Sequence Entries Research Articles

IPhyDSDB: Phytoplasma Disease and Symptom Database.

Phytoplasmas are small, intracellular bacteria that infect a vast range of plant species, causing significant economic losses and impacting agriculture and farmers' livelihoods. Early and rapid diagnosis of phytoplasma infections is crucial for preventing the spread of these diseases, particularly through early symptom recognition in the field by farmers and growers. A symptom database for phytoplasma infections can assist in recognizing the symptoms and enhance early detection and management. In this study, nearly 35,000 phytoplasma sequence entries were retrieved from the NCBI nucleotide database using the keyword "phytoplasma" and information on phytoplasma disease-associated plant hosts and symptoms was gathered. A total of 945 plant species were identified to be associated with phytoplasma infections. Subsequently, links to symptomatic images of these known susceptible plant species were manually curated, and the Phytoplasma Disease Symptom Database (iPhyDSDB) was established and implemented on a web-based interface using the MySQL Server and PHP programming language. One of the key features of iPhyDSDB is the curated collection of links to symptomatic images representing various phytoplasma-infected plant species, allowing users to easily access the original source of the collected images and detailed disease information. Furthermore, images and descriptive definitions of typical symptoms induced by phytoplasmas were included in iPhyDSDB. The newly developed database and web interface, equipped with advanced search functionality, will help farmers, growers, researchers, and educators to efficiently query the database based on specific categories such as plant host and symptom type. This resource will aid the users in comparing, identifying, and diagnosing phytoplasma-related diseases, enhancing the understanding and management of these infections.

Analysis of Antimicrobial Resistance in Bacterial Pathogens Recovered from Food and Human Sources: Insights from 639,087 Bacterial Whole-Genome Sequences in the NCBI Pathogen Detection Database.

Understanding the role of foods in the emergence and spread of antimicrobial resistance necessitates the initial documentation of antibiotic resistance genes within bacterial species found in foods. Here, the NCBI Pathogen Detection database was used to query antimicrobial resistance gene prevalence in foodborne and human clinical bacterial isolates. Of the 1,843,630 sequence entries, 639,087 (34.7%) were assigned to foodborne or human clinical sources with 147,788 (23.14%) from food and 427,614 (76.88%) from humans. The majority of foodborne isolates were either Salmonella (47.88%), Campylobacter (23.03%), Escherichia (11.79%), or Listeria (11.3%), and the remaining 6% belonged to 20 other genera. Most foodborne isolates were from meat/poultry (95,251 or 64.45%), followed by multi-product mixed food sources (29,892 or 20.23%) and fish/seafood (6503 or 4.4%); however, the most prominent isolation source varied depending on the genus/species. Resistance gene carriage also varied depending on isolation source and genus/species. Of note, Klebsiella pneumoniae and Enterobacter spp. carried larger proportions of the quinolone resistance gene qnrS and some clinically relevant beta-lactam resistance genes in comparison to Salmonella and Escherichia coli. The prevalence of mec in S. aureus did not significantly differ between meat/poultry and multi-product sources relative to clinical sources, whereas this resistance was rare in isolates from dairy sources. The proportion of biocide resistance in Bacillus and Escherichia was significantly higher in clinical isolates compared to many foodborne sources but significantly lower in clinical Listeria compared to foodborne Listeria. This work exposes the gaps in current publicly available sequence data repositories, which are largely composed of clinical isolates and are biased towards specific highly abundant pathogenic species. We also highlight the importance of requiring and curating metadata on sequence submission to not only ensure correct information and data interpretation but also foster efficient analysis, sharing, and collaboration. To effectively monitor resistance carriage in food production, additional work on sequencing and characterizing AMR carriage in common commensal foodborne bacteria is critical.

The Distribution Properties of Consecutive Quadratic Residue Sequences

We consider any prime number p . Let k , s be two positive integers. We are interested in the arithmetic progressions (sequences) with the common difference s and length k , where the sequence entries are from the set of quadratic residue modulo p or the set of quadratic nonresidue modulo p . The numbers of such sequences are denoted as N p k , s and N p ′ k , s , respectively. In this paper, we apply analytic number theory methods, in particular, properties of Legendre’s symbol modulo p and character sums, to study the numbers N p k , s and N p ′ k , s . Exact formulas are given for certain values of k and s under some restrictions. In addition, estimation formulas in other cases are given.

