Query Sequence Research Articles

Rapid multiple protein sequence search by parallel and heterogeneous computation.

Protein sequence database search and multiple sequence alignment generation is a fundamental task in many bioinformatics analyses. As the data volume of sequences continues to grow rapidly, there is an increasing need for efficient and scalable multiple sequence query algorithms for super-large databases without expensive time and computational costs. We introduce Chorus, a novel protein sequence query system that leverages parallel model and heterogeneous computation architecture to enable users to query thousands of protein sequences concurrently against large protein databases on a desktop workstation. Chorus achieves over 100× speedup over BLASTP without sacrificing sensitivity. We demonstrate the utility of Chorus through a case study of analyzing a ∼1.5-TB large-scale metagenomic datasets for novel CRISPR-Cas protein discovery within 30 min. Chorus is open-source and its code repository is available at https://github.com/Bio-Acc/Chorus.

Genome-wide identification and in silico characterization of major RNAi gene families in date palm (Phoenix dactylifera)

BackgroundDates contain various minerals that are essential for good health. The major RNA interference (RNAi) gene families play a vital role in plant growth and development by controlling the expression of protein-coding genes against different biotic and abiotic stresses. However, these gene families for date palm are not yet studied. Therefore, this study has explored major RNAi genes and their characteristics in date palm.ResultsWe have identified 4 PdDCLs, 7 PdAGOs, and 3 PdRDRs as RNAi proteins from the date palm genome by using AtRNAi genes as query sequences in BLASTp search. Domain analysis of predicted RNAi genes has revealed the Helicase_C, Dicer_dimer, PAZ, RNase III, and Piwi domains that are associated with the gene silencing mechanisms. Most PdRNAi proteins have been found in the nucleus and cytosol associated with the gene silencing actions. The gene ontology (GO) enrichment analysis has revealed some important GO terms including RNA interference, dsRNA fragmentation, and ribonuclease_III activity that are related to the protein-coding gene silencing mechanisms. Gene regulatory network (GRN) analysis has identified PAZ and SNF2 as the transcriptional regulators of PdRNAi genes. Top-ranked 10 microRNAs including Pda-miR156b, Pda-miR396a, Pda-miR166a, Pda-miR167d, and Pda-miR529a have been identified as the key post-transcriptional regulators of PdRNAi genes that are associated with different biotic/abiotic stresses. The cis-acting regulatory element analysis of PdRNAi genes has detected some vital cis-acting elements including ABRE, MBS, MYB, MYC, Box-4, G-box, I-box, and STRE that are linked with different abiotic stresses.ConclusionThe results of this study might be valuable resources for the improvement of different characteristics in date palm by further studies in wet-lab.

RDCanon: A Python Package for Canonicalizing the Order of Tokens in SMARTS Queries.

SMARTS is a widely used language in cheminformatics for defining substructural queries for database lookups, reaction templates for chemical transformations, and other applications. As an extension to SMILES, many SMARTS patterns can represent the same query. Despite this, no canonicalization algorithm invariant of the line notation sequence or atomic numbering is publicly available. Here, we introduce RDCanon, an open-source Python package that can be used to standardize SMARTS queries. RDCanon is designed to ensure that the sequence of atomic queries remains consistent for all graphs representing the same substructure query and to ensure a canonical sequence of primitives within each individual atom query; furthermore, the algorithm can be applied to canonicalize the order of reactants, agents, and products and their atom map numbers in reaction SMARTS templates. As part of its canonicalization algorithm, RDCanon provides a mechanism in which the canonicalized SMARTS is optimized for speed against specific molecular databases. Several case studies are provided to showcase improved efficiency in substructure matching and retrosynthetic analysis.

