Search

matlab incorporates a number of bioinformatics function packaged together in its Bioinformatics Toolbox. The Bioinformatics Toolbox provides functions for creating objects such as biograph, the gene ontology and trees for phylogenetic analysis. In addition, this toolbox provides a number of functions for downloading sequences from DNA, protein and structure databases. Sequence analysis functions are provided for performing statistical analysis on the sequence data as well as for comparing DNA and protein sequences using the standard alignment methods (Needleman-Wunsch and Smith-Waterman) for global and local alignments. Capabilities for finding multiple sequence alignments, physlogenetic analysis, visualization and modeling sequence profiles using Hidden Markov methods are included within in the Bioinformatics Toolbox. The toolbox provides extensive functionality for retrieving, writing and and analyzing microarray data. The richness of tools included makes it possible to apply the concepts covered in the subsequent chapters within the framework of the same application. This provides an overview of the basic functions that enable reading, writing and display of biological sequence data.

The pTARGET web server enables prediction of nine distinct protein subcellular localizations in eukaryotic non-plant species. Predictions are made using a new algorithm [C. Guda and S. Subramaniam (2005) pTARGET [corrected] a new method for predicting protein subcellular localization in eukaryotes. Bioinformatics, 21, 3963-3969], which is primarily based on the occurrence patterns of location-specific protein functional domains in different subcellular locations. We have implemented a relational database, PreCalcDB, to store pre-computed prediction results for all eukaryotic non-plant protein sequences in the public domain that includes about 770,000 entries. Queries can be made by entering protein sequences or by uploading a file containing up to 5000 protein sequences in FASTA format. Prediction results for queries with matching entries in the PreCalcDB will be retrieved instantly; while for the missing ones new predictions will be computed and sent by email. Pre-computed predictions can also be downloaded for complete proteomes of Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila, Mus musculus and Homo sapiens. The server, its documentation and the data are accessible from http://bioinformatics.albany.edu/~ptarget.

FeatureScan is a software package aiming to reveal novel types of DNA sequence similarity by comparing physico-chemical properties. Thirty-eight different parameters of DNA double strands such as charge, melting enthalpy, conformational parameters and the like are provided. As input FeatureScan requires two sequences, a pattern sequence and a target sequence, search conditions are set by selecting a specific DNA parameter and a threshold value. Search results are displayed in FASTA format and directly linked to external genome databases/browsers (ENSEMBL, NCBI, UCSC). An Internet version of FeatureScan is accessible at http://genome.gbf.de/featurescan/. As part of the HOBIT initiative (http://hobit.sourceforge.net/) FeatureScan is also accessible as a web service at its above home page. Currently, several preloaded genomes are provided at this Internet website (Homo sapiens, Mus musculus, Rattus norvegicus and four strains of Escherichia coli) as target sequences. Standalone executables of FeatureScan are available on request.

Pathway Analyst (Path-A) is a publicly available web server (http://path-a.cs.ualberta.ca) that predicts metabolic pathways. It takes a FASTA format file containing a set of query protein sequences from a single organism (a partial or complete proteome) and identifies those sequences that are likely to participate in any of its supported metabolic pathways (currently 10). Path-A uses a number of machine-learning and sequence analysis techniques (e.g. SVM, BLAST and HMM) to predict pathways. Each machine-learned classifier exploits similarity between sequences in the pathways of its model organisms and sequences in the query set. It predicts the pathways that are present in the query organism and annotates each predicted reaction and catalyst, using the appropriate sequences from the query set. Path-A also provides a browsable and searchable database of the pathways for the model organisms that are used to make its predictions. Path-A's predictor sets (using different classifier technologies) have been evaluated using standard cross-validation techniques on a dataset of 10 metabolic pathways across 13 model organisms--a total of 125 organism-specific pathways. The most accurate classifier technology obtained a mean precision of 78.3% and a mean recall of 92.6% in predicting all catalyst proteins, of all reactions, in all pathways present in the dataset. Although Path-A currently only supports metabolic pathways, the underlying prediction techniques are general enough for other types of pathways. Consequently, it is our intent to extend Path-A to predict other types of pathways, including signalling pathways.

The snoRNA-LBME-db is a dedicated database containing human C/D box and H/ACA box small nucleolar RNAs (snoRNAs), and small Cajal body-specific RNAs (scaRNAs). C/D box and H/ACA box snoRNAs are part of ribonucleoparticles that guide 2'-O-ribose methylation and pseudouridilation, respectively, of selected residues of 28S, 18S or 5.8S rRNAs or of the spliceosomal U6 RNA. Similarly, scaRNAs guide modifications of the spliceosomal RNAs transcribed by RNA polymerase II (U1, U2, U4, U5 and U12) and are often composed of both C/D box and H/ACA box domains. However, some snoRNAs do not function as modification guide RNAs, but rather as RNA chaperones during the maturation of pre-rRNA. The database was built by a compilation of the literature, and comprises human sno/scaRNAs that were experimentally verified, as well as the human orthologs of snoRNAs that were cloned in other vertebrate species, and some snoRNAs that are predicted by bioinformatics search in loci submitted to genomic imprinting, but have not all been experimentally verified. For each entry, the database identifies the modified nucleotide(s) in the target RNA(s), indicates the corresponding predicted base pairing, gives a few pertinent references and provides a link to the position of the sno/scaRNA on the UCSC Genome Browser. The 'Find guide RNA' function allows one to find the sno/scaRNAs predicted to guide the modification of a particular nucleotide in the rRNA and spliceosomal RNA sequences. The 'Browse' function allows one to download the sequences of selected sno/scaRNAs in the FASTA format. The database is available online at http://www-snorna.biotoul.fr/. It can also be accessed from the human UCSC Genome Browser via the sno/miRNA track.

POWER, the PhylOgenetic WEb Repeater, is a web-based service designed to perform user-friendly pipeline phylogenetic analysis. POWER uses an open-source LAMP structure and infers genetic distances and phylogenetic relationships using well-established algorithms (ClustalW and PHYLIP). POWER incorporates a novel tree builder based on the GD library to generate a high-quality tree topology according to the calculated result. POWER accepts either raw sequences in FASTA format or user-uploaded alignment output files. Through a user-friendly web interface, users can sketch a tree effortlessly in multiple steps. After a tree has been generated, users can freely set and modify parameters, select tree building algorithms, refine sequence alignments or edit the tree topology. All the information related to input sequences and the processing history is logged and downloadable for the user's reference. Furthermore, iterative tree construction can be performed by adding sequences to, or removing them from, a previously submitted job. POWER is accessible at http://power.nhri.org.tw.