Development of Conventional Multiplex PCR Assays for the Identification of 21 West Palaearctic Biting Midge Taxa (Diptera: Ceratopogonidae) Belonging to the Culicoides Subgenus Culicoides, including Recently Discovered Species and Genetic Variants

In 2006 and 2011, two biting-midge-borne arboviruses of high veterinary importance emerged for the first time in Central Europe: bluetongue virus (BTV) and Schmallenberg virus (SBV). Members of the native Obsoletus and Pulicaris Groups were soon identified as the potential vectors. However, despite several years of extensive taxonomic research on these groups, correct species identification and differentiation from closely related species are still challenging due to isomorphic features, the existence of cryptic species and obsolete PCR identification assays. At present, 17 valid West Palaearctic biting midge species of the Culicoides subgenus Culicoides, including the Pulicaris Group, are known, and additional genetic variants have been described. For many of them, no identification tests are available, and their roles in disease transmission have remained unknown. In this study, 465 GenBank DNA sequence entries of the mitochondrial cytochrome c oxidase subunit I (COI) gene were used to design PCR primers as specific genetic markers for 21 West Palaearctic biting midge taxa of the Culicoides subgenus Culicoides. During their validation with DNA from field-collected biting midges and synthetic DNA from biting midge genotypes not available from the field, all primers detected their target taxa, while few showed cross-reactions. Our results indicate the great potential of the new primers in PCR assays and clearly demonstrate the suitability of the COI gene as an excellent marker for the identification of different biting midge species and genetic variants of the Culicoides subgenus Culicoides.

PepLab Platform: Database and Software Tools for Analysis of Food-Derived Bioactive Peptides

Research on food-derived bioactive peptides is expanding and the need for a convenient online platform that combines a foolproof and intuitive user interface with a reliable database and tools for prediction and analysis is rising. In this regard, this paper presents an open-access web-based platform PepLab (Peptides Laboratory). The database contains 2764 peptide sequence entries, grouped into sixteen classes according to their biological activity and into seven classes according to the source from which they were derived. Moreover, it includes bioinformatic tools for their processing and analysis. The DMpep tool allows extracting information about a number of physicochemical characteristics of a peptide or a set of peptides, including those that are not recorded in the database. The main advantages of PepLab are a user-friendly interface, a responsive design, and optimized search engines for better visibility on the Internet. Prediction of non-reported activity is available based on amino acid sequence analysis. In addition, users can download data and results in a convenient format (text and/or graphic) that was limited in the existing platforms. In this way, the presented PepLab platform will be helpful for researchers from various fields—bioinformatics, pharmaceuticals, food sciences, dietetics, biotechnology, analytical chemistry, etc.

A study of the matrix classes (ℓα, ℓα) and (ℓα, c), 0 <α ≤ 1

In this paper, entries of sequences, infinite series and infinite matrices are real or complex numbers. The present paper is a continuation of [8], where we established some properties of the matrix class \((\ell_\alpha, \ell_\alpha)\), \(0 < \alpha \leq 1\). In this paper, we also record some properties of the class \((\ell_\alpha, c)\) and the sequence space \(\ell_\alpha\), \(0 < \alpha \leq 1\).

ON BOUNDEDNESS WITH SPEED λ IN ULTRAMETRIC FIELDS

In the present paper, K denotes a complete, non-trivially valued, ultrametric (or non-archimedean) field. Entries of sequences, infinite series and infinite matrices are in K. Following Kangro [2, 3, 4], we introduce the concept of boundedness with speed λ or λ-boundedness. We then obtain a characterization of the matrix class (mλ , mµ ), where mλ denotes the set of all λ-bounded sequences in K. We conclude the paper with a remark about the matrix class (c λ , mµ ), where c λ denotes the set of all λ-convergent sequences in K.

SIGNS OF LEARNING

Australia’s education system has historically been premised on state and territory-based curricula, until the development of a national curriculum in each learning area, released in stages from 2014. This paper will discuss the curriculum development process for the Auslan curriculum in the Languages learning area, which delivered an endorsed blueprint for the formal teaching and learning of Auslan in Australian schools from 2017. Unique features of the curriculum will be outlined, such as the dual-pathways for first and second language learners of Auslan, and the different sequence entry points, specifically designed to meet the needs of deaf children in both the early primary school years, as well as those who are late learners of a first language, entering secondary school with limited spoken and/or signed language later in childhood. The paper will provide an overview of the structure and content of the curriculum and the nature of the learners, and conclude with some of the opportunities and challenges arising from this initiative, such as the ongoing lack of quality resources that exist for its day-to-day implementation in schools nation-wide.

