in the following problem: how to automatically assess the quality of such an exploration. We study this problem under the following angles. First, we formulate the hypothesis that the quality of the exploration can be measured by evaluating the improvement of the skill of writing queries that contribute to the exploration. Second, we restrict to a particular use case of database exploration, namely OLAP explorations of data cubes. Third, we propose to use simple query features to model its contribution to an exploration. The first hypothesis allows to use the Knowledge Tracing, a popular model for skill acquisition, to measure the evolution of the ability to write contributive queries. The restriction to OLAP exploration allows to take advantage of well known OLAP primitives and schema. Finally, using query features allows to apply a supervised learning approach to model query contribution. We show on both real and artificial explorations that automatic assessment of OLAP explorations is feasible and is consistent with the user’s and expert’s viewpoints.

Mining and Functional Verification of Calcitonin Genes from the Genome Database.

With more countries in the world entering elderly society, osteoporosis is a common disease among the elderly, especially middle-aged and elderly women. Although calcitonin is an effective drug used to treat osteoporosis in clinical practice, it also exists such problems as high cost, short half-life, and high immunogenicity. Therefore, to explore more efficient calcitonin has important clinical significance. Given the emergence of new-generation gene sequencing, numerous genome sequences of marine species have been revealed. This study aimed to identify new, highly active Calcitonins (CTs) from the gene database. Candidate CT sequences were obtained by BLAST and analyzed. The evolutionary tree of these sequences was constructed using the Neighbor-Joining method of MEGA 7 software. Secondary structures were analyzed by Circular Dichroism (CD). The biological activities of CTs were estimated using the standard of the rat hypocalcemic activity assay in vivo. The half-life and immunogenicity of CT sequences were determined by ELISA. The physicochemical properties of peptides were analyzed with ProtParam and HeliQuest. A total of 64 candidate CT gene and amino acid sequences from different species were obtained by BLAST using the salmon CT (sCT) sequence as the query sequence. These sequences were clustered to 27 different CT polypeptide sequences, and then the evolutionary tree was constructed. A total of 13 sequences were selected for chemical synthesis and activity assay. Results showed that although their secondary structures were similar, four types of candidate CTs had 30% higher activities than sCT, three other types had similar activities to sCT, and the remaining four types had much lower activities than sCT. Among the three designed CTs, the activities of CT-01 and CT-02 were at least 50% higher than those of sCT. Furthermore, all three CT sequences had a similar half-life to sCT and lower immunogenicity. CTs from Monodelphis domestica, Gallus gallus, Ornithorhynchus anatinus, and Carassius auratus had high activities. The exploration and mining of the marine-life genome database can be extremely valuable considering broad application prospect.

<bold>CASTLE:</bold> Enhancing the Utility of Inequality Query Auditing Without Denial Threats

We consider a private data set composed of a set of individuals, and the data are outsourced to a remote cloud server. We revisit the classic query auditing problem in this outsourcing scenario; the cloud audits each newly arrived query on a single attribute, and the query is rejected if answering it compromises any individual's privacy. Various query auditing issues have been studied and addressed before. However, previous auditing schemes either have the difficulty of removing denial threats, or lack the analysis of utility (which is defined as the number of answered queries). In this paper, we study the auditing of a sequence of polynomial-time computable queries. Each query is of format f(X̃) a, where f is any polynomial function, X̃ is a subset of the private data set, and the answer is either “yes” or “no”. Existing methods cannot be applied directly to audit such a query, because it intermingles several types of functions (e.g., sum and max/min). Hence, we propose CASTLE, which is an inequality query auditing scheme that evaluates the risk of answering a query based on the query history and determines whether a newly arrived query should be answered correctly against a denial threat. Furthermore, to overcome the limitations of the existing query auditing mechanisms, which are of low utility, we relax CASTLE to increase the utility by returning answers with slight perturbations. We show that our method can be applied to audit intermingled equality queries with an extension. Experiments are conducted to evaluate the efficiency and effectiveness of our methods.

BLAST-XYPlot Viewer: A Tool for Performing BLAST in Whole-Genome Sequenced Bacteria/Archaea and Visualize Whole Results Simultaneously.