This paper describes an extensible, open-source (GPL) data repository and retrieval system that supports fast, efficient, keyword based retrieval of genomic sequences from multiple libraries with retrieved sequences post-processed by FASTA, Smith-Waterman and other analysis software. This application is implemented for Linux and is written in Mumps, C, and C++ with supporting components that include the Berkeley Data Base, the Perl Compatible Regular Expression Library, GLADE, and tools such as FASTA, Smith-Waterman, and modules from EMBOSS. The package described here can quickly index data sets of up to 256 terabytes using a B-tree based multi-dimensional data model. An example is presented that indexes the text of the full NCBI Genbank library.

Motivation: Anchoring of proteins to the extracytosolic leaflet of membranes via C-terminal attachment of glycosylphosphatidylinositol (GPI) is ubiquitous and essential in eukaryotes. The signal for GPI-anchoring is confined to the C-terminus of the target protein. In order to identify anchoring signals in silico, we have trained neural networks on known GPI-anchored proteins, systematically optimizing input parameters. Results: A Kohonen self-organizing map, GPI-SOM, was developed that predicts GPI-anchored proteins with high accuracy. In combination with SignalP, GPI-SOM was used in genome-wide surveys for GPI-anchored proteins in diverse eukaryotes. Apart from specialized parasites, a general trend towards higher percentages of GPI-anchored proteins in larger proteomes was observed. Availability: GPI-SOM is accessible on-line at http://gpi.unibe.ch. The source code (written in C) is available on the same website. Contact: pascal.maeser@izb.unibe.ch Supplementary information: Positive training set, performance test sets and lists of predicted GPI-anchored proteins from different eukaryotes in fasta format.

Summary: Protein secondary structure and solvent accessibility predictions are a fundamental intermediate step towards protein structure and function prediction. We present new systems for the ab initio prediction of protein secondary structure and solvent accessibility, Porter 4.0 and PaleAle 4.0. Porter 4.0 predicts secondary structure correctly for 82.2% of residues. PaleAle 4.0’s accuracy is 80.0% for prediction in two classes with a 25% accessibility threshold. We show that the increasing training set sizes that come with the continuing growth of the Protein Data Bank keep yielding prediction quality improvements and examine the impact of protein resolution on prediction performances. Availability: Porter 4.0 and PaleAle 4.0 are freely available for academic users at http://distillf.ucd.ie/porterpaleale/. Up to 64 kb of input in FASTA format can be processed in a single submission, with predictions now being returned to the user within a single web page and, optionally, a single email. Contact: gianluca.pollastri@ucd.ie

The Clustal series of programs are widely used in molecular biology for the multiple alignment of both nucleic acid and protein sequences and for preparing phylogenetic trees. The popularity of the programs depends on a number of factors, including not only the accuracy of the results, but also the robustness, portability and user-friendliness of the programs. New features include NEXUS and FASTA format output, printing range numbers and faster tree calculation. Although, Clustal was originally developed to run on a local computer, numerous Web servers have been set up, notably at the EBI (European Bioinformatics Institute) (http://www.ebi.ac.uk/clustalw/).

Primer Design Assistant (PDA) is a web interface primer design service combined with thermodynamic theory to evaluate the fitness of primers. It runs in a Linux-Apache-MySQL-PHP structure on a PC equipped with dual CPU (Intel Pentium III 1.4 GHz) and 512 Mb of RAM. A succinct user interface of PDA is accomplished by built-in parameters setting. Advanced options on 5' GC content, 3' GC content, dimer check and hairpin check are available. The option of covered region constrains the PCR product to cover a user-defined segment. PDA accepts single sequence query or multiple ones in FASTA format. It produces optimal and homogenous primer pairs that meet the need in experimental design with large-scaled PCR amplifications. Considering the system loading, the size of a submitted sequence is limited to 10 kb and the total sequence number in a query is limited to 20. The authors may be contacted regarding other requirements for primer design. The web application can be found at http://dbb.nhri.org.tw/primer/.

Summary: To address the impending need for exploring rapidly increased transcriptomics data generated for non-model organisms, we developed CBrowse, an AJAX-based web browser for visualizing and analyzing transcriptome assemblies and contigs. Designed in a standard three-tier architecture with a data pre-processing pipeline, CBrowse is essentially a Rich Internet Application that offers many seamlessly integrated web interfaces and allows users to navigate, sort, filter, search and visualize data smoothly. The pre-processing pipeline takes the contig sequence file in FASTA format and its relevant SAM/BAM file as the input; detects putative polymorphisms, simple sequence repeats and sequencing errors in contigs and generates image, JSON and database-compatible CSV text files that are directly utilized by different web interfaces. CBowse is a generic visualization and analysis tool that facilitates close examination of assembly quality, genetic polymorphisms, sequence repeats and/or sequencing errors in transcriptome sequencing projects. Availability: CBrowse is distributed under the GNU General Public License, available at http://bioinfolab.muohio.edu/CBrowse/ Contact: liangc@muohio.edu or liangc.mu@gmail.com; glji@xmu.edu.cn Supplementary Information: Supplementary data are available at Bioinformatics online.

Similarity searches are a powerful method for solving important biological problems such as database scanning, evolutionary studies, gene prediction, and protein structure prediction. FASTA is a widely used sequence comparison tool for rapid database scanning. Here we describe the GWFASTA server that was developed to assist the FASTA user in similarity searches against partially and/or completely sequenced genomes. GWFASTA consists of more than 60 microbial genomes, eight eukaryote genomes, and proteomes of annotated genomes. In fact, it provides the maximum number of databases for similarity searching from a single platform. GWFASTA allows the submission of more than one sequence as a single query for a FASTA search. It also provides integrated post-processing of FASTA output, including compositional analysis of proteins, multiple sequences alignment, and phylogenetic analysis. Furthermore, it summarizes the search results organism-wise for prokaryotes and chromosome-wise for eukaryotes. Thus, the integration of different tools for sequence analyses makes GWFASTA a powerful tool for biologists.

RADARS, a rapid, automated, data archiving and retrieval software system for high-throughput proteomic mass spectral data processing and storage, is described. The majority of mass spectrometer data files are compatible with RADARS, for consistent processing. The system automatically takes unprocessed data files, identifies proteins via in silico database searching, then stores the processed data and search results in a relational database suitable for customized reporting. The system is robust, used in 24/7 operation, accessible to multiple users of an intranet through a web browser, may be monitored by Virtual Private Network, and is secure. RADARS is scalable for use on one or many computers, and is suited to multiple processor systems. It can incorporate any local database in FASTA format, and can search protein and DNA databases online. A key feature is a suite of visualisation tools (many available gratis), allowing facile manipulation of spectra, by hand annotation, reanalysis, and access to all procedures. We also described the use of Sonar MS/MS, a novel, rapid search engine requiring 40 MB RAM per process for searches against a genomic or EST database translated in all six reading frames. RADARS reduces the cost of analysis by its efficient algorithms: Sonar MS/MS can identifiy proteins without accurate knowledge of the parent ion mass and without protein tags. Statistical scoring methods provide close-to-expert accuracy and brings robust data analysis to the non-expert user.