Development of a Sequence Searchable Database of Celiac Disease-Associated Peptides and Proteins for Risk Assessment of Novel Food Proteins.

Celiac disease (CeD) is an autoimmune enteropathy induced by prolamin and glutelin proteins in wheat, barley, rye, and triticale recognized by genetically restricted major histocompatibility (MHC) receptors. Patients with CeD must avoid consuming these proteins. Regulators in Europe and the United States expect an evaluation of CeD risks from proteins in genetically modified (GM) crops or novel foods for wheat-related proteins. Our database includes evidence-based causative peptides and proteins and two amino acid sequence comparison tools for CeD risk assessment. Sequence entries are based on the review of published studies of specific gluten-reactive T cell activation or intestinal epithelial toxicity. The initial database in 2012 was updated in 2018 and 2022. The current database holds 1,041 causative peptides and 76 representative proteins. The FASTA sequence comparison of 76 representative CeD proteins provides an insurance for possible unreported epitopes. Validation was conducted using protein homologs from Pooideae and non-Pooideae monocots, dicots, and non-plant proteins. Criteria for minimum percent identity and maximum E-scores are guidelines. Exact matches to any of the 1,041 peptides suggest risks, while FASTA alignment to the 76 CeD proteins suggests possible risks. Matched proteins should be tested further by CeD-specific CD4/8+ T cell assays or in vivo challenges before their use in foods.

Knowledge Extraction from Specimen-Derived Data from GenBank to Enrich Biodiversity Information

DNA barcoding and environmental DNA (eDNA) are increasing the need for the utilization of gene sequences in the field of biodiversity. GBIF (Global Biodiversity Information Facility) and GGBN (Global Genome Biodiversity Network) are taking action on the treatment of gene sequences in the field of biodiversity (Finstad et al. 2020). Gene sequences have been collected and published by INSDC (International Nucleotide Sequence Database Collaboration) for over 30 years (Arita et al. 2020). Biodiversity information has been collected using standards such as Darwin Core (Wieczorek et al. 2012), but INSDC gene sequences are stored in their own format. In the field of bioinformatics, researchers are also organizing the BioHackathon series, notably the NBDC/DBCLS BioHackathon and the spin-off Biohackathon Europe, to standardize data through the Semantic Web (Garcia Castro et al. 2021, Vos et al. 2020), but the linkage with biodiversity information has just begun. In this study, as an example of linking gene sequence information with biodiversity information, I attempted to construct an infrastructure for knowledge extraction by utilising gene sequence entries derived from museum specimens from GenBank (Sayers et al. 2020). I have previously surveyed the BOLD (The Barcode of Life Data System) (Ratnasingham and Hebert 2007) IDs listed in GenBank (Nakazato 2020). I downloaded the fish and insect data from the GenBank FTP (file transfer protocol) site. Then I extracted the descriptions in the "specimen_voucher" field and obtained 749,627 (28% of the fish entries in GenBank) and 1,621,890 (13%) specimen IDs, respectively. I also extracted from the "note" field approximately 1000 entries describing the type of the specimen, such as "holotype", "lectotype", and "paratype". These extracts include descriptions written in natural language. NCBI (National Center for Biotechnology Information) publishes the BioCollections database (Sharma et al. 2019), and these data may be able to refine the description. In the future, I plan to map these extracted IDs to the collection IDs in the biodiversity information database. This will enable us to enrich the biodiversity information with GenBank descriptions, for example, by adding articles listed in GenBank as references to the specimen data.

Some properties of the matrix class (l suffix alpha,l suffix alpha), 0

Throughout the present paper, entries of sequences, infinite series and infinite matrices are real or complex numbers. The class \((\ell_\alpha, \ell_\alpha)\), \(0 < \alpha \leq 1\), of all infinite matrices transforming sequences in \(\ell_\alpha\) to sequences in \(\ell_\alpha\) is characterized. The structure of \((\ell_\alpha, \ell_\alpha)\), \(0 < \alpha \leq 1\), is then discussed. Following Fridy [\emph{Properties of absolute summability matrices}, Proc. Amer. Math. Soc. 24 (1970), 583--585], a Steinhaus type result involving the class \((\ell_\alpha, \ell_\alpha)\) is also proved.