Sibyl: Forecasting Time-Evolving Query Workloads

Database systems often rely on historical query traces to perform workload-based performance tuning. However, real production workloads are time-evolving, making historical queries ineffective for optimizing future workloads. To address this challenge, we propose SIBYL, an end-to-end machine learning-based framework that accurately forecasts a sequence of future queries, with the entire query statements, in various prediction windows. Drawing insights from real-workloads, we propose template-based featurization techniques and develop a stacked-LSTM with an encoder-decoder architecture for accurate forecasting of query workloads. We also develop techniques to improve forecasting accuracy over large prediction windows and achieve high scalability over large workloads with high variability in arrival rates of queries. Finally, we propose techniques to handle workload drifts. Our evaluation on four real workloads demonstrates that SIBYL can forecast workloads with an 87.3% median F1 score, and can result in 1.7× and 1.3× performance improvement when applied to materialized view selection and index selection applications, respectively.

Lambda3: homology search for protein, nucleotide, and bisulfite-converted sequences.

Local alignments of query sequences in large databases represent a core part of metagenomic studies and facilitate homology search. Following the development of NCBI Blast, many applications aimed to provide faster and equally sensitive local alignment frameworks. Most applications focus on protein alignments, while only few also facilitate DNA-based searches. None of the established programs allow searching DNA sequences from bisulfite sequencing experiments commonly used for DNA methylation profiling, for which specific alignment strategies need to be implemented. Here, we introduce Lambda3, a new version of the local alignment application Lambda. Lambda3 is the first solution that enables the search of protein, nucleotide as well as bisulfite-converted nucleotide query sequences. Its protein mode achieves comparable performance to that of the highly optimized protein alignment application Diamond, while the nucleotide mode consistently outperforms established local nucleotide aligners. Combined, Lambda3 presents a universal local alignment framework that enables fast and sensitive homology searches for a wide range of use-cases. Lambda3 is free and open-source software publicly available at https://github.com/seqan/lambda/.

Evaluating the reliability of DNA Barcoding for Central American Pacific shallow water echinoderms identification: a molecular taxonomy and database accuracy analysis

Introduction: Molecular divergence thresholds have been proposed to distinguish recently separated evolutive units, often displaying more accurate putative species assignments in taxonomic research compared to traditional morphological approaches. This makes DNA barcoding an attractive identification tool for a variety of marine invertebrates, especially for cryptic species complexes. Although GenBank and the Barcode of Life Data System (BOLD) are the major sequence repositories worldwide, very few have tested their performance in the identification of echinoderm sequences.
 Objective: We use COI echinoderm sequences from local samples and the molecular identification platforms from GenBank and BOLD, in order to test their accuracy and reliability in the DNA barcoding identification for Central American shallow water echinoderms, at genus and species level.
 Methods: We conducted sampling, tissue extraction, COI amplification, sequencing, and taxonomic identification for 475 specimens. The 348 obtained sequences were individually enquired with BLAST in GenBank as well as using the Identification System (IDS) in BOLD. Query sequences were classified depending on the best match result. McNemar’s chi-squared, Kruskal-Wallis’s and Mann-Whitney’s U tests were performed to prove differences between the results from both databases. Additionally, we recorded an updated list of species reported for the shallow waters of the Central American Pacific.
 Results: We found 324 echinoderm species reported for Central American Pacific shallow waters. Only 118 and 110 were present in GenBank and BOLD databases respectively. We proposed 325 solved morphology-based identities and 21 provisional identifications in 50 putative taxa. GenBank retrieved 348 molecular-based identifications in 58 species, including twelve provisional identifications in tree taxa. BOLD recovered 170 COI identifications in 23 species with one provisional identification. Nevertheless, 178 sequences retrieved unmatched terms (in 34 morphology-based taxa). Only 86 sequences (25 %) were retrieved as correct identifications and 128 (37 %) as identification errors in both platforms. We include 84 sequences for eleven species not represented in GenBank and 65 sequences for ten species in BOLD Echinoderm COI databases. The identification accuracy using BLAST (175 correct and 152 incorrect identifications) was greater than with IDS engine (110 correct and 218 identification errors), therefore GenBank outperforms BOLD (Kruskal-Wallis = 41.625, df = 1, p < 0.001).
 Conclusions: Additional echinoderm sample references are needed to improve the utility of the evaluated DNA barcoding identification tools. Identification discordances in both databases may obey specific parameters used in each search algorithm engine and the available sequences. We recommend the use of barcoding as a complementary identification source for Central American Pacific shallow water echinoderm species.