We construct a virus database called VirusDB (http://yaulab.math.tsinghua.edu.cn/VirusDB/) and an online inquiry system to serve people who are interested in viral classification and prediction. The database stores all viral genomes, their corresponding natural vectors, and the classification information of the single/multiple-segmented viral reference sequences downloaded from National Center for Biotechnology Information. The online inquiry system serves the purpose of computing natural vectors and their distances based on submitted genomes, providing an online interface for accessing and using the database for viral classification and prediction, and back-end processes for automatic and manual updating of database content to synchronize with GenBank. Submitted genomes data in FASTA format will be carried out and the prediction results with 5 closest neighbors and their classifications will be returned by email. Considering the one-to-one correspondence between sequence and natural vector, time efficiency, and high accuracy, natural vector is a significant advance compared with alignment methods, which makes VirusDB a useful database in further research. Refereed/Peer-reviewed

Modern next-generation sequencing technologies can generate huge volumes of data. One popular and useful tool to analyze these huge amount of data is the so called de Bruijn graph. Because of the huge number of nodes, in de Bruijn Graph based genome assembly the main barrier is the memory and runtime. And, this area has been the focus of significant attention in the contemporary literature. We present an algorithm that makes a balance between memory and runtime. Our approach stores the de Bruijn graph in a hash table with an auxiliary data structure which improves the total memory usage and runtime with no false positives. In the whole assembly process, generally the graph construction procedure takes the major share of the time. Our approach presents significant advancement in this aspect. All the data files (in FASTA format) along with the program code are available for downloaded at the following link: https://drive.google.com/folderview?id=0B3D-hZtRZ933V1dMOVBHUkNJM00&usp=sharing Please contact M. Sohel Rahman (msrahman@cse.buet.ac.bd)

Author(s): Hunter, Wayne B.; Reese, Justin; International Psyllid Genome Consortium, The | Abstract: The Asian citrus psyllid, Diaphorina citri (Hemiptera), is an important pest of citrus because it vectors bacteria responsible for huanglongbing, which is one of the most serious diseases of citrus worldwide. The first genome draft of D. citri (DIACI_1.0) was completed in 2011 (ARS, Ft. Pierce, FL), however, gaps in the assembly prompted additional sequencing using the long run PacBio system at the Los Alamos National Lab, NM. The revised draft genome (DIACI_1.1) was assembled using the new software PB-Jelly, with an improved N50 of 38 kb (up from 25 kb) and an increase by over 10 million in the number of resolved bases. The genome and transcriptome have been submitted into the public domain at the National Center for Biotechnology Information, NCBI, and the genome is currently being submitted to be processed and made available for access by the larger research community [http://www.ncbi.nlm.nih.gov/genome?LinkName=bioproject_genomeafrom_uid=29447]. The psyllid transcriptome identified over 25,600 predicted genes, and is supported by an additional 19,598 previous EST’s. Life-stage specific transcripts were identified for adults, nymphs and eggs. BlastX analyses showed that the psyllid genome has the most similarity to the Pea Aphid, Acyrthosiphon pisum, another hemipteran. The transcriptome data was provided to the Innocentive® Challenge Program in 2011 to advance efforts for RNAi development against psyllids. Other researchers are also using these data to develop strategies to suppress psyllid populations. Efforts are now focused on annotation of the psyllid genome which will provide more information on the genetic basis of psyllid biology. Free Downloads of the most recent datasets can be found at: http://www.psyllid.org/node/10 which includes the transcriptome and predicted protein files in FASTA format, and the gene locations on the DIACI_1.1 Genome assembly (GFF3 format).

Background Next generation sequencing technology has allowed efficient production of draft genomes for many organisms of interest. However, most draft genomes are just collections of independent contigs, whose relative positions and orientations along the genome being sequenced are unknown. Although several tools have been developed to order and orient the contigs of draft genomes, more accurate tools are still needed. Results In this study, we present a novel reference-based contig assembly (or scaffolding) tool, named as CAR, that can efficiently and more accurately order and orient the contigs of a prokaryotic draft genome based on a reference genome of a related organism. Given a set of contigs in multi-FASTA format and a reference genome in FASTA format, CAR can output a list of scaffolds, each of which is a set of ordered and oriented contigs. For validation, we have tested CAR on a real dataset composed of several prokaryotic genomes and also compared its performance with several other reference-based contig assembly tools. Consequently, our experimental results have shown that CAR indeed performs better than all these other reference-based contig assembly tools in terms of sensitivity, precision and genome coverage. Conclusions CAR serves as an efficient tool that can more accurately order and orient the contigs of a prokaryotic draft genome based on a reference genome. The web server of CAR is freely available at http://genome.cs.nthu.edu.tw/CAR/ and its stand-alone program can also be downloaded from the same website. Electronic supplementary material The online version of this article (doi:10.1186/s12859-014-0381-3) contains supplementary material, which is available to authorized users.

We present a web server for pair-wise alignment of membrane protein sequences, using the program AlignMe. The server makes available two operational modes of AlignMe: (i) sequence to sequence alignment, taking two sequences in fasta format as input, combining information about each sequence from multiple sources and producing a pair-wise alignment (PW mode); and (ii) alignment of two multiple sequence alignments to create family-averaged hydropathy profile alignments (HP mode). For the PW sequence alignment mode, four different optimized parameter sets are provided, each suited to pairs of sequences with a specific similarity level. These settings utilize different types of inputs: (position-specific) substitution matrices, secondary structure predictions and transmembrane propensities from transmembrane predictions or hydrophobicity scales. In the second (HP) mode, each input multiple sequence alignment is converted into a hydrophobicity profile averaged over the provided set of sequence homologs; the two profiles are then aligned. The HP mode enables qualitative comparison of transmembrane topologies (and therefore potentially of 3D folds) of two membrane proteins, which can be useful if the proteins have low sequence similarity. In summary, the AlignMe web server provides user-friendly access to a set of tools for analysis and comparison of membrane protein sequences. Access is available at http://www.bioinfo.mpg.de/AlignMe.