Performance‐Price‐Ratio Utility: Market Equilibrium Analysis and Empirical Calibration Studies

In this study, as an alternative to the surplus utility, we propose a performance‐price‐ratio (PPR) utility and specialize the customer choice model with a PPR maximization criterion. With the PPR‐based choice model, we investigate a pricing game between retailers in a competitive market. Through transferring the decision variable from price to performance‐price ratio, the first‐order condition of the game becomes a linear equation system, which enables us to develop a closed‐form equilibrium solution. The solution reveals how product performance and price sensitivity affect equilibrium pricing, market share, and revenue. With the developed theoretical results, we carry out empirical studies with a rich data set obtained from the China TV market. At the individual product level and the brand level, we calibrate the PPR‐based choice model and the widely used surplus‐based model with a Bayesian information criteria‐based stepwise multivariate regression method. The regression results suggest that the PPR‐based model fits both the product‐level and brand‐level data better than the surplus‐based model. Through the stepwise selection of independent variables, we find that the leading features affecting a TV’s performance are 3D TV functionality and its screen size. Moreover, we explore how the market equilibrium evolves with the game decision sequence, market structure, and new product entry.

Quantitative proteomics and phosphoproteomics of Trypanosoma cruzi epimastigote cell cycle

The protozoan Trypanosoma cruzi is the causative agent of the neglected infectious illness Chagas disease. During its life cycle it differentiates into replicative and non-replicative life stages. So far, T. cruzi cell division has been investigated by transcriptomics but not by proteomics approaches. Here we show the first quantitative proteome analysis of T. cruzi cell division. T. cruzi epimastigote cultures were subject to synchronization with hydroxyurea and harvested at different time points. Analysis by flow cytometry, bright field and fluorescence microscopy indicated that samples collected at 0 h, 2 h, 6 h and 14 h overrepresented G1, G1-S, S and M cell cycle phases, respectively. After trypsin digestion of these samples, the resulting peptides were labelled with iTRAQ and subjected to LC-MS/MS. Also, iTRAQ-labelled phosphopeptides were enriched with TiO2 to access the phosphoproteome. Overall, 597 protein groups and 94 phosphopeptides presented regulation with the most remarkable variation in abundance at 6 h (S-phase). Comparison of our proteomic data to previous transcriptome-wise analysis of epimastigote cell cycle showed 16 sequence entries in common, with the highest mRNA/protein correlation observed in transcripts with peak abundance in G1-phase. Our data revealed regulated proteins and phosphopeptides which play important roles in the control of cell division in other organisms and some of them were previously detected in the nucleus or associated with T. cruzi chromatin.

Characterization of the matrix class (ℓα,ℓβ), 0 &lt; α ≤ β ≤ 1

Throughout the present paper, entries of sequences, infinite series and infinite matrices are real or complex numbers. In this paper, we characterize the matrix class (??,??), 0 &lt; ? ? ? ? 1.

NemChR-DB: a database of parasitic nematode chemosensory G-protein coupled receptors

Nematode Chemosensory G-Protein Coupled Receptors have expanded within nematodes, where they play important roles in foraging and host-seeking behaviour. Nematode Chemosensory G-Protein Coupled Receptors are most highly expressed during free-living stages when chemosensory signalling is required for host detection and nematode activation in various parasitic nematodes, and therefore position Nematode Chemosensory G-Protein Coupled Receptors at the transition from infective to parasitic stages, making them important regulators to study in terms of host-seeking and host specificity. To facilitate the analysis of Nematode Chemosensory G-Protein Coupled Receptors, here we describe an integrative database of nematode chemoreceptors called NemChR-DB. This database enables users to study diverse parasitic nematode chemoreceptors, functionally explore sequence entries through structural and literature-based annotations, and perform cross-species comparisons.

