Biological Sequence Databases Research Articles

Strategies to improve usability and preserve accuracy in biological sequence databases.

Biology is increasingly dependent on large-scale analysis, such as proteomics, creating a requirement for efficient bioinformatics. Bioinformatic predictions of biological functions rely upon correctly annotated database sequences, and the presence of inaccurately annotated or otherwise poorly described sequences introduces noise and bias to biological analyses. Accurate annotations are, for example, pivotal for correct identification of polypeptide fragments. However, standards for how sequence databases are organized and presented are currently insufficient. Here, we propose five strategies to address fundamental issues in the annotation of sequence databases: (i) to clearly separate experimentally verified and unverified sequence entries; (ii) to enable a system for tracing the origins of annotations; (iii) to separate entries with high-quality, informative annotation from less useful ones; (iv) to integrate automated quality-control software whenever such tools exist; and (v) to facilitate postsubmission editing of annotations and metadata associated with sequences. We believe that implementation of these strategies, for example as requirements for publication of database papers, would enable biology to better take advantage of large-scale data.

Parallel algorithms for large-scale biological sequence alignment on Xeon-Phi based clusters.

BackgroundComputing alignments between two or more sequences are common operations frequently performed in computational molecular biology. The continuing growth of biological sequence databases establishes the need for their efficient parallel implementation on modern accelerators.ResultsThis paper presents new approaches to high performance biological sequence database scanning with the Smith-Waterman algorithm and the first stage of progressive multiple sequence alignment based on the ClustalW heuristic on a Xeon Phi-based compute cluster. Our approach uses a three-level parallelization scheme to take full advantage of the compute power available on this type of architecture; i.e. cluster-level data parallelism, thread-level coarse-grained parallelism, and vector-level fine-grained parallelism. Furthermore, we re-organize the sequence datasets and use Xeon Phi shuffle operations to improve I/O efficiency.ConclusionsEvaluations show that our method achieves a peak overall performance up to 220 GCUPS for scanning real protein sequence databanks on a single node consisting of two Intel E5-2620 CPUs and two Intel Xeon Phi 7110P cards. It also exhibits good scalability in terms of sequence length and size, and number of compute nodes for both database scanning and multiple sequence alignment. Furthermore, the achieved performance is highly competitive in comparison to optimized Xeon Phi and GPU implementations. Our implementation is available at https://github.com/turbo0628/LSDBS-mpi.

BISQUE: locus- and variant-specific conversion of genomic, transcriptomic and proteomic database identifiers

Biological sequence databases are integral to efforts to characterize and understand biological molecules and share biological data. However, when analyzing these data, scientists are often left holding disparate biological currency-molecular identifiers from different databases. For downstream applications that require converting the identifiers themselves, there are many resources available, but analyzing associated loci and variants can be cumbersome if data is not given in a form amenable to particular analyses. Here we present BISQUE, a web server and customizable command-line tool for converting molecular identifiers and their contained loci and variants between different database conventions. BISQUE uses a graph traversal algorithm to generalize the conversion process for residues in the human genome, genes, transcripts and proteins, allowing for conversion across classes of molecules and in all directions through an intuitive web interface and a URL-based web service. BISQUE is freely available via the web using any major web browser (http://bisque.yulab.org/). Source code is available in a public GitHub repository (https://github.com/hyulab/BISQUE). haiyuan.yu@cornell.edu Supplementary data are available at Bioinformatics online.

A Hybrid Parallel Implementation of the Aho–Corasick and Wu–Manber Algorithms Using NVIDIA CUDA and MPI Evaluated on a Biological Sequence Database

Multiple matching algorithms are used to locate the occurrences of patterns from a finite pattern set in a large input string. Aho–Corasick and Wu–Manber, two of the most well known algorithms for multiple matching require an increased computing power, particularly in cases where large-size datasets must be processed, as is common in computational biology applications. Over the past years, Graphics Processing Units (GPUs) have evolved to powerful parallel processors outperforming Central Processing Units (CPUs) in scientific calculations. Moreover, multiple GPUs can be used in parallel, forming hybrid computer cluster configurations to achieve an even higher processing throughput. This paper evaluates the speedup of the parallel implementation of the Aho–Corasick and Wu–Manber algorithms on a hybrid GPU cluster, when used to process a snapshot of the Expressed Sequence Tags of the human genome and for different problem parameters.