Location Privacy Protection Game Against Adversary Through Multi-User Cooperative Obfuscation

In location-based services(LBSs), it is promising for users to crowdsource and share their Point-of-Interest(PoI) information with each other in a common cache to reduce query frequency and preserve location privacy. Yet most studies on multi-user privacy preservation overlook the opportunity of leveraging their service flexibility. This paper is the first to study multiple users' strategic cooperation against an adversary's optimal inference attack, by leveraging mutual service flexibility. We formulate the multi-user privacy cooperation against the adversary as a max-min adversarial game and solve it in a linear program. Unlike the vast literature, even if a user finds the cached information useful, we prove it beneficial to still query the platform to further confuse the adversary. As the linear program's computational complexity still increases superlinearly with the number of users' possible locations, we propose a binary obfuscation scheme in two opposite spatial directions to achieve guaranteed performance with only constant complexity. Perhaps surprisingly, a user with a greater service flexibility should query with a less obfuscated location to add confusion. Finally, we provide guidance on the optimal query sequence among LBS users. Simulation results show that our crowdsourced privacy protection scheme greatly improves users' privacy as compared with existing approaches.

IDV Typer: An Automated Tool for Lineage Typing of Influenza D Viruses Based on Return Time Distribution.

Influenza D virus (IDV) is the most recent addition to the Orthomyxoviridae family and cattle serve as the primary reservoir. IDV has been implicated in Bovine Respiratory Disease Complex (BRDC), and there is serological evidence of human infection of IDV. Evolutionary changes in the IDV genome have resulted in the expansion of genetic diversity and the emergence of multiple lineages that might expand the host tropism and potentially increase the pathogenicity to animals and humans. Therefore, there is an urgent need for automated, accurate and rapid typing tools for IDV lineage typing. Currently, IDV lineage typing is carried out using BLAST-based searches and alignment-based molecular phylogeny of the hemagglutinin-esterase fusion (HEF) gene sequences, and lineage is assigned to query sequences based on sequence similarity (BLAST search) and proximity to the reference lineages in the tree topology, respectively. To minimize human intervention and lineage typing time, we developed IDV Typer server, implementing alignment-free method based on return time distribution (RTD) of k-mers. Lineages are assigned using HEF gene sequences. The server performs with 100% sensitivity and specificity. The IDV Typer server is the first application of an RTD-based alignment-free method for typing animal viruses.

Fish feed composition by high-throughput sequencing analysis: Parasite risk assessment

The use of wild small fish species as feed for aquaculture has clearly an economic incentive by speeding the growth of farmed species. Since feed ingredients are sourced from wild fisheries the farmed species could contain natural contaminants which may introduce food safety concerns. In this study, we used High-Throughput Sequencing (HTS) to explore the whole DNA profile of ten dry commercial feeds commonly used by Spanish fish farming companies. The feeds were mainly made of species within the genus Sprattus, Ammodytes and Clupea, and vegetables of the genus Triticum. In the feeds, DNA sequences of parasitic nematodes of fishes (˂1 % total OTUs) were also identified. A taxonomic assignment of query sequences, using a phylogeny-based approach, estimation of pairwise nucleotide identities within and between sequence groups and haplotype network analysis, allow assign short query sequences to the species Phocanema krabbei (Anisakidae) and Hysterothylacium aduncum (Rhaphidascarididae). Both species were identified as ingredient in two and six fish feeds, respectively. This result is of highly concern regarding dietetic recommendations to sensitized patients to anisakids, considering the growing evidence on the possible allergenic potential of both genera, and the recent data on the transfer of anisakid heat-resistant allergens from fishmeal to farm and aquaculture animals.