One of the most commonly used tools to compare protein or DNA sequences against databases is BLAST. We introduce a web tool that allows the performance of BLAST-searches of protein/DNA sequences in whole-genome sequenced bacteria/archaea, and displays a large amount of BLAST-results simultaneously. The circular bacterial replicons are projected as horizontal lines with fixed length of 360, representing the degrees of a circle. A coordinate system is created with length of the replicon along the x-axis and the number of replicon used on the y-axis. When a query sequence matches with a gene/protein of a particular replicon, the BLAST-results are depicted as an “x,y” position in a specially adapted plot. This tool allows the visualization of the results from the whole data to a particular gene/protein in real time with low computational resources.

DNA barcoding of economically important freshwater fish species from north-central Nigeria uncovers cryptic diversity.

This study examines the utility of morphology and DNA barcoding in species identification of freshwater fishes from north‐central Nigeria. We compared molecular data (mitochondrial cytochrome c oxidase subunit I (COI) sequences) of 136 de novo samples from 53 morphologically identified species alongside others in GenBank and BOLD databases. Using DNA sequence similarity‐based (≥97% cutoff) identification technique, 50 (94.30%) and 24 (45.30%) species were identified to species level using GenBank and BOLD databases, respectively. Furthermore, we identified cases of taxonomic problems in 26 (49.00%) morphologically identified species. There were also four (7.10%) cases of mismatch in DNA barcoding in which our query sequence in GenBank and BOLD showed a sequence match with different species names. Using DNA barcode reference data, we also identified four unknown fish samples collected from fishermen to species level. Our Neighbor‐joining (NJ) tree analysis recovers several intraspecific species clusters with strong bootstrap support (≥95%). Analysis uncovers two well‐supported lineages within Schilbe intermedius. The Bayesian phylogenetic analyses of Nigerian S. intermedius with others from GenBank recover four lineages. Evidence of genetic structuring is consistent with geographic regions of sub‐Saharan Africa. Thus, cryptic lineage diversity may illustrate species’ adaptive responses to local environmental conditions. Finally, our study underscores the importance of incorporating morphology and DNA barcoding in species identification. Although developing a complete DNA barcode reference library for Nigerian ichthyofauna will facilitate species identification and diversity studies, taxonomic revisions of DNA sequences submitted in databases alongside voucher specimens are necessary for a reliable taxonomic and diversity inventory.

PrESOgenesis: A two-layer multi-label predictor for identifying fertility-related proteins using support vector machine and pseudo amino acid composition approach

Successful spermatogenesis and oogenesis are the two genetically independent processes preceding embryo development. To date, several fertility-related proteins have been described in mammalian species. Nevertheless, further studies are required to discover more proteins associated with the development of germ cells and embryogenesis in order to shed more light on the processes. This work builds on our previous software (OOgenesis_Pred), mainly focusing on algorithms beyond what was previously done, in particular new fertility-related proteins and their classes (embryogenesis, spermatogenesis and oogenesis) based on the support vector machine according to the concept of Chou’s pseudo-amino acid composition features. The results of five-fold cross validation, as well as the independent test demonstrated that this method is capable of predicting the fertility-related proteins and their classes with accuracy of more than 80%. Moreover, by using feature selection methods, important properties of fertility-related proteins were identified that allowed for their accurate classification. Based on the proposed method, a two-layer classifier software, named as “PrESOgenesis” (https://github.com/mrb20045/PrESOgenesis) was developed. The tool identified a query sequence (protein or transcript) as fertility or non-fertility-related protein at the first layer and then classified the predicted fertility-related protein into different classes of embryogenesis, spermatogenesis or oogenesis at the second layer.

Efficient Learning-Free Keyword Spotting.

In this article, a method for segmentation-based learning-free Query by Example (QbE) keyword spotting on handwritten documents is proposed. The method consists of three steps, namely preprocessing, feature extraction and matching, which address critical variations of text images (e.g., skew, translation, different writing styles). During the feature extraction step, a sequence of descriptors is generated using a combination of a zoning scheme and a novel appearance descriptor, referred as modified Projections of Oriented Gradients. The preprocessing step, which includes contrast normalization and main-zone detection, aims to overcome the shortcomings of the appearance descriptor. Moreover, an uneven zoning scheme is introduced by applying a denser zoning only on query images for a more detailed representation. This leads to a significant reduction in storage requirements of a document collection. The distance between the query and word sequences is efficiently computed by the proposed Selective Matching algorithm. This algorithm is further extended to handle an augmented set of images originating from a single query image. The efficiency of the proposed method is demonstrated by experimentation conducted on seven publicly available datasets. In these experiments, the proposed method significantly outperforms all state-of-the-art learning-free techniques.

D-GENIES: dot plot large genomes in an interactive, efficient and simple way.