Accelerating Smith-Waterman Alignment for Protein Database Search Using Frequency Distance Filtration Scheme Based on CPU-GPU Collaborative System.

The Smith-Waterman (SW) algorithm has been widely utilized for searching biological sequence databases in bioinformatics. Recently, several works have adopted the graphic card with Graphic Processing Units (GPUs) and their associated CUDA model to enhance the performance of SW computations. However, these works mainly focused on the protein database search by using the intertask parallelization technique, and only using the GPU capability to do the SW computations one by one. Hence, in this paper, we will propose an efficient SW alignment method, called CUDA-SWfr, for the protein database search by using the intratask parallelization technique based on a CPU-GPU collaborative system. Before doing the SW computations on GPU, a procedure is applied on CPU by using the frequency distance filtration scheme (FDFS) to eliminate the unnecessary alignments. The experimental results indicate that CUDA-SWfr runs 9.6 times and 96 times faster than the CPU-based SW method without and with FDFS, respectively.

High Performance Pattern Matching on Heterogeneous Platform

Summary Pattern discovery is one of the fundamental tasks in bioinformatics and pattern recognition is a powerful technique for searching sequence patterns in the biological sequence databases. Fast and high performance algorithms are highly demanded in many applications in bioinformatics and computational molecular biology since the significant increase in the number of DNA and protein sequences expand the need for raising the performance of pattern matching algorithms. For this purpose, heterogeneous architectures can be a good choice due to their potential for high performance and energy efficiency. In this paper we present an efficient implementation of Aho-Corasick (AC) which is a well known exact pattern matching algorithm with linear complexity, and Parallel Failureless Aho-Corasick (PFAC) algorithm which is the massively parallelized version of AC algorithm without failure transitions, on a heterogeneous CPU/GPU architecture. We progressively redesigned the algorithms and data structures to fit on the GPU architecture. Our results on different protein sequence data sets show that the new implementation runs 15 times faster compared to the original implementation of the PFAC algorithm.

BLASTGrabber: a bioinformatic tool for visualization, analysis and sequence selection of massive BLAST data.

BackgroundAdvances in sequencing efficiency have vastly increased the sizes of biological sequence databases, including many thousands of genome-sequenced species. The BLAST algorithm remains the main search engine for retrieving sequence information, and must consequently handle data on an unprecedented scale. This has been possible due to high-performance computers and parallel processing. However, the raw BLAST output from contemporary searches involving thousands of queries becomes ill-suited for direct human processing. Few programs attempt to directly visualize and interpret BLAST output; those that do often provide a mere basic structuring of BLAST data.ResultsHere we present a bioinformatics application named BLASTGrabber suitable for high-throughput sequencing analysis. BLASTGrabber, being implemented as a Java application, is OS-independent and includes a user friendly graphical user interface. Text or XML-formatted BLAST output files can be directly imported, displayed and categorized based on BLAST statistics. Query names and FASTA headers can be analysed by text-mining. In addition to visualizing sequence alignments, BLAST data can be ordered as an interactive taxonomy tree. All modes of analysis support selection, export and storage of data. A Java interface-based plugin structure facilitates the addition of customized third party functionality.ConclusionThe BLASTGrabber application introduces new ways of visualizing and analysing massive BLAST output data by integrating taxonomy identification, text mining capabilities and generic multi-dimensional rendering of BLAST hits. The program aims at a non-expert audience in terms of computer skills; the combination of new functionalities makes the program flexible and useful for a broad range of operations.