The FASTA programs search protein and deoxyribonucleic acid (DNA) databases for sequences with statistically significant similarity. The programs compare proteins, DNA and short peptides and oligonucleotides, and run on most popular computers. FASTA and BLAST both seek to identify homologous proteins or DNA sequences. BLAST is faster, but FASTA is more flexible, providing both rigorous (SSEARCH, LALIGN, GGSEARCH and GLSEARCH) and heuristic (FASTA, FASTX/Y, TFASTX/Y and FASTS/M/F) algorithms, a wider range of scoring matrices and different approaches for estimating statistical significance. In addition, the FASTA programs offer options to search a small, representative database, but then the report results from a larger sequence set linked to the initial significant hits. The FASTA programs can also annotate the alignments to include the conservation state of aligned functional residues, such as active sites, and subalignment scores associated with domain boundaries. The FASTA programs provide flexible and rigorous alternatives to BLAST for protein, translated-DNA and DNA alignment. Key Concepts: The FASTA program uses a heuristic (approximate) strategy for finding similar sequences, but the FASTA package includes SSEARCH and GGSEARCH, which provide rigorous algorithms. Homologs can be identified because they share excess (statistically significant) sequence similarity. E()-values (expect-values) report the significance (expectation) of a sequence similarity score. Sequence alignments are more accurate when the scoring matrix matches the evolutionary distance of the aligned sequences. The FASTA programs can align against sequences not included in the initial search using library expansion. The FASTA programs can use external annotations to modify aligned sequences and to partition similarity scores. Keywords: sequence similarity; homology; statistical significance; protein sequence comparison; DNA sequence comparison

The computer program EXONSAMPLER automates the sampling of thousands of exon sequences from publicly available reference genome sequences and gene annotation databases. It was designed to provide exon sequences for the efficient, next-generation gene sequencing method called exon capture. The exon sequences can be sampled by a list of gene name abbreviations (e.g. IFNG, TLR1), or by sampling exons from genes spaced evenly across chromosomes. It provides a list of genomic coordinates (a bed file), as well as a set of sequences in fasta format. User-adjustable parameters for collecting exon sequences include a minimum and maximum acceptable exon length, maximum number of exonic base pairs (bp) to sample per gene, and maximum total bp for the entire collection. It allows for partial sampling of very large exons. It can preferentially sample upstream (5 prime) exons, downstream (3 prime) exons, both external exons, or all internal exons. It is written in the Python programming language using its free libraries. We describe the use of EXONSAMPLER to collect exon sequences from the domestic cow (Bos taurus) genome for the design of an exon-capture microarray to sequence exons from related species, including the zebu cow and wild bison. We collected ~10% of the exome (~3 million bp), including 155 candidate genes, and ~16,000 exons evenly spaced genomewide. We prioritized the collection of 5 prime exons to facilitate discovery and genotyping of SNPs near upstream gene regulatory DNA sequences, which control gene expression and are often under natural selection.

The objective of database AsMamDB is to facilitate the systematic study of alternatively spliced genes of mammals. Version 1.0 of AsMamDB contains 1563 alternatively spliced genes of human, mouse and rat, each associated with a cluster of nucleotide sequences. The main information provided by AsMamDB includes gene alternative splicing patterns, gene structures, locations in chromosomes, products of genes and tissues where they express. Alternative splicing patterns are represented by multiple alignments of various gene transcripts and by graphs of their topological structures. Gene structures are illustrated by exon, intron and various regulatory elements distributions. There are 4204 DNAs, 3977 mRNAs, 8989 CDSs and 126 931 ESTs in the current database. More than 130 000 GenBank entries are covered and 4443 MEDLINE records are linked. DNA, mRNA, exon, intron and relevant regulatory element sequences are provided in FASTA format. More information can be obtained by using the web-based multiple alignment tool Asalign and various category lists. AsMamDB can be accessed at http://166.111.30.6 5/ASMAM DB.html.

UNLABELLED To investigate the functional sites on a protein and the prediction of binding sites (residues)in proteins, it is often required to identify the binding site residues at different distance threshold from protein three dimensional (3D)structures. For the study of a particular protein chain and its interaction with the ligand in complex form, researchers have to parse the output of different available tools or databases for finding binding-site residues. Here we have developed a tool for calculating amino acid contact distances in proteins at different distance threshold from the 3D-structure of the protein. For an input of protein 3D-structure, ContPro can quickly find all binding-site residues in the protein by calculating distances and also allows researchers to select the different distance threshold, protein chain and ligand of interest. Additionally, it can also parse the protein model (in case of multi model protein coordinate file)and the sequence of selected protein chain in Fasta format from the input 3D-structure. The developed tool will be useful for the identification and analysis of binding sites of proteins from 3D-structure at different distance thresholds. AVAILABILITY IT CAN BE ACCESSED AT: http://procarb.org/contpro/

To aid studies of molecular evolution and to assist in gene prediction research, we have constructed an Exon-Intron Database (EID) in FASTA format. Currently, the database is derived from GenBank release 112, and it contains 51 289 protein-coding genes (287 209 exons) that harbor introns, along with extensive descriptions of each gene and its DNA and protein sequences, as well as splice motif information. There is 17% redundancy inherited from GenBank-a purge at the 99% identity level reduced the database to 42 460 genes (243 589 exons). We have created subdatabases of genes whose intron positions have been experimentally determined. One such database, constructed by comparing genomic and mRNA sequences, contains 11 242 genes (62 474 exons). A larger database of 22 196 genes (105 595 exons) was constructed by selecting on keywords to eliminate computer-predicted genes. By examining the two nucleotides adjacent to the intron boundary, we infer that there is a 2% rate of errors or other deviations from the standard GTellipsisAG motif in nuclear genes. This criterion can be used to eliminate 4921 genes from the overall database. Various tools are provided to enable generation of user-specific subsets of the EID. The EID distribution can be obtained from http://mcb.harvard.edu/gilbert/EID

UNLABELLED Conformational switches observed in the protein backbone play a key role in a variety of fundamental biological activities. This paper describes a web-server that implements a pattern recognition algorithm trained on the examples from the Database of Macromolecular Movements to predict residue positions involved in conformational switches. Prediction can be performed at an adjustable false positive rate using a user-supplied protein sequence in FASTA format or a structure in a Protein Data Bank (PDB) file. If a protein sequence is submitted, then the web-server uses sequence-derived information only (such as evolutionary conservation of residue positions). If a PDB file is submitted, then the web-server uses sequence-derived information and residue solvent accessibility calculated from this file. AVAILABILITY FlexPred is publicly available at http://flexpred.rit.albany.edu.