Dot plots are widely used to quickly compare sequence sets. They provide a synthetic similarity overview, highlighting repetitions, breaks and inversions. Different tools have been developed to easily generated genomic alignment dot plots, but they are often limited in the input sequence size. D-GENIES is a standalone and web application performing large genome alignments using minimap2 software package and generating interactive dot plots. It enables users to sort query sequences along the reference, zoom in the plot and download several image, alignment or sequence files. D-GENIES is an easy-to-install, open-source software package (GPL) developed in Python and JavaScript. The source code is available at https://github.com/genotoul-bioinfo/dgenies and it can be tested at http://dgenies.toulouse.inra.fr/.

Cascade Ligand- and Structure-Based Virtual Screening to Identify New Trypanocidal Compounds Inhibiting Putrescine Uptake.

Chagas disease is a neglected tropical disease endemic to Latin America, though migratory movements have recently spread it to other regions. Here, we have applied a cascade virtual screening campaign combining ligand- and structure-based methods. In order to find novel inhibitors of putrescine uptake in Trypanosoma cruzi, an ensemble of linear ligand-based classifiers obtained by has been applied as initial screening filter, followed by docking into a homology model of the putrescine permease TcPAT12. 1,000 individual linear classifiers were inferred from a balanced dataset. Subsequently, different schemes were tested to combine the individual classifiers: MIN operator, average ranking, average score, average voting, with MIN operator leading to the best performance. The homology model was based on the arginine/agmatine antiporter (AdiC) from Escherichia coli as template. It showed 64% coverage of the entire query sequence and it was selected based on the normalized Discrete Optimized Protein Energy parameter and the GA341 score. The modeled structure had 96% in the allowed area of Ramachandran's plot, and none of the residues located in non-allowed regions were involved in the active site of the transporter. Positivity Predictive Value surfaces were applied to optimize the score thresholds to be used in the ligand-based virtual screening step: for that purpose Positivity Predictive Value was charted as a function of putative yields of active in the range 0.001–0.010 and the Se/Sp ratio. With a focus on drug repositioning opportunities, DrugBank and Sweetlead databases were subjected to screening. Among 8 hits, cinnarizine, a drug frequently prescribed for motion sickness and balance disorder, was tested against T. cruzi epimastigotes and amastigotes, confirming its trypanocidal effects and its inhibitory effects on putrescine uptake. Furthermore, clofazimine, an antibiotic with already proven trypanocidal effects, also displayed inhibitory effects on putrescine uptake. Two other hits, meclizine and butoconazole, also displayed trypanocidal effects (in the case of meclizine, against both epimastigotes and amastigotes), without inhibiting putrescine uptake.

HotSpot Wizard 3.0: web server for automated design of mutations and smart libraries based on sequence input information.

HotSpot Wizard is a web server used for the automated identification of hotspots in semi-rational protein design to give improved protein stability, catalytic activity, substrate specificity and enantioselectivity. Since there are three orders of magnitude fewer protein structures than sequences in bioinformatic databases, the major limitation to the usability of previous versions was the requirement for the protein structure to be a compulsory input for the calculation. HotSpot Wizard 3.0 now accepts the protein sequence as input data. The protein structure for the query sequence is obtained either from eight repositories of homology models or is modeled using Modeller and I-Tasser. The quality of the models is then evaluated using three quality assessment tools—WHAT_CHECK, PROCHECK and MolProbity. During follow-up analyses, the system automatically warns the users whenever they attempt to redesign poorly predicted parts of their homology models. The second main limitation of HotSpot Wizard’s predictions is that it identifies suitable positions for mutagenesis, but does not provide any reliable advice on particular substitutions. A new module for the estimation of thermodynamic stabilities using the Rosetta and FoldX suites has been introduced which prevents destabilizing mutations among pre-selected variants entering experimental testing. HotSpot Wizard is freely available at http://loschmidt.chemi.muni.cz/hotspotwizard.

AC-DIAMOND v1: accelerating large-scale DNA–protein alignment

AC-DIAMOND (v1) is a DNA-protein alignment tool designed to tackle the efficiency challenge of aligning large amount of reads or contigs to protein databases. When compared with the previously most efficient method DIAMOND, AC-DIAMOND gains a 6- to 7-fold speed-up, while retaining a similar degree of sensitivity. The improvement is rooted at two aspects: first, using a compressed index of seeds with adaptive-length to speed-up the matching between query and reference sequences; second, adopting a compact form of dynamic programing to fully utilize the parallelism of the SIMD capability. Software source codes and binaries available at https://github.com/Maihj/AC-DIAMOND/. Supplementary data are available at Bioinformatics online.