Landing, consumption, and DNA barcoding of commercial seabream (Perciformes: Sparidae) in Kuwait

Abstract To our knowledge, this is the first inclusive assessment of the status of Sparidae fisheries in the territorial waters of Kuwait, integrating information on catch trends, consumer preferences, recreational fishing behaviour, and molecular based identification of fish species by DNA barcoding Fisheries landing data were obtained from the official fisheries bulletin released by the Kuwait Central Statistical Bureau. Surveys were conducted to assess consumer preferences on seabream species, landed seabream species, and the behaviour of recreational anglers towards seabream species. DNA barcodes were generated to authenticate commercial seabream using the mitochondrial cytochrome c oxidase subunit I (COXI) gene and then compared with GenBank sequence entries; these sequences were then used to construct a neighbour‐joining (NJ) phylogenetic tree. Yellowfin seabream Acanthopagrus latus was the prime and most favoured commercial seabream species (45%), followed by Sparidentex hasta, which was the top‐rated secondary, unassessed seabream species. Approximately 54% of households conduct recreational fishing more than twice a month, and most (77%) primarily target S. hasta and Ac. latus. Consumer demand for secondary seabreams, which were not assessed and are listed in the bulletin under the category ‘others’, was evident. The NJ phylogenetic tree revealed that Arabian Gulf seabreams, including those of Kuwait, are genetically different from their counterparts inhabiting neighbouring waters. The data presented here highlight the urgency to modify the existing fisheries list using accurate identification tools, such as DNA barcoding, for the sustainable and conservation‐oriented management of local fisheries, which are in decline.

Genetic composition of an exponentially growing cell population

We study a simple model of DNA evolution in a growing population of cells. Each cell contains a nucleotide sequence which randomly mutates at cell division. Cells divide according to a branching process. Following typical parameter values in bacteria and cancer cell populations, we take the mutation rate to zero and the final number of cells to infinity. We prove that almost every site (entry of the nucleotide sequence) is mutated in only a finite number of cells, and these numbers are independent across sites. However independence breaks down for the rare sites which are mutated in a positive fraction of the population. The model is free from the popular but disputed infinite sites assumption. Violations of the infinite sites assumption are widespread while their impact on mutation frequencies is negligible at the scale of population fractions. Some results are generalised to allow for cell death, selection, and site-specific mutation rates. For illustration we estimate mutation rates in a lung adenocarcinoma.

Current situation of DNA Barcoding data in biodiversity and genomics databases and data integration for museomics

The museomics activity regards museum-preserved specimens as rich resources for DNA studies by extracting and analyzing DNA from these specimens in conjunction with their biodiversity information. Also in biodiversity field, DNA sequence data such as DNA barcoding has become essential as evidence for species identification and phylogenetic analysis as well as occurrence and morphological information. To accelerate biodiversity informatics, it is important to utilize both biodiversity occurrence and morphology data, and bioinformatics sequencing data. There are many databases for biodiversity domain such as GBIF (The Global Biodiversity Information Facility) for species occurrence records, EoL (The Encyclopedia of Life) as a knowledge base of all species, and BOLD (The Barcode of Life Data) for DNA barcoding data. In genomics science, molecular data involving DNA and protein sequences have been captured by the DNA Data Bank in Japan (DDBJ), the European Bioinformatics Institute (EBI, UK), and the National Center for Biotechnology Information (NCBI, US) under the International Nucleotide Sequence Database Collaboration (INSDC) for more than 30 years. Recently, NCBI launched a new database called BioCollections, including 7,930 culture collections, museums, herbaria, and other natural history collections. In addition, we can submit biodiversity information such as specimen voucher IDs, BOLD IDs, and latitude/longitude with DNA sequences. To find out the current situation, I downloaded GenBank (Nucleotide) files (updated at 22 Feb 2019) from the NCBI FTP (file transfer protocol) site and extracted biodiversity features including specimen voucher IDs and BOLD IDs. For Insecta, there are 2,427,343 sequence entries with specimen voucher ID and 1,766,142 entries with BOLD ID of 3,389,495 total entries. The most abundant species with voucher IDs is “Cecidomyiidae sp. BOLD−2016” (Diptera) (35,861 sequence entries). The most frequently referred voucher ID is “USNMENT00921257” (1510 sequence entries), indicating Stenamma megamanni (Hymenoptera, Formicidae, Myrmicinae). For flowering plants (Magnoliophyta), of 3,094,140 total entries, 1,109,420 sequence entries are assigned with voucher IDs and 73,409 entries with BOLD IDs. Additionally, 79,891 matK entries and 63,821 rbcL entries are submitted with voucher IDs, without BOLD IDs. I also retrieved BOLD data for Insecta and flowering plants. The 2,368,801 GenBank entries are referred from 4,176,481 BOLD total entries for Insecta, and the 259,245 GenBank entries from 345,706 BOLD entries for flowering plants. Some DNA barcoding data exist redundantly in BOLD database because BOLD imports sequences from NCBI submitted as DNA barcoding data in BOLD. These entries have different BOLD IDs but same BIN_URL is assigned. Recently, high-throughput sequencing technology, also called next-generation sequencing technology (NGS), has made a great impact in genomic science. Biodiversity researchers became to perform not only DNA barcoding but also RNA-Seq with NGS. NGS also accelerates museomics activity. NGS data are archived to the Sequence Read Archive (SRA) database, and sample information is described in BioSample database in INSDC. To utilize NGS data for biodiversity field, we will need to integrate such databases and other biodiversity databases. We, Database Center for Life Science, tackle to integrate life science data with Semantic Web technology. We held annual meetings to integrate life science data, called BioHackathons, in which researchers from all over the world participated. We began to RDFize BioSample data, but we should import existing schemes used in the biodiversity field including Darwin Core.

