Sequence Similarity Search Tools Research Articles

IGTCM: An integrative genome database of traditional Chinese medicine plants.

Fully understanding traditional Chinese medicines (TCMs) is still challenging because of the extreme complexity of their chemical components and mechanisms of action. The TCM Plant Genome Project aimed to obtain genetic information, determine gene functions, discover regulatory networks of herbal species, and elucidate the molecular mechanisms involved in the disease prevention and treatment, thereby accelerating the modernization of TCMs. A comprehensive database that contains TCM-related information will provide a vital resource. Here, we present an integrative genome database of TCM plants (IGTCM) that contains 14,711,220 records of 83 annotated TCM-related herb genomes, including 3,610,350 genes, 3,534,314 proteins and corresponding coding sequences, and 4,032,242 RNAs, as well as 1033 non-redundant component records for 68 herbs, downloaded and integrated from the GenBank and RefSeq databases. For minimal interconnectivity, each gene, protein, and component was annotated using the eggNOG-mapper tool and Kyoto Encyclopedia of Genes and Genomes database to acquire pathway information and enzyme classifications. These features can be linked across several species and different components. The IGTCM database also provides visualization and sequence similarity search tools for data analyses. These annotated herb genome sequences in IGTCM database are a necessary resource for systematically exploring genes related to the biosynthesis of compounds that have significant medicinal activities and excellent agronomic traits that can be used to improve TCM-related varieties through molecular breeding. It also provides valuable data and tools for future research on drug discovery and the protection and rational use of TCM plant resources. The IGTCM database is freely available at http://yeyn.group:96/.

RareLSD: a manually curated database of lysosomal enzymes associated with rare diseases.

RareLSD is a manually curated database of lysosomal enzymes associated with rare diseases that maintains comprehensive information of 63 unique lysosomal enzymes and 93 associated disorders. Each entry provides a complete information on the disorder that includes the name of the disease, organ affected, age of onset, available drug, inheritance pattern, defected enzyme and single nucleotide polymorphism. To facilitate users in designing drugs against these diseases, we predicted and maintained structures of lysosomal enzymes. Our information portal also contains information on biochemical assays against disease-associated enzymes obtained from PubChem. Each lysosomal entry is supported by information that includes disorders, inheritance pattern, drugs, family members, active inhibitors, etc. Eventually, a user-friendly web interface has been developed to facilitate the users in searching and browsing data in RareLSD with a wide range of options. RareLSD is integrated with sequence similarity search tools (e.g. BLAST and Smith-Waterman algorithm) for analysis. It is built on responsive templates that are compatible with most of browsers and screens including smartphones and gadgets (mobile, iPhone, iPad, tablets, etc.).

Sequence Similarity Searching.

Sequence similarity searching has become an important part of the daily routine of molecular biologists, bioinformaticians and biophysicists. With the rapidly growing sequence databanks, this computational approach is commonly applied to determine functions and structures of unannotated sequences, to investigate relationships between sequences, and to construct phylogenetic trees. We introduce arguably the most popular BLAST-based family of the sequence similarity search tools. We explain basic concepts related to the sequence alignment and demonstrate how to search the current databanks using Web site versions of BLASTP, PSI-BLAST and BLASTN. We also describe the standalone BLAST+ tool. Moreover, this unit discusses the inputs, parameter settings and outputs of these tools. Lastly, we cover recent advances in the sequence similarity searching, focusing on the fast MMseqs2 method. © 2018 by John Wiley & Sons, Inc.

A Massively Parallel Sequence Similarity Search for Metagenomic Sequencing Data.