Parallel Continuous Flow: A Parallel Suffix Tree Construction Tool for Whole Genomes

The construction of suffix trees for very long sequences is essential for many applications, and it plays a central role in the bioinformatic domain. With the advent of modern sequencing technologies, biological sequence databases have grown dramatically. Also the methodologies required to analyze these data have become more complex everyday, requiring fast queries to multiple genomes. In this article, we present parallel continuous flow (PCF), a parallel suffix tree construction method that is suitable for very long genomes. We tested our method for the suffix tree construction of the entire human genome, about 3GB. We showed that PCF can scale gracefully as the size of the input genome grows. Our method can work with an efficiency of 90% with 36 processors and 55% with 172 processors. We can index the human genome in 7 minutes using 172 processes.

An improved distance matrix computation algorithm for multicore clusters.

Distance matrix has diverse usage in different research areas. Its computation is typically an essential task in most bioinformatics applications, especially in multiple sequence alignment. The gigantic explosion of biological sequence databases leads to an urgent need for accelerating these computations. DistVect algorithm was introduced in the paper of Al-Neama et al. (in press) to present a recent approach for vectorizing distance matrix computing. It showed an efficient performance in both sequential and parallel computing. However, the multicore cluster systems, which are available now, with their scalability and performance/cost ratio, meet the need for more powerful and efficient performance. This paper proposes DistVect1 as highly efficient parallel vectorized algorithm with high performance for computing distance matrix, addressed to multicore clusters. It reformulates DistVect1 vectorized algorithm in terms of clusters primitives. It deduces an efficient approach of partitioning and scheduling computations, convenient to this type of architecture. Implementations employ potential of both MPI and OpenMP libraries. Experimental results show that the proposed method performs improvement of around 3-fold speedup upon SSE2. Further it also achieves speedups more than 9 orders of magnitude compared to the publicly available parallel implementation utilized in ClustalW-MPI.

Fast and Efficient Hashing for Sequence Similarity Search using Substring Extraction in DNA Sequence Databases

Emergent interest in genomic research has resulted in the creation of huge biological sequence databases, however search and retrieval of relevant information from these databases takes a lot of processing time, when performed conventionally as size of databases containing DNA sequences is huge. Hence, providing an efficient searching mechanism is mandatory. In this paper we present an efficient search mechanism using Hashing techniques. Initially, the data is hashed and indexed according to different window sizes. During this process, we eliminate redundancies and only record patterns with distinct elements and provide them with corresponding hash values. During the search phase, the search string is checked for the size of the window and if it exceeds the maximum limit of 4, then it is divided. The first part is considered as the search string and the search is made. After the confirmation of the index, the strings that follow the current indexed string are matched with the search string and finally the confirmation is made. The simulation results show that the current methodology provides faster results, while occupying lesser memory.

Increasing Efficiency of Computation Time For Hit Detection In BLASTN

For biologists very hard time is with analyzing the uniqueness between two sample sequences such as DNA, RNA and protein sequences. A Biosequence represents a single, continuous molecule of nucleic acid or protein. It can be anything from a band on a gel to a complete chromosome. That's to design for a huge database system which finds similarities between two sequences that have biological significance. In such condition we have to compromise in computation time, this can be overcome through implementation BLASTN process. In this paper the BLAST process will be working more efficient by a new approach for biological sequence database scanning. The scanning is performed with reconfigurable FPGA base hardware by comparing sequence one to many sequences from the database. The experimental sequence matching reduces the computation time of BLAST. [1] [2]

EASER: Ensembl Easy Sequence Retriever

The rapid advances in genome sequencing technologies have increased the pace at which biological sequence databases are becoming available to the broad scientific community. Thus, obtaining and preparing an appropriate sequence dataset is a crucial first step for all types of genomic analyses. Here, we present a script that can widely facilitate the easy, fast, and effortless downloading and preparation of a proper biological sequence dataset for various genomics studies. This script retrieves Ensembl defined genomic features, associated with a given Ensembl identifier. Coding (CDS) and genomic sequences can be easily retrieved based on a selected relationship from a set of relationship types, either considering all available organisms or a user specified subset of organisms. The script is very user-friendly and by default starts with an interactive mode if no command-line options are specified.