An enhanced state-aware model learning approach for security analysis in lightweight protocol implementations

Owing to the emergence and rapid advances of new-generation information and digitalization technologies, the concept of model-driven digital twin has received widespread attentions and is developing vigorously. Driven by data and simulators, the digital twin can create the virtual twins of physical objects to perform monitoring, simulation, prediction, optimization, and so on. Hence, the application of digital twin can increase efficiency and security of systems by providing reliable model and decision supports. In this paper, we propose a state-aware model learning method to simulate and analyze the lightweight protocol implementations in edge/cloud environments. We introduce the data flow of program execution and network interaction inputs/outputs (I/O) into the extended finite state machine (EFSM) to expand the modeling scope and insight. We aim to calibrate the states and construct an accurate state-machine model using a digital twin based layered approach to reasonably reflect the correlation of a device’s external behavior and internal data. This, in turn, improves our ability to verify the logic and evaluate the security for protocol implementations. This method firstly involves instrumenting the target device to monitor variable activity during its execution. We then employ learning algorithms to produce multiple rounds of message queries. Both the I/O data corresponding to these query sequences and the state calibration information derived from filtered memory variables are obtained through the mapper and execution monitor, respectively. These two aspects of information are combined to dynamically and incrementally construct the protocol’s state machine. We apply this method to develop SALearn and evaluate the effectiveness of SALearn on two lightweight protocol implementations. Our experimental results indicate that SALearn outperforms existing protocol model learning tools, achieving higher learning efficiency and uncovering more interesting states and security issues. In total, we identified two violation scenarios of rekey logic. These situations also reflect the differences in details between different implementations.

Hound: a novel tool for automated mapping of genotype to phenotype in bacterial genomes assembled de novo.

Increasing evidence suggests that microbial species have a strong within species genetic heterogeneity. This can be problematic for the analysis of prokaryote genomes, which commonly relies on a reference genome to guide the assembly process. Differences between reference and sample genomes will therefore introduce errors in final assembly, jeopardizing the detection from structural variations to point mutations-critical for genomic surveillance of antibiotic resistance. Here we present Hound, a pipeline that integrates publicly available tools to assemble prokaryote genomes de novo, detect user-given genes by similarity to report mutations found in the coding sequence, promoter, as well as relative gene copy number within the assembly. Importantly, Hound can use the query sequence as a guide to merge contigs, and reconstruct genes that were fragmented by the assembler. To showcase Hound, we screened through 5032 bacterial whole-genome sequences isolated from farmed animals and human infections, using the amino acid sequence encoded by blaTEM-1, to detect and predict resistance to amoxicillin/clavulanate which is driven by over-expression of this gene. We believe this tool can facilitate the analysis of prokaryote species that currently lack a reference genome, and can be scaled either up to build automated systems for genomic surveillance or down to integrate into antibiotic susceptibility point-of-care diagnostics.

NanoBERT: a deep learning model for gene agnostic navigation of the nanobody mutational space.

Nanobodies are a subclass of immunoglobulins, whose binding site consists of only one peptide chain, bestowing favorable biophysical properties. Recently, the first nanobody therapy was approved, paving the way for further clinical applications of this antibody format. Further development of nanobody-based therapeutics could be streamlined by computational methods. One of such methods is infilling-positional prediction of biologically feasible mutations in nanobodies. Being able to identify possible positional substitutions based on sequence context, facilitates functional design of such molecules. Here we present nanoBERT, a nanobody-specific transformer to predict amino acids in a given position in a query sequence. We demonstrate the need to develop such machine-learning based protocol as opposed to gene-specific positional statistics since appropriate genetic reference is not available. We benchmark nanoBERT with respect to human-based language models and ESM-2, demonstrating the benefit for domain-specific language models. We also demonstrate the benefit of employing nanobody-specific predictions for fine-tuning on experimentally measured thermostability dataset. We hope that nanoBERT will help engineers in a range of predictive tasks for designing therapeutic nanobodies. https://huggingface.co/NaturalAntibody/.