UNLABELLED Diabetic retinopathy, is a microvascular complication of diabetes mellitus and is a major cause of adult blindness. Despite advances in diagnosis and treatment the pathogenesis of diabetic retinopathy is not well understood. Results from epidemiological studies of diabetic patients suggest that there are familial predispositions to diabetes and to diabetic retinopathy. Therefore the main purpose of this database is to help both scientists and doctors in studying the candidate genes responsible for causing diabetic retinopathy. For each candidate gene official symbol, chromosome map, number of exons, GT-AG introns, motif, polymorphic variation and 3D structure are given respectively. In addition to molecular class and function of these genes, this database also provides links to download the corresponding nucleotide and amino acid sequences in FASTA format which may be further used for computational approaches. Therefore this database will increase the understanding of the genetics underlying the development or progression of diabetic retinopathy and will have an impact on future diagnostic, prevention and intervention strategies. AVAILABILITY The database is freely available at http: diaretinopathydatabase.com.

The Roche 454 GS Junior sequencing platform allows locus-specific DNA methylation analysis using deep bisulfite amplicon sequencing. However, bisulfite-converted DNA reads may contain long T homopolymers, and the main sources of errors on pyrosequencing platforms are homopolymer over- and undercalls. Furthermore, existing tools do not always meet the analysis requirements for complex assay designs with multiple regions of interest (ROIs) from multiple samples. We have developed the amplikyzer software package to address the above challenges. It directly aligns the intensity sequences from standard flowgram files (SFF format) to given amplicon reference sequences, without converting to nucleotide FASTA format first, avoiding information loss by rounding flow intensities, and taking special measures to correctly process long homopolymers. It offers a variety of options to analyze complex multiplexed samples with several regions of interest and outputs useful statistics and publication-quality analysis plots without mandatory manual interaction. This allows our software to be used as part of automated pipelines as well as interactively. The underlying analysis algorithms, using a novel hybrid flowgram-DNA sequence representation are described in detail. We also discuss configuration options and use cases of our open source amplikyzer software and present exemplary results. The software, including required libraries, is available at https://bitbucket.org/svenrahmann/amplikyzer/downloads.

Two colonial ascidians Didemnum granulatum and D. psammathodes were collected from Tuticorin coastal waters. These ascidians were sequenced at 603 and 576 bp region of the mitochondrial cytochrome oxidase subunit I gene (COI) for phylogenetic analysis. Barcode sequences were extracted via FASTA format from NCBI. The genetic distances of submitted DNA sequences were compared with related ascidian species. Didemnum granulatum (JQ013198) sequence shows maximum identical 99% with D. vexillum. Didemnum psammathodes (JN624758) sequence submitted at present shows maximum identical 100% with another D. psammathodes sequence which was already submitted in NCBI. The sequence also shows maximum identical 90–89% with D. vexillum. From the present study it is concluded that precise and accurate identification of ascidians could be performed using the barcode sequences of the mitochondrial DNA (in the COI gene).

The advent of inexpensive RNA-seq technologies and other deep sequencing technologies for RNA has the promise to radically improve genomic annotation, providing information on transcribed regions and splicing events in a variety of cellular conditions. Using MS-based proteogenomics, many of these events can be confirmed directly at the protein level. However, the integration of large amounts of redundant RNA-seq data and mass spectrometry data poses a challenging problem. Our paper addresses this by construction of a compact database that contains all useful information expressed in RNA-seq reads. Applying our method to cumulative C. elegans data reduced 496.2 GB of aligned RNA-seq SAM files to 410 MB of splice graph database written in FASTA format. This corresponds to 1000× compression of data size, without loss of sensitivity. We performed a proteogenomics study using the custom data set, using a completely automated pipeline, and identified a total of 4044 novel events, including 215 novel genes, 808 novel exons, 12 alternative splicings, 618 gene-boundary corrections, 245 exon-boundary changes, 938 frame shifts, 1166 reverse strands, and 42 translated UTRs. Our results highlight the usefulness of transcript + proteomic integration for improved genome annotations.

MITOPRED web server enables prediction of nucleus-encoded mitochondrial proteins in all eukaryotic species. Predictions are made using a new algorithm based primarily on Pfam domain occurrence patterns in mitochondrial and non-mitochondrial locations. Pre-calculated predictions are instantly accessible for proteomes of Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila, Homo sapiens, Mus musculus and Arabidopsis species as well as all the eukaryotic sequences in the Swiss-Prot and TrEMBL databases. Queries, at different confidence levels, can be made through four distinct options: (i) entering Swiss-Prot/TrEMBL accession numbers; (ii) uploading a local file with such accession numbers; (iii) entering protein sequences; (iv) uploading a local file containing protein sequences in FASTA format. Automated updates are scheduled for the pre-calculated prediction database so as to provide access to the most current data. The server, its documentation and the data are available from http://mitopred.sdsc.edu.

Aim Less than 7% of HLA genes in IMGT/HLA are reported with full-length sequences. Recent high-throughput advances with NGS offers efficient full-length HLA typing. ∼160,000 HLA loci were reported by HML. A fully automatic annotation pipeline is needed to predict HLA exon–intron architecture and characterize genetic variation within exons and introns. Methods A program hml2fa was created to parse consensus sequences from HML to fasta format, then Clustal-Omega was applied for multiple sequence alignment with references. ExonFinder was created to predict exon–intron structures. UniqueSeq was used to remove sequence duplications for uniqueness analysis. Meanwhile, alignment sequence weights were calculated then bar plotted for validation. Results Sequence weight distribution indicated good alignment coverage especially in exon regions. Uniqueness analysis revealed thousands of full-length HLA variants that are potential novel alleles. In consideration of unique CDS and protein products, ∼50% of the variation was found in exons, resulting in 20–25% non-synonymous changes in class I HLA genes. Further analyses showed that exons 2 and 3 have significant high variability (p Conclusions This annotation pipeline can be used to predict HLA exon–intron structure and explore genetic variation on a large-scale. Surprisingly large numbers of intronic variants may affect HLA function and impact selection of matches for best transplant outcomes. Download : Download high-res image (307KB) Download : Download full-size image