Sequence similarity searches have been widely used in the analyses of metagenomic sequencing data. Finding homologous sequences in a reference database enables the estimation of taxonomic and functional characteristics of each query sequence. Because current metagenomic sequencing data consist of a large number of nucleotide sequences, the time required for sequence similarity searches account for a large proportion of the total time. This time-consuming step makes it difficult to perform large-scale analyses. To analyze large-scale metagenomic data, such as those found in the human oral microbiome, we developed GHOST-MP (Genome-wide HOmology Search Tool on Massively Parallel system), a parallel sequence similarity search tool for massively parallel computing systems. This tool uses a fast search algorithm based on suffix arrays of query and database sequences and a hierarchical parallel search to accelerate the large-scale sequence similarity search of metagenomic sequencing data. The parallel computing efficiency and the search speed of this tool were evaluated. GHOST-MP was shown to be scalable over 10,000 CPU (Central Processing Unit) cores, and achieved over 80-fold acceleration compared with mpiBLAST using the same computational resources. We applied this tool to human oral metagenomic data, and the results indicate that the oral cavity, the oral vestibule, and plaque have different characteristics based on the functional gene category.

Using PFP and ESG Protein Function Prediction Web Servers.

Elucidating biological function of proteins is a fundamental problem in molecular biology and bioinformatics. Conventionally, protein function is annotated based on homology using sequence similarity search tools such as BLAST and FASTA. These methods perform well when obvious homologs exist for a query sequence; however, they will not provide any functional information otherwise. As a result, the functions of many genes in newly sequenced genomes are left unknown, which await functional interpretation. Here, we introduce two webservers for function prediction methods, which effectively use distantly related sequences to improve function annotation coverage and accuracy: Protein Function Prediction (PFP) and Extended Similarity Group (ESG). These two methods have been tested extensively in various benchmark studies and ranked among the top in community-based assessments for computational function annotation, including Critical Assessment of Function Annotation (CAFA) in 2010-2011 (CAFA1) and 2013-2014 (CAFA2). Both servers are equipped with user-friendly visualizations of predicted GO terms, which provide intuitive illustrations of relationships of predicted GO terms. In addition to PFP and ESG, we also introduce NaviGO, a server for the interactive analysis of GO annotations of proteins. All the servers are available at http://kiharalab.org/software.php .

Machine Learning Algorithm for Predicting Ethylene Responsive Transcription Factor in Rice Using an Ensemble Classifier

Ethylene response factor (ERF) constitutes one of the most important gene families which are related to environmental responses and tolerancein plants . ERF genes are defined by the domain AP2/ERF, which comprises approximately 60 amino acids and are involved in DNA binding. Development of computational tools using machine learning tools will definitely enhance rice genome annotation. Machine learning algorithm involves construction and study of systems that can learn from data, rather than follow only explicitly programmed instructions. This study primarily emphasizes on the development of prediction tool, ERFPred, for drought responsive protein ERF in rice using machine learning algorithms. We have used fourteen different feature extraction methods including amino acid features, dipeptide, tripeptide, hybrid methods and exchange group features. Using, Random Forest classifier, we have obtained a precision rate of 100% for the ERFPred tool. To prove that species specific tool is better than an All plant tool, a general tool for plants, two different approaches were used and validated. The results obtained were also further compared with sequence similarity search tool, PSI-BLAST.

Div-BLAST: diversification of sequence search results.

Sequence similarity tools, such as BLAST, seek sequences most similar to a query from a database of sequences. They return results significantly similar to the query sequence and that are typically highly similar to each other. Most sequence analysis tasks in bioinformatics require an exploratory approach, where the initial results guide the user to new searches. However, diversity has not yet been considered an integral component of sequence search tools for this discipline. Some redundancy can be avoided by introducing non-redundancy during database construction, but it is not feasible to dynamically set a level of non-redundancy tailored to a query sequence. We introduce the problem of diverse search and browsing in sequence databases that produce non-redundant results optimized for any given query. We define diversity measures for sequences and propose methods to obtain diverse results extracted from current sequence similarity search tools. We also propose a new measure to evaluate the diversity of a set of sequences that is returned as a result of a sequence similarity query. We evaluate the effectiveness of the proposed methods in post-processing BLAST and PSI-BLAST results. We also assess the functional diversity of the returned results based on available Gene Ontology annotations. Additionally, we include a comparison with a current redundancy elimination tool, CD-HIT. Our experiments show that the proposed methods are able to achieve more diverse yet significant result sets compared to static non-redundancy approaches. In both sequence-based and functional diversity evaluation, the proposed diversification methods significantly outperform original BLAST results and other baselines. A web based tool implementing the proposed methods, Div-BLAST, can be accessed at cedar.cs.bilkent.edu.tr/Div-BLAST

CLAST: CUDA implemented large-scale alignment search tool.