Mass spectrometric characterization of the zein protein composition in maize flour extracts upon protein separation by SDS-PAGE and 2D gel electrophoresis.

Highly homogenous α zein protein was isolated from maize kernels in an environment-friendly process using 95% ethanol as solvent. Due to the polyploidy and genetic polymorphism of the plant source, the application of high resolution separation methods in conjunction with precise analytical methods, such as MALDI-TOF-MS, is required to accurately estimate homogeneity of products that contain natural zein protein. The α zein protein product revealed two main bands in SDS-PAGE analysis, one at 25kDa and other at 20kDa apparent molecular mass. Yet, high resolution 2DE revealed approximately five protein spot groups in each row, the first at ca. 25kDa and the second at ca. 20kDa. Peptide mass fingerprinting data of the proteins in the two dominant SDS-PAGE bands matched to 30 amino acid sequence entries out of 102 non-redundant data base entries. MALDI-TOF-MS peptide mapping of the proteins from all spots indicated the presence of only α zein proteins. The most prominent ion signals in the MALDI mass spectra of the protein mixture of the 25kDa SDS gel band after in-gel digestion were found at m/z 1272.6 and m/z 2009.1, and the most prominent ion signals of the protein mixture of the 20kDa band after in-gel digestion were recorded at m/z 1083.5 and m/z 1691.8. These ion signals have been found typical for α zein proteins and may serve as marker ion signals which upon chymotryptic digestion reliably indicate the presence of α zein protein in two hybrid corn products.

A Comparison of Three Different Bioinformatics Analyses of the 16S-23S rRNA Encoding Region for Bacterial Identification.

Rapid and reliable identification of bacterial pathogens directly from patient samples is required for optimizing antimicrobial therapy. Although Sanger sequencing of the 16S ribosomal RNA (rRNA) gene is used as a molecular method, species identification and discrimination is not always achievable for bacteria as their 16S rRNA genes have sometimes high sequence homology. Recently, next generation sequencing (NGS) of the 16S–23S rRNA encoding region has been proposed for reliable identification of pathogens directly from patient samples. However, data analysis is laborious and time-consuming and a database for the complete 16S–23S rRNA encoding region is not available. Therefore, a better, faster, and stronger approach is needed for NGS data analysis of the 16S–23S rRNA encoding region. We compared speed and diagnostic accuracy of different data analysis approaches: de novo assembly followed by Basic Local Alignment Search Tool (BLAST), operational taxonomic unit (OTU) clustering, or mapping using an in-house developed 16S–23S rRNA encoding region database for the identification of bacterial species. De novo assembly followed by BLAST using the in-house database was superior to the other methods, resulting in the shortest turnaround time (2 h and 5 min), approximately 2 h less than OTU clustering and 4.5 h less than mapping, and a sensitivity of 80%. Mapping was the slowest and most laborious data analysis approach with a sensitivity of 60%, whereas OTU clustering was the least laborious approach with 70% sensitivity. Although the in-house database requires more sequence entries to improve the sensitivity, the combination of de novo assembly and BLAST currently appears to be the optimal approach for data analysis.