Aim To expand the analytical potential of NGS in HLA typing. Studies based on SNP analysis have revealed the DNA sequence of HLA genes to carry important biological information typically overlooked in the simple exercise of allele assignment. NGS software is optimised to match sample sequences against databases of HLA allele sequence references. The goal here, however, is to create a method to open the data collected by NGS to any kind of query. Methods BLAST is still one of the most robust and efficient sequence-matching and sequence-alignment methods. Typically BAST databases are built with reference sequences against which a sample sequence is queried for matches. Here we reverse the approach and build a database of sample sequences against which we query for matches for particular sequences of interest following these steps: 1. Compile a database of sample sequences included in a given study. 2. Convert sample sequences to FASTA format. 3. Create a BLAST database with the study sample sequences. 4. Run any BLAST query against the BLAST database of the study sample sequences. Results This method was implemented using a dataset collected for typing purposes. The HLA-C 3’UTR polymorphisms which bind to miRNAs and regulate the level of expression (Nature 2011; 472:7344) were used. The BLAST output provided accurate information about which sequences carried the query polymorphism, which matched what is known about the association of these SNPs with HLA-C alleles. This method has also been successfully used to look for mutations that render HLA alleles null, and other SNP analysis queries. Conclusions NGS provides data that goes beyond the need for simple allele assignment. The method presented here provides (1) a robust and reliable way to store this accumulated information, (2) a quick and simple way to query this database of sequence data; and (3) an open method to ask any sequence question. M. Li: Employee; Company/Organization; Immucor.

Summary: PISCES is a public server for culling sets of protein sequences from the Protein Data Bank (PDB) by sequence identity and structural quality criteria. PISCES can provide lists culled from the entire PDB or from lists of PDB entries or chains provided by the user. The sequence identities are obtained from PSI-BLAST alignments with position-specific substitution matrices derived from the non-redundant protein sequence database. PISCES therefore provides better lists than servers that use BLAST, which is unable to identify many relationships below 40% sequence identity and often overestimates sequence identity by aligning only well-conserved fragments. PDB sequences are updated weekly. PISCES can also cull non-PDB sequences provided by the user as a list of GenBank identifiers, a FASTA format file, or BLAST/PSI-BLAST output. Availability: The server is located at http://www.fccc.edu/research/labs/dunbrack/pisces Contact: rl_dunbrack@fccc.edu * To whom correspondence should be addressed.

The increased throughput and decreased cost of next-generation sequencing (NGS) have shifted the bottleneck genomic research from sequencing to annotation, analysis and accessibility. This is particularly challenging for research communities working on organisms that lack the basic infrastructure of a sequenced genome, or an efficient way to utilize whatever sequence data may be available. Here we present a new database, the Assembled Searchable Giant Arthropod Read Database (ASGARD). This database is a repository and search engine for transcriptomic data from arthropods that are of high interest to multiple research communities but currently lack sequenced genomes. We demonstrate the functionality and utility of ASGARD using de novo assembled transcriptomes from the milkweed bug Oncopeltus fasciatus, the cricket Gryllus bimaculatus and the amphipod crustacean Parhyale hawaiensis. We have annotated these transcriptomes to assign putative orthology, coding region determination, protein domain identification and Gene Ontology (GO) term annotation to all possible assembly products. ASGARD allows users to search all assemblies by orthology annotation, GO term annotation or Basic Local Alignment Search Tool. User-friendly features of ASGARD include search term auto-completion suggestions based on database content, the ability to download assembly product sequences in FASTA format, direct links to NCBI data for predicted orthologs and graphical representation of the location of protein domains and matches to similar sequences from the NCBI non-redundant database. ASGARD will be a useful repository for transcriptome data from future NGS studies on these and other emerging model arthropods, regardless of sequencing platform, assembly or annotation status. This database thus provides easy, one-stop access to multi-species annotated transcriptome information. We anticipate that this database will be useful for members of multiple research communities, including developmental biology, physiology, evolutionary biology, ecology, comparative genomics and phylogenomics. Database URL: asgard.rc.fas.harvard.edu.

Traditional methods for the isolation and identification of fungal ­microsatellite markers mostly rely on construction of microsatellite-enriched DNA libraries and Sanger sequencing of clones from these libraries. These methods are time-consuming, labor-intensive, and relatively expensive. In this chapter, a quick and cost-effective approach is described for the discovery of microsatellites in fungi based on direct shotgun pyrosequencing. With this approach, high molecular weight DNA is extracted from the fungus of interest and subjected to 454 genome sequencing. The sequence reads are assembled into contigs or unique sequences in a fasta format. Then, free softwares such as MSATCOMMANDER and QDD are used for microsatellite search and primer design. The designed primer pairs are tested for PCR amplification and polymorphism using DNA samples from a diverse ­collection of fungal isolates.

The rapid expansion in the amount of biological data being generated worldwide is exceeding efforts to manage analysis of the data. Annotation can be specified as any piece of information associated with an amino acid sequence. Annotation is a process of relating additional information with a particular point in a piece of information. The present task is completely based on the biological database Protein Data Bank (PDB). The main outcome of the work is to extract annotation field information from pdb files in suitable way and accumulate it in a condensed manner. During the course of work, the FASTA sequence representation is made and this sequence is used to generate the annotation id which is one of the major annotation field.