A Pitfall of Iterative Ψ-Blast

The BLAST program [1] stands as a widely used tool for the search of the most similar sequences, while the iterative Ψ-BLAST program [2] offers a high sensitivity for detecting remote homologs of the query sequence through an iterative usage of the BLAST search. However, the number of iterations that have to be used by the Ψ-BLAST is rather poorly justified in the literature. Our study shows that, as the number of iterations increases, ΨBLAST rapidly loses the ability to be guided by the query sequence in the search for homologs. When working with the non-redundant (nr) sequence database of 2021, Ψ-BLAST, already after the second iteration, retains the query sequence at the top of the list of the found homologs to this sequence in only 18% of cases. Moreover, a query sequence is still listed among homologs found by Ψ-BLAST after the recommended 10 iterations in only 35% of cases. Using a considerably smaller nr database-2011 as a reference, we reveal that these effects intensify over time. Our findings underscore the necessity for circumspection when interpreting Ψ-BLAST outcomes; the degree of vigilance must increase with the database size. A vigilant monitoring of the position of the query sequence in the array of detected homologs is needed. We recommend using the disappearance of the query sequence from the list of homologs produced by Ψ-BLAST as a criterion to conclude the Ψ-BLAST iterations.

MAPLE: MArkov Process Leakage attacks on Encrypted Search

Encrypted search algorithms (ESAs) enable private search on encrypted data and can be constructed from a variety of cryptographic primitives. All knownsub-linear ESA algorithms leak information and, therefore, the design of leakage attacks is an important way to ascertain whether a given leakage profile is exploitable in practice. Recently,Oya and Kerschbaum(Usenix '22) presented an attack called IHOP that targets the query equality pattern which reveals if and when two queries are for the same keyword of a sequence of dependent queries. In this work, we continue the study of query equality leakage on dependent queries and present two new attacks in this setting which can work either as known-distribution or known-sample attacks. They model query distributions as Markov processes and leverage insights and techniques from stochastic processes and machine learning. We implement our attacks and evaluate them on real-world query logs. Our experiments show that they outperform the state-of-the-art in most settings but also have limitations inpractical settings.

In silico-based Safety Assessment of the EPSPS CP4 single event expressed in genetically modified crops

In silico-based Safety Assessment of the EPSPS CP4 single event expressed in genetically modified crops

Scalable neighbour search and alignment with uvaia.

Despite millions of SARS-CoV-2 genomes being sequenced and shared globally, manipulating such data sets is still challenging, especially selecting sequences for focused phylogenetic analysis. We present a novel method, uvaia, which is based on partial and exact sequence similarity for quickly extracting database sequences similar to query sequences of interest. Many SARS-CoV-2 phylogenetic analyses rely on very low numbers of ambiguous sites as a measure of quality since ambiguous sites do not contribute to single nucleotide polymorphism (SNP) differences. Uvaia overcomes this limitation by using measures of sequence similarity which consider partially ambiguous sites, allowing for more ambiguous sequences to be included in the analysis if needed. Such fine-grained definition of similarity allows not only for better phylogenetic analyses, but could also lead to improved classification and biogeographical inferences. Uvaia works natively with compressed files, can use multiple cores and efficiently utilises memory, being able to analyse large data sets on a standard desktop.

GCOC: A Genome Classifier-On-Chip based on Similarity Search Content Addressable Memory.

GCOC is a genome classification system-on-chip (SoC) that classifies genomes by k-mer matching, an approach that divides a DNA query sequence into a set of short DNA fragments of size k, which are searched in a reference genome database, with the underlying assumption that sequenced DNA reads of the same organism (or its close variants) share most of such k-mers. At the core of GCOC is a similarity, or approximate search-capable Content Addressable Memory (SAS-CAM), which in addition to exact match, also supports approximate, or Hamming distance tolerant search. Classification operation is controlled by an embedded RISC-V processor. GCOC classification platform was designed and manufactured in a commercial 65nm process. We conduct a thorough analysis of GCOC classification efficiency as well as its performance, silicon area, and power consumption using silicon measurements. GCOC classifies 769.2K short DNA reads/sec. The silicon area of GCOC SoC is 3.12mm2 and its power consumption is 1.27mW. We envision GCOC deployed as a field (for example at points of care) portable classifier where the classification is required to be real-time, easy to operate and energy efficient.