Theoretical Study on Big Data with Data Mining

Identify and find trend of large size and complex data is very important area for researcher. Find very complex data and its trend change as per requirement is big data mining which can be used in many applications like financial, retail etc. Responses will be quick from big data mining. That could be possible by design and generate framework. In which, query will be analyzed first and compared keywords with big data according to sequence and arrangement of query. Finally user will get information with most relevant comparison. Different types of feasible study were required to adoptability of framework like technical or social study. That model can be safe by using some security features like alerts on data copy, add security check before the retrieval information and search in local directory first.

Accuracy of taxonomy prediction for 16S rRNA and fungal ITS sequences.

Prediction of taxonomy for marker gene sequences such as 16S ribosomal RNA (rRNA) is a fundamental task in microbiology. Most experimentally observed sequences are diverged from reference sequences of authoritatively named organisms, creating a challenge for prediction methods. I assessed the accuracy of several algorithms using cross-validation by identity, a new benchmark strategy which explicitly models the variation in distances between query sequences and the closest entry in a reference database. When the accuracy of genus predictions was averaged over a representative range of identities with the reference database (100%, 99%, 97%, 95% and 90%), all tested methods had ≤50% accuracy on the currently-popular V4 region of 16S rRNA. Accuracy was found to fall rapidly with identity; for example, better methods were found to have V4 genus prediction accuracy of ∼100% at 100% identity but ∼50% at 97% identity. The relationship between identity and taxonomy was quantified as the probability that a rank is the lowest shared by a pair of sequences with a given pair-wise identity. With the V4 region, 95% identity was found to be a twilight zone where taxonomy is highly ambiguous because the probabilities that the lowest shared rank between pairs of sequences is genus, family, order or class are approximately equal.

EBWS: Essential Bioinformatics Web Services for Sequence Analyses.

The Essential Bioinformatics Web Services (EBWS) are implemented on a new PHP-based server that provides useful tools for analyses of DNA, RNA, and protein sequences applying a user-friendly interface. Nine Web-based applets are currently available on the Web server. They include reverse complementary DNA and random DNA/RNA/peptide oligomer generators, a pattern sequence searcher, a DNA restriction cutter, a prokaryotic ORF finder, a random DNA/RNA mutation generator. It also includes calculators of melting temperature (TM) of DNA/DNA, RNA/RNA, and DNA/RNA hybrids, a guide RNA (gRNA) generator for the CRISPR/Cas9 system and an annealing temperature calculator for multiplex PCR. The pattern-searching applet has no limitations in the number of motif inputs and applies a toolbox of Regex quantifiers that can be used for defining complex sequence queries of RNA, DNA, and protein sequences. The DNA enzyme digestion program utilizes a large database of 1502 restriction enzymes. The gRNA generator has a database of 25 bacterial genomes searchable for gRNA target sequences and has an option for searching in any genome sequence given by the user. All programs are permanently available online at http://penchovsky.atwebpages.com/applications.php without any restrictions.

Gene Unprediction with Spurio: A tool to identify spurious protein sequences.

We now have access to the sequences of tens of millions of proteins. These protein sequences are essential for modern molecular biology and computational biology. The vast majority of protein sequences are derived from gene prediction tools and have no experimental supporting evidence for their translation. Despite the increasing accuracy of gene prediction tools there likely exists a large number of spurious protein predictions in the sequence databases. We have developed the Spurio tool to help identify spurious protein predictions in prokaryotes. Spurio searches the query protein sequence against a prokaryotic nucleotide database using tblastn and identifies homologous sequences. The tblastn matches are used to score the query sequence's likelihood of being a spurious protein prediction using a Gaussian process model. The most informative feature is the appearance of stop codons within the presumed translation of homologous DNA sequences. Benchmarking shows that the Spurio tool is able to distinguish spurious from true proteins. However, transposon proteins are prone to be predicted as spurious because of the frequency of degraded homologs found in the DNA sequence databases. Our initial experiments suggest that less than 1% of the proteins in the UniProtKB sequence database are likely to be spurious and that Spurio is able to identify over 60 times more spurious proteins than the AntiFam resource. The Spurio software and source code is available under an MIT license at the following URL: https://bitbucket.org/bateman-group/spurio.