Motivation and Objectives In genotyping analysis often researchers need to merge together genetic datasets coming from different genotyping platforms that use different sets of Single Nucleotide Polymorphisms (SNPs) to represent genetic polymorphisms. In order to do this, it is necessary to know the exact position of a SNP in a chromosome and update this information when new builds of the reference genome are available. In this work, we present G-SNPM (GPU SNP Mapping) a GPU-based tool to map SNPs on a genome. Methods G-SNPM is a tool that maps a short sequence (read) representative of a SNP against a reference DNA sequence in order to find the absolute position of the SNP in that sequence. Several tools have been devised to perform short-read mapping. Without aiming to be exhaustive, we can cite some solutions: MAQ (Li and Durbin, 2008), RMAP (Smith et al., 2008; Smith et al., 2009), Bowtie (Langmead et al., 2009), BWA (Li and Durbin, 2009), CloudBurst (Schatz, 2009), and SHRiMP (Rumble et al., 2009). A comparative study aimed at assessing the accuracy and the runtime performance of six state-of-the-art next-generation sequencing read alignment tools (Ruffalo et al., 2011) highlighted that among all SOAPv2 (Li et al., 2009) is the one that shows the higher accuracy. Recently, it has been proposed SOAPv3 (Liu et al., 2012) the GPU-based evolution of the SOAPv2 aligner. Experimental results shown that SOAPv3 outperforms notably both BWA and Bowtie. When tested to align millions of 100-bp read pairs to the human genome it resulted at least 7.5 times faster than BWA, and 20 times faster than Bowtie. Moreover, SOAPv3 that not exploits heuristics is able to align correctly slightly more reads than BWA and Bowtie. The current release of SOAPv3 supports alignments with up to four mismatches while it does not support indels. In G-SNPM each SNP is mapped on its related chromosome by means an automatic three stage pipeline. In the first stage, G-SNPM uses SOAPv3 to parallel align on a reference chromosome its related reads representative of a SNP. Due to the fact that SOAPv3 does not support indels, it might not be able to align some reads. Then, in the second stage G-SNPM uses another short-read mapping tool to align the unmapped reads. In particular, in this stage it is used SHRiMP which exploits specialized vector computing hardware to speed-up the dynamic programming algorithm of Smith-Waterman. Finally, in the third stage, G-SNPM analyses the alignments of the reads mapped by SOAPv3 and SHRiMP to calculate the absolute position of each SNP. An output file is generated which for each SNP reports its name, the related chromosome, the original SNP position, and the mapped SNP position. Moreover, information about the alignment as the strand, number of mismatches, and indels are also provided (see Figure 1). In G-SNPM reference DNA sequences are accepted in standard FASTA format, whereas SNPs must be represented through two files: a FASTA file with the representative reads of the SNPs, and another flat file with information about the SNP, in particular the original absolute SNP position and its alleles. Currently, automatic generation of these files is provided for SNP probes of the Illumina Chip. G-SNPM analyses Illumina files to automatically generate the previous described files for each chromosome. Results and Discussion The tool has been tested in the problem of remapping all the SNP probes of the Illumina Chip HumanOmni 1S (version 1), in order to find the map positions of each SNP in the build 37.3 of the refseq. To assess the performance of G-SNPM we compared its performance with those obtained by mapping the same SNPs with the state-of-the-art short-read mapping tool BWA. Experimental results shown that in the task of mapping around 1.2 million of SNPs BWA has been unable to map 55 SNPs (maximum edit distance 4% and up to two gap opening), whereas G-SNPM mapped correctly all SNPs. In particular, 178 SNPs has been mapped with SHRiMP in the second stage of the pipeline. Results shown that BWA has been able to map more reads than SOAPv3. Since SOAPv3 does not support indels, it might be unable to align some reads. However, it should be pointed out that differently that SOAPv3, BWA is designed not to miss any potential alignment resulting in many incorrect mapped reads (Ruffalo et al., 2011). Currently, G-SNPM runs on linux and it is freely available as a standalone application at the address http://www.itb.cnr.it/web/bioinformatics/g-snpm . To use G-SNPM is required a computer equipped with a CUDA enabled GPU card based on the Fermi architecture. We assessed G-SNPM with a NVIDIA GeForce GTX 480 card. Acknowledgements This work has been supported by the Italian Ministry Education and Research (MIUR) through the Flagship “InterOmics”, ITALBIONET (RBPR05ZK2Z), HIRMA (RBAP11YS7K) and the European “MIMOMICS” projects. References Langmead B, Trapnell C, et al. (2009). Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10:R25. Li H, Ruan J, Durbin R (2008). Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res. , 18(11):1851–8. Li H and Durbin R (2009). Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25(14):1754-1760 Li R, Yu C, Li Y, et al. (2009). SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics, 25(15). Liu CM, Wong T, et al. (2012). SOAP3: ultra-fast GPU-based parallel alignment tool for short reads. Bioinformatics, 28(6):878-9. Ruffalo M, LaFramboise T, Koyuturk M (2011). Comparative analysis of algorithms for next-generation sequencing read alignment. Bioinformatics 15;27(20):2790-6. Rumble SM, Lacroute P, et al. (2009). SHRiMP: Accurate Mapping of Short Color-space Reads. PLoS Comput Biol 5(5):e1000386. doi:10.1371/journal.pcbi.1000386. Schatz MC (2009). CloudBurst: highly sensitive read mapping with MapReduce. Bioinformatics, 25(11):1363–9. Smith AD, Chung WY, et al. (2009). Updates to the RMAP short-read mapping software. Bioinformatics, 25(21):2841–2842. Smith AD, Xuan Z, Zhang MQ (2008). Using quality scores and longer reads improves accuracy of solexa read mapping. BMC Bioinformatics, 9:128. Note: Figures and tables are available in PDF version only.

Background: Predicting protein function has become increasingly demanding in the era of next generation sequencing technology. The task to assign a curator-reviewed function to every single sequence is impracticable. Bioinformatics tools, easy to use and able to provide automatic and reliable annotations at a genomic scale, are necessary and urgent. In this scenario, the Gene Ontology has provided the means to standardize the annotation classification with a structured vocabulary which can be easily exploited by computational methods.Results: Argot2 is a web-based function prediction tool able to annotate nucleic or protein sequences from small datasets up to entire genomes. It accepts as input a list of sequences in FASTA format, which are processed using BLAST and HMMER searches vs UniProKB and Pfam databases respectively; these sequences are then annotated with GO terms retrieved from the UniProtKB-GOA database and the terms are weighted using the e-values from BLAST and HMMER. The weighted GO terms are processed according to both their semantic similarity relations described by the Gene Ontology and their associated score. The algorithm is based on the original idea developed in a previous tool called Argot. The entire engine has been completely rewritten to improve both accuracy and computational efficiency, thus allowing for the annotation of complete genomes.Conclusions: The revised algorithm has been already employed and successfully tested during in-house genome projects of grape and apple, and has proven to have a high precision and recall in all our benchmark conditions. It has also been successfully compared with Blast2GO, one of the methods most commonly employed for sequence annotation. The server is freely accessible at http://www.medcomp.medicina.unipd.it/Argot2, Journal Article, info:eu-repo/semantics/published

There are many instances in molecular biology when it is necessary to identify ORFs in a DNA sequence. While programs exist for displaying protein translations in multiple ORFs in alignment with a DNA sequence, they are often expensive, exist as add-ons to software that must be purchased, or are only compatible with a particular operating system. JavaScript DNA Translator is a shareware application written in JavaScript, a scripting language interpreted by the Netscape® Communicator and Internet Explorer Web browsers, which makes it compatible with several different operating systems. While the program uses a familiar Web page interface, it requires no connection to the Internet since calculations are performed on the user’s own computer. The program analyzes one or multiple DNA sequences and generates translations in up to six reading frames aligned to a DNA sequence, in addition to displaying translations as separate sequences in FASTA format. ORFs within a reading frame can also be displayed as separate sequences. Flexible formatting options are provided, including the ability to hide ORFs below a minimum size specified by the user. The program is available free of charge at the BioTechniques Software Library (www.Biotechniques.com).