BackgroundMetagenomics is a powerful methodology to study microbial communities, but it is highly dependent on nucleotide sequence similarity searching against sequence databases. Metagenomic analyses with next-generation sequencing technologies produce enormous numbers of reads from microbial communities, and many reads are derived from microbes whose genomes have not yet been sequenced, limiting the usefulness of existing sequence similarity search tools. Therefore, there is a clear need for a sequence similarity search tool that can rapidly detect weak similarity in large datasets.ResultsWe developed a tool, which we named CLAST (CUDA implemented large-scale alignment search tool), that enables analyses of millions of reads and thousands of reference genome sequences, and runs on NVIDIA Fermi architecture graphics processing units. CLAST has four main advantages over existing alignment tools. First, CLAST was capable of identifying sequence similarities ~80.8 times faster than BLAST and 9.6 times faster than BLAT. Second, CLAST executes global alignment as the default (local alignment is also an option), enabling CLAST to assign reads to taxonomic and functional groups based on evolutionarily distant nucleotide sequences with high accuracy. Third, CLAST does not need a preprocessed sequence database like Burrows–Wheeler Transform-based tools, and this enables CLAST to incorporate large, frequently updated sequence databases. Fourth, CLAST requires <2 GB of main memory, making it possible to run CLAST on a standard desktop computer or server node.ConclusionsCLAST achieved very high speed (similar to the Burrows–Wheeler Transform-based Bowtie 2 for long reads) and sensitivity (equal to BLAST, BLAT, and FR-HIT) without the need for extensive database preprocessing or a specialized computing platform. Our results demonstrate that CLAST has the potential to be one of the most powerful and realistic approaches to analyze the massive amount of sequence data from next-generation sequencing technologies.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0406-y) contains supplementary material, which is available to authorized users.

KLAST – high-performance sequence similarity search tool

KLAST – high-performance sequence similarity search tool

The hypothalamic–neurohypophysial system: genomes, genes and genetics

The hypothalamic–neurohypophysial system: genomes, genes and genetics

Chain-RNA: A Comparative ncRNA Search Tool Based on the Two-Dimensional Chain Algorithm

Noncoding RNA (ncRNA) identification is highly important to modern biology. The state-of-the-art method for ncRNA identification is based on comparative genomics, in which evolutionary conservations of sequences and secondary structures provide important evidence for ncRNA search. For ncRNAs with low sequence conservation but high structural similarity, conventional local alignment tools such as BLAST yield low sensitivity. Thus, there is a need for ncRNA search methods that can incorporate both sequence and structural similarities. We introduce chain-RNA, a pairwise structural alignment tool that can effectively locate cross-species conserved RNA elements with low sequence similarity. In chain-RNA, stem-loop structures are extracted from dot plots generated by an efficient local-folding algorithm. Then, we formulate stem alignment as an extended 2D chain problem and employ existing chain algorithms. Chain-RNA is tested on a data set containing annotated ncRNA homologs and is applied to novel ncRNA search in a transcriptomic data set. The experimental results show that chain-RNA has better tradeoff between sensitivity and false positive rate in ncRNA prediction than conventional sequence similarity search tools and is more time efficient than structural alignment tools. The source codes of chain-RNA can be downloaded at http://sourceforge.net/projects/chain-rna/ or at http://www.cse.msu.edu/~leijikai/chain-rna/.