Molecular cloning and characterization of porcine indoleamine 2, 3-dioxygenase and its expression in various tissues

In order to confirm the existence of indoleamine 2, 3-dioxygenase (IDO) gene in swine, and to clone the novel gene followed by the molecule structure properties and expression pattern analysis, the porcine mRNA sequences homologous to human IDO were obtained from GenBank database by bioinformatics method. By using RT-PCR, the IDO gene was cloned from porcine endothelial cell line and the accuracy of the nucleic acid sequence was confirmed, and the expression pattern of the gene was detected. The three-dimensional structure model of porcine IDO was built referring to the tertiary structure of human IDO using biological sequence analysis software and database. The results showed that the porcine IDO was identified by sequencing. The nucleotide sequences were confirmed as a novel gene after submitted to Genbank. Porcine IDO was expressed in the lung, thymus, epididymis and anterior chamber with a basic level, however in peripheral blood mononuclear cells (PBMCs) the IDO gene was highly expressed. The three-dimensional structure model of porcine IDO was similar to that of human IDO. It was suggested that identification of the structure information of porcine IDO is essential to further investigate the immunologic function of the gene. Study of IDO on NK cells-mediated xenograft rejection will be a novel therapeutic target for the development of xenotransplantation.

On compressing and indexing repetitive sequences

We introduce LZ-End, a new member of the Lempel–Ziv family of text compressors, which achieves compression ratios close to those of LZ77 but is much faster at extracting arbitrary text substrings. We then build the first self-index based on LZ77 (or LZ-End) compression, which in addition to text extraction offers fast indexed searches on the compressed text. This self-index is particularly effective for representing highly repetitive sequence collections, which arise for example when storing versioned documents, software repositories, periodic publications, and biological sequence databases.

Taxonomic classification of metagenomic shotgun sequences with CARMA3

The vast majority of microbes are unculturable and thus cannot be sequenced by means of traditional methods. High-throughput sequencing techniques like 454 or Solexa-Illumina make it possible to explore those microbes by studying whole natural microbial communities and analysing their biological diversity as well as the underlying metabolic pathways. Over the past few years, different methods have been developed for the taxonomic and functional characterization of metagenomic shotgun sequences. However, the taxonomic classification of metagenomic sequences from novel species without close homologue in the biological sequence databases poses a challenge due to the high number of wrong taxonomic predictions on lower taxonomic ranks. Here we present CARMA3, a new method for the taxonomic classification of assembled and unassembled metagenomic sequences that has been adapted to work with both BLAST and HMMER3 homology searches. We show that our method makes fewer wrong taxonomic predictions (at the same sensitivity) than other BLAST-based methods. CARMA3 is freely accessible via the web application WebCARMA from http://webcarma.cebitec.uni-bielefeld.de.

A Fast Hybrid Algorithm Approach for the Exact String Matching Problem Via Berry Ravindran and Alpha Skip Search Algorithms

Problem statement: String matching algorithm had been an essential means for searching biological sequence database. With the constant expansion in scientific data such as DNA and Protein; the development of enhanced algorithms have even become more critical as the major concern had always been how to raise the performances of these search algorithms to meet challenges of scientific information. Approach: Therefore a new hybrid algorithm comprising Berry Ravindran (BR) and Alpha Skip Search (ASS) is presented. The concept is based on BR shift function and combines with ASS to ensure improved performance. Results: The results obtained in percentages from the proposed hybrid algorithm displayed superior results in terms of number of attempts and number of character comparisons than the original algorithms when various types of data namely DNA, Protein and English text are applied to appraise the hybrid performances. The enhancement of the proposed hybrid algorithm performs better at 71%, 60% and 63% when compared to Berry-Ravindran in DNA, Protein and English text correspondingly. Moreover the rate of enhancement over Alpha Skip Search algorithm in DNA, Protein and English text are 48%, 28% and 36% respectively. Conclusion: The new proposed hybrid algorithm is relevant for searching biological science sequence database and also other string search systems.