Surface proteins, such as those located in the cell wall of fungi, play an important role in the interaction with the surrounding environment. For instance, they mediate primary host-pathogen interactions and are crucial to the establishment of biofilms and fungal infections. Surface localization of proteins is determined by specific sequence features and can be predicted by combining different freely available web servers. However, user-friendly tools that allow rapid analysis of large datasets (whole proteomes or larger) in subsequent analyses were not yet available. Here, we present the web tool ProFASTA, which integrates multiple tools for rapid scanning of protein sequence properties in large datasets and returns sequences in FASTA format. ProFASTA also allows for pipeline filtering of proteins with cell surface characteristics by analysis of the output created with SignalP, TMHMM and big-PI. In addition, it provides keyword, iso-electric point, composition and pattern scanning. Furthermore, ProFASTA contains all fungal protein sequences present in the NCBI Protein database. As the full fungal NCBI Taxonomy is included, sequence subsets can be selected by supplying a taxon name. The usefulness of ProFASTA is demonstrated here with a few examples; in the recent past, ProFASTA has already been applied successfully to the annotation of covalently-bound fungal wall proteins as part of community-wide genome annotation programs. ProFASTA is available at: http://www.bioinformatics.nl/tools/profasta/.

There is a great need for standards in the orthology field. Users must contend with different ortholog data representations from each provider, and the providers themselves must independently gather and parse the input sequence data. These burdensome and redundant procedures make data comparison and integration difficult. We have designed two XML-based formats, SeqXML and OrthoXML, to solve these problems. SeqXML is a lightweight format for sequence recordscthe input for orthology prediction. It stores the same sequence and metadata as typical FASTA format records, but overcomes common problems such as unstructured metadata in the header and erroneous sequence content. XML provides validation to prevent data integrity problems that are frequent in FASTA files. The range of applications for SeqXML is broad and not limited to ortholog prediction. We provide read/write functions for BioJava, BioPerl, and Biopython. OrthoXML was designed to represent ortholog assignments from any source in a consistent and structured way, yet cater to specific needs such as scoring schemes or meta-information. A unified format is particularly valuable for ortholog consumers that want to integrate data from numerous resources, e.g. for gene annotation projects. Reference proteomes for 61 organisms are already available in SeqXML, and 10 orthology databases have signed on to OrthoXML. Adoption by the entire field would substantially facilitate exchange and quality control of sequence and orthology information.

We have developed suite of bioinformatics algorithms for automated identification and classification of microbes based on comparative analysis of protein sequences. This application uses sequence information of microbial proteins revealed by mass spectrometry-based proteomics for identification and phyloproteomics classification. The algorithms transforms results of searching product ion spectra of peptide ions against a protein database, performed by commercially available software (e.g. SEQUEST), into a taxonomically meaningful and easy to interpret output. To achieve this goal we constructed a custom protein database composed of theoretical proteomes derived from all fully sequenced bacterial genomes (1204 microorganisms as of August 25th, 2010) in a FASTA format. Each protein sequence in the database is supplemented with information on a source organism and chromosomal position of each protein coding open reading frame (ORF) is embedded into the protein sequence header. In addition this information is linked with a taxonomic position of each database bacterium. ABOid analyzes SEQUEST search results files to provide the probabilities that peptide sequence assignments to a product ion mass spectrum (MS/MS) are correct and uses the accepted spectrum–to-sequence matches to generate a sequence-to-organism (STO) matrix of assignments. Because peptide sequences are differentially present or absent in various strains being compared this allows for the classification of bacterial species in a high throughput manner. For this purpose, STO matrices of assignments, viewed as assignment bitmaps, are next analyzed by a ABOid module that uses phylogenetic relationships between bacterial species as a part of decision tree process, and by applying multivariate statistical techniques (principal component and cluster analysis), to reveal relationship of the analyzed unknown sample to the database microorganisms. Our bacterial classification and identification algorithm uses assignments of an analyzed organism to taxonomic groups based on an organized scheme that begins at the phylum level and follows through classes, orders, families and genus down to strain level.

Many proteins exist in vivo as oligomers with different quaternary structural attributes rather than as individual chains. These proteins are the structural components of various biological functions, including cooperative effects, allosteric mechanisms and ion-channel gating. With the dramatic increase in the number of protein sequences submitted to the public databank, it is important for both basic research and drug discovery research to acquire the knowledge about possible quaternary structural attributes of their interested proteins in a timely manner. A high-throughput method (DWT_SVM), fusing discrete wavelet transform (DWT) and support vector machine (SVM) classifier algorithm with various physicochemical features, has been developed to predict protein quaternary structure. The accuracy in distinguishing candidate proteins as homo-oligomer or hetero-oligomer using the dataset R2720 was 85.95% and 85.49% respectively by jackknife, showing that DWT_SVM is guide promising in predicting protein quaternary structures. The online service is available at http://bioinfo.ncu.edu.cn/Services.aspx . Protein sequences in FASTA format can be directly fed to the system OligoPred. The processed results will be presented in a diagram that includes the information of feature extraction and the classification error rate.

Alternative splicing (AS) is a post-transcriptional process considered to be responsible for the huge diversity of proteins in higher eukaryotes. AS events are regulated by different splicing factors (SFs) that bind to sequence elements on the RNA. SFmap is a web server for predicting putative SF binding sites in genomic data (http://sfmap.technion.ac.il). SFmap implements the COS(WR) algorithm, which computes similarity scores for a given regulatory motif based on information derived from its sequence environment and its evolutionary conservation. Input for SFmap is a human genomic sequence or a list of sequences in FASTA format that can either be uploaded from a file or pasted into a window. SFmap searches within a given sequence for significant hits of binding motifs that are either stored in our database or defined by the user. SFmap results are provided both as a text file and as a graphical web interface.

We present the jump-start simultaneous alignment and tree construction using hidden Markov models (SATCHMO-JS) web server for simultaneous estimation of protein multiple sequence alignments (MSAs) and phylogenetic trees. The server takes as input a set of sequences in FASTA format, and outputs a phylogenetic tree and MSA; these can be viewed online or downloaded from the website. SATCHMO-JS is an extension of the SATCHMO algorithm, and employs a divide-and-conquer strategy to jump-start SATCHMO at a higher point in the phylogenetic tree, reducing the computational complexity of the progressive all-versus-all HMM-HMM scoring and alignment. Results on a benchmark dataset of 983 structurally aligned pairs from the PREFAB benchmark dataset show that SATCHMO-JS provides a statistically significant improvement in alignment accuracy over MUSCLE, Multiple Alignment using Fast Fourier Transform (MAFFT), ClustalW and the original SATCHMO algorithm. The SATCHMO-JS webserver is available at http://phylogenomics.berkeley.edu/satchmo-js. The datasets used in these experiments are available for download at http://phylogenomics.berkeley.edu/satchmo-js/supplementary/.