MitoZoa 2.0: a database resource and search tools for comparative and evolutionary analyses of mitochondrial genomes in Metazoa

The MITOchondrial genome database of metaZOAns (MitoZoa) is a public resource for comparative analyses of metazoan mitochondrial genomes (mtDNA) at both the sequence and genomic organizational levels. The main characteristics of the MitoZoa database are the careful revision of mtDNA entry annotations and the possibility of retrieving gene order and non-coding region (NCR) data in appropriate formats. The MitoZoa retrieval system enables basic and complex queries at various taxonomic levels using different search menus. MitoZoa 2.0 has been enhanced in several aspects, including: a re-annotation pipeline to check the correctness of protein-coding gene predictions; a standardized annotation of introns and of precursor ORFs whose functionality is post-transcriptionally recovered by RNA editing or programmed translational frameshifting; updates of taxon-related fields and a BLAST sequence similarity search tool. Database novelties and the definition of standard mtDNA annotation rules, together with the user-friendly retrieval system and the BLAST service, make MitoZoa a valuable resource for comparative and evolutionary analyses as well as a reference database to assist in the annotation of novel mtDNA sequences. MitoZoa is freely accessible at http://www.caspur.it/mitozoa.

BOV – a web-based BLAST output visualization tool

BackgroundThe BLAST program is one of the most widely used sequence similarity search tools for genomic research, even by those biologists lacking extensive bioinformatics training. As the availability of sequence data increases, more researchers are downloading the BLAST program for local installation and performing larger and more complex tasks, including batch queries. In order to manage and interpret the results of batch queries, a host of software packages have been developed to assist with data management and post-processing. Among these programs, there is almost a complete lack of visualization tools to provide graphic representation of complex BLAST pair-wise alignments. We have developed a web-based program, BLAST Output Visualization Tool (BOV), that allows users to interactively visualize the matching regions of query and database hit sequences, thereby allowing the user to quickly and easily dissect complex matching patterns.ResultsUsers can upload the standard BLAST output in pair-wise alignment format as input to the web server (including batch queries generated installing and running the stand-alone BLAST program on a local server). The program extracts the alignment coordinates of matching regions between the query and the corresponding database hit sequence. The coordinates are used to plot each matching region as colored lines or trapezoids. Using the straightforward control panels throughout the web site, each plotted matching region can be easily explored in detail by, for example, highlighting the region of interest or examining the raw pair-wise sequence alignment. Tutorials are provided at the website to guide users step-by-step through the functional features of BOV.ConclusionBOV provides a user-friendly web interface to visualize the standard BLAST output for investigating wide-ranging genomic problems, including single query and batch query datasets. In particular, this software is valuable to users interested in identifying regions of co-linearity, duplication, translocation, and inversion among sequences. A web server hosting BOV is accessible via and the software is freely available for local installations.

Protein structural similarity search by Ramachandran codes

BackgroundProtein structural data has increased exponentially, such that fast and accurate tools are necessary to access structure similarity search. To improve the search speed, several methods have been designed to reduce three-dimensional protein structures to one-dimensional text strings that are then analyzed by traditional sequence alignment methods; however, the accuracy is usually sacrificed and the speed is still unable to match sequence similarity search tools. Here, we aimed to improve the linear encoding methodology and develop efficient search tools that can rapidly retrieve structural homologs from large protein databases.ResultsWe propose a new linear encoding method, SARST (Structural similarity search Aided by Ramachandran Sequential Transformation). SARST transforms protein structures into text strings through a Ramachandran map organized by nearest-neighbor clustering and uses a regenerative approach to produce substitution matrices. Then, classical sequence similarity search methods can be applied to the structural similarity search. Its accuracy is similar to Combinatorial Extension (CE) and works over 243,000 times faster, searching 34,000 proteins in 0.34 sec with a 3.2-GHz CPU. SARST provides statistically meaningful expectation values to assess the retrieved information. It has been implemented into a web service and a stand-alone Java program that is able to run on many different platforms.ConclusionAs a database search method, SARST can rapidly distinguish high from low similarities and efficiently retrieve homologous structures. It demonstrates that the easily accessible linear encoding methodology has the potential to serve as a foundation for efficient protein structural similarity search tools. These search tools are supposed applicable to automated and high-throughput functional annotations or predictions for the ever increasing number of published protein structures in this post-genomic era.