Phylogeny of a Mosquitocidal Bacillus thuringiensis var israelensis

Objectives: The study aims to compare the 16srRNA sequence of Bacillus thuringiensis var israelensis available in the National Center for Biotechnology Information, database, the sequence length, the relatedness, identity, and ancestral property by constructing a phylogenetic tree and by basic local alignment tool. Methods: The 16srRNA sequence of all the Bacillus thuringiensis var israelensis was retrieved from the database. A basic local alignment tool was used to study the description of the sequences, the percent identity, graphical summary, and alignment. Mega software was used to draw the phylogenetic tree. Results: The total number of mosquitocidal 16srRNA gene sequences was 19. One sequence VCRC-B17 strain was the complete genomic sequence deposited other 18 hits were partial sequences. The Expected value, percent identity, maximum, and total score of 18 hits shows a significant match with the query sequence VCRC-B17. The graphic summary result displays that the 18 hits align with the query sequence as they are coded with the highest score coding color bar, red with a ≥ 200 alignment score. The phylogenetic tree did not support the monophyletic status indicating there is a diversity in the strain. Conclusion: The 19 sequences deposited in the database are identified as a mosquito larvicidal Bacillus thuringiensis var israelensis based on the alignment score or match even though they do not possess an equal base pair length with the query sequence.

Phylogenetic Analysis and Lipoxygenase (LOX) Gene Family Variation in The Pistachio

Lipoxygenases (LOX) gene family is a type of nonheme iron-containing dioxygenases, which has a very important aspect in plant development and fruit quality. LOX gene, which is responsible for lipid oxidation, the main role for the biosynthesis pathway of unsaturated fatty acids. Although some studies have investigated the LOX gene family in several species including arabidopsis, soybean, peanut and apple, there is no information from Pistachio; and the phylogeny of this gene family in the Pistacia genus is still not determined. In this study, Arabidopsis thaliana LOX1 gene (NCBI Reference Sequence: NM_104376.3) was selected and used as a query sequence for performing a BLASTN search. Among all sequence query which was found by NCBI platform, 9 sequences were selected for further analysis. Phylogenetic tree of full-length LOX gene sequences from the Pistacia genus was constructed using the Maximum Likelihood method with MEGA software. By using phylogenic analysis, we identified variations in gene structure and revealed the phylogenetic evolutionary relationship of this gene family. Additionally, this may serve as a reference value for assessing the genetic relationships among various LOX genes in Pistacia genus species. This variations provides us the possibility the design the primer to achieve us to find the exact LOX gene in Pistacia genus and future research on the evolutionary history and transgenic research on LOX genes.

Taxalogue: a toolkit to create comprehensive CO1 reference databases

Background Taxonomic identification through DNA barcodes gained considerable traction through the invention of next-generation sequencing and DNA metabarcoding. Metabarcoding allows for the simultaneous identification of thousands of organisms from bulk samples with high taxonomic resolution. However, reliable identifications can only be achieved with comprehensive and curated reference databases. Therefore, custom reference databases are often created to meet the needs of specific research questions. Due to taxonomic inconsistencies, formatting issues, and technical difficulties, building a custom reference database requires tremendous effort. Here, we present taxalogue, an easy-to-use software for creating comprehensive and customized reference databases that provide clean and taxonomically harmonized records. In combination with extensive geographical filtering options, taxalogue opens up new possibilities for generating and testing evolutionary hypotheses. Methods taxalogue collects DNA sequences from several online sources and combines them into a reference database. Taxonomic incongruencies between the different data sources can be harmonized according to available taxonomies. Dereplication and various filtering options are available regarding sequence quality or metadata information. taxalogue is implemented in the open-source Ruby programming language, and the source code is available at https://github.com/nwnoll/taxalogue. We benchmark four reference databases by sequence identity against eight queries from different localities and trapping devices. Subsamples from each reference database were used to compare how well another one is covered. Results taxalogue produces reference databases with the best coverage at high identities for most tested queries, enabling more accurate, reliable predictions with higher certainty than the other benchmarked reference databases. Additionally, the performance of taxalogue is more consistent while providing good coverage for a variety of habitats, regions, and sampling methods. taxalogue simplifies the creation of reference databases and makes the process reproducible and transparent. Multiple available output formats for commonly used downstream applications facilitate the easy adoption of taxalogue in many different software pipelines. The resulting reference databases improve the taxonomic classification accuracy through high coverage of the query sequences at high identities.