The DNA bank network: the start from a german initiative.

The explicit aim of the DNA Bank Network is to close the divide between biological specimen collections and molecular sequence databases. It provides a technically optimized DNA and tissue collection service facility in the interest of all biological research, with access to well-documented DNA-containing samples and voucher specimens as well as to corresponding molecular data stored in public sequence databases. The Network enables scientists to (i) query and order DNA samples of organisms collected from natural habitats via a shared Web portal, (ii) store DNA samples for reference under optimal conditions after project completion or data publication, (iii) obtain DNA material to conduct new studies or to extend and complement previous investigations, and (iv) support good scientific practice as the deposition of DNA samples and related specimens facilitates the verification of published results.

Consistent annotation of gene expression arrays

BackgroundGene expression arrays are valuable and widely used tools for biomedical research. Today's commercial arrays attempt to measure the expression level of all of the genes in the genome. Effectively translating the results from the microarray into a biological interpretation requires an accurate mapping between the probesets on the array and the genes that they are targeting. Although major array manufacturers provide annotations of their gene expression arrays, the methods used by various manufacturers are different and the annotations are difficult to keep up to date in the rapidly changing world of biological sequence databases.ResultsWe have created a consistent microarray annotation protocol applicable to all of the major array manufacturers. We constantly keep our annotations updated with the latest Ensembl Gene predictions, and thus cross-referenced with a large number of external biomedical sequence database identifiers. We show that these annotations are accurate and address in detail reasons for the minority of probesets that cannot be annotated. Annotations are publicly accessible through the Ensembl Genome Browser and programmatically through the Ensembl Application Programming Interface. They are also seamlessly integrated into the BioMart data-mining tool and the biomaRt package of BioConductor.ConclusionsConsistent, accurate and updated gene expression array annotations remain critical for biological research. Our annotations facilitate accurate biological interpretation of gene expression profiles.

Enabling HMMER for the Grid with COMP Superscalar

Abstract The continuously increasing size of biological sequence databases has motivated the development of analysis suites that, by means of parallelization, are capable of performing faster searches on such databases. However, many of these tools are not suitable for execution on mid-to-large scale parallel infrastructures such as computational Grids. This paper shows how COMP Superscalar can be used to effectively parallelize on the Grid a sequence analysis program. In particular, we present a sequential version of the HMMER hmmpfam tool that, when run with COMP Superscalar, is decomposed into tasks and run on a set of distributed resources, not burdening the programmer with parallelization efforts. Although performance is not a main objective of this work, we also present some test results where COMP Superscalar, using a new pre-scheduling technique, clearly outperforms a well-known parallelization of the hmmpfam algorithm.

Estimating the Gumbel scale parameter for local alignment of random sequences by importance sampling with stopping times

The gapped local alignment score of two random sequences follows a Gumbel distribution. If computers could estimate the parameters of the Gumbel distribution within one second, the use of arbitrary alignment scoring schemes could increase the sensitivity of searching biological sequence databases over the web. Accordingly, this article gives a novel equation for the scale parameter of the relevant Gumbel distribution. We speculate that the equation is exact, although present numerical evidence is limited. The equation involves ascending ladder variates in the global alignment of random sequences. In global alignment simulations, the ladder variates yield stopping times specifying random sequence lengths. Because of the random lengths, and because our trial distribution for importance sampling occurs on a different sample space from our target distribution, our study led to a mapping theorem, which led naturally in turn to an efficient dynamic programming algorithm for the importance sampling weights. Numerical studies using several popular alignment scoring schemes then examined the efficiency and accuracy of the resulting simulations.