AllerTool: a web server for predicting allergenicity and allergic cross-reactivity in proteins

Assessment of potential allergenicity and patterns of cross-reactivity is necessary whenever novel proteins are introduced into human food chain. Current bioinformatic methods in allergology focus mainly on the prediction of allergenic proteins, with no information on cross-reactivity patterns among known allergens. In this study, we present AllerTool, a web server with essential tools for the assessment of predicted as well as published cross-reactivity patterns of allergens. The analysis tools include graphical representation of allergen cross-reactivity information; a local sequence comparison tool that displays information of known cross-reactive allergens; a sequence similarity search tool for assessment of cross-reactivity in accordance to FAO/WHO Codex alimentarius guidelines; and a method based on support vector machine (SVM). A 10-fold cross-validation results showed that the area under the receiver operating curve (A(ROC)) of SVM models is 0.90 with 86.00% sensitivity (SE) at specificity (SP) of 86.00%. AllerTool is freely available at http://research.i2r.a-star.edu.sg/AllerTool/.

Characterization of BGTG‐1, a tergal gland‐secreted alpha‐amylase, from the German cockroach, Blattella germanica (L.)

The protein fraction of the German cockroach, Blattella germanica (L.), tergal gland secretion was examined. SDS-PAGE separation of proteins present in B. germanica tergal gland secretion revealed a tergal gland-secreted protein, BGTG-1, at approximately 63 kDa. BGTG-1 first appeared in tergal gland secretion at 2 days postimaginal moult and the amount of protein observed increased through day 5. A 2051 bp cDNA sequence, bgtg-1, was obtained by RACE polymerase chain reaction and contains a 1494 bp ORF encoding a predicted protein of 498 amino acids. In a Northern hybridization experiment using total RNA from B. germanica tergal gland tissue, a (32)P-labelled bgtg-1 probe hybridized to an RNA approximately 2000 bp and confirmed the 2051 bp cDNA size obtained by RACE PCR. Using the BLASTx sequence similarity search tool, the top match to the bgtg-1 ORF was found to be an alpha-amylase from Drosophila kikkawai (e-value = 1 x 10(-178)). Alignment of the bgtg-1 deduced protein sequence with alpha-amylases from fruit fly, Drosophila melanogaster, honey bee, Apis mellifera (L.) and yellow mealworm, Tenebrio molitor (L.), revealed conserved residues throughout the ORF and sequence identities ranging from 58.4 to 58.2%. Using a gel-based assay, degradation of starch by native BGTG-1 was demonstrated in vitro and we propose that BGTG-1 may be involved in processing phagostimulatory sugars present in B. germanica tergal gland secretion.

Using GenBank data for plant identification: possibilities and limitations using the ITS 1 of Asteraceae species belonging to the tribes Lactuceae and Anthemideae

AbstractAs a contribution to establish taxonomic identification methods using DNA sequence data, we evaluate to which extent the present set‐up of the nucleotide databases (NCBI, EBI, GenomeNet) allows to use them for reliable routine plant identification applying the implemented sequence similarity and homology search tools. We screened newly determined ITS 1 sequences from Asteraceae (tribes Lactuceae, Anthemideae) against the molecular nucleotide databases where sequences from that exact species, from other species of the same genus, or only from other genera of the same family had already been published. With the present set up of the BLAST® algorithms [MEGABLAST, BLASTN] 100% correct family, 99% correct tribe, and 98% precise genus affiliation could be achieved in Lactuceae representatives and 100% correct family, 100% correct tribe, and 95% precise genus affiliation in Anthemideae. Correct species detection varied between 86% accuracy for Lactuceae and 91% in Anthemideae. Necessary optimization criteria are proposed as well as further requirements for a standardized molecular identification system.