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Non-coding RNA (ncRNA) is involved in many biological processes and diseases in all species. Many ncRNA datasets exist that provide a sequential representation of data that best suits biomedical purposes. However, for ncRNA identification and analysis, statistical learning methods require hidden numerical features from the data. The extraction of hidden features, their analysis, and usage of a suitable set of features is crucial towards any statistical learning methods performance. Furthermore, a wealth of sequence intrinsic features has been proposed for ncRNA identification. Therefore, a systematic review and selection of these features are warranted. First, fasta format sequence datasets are generated from RNACentral representing many ncRNA types across a number of species. Next, a features dataset is created per fasta dataset consisting of 17 most frequently reported sequence intrinsic features. The features dataset is available from the FexRNA platform developed as part of this work. In addition, the features datasets are explored and analysed in terms of statistical information, univariate and bivariate analysis. For the feature selection (FS), a two-fold hierarchal FS framework based on majority voting and correlation is proposed and evaluated. Therefore, the FexRNA platform provides a useful platform for information about ncRNA features datasets, features analysis, and selection.

Aim: In this study, a wide range of in silico investigation of Bubalus bubalis (BB) heat shock protein 70 (HSP70) and heat shock factor-1 (HSF1) has been performed, ranging from sequence evaluation among species to homology modeling along with their docking studies to decipher the interacting residues of both molecules. Materials and Methods: Protein sequences of BB HSP70 and HSF1 were retrieved from NCBI database in FASTA format. Primary and secondary structure prediction were computed using Expasy ProtParam server and Phyre2 server, respectively. TMHMM server was used to identify the transmembrane regions in HSP70. Multiple sequence alignment and comparative analysis of the protein was carried out using MAFFT and visualization was created using ESPript 3.0. Phylogenetic analysis was accomplished by COBALT. Interactions of HSP70 with other proteins were studied using STRING database. Modeller 9.18, RaptorX, Swiss-Modeller, Phyre2, and I-TASSER were utilized to design the three-dimensional structure of these proteins followed by refinement; energy minimization was accomplished using ModRefiner and SPDBV program. Stereochemical quality along with the accuracy of the predicted models and their visualization was observed by PROCHECK program of PDBsum and UCSF Chimera, respectively. ClusPro 2.0 server was accessed for the docking of the receptor protein with the ligand. Results: The lower value of Grand Average of Hydropathy indicates the more hydrophilic nature of HSP70 protein. Value of the instability index (II) classified the protein as stable. No transmembrane region was reported for HSP70 by TMHMM server. Phylogenetic analysis based on multiple sequence alignments (MSAs) by COBALT indicated more evolutionarily closeness of Bos indicus (BI) with Bos taurus as compared to BI and BB. STRING database clearly indicates the HSF1 as one of the interacting molecules among 10 interacting partners with HSP 70. The best hit of 3D model of HSP70 protein and HSF1 was retrieved from I-TASSER and Phyre2, respectively. Interacting residues and type of bonding between both the molecules which were docked by ClusPro 2.0 were decoded by PIC server. Hydrophobic interactions, protein-protein main-chain-side-chain hydrogen bonds, and protein-protein side-chain-side-chain hydrogen bonds were delineated in this study. Conclusion: This is the first-ever study on in silico interaction of HSP70 and HSF1 proteins in BB. Several bioinformatics web tools were utilized to study secondary structure along with comparative modeling, physicochemical properties, and protein-protein interaction. The various interacting amino acid residues of both proteins have been indicated in this study.

Background: Todays, one of the most important problems in detection of human pathogens, is lack of positive control. The idea of using hybrid vectors, containing genes of different pathogens, can overcome this limitation. We can design specific primers for each region and use the hybrid vector as positive control sample in PCR. In this research we designed a hybrid vector and relevant primers for detection of Variolla and Burkholderia. Materials and Methods: In this study 16srRNA and HA genes were chosen to be located on the vector, to represent of Burkholderia and Variolla, in respectively. The sequence of these genes obtained from NCBI in FASTA format and aligned in BioEdit software for finding conserve region of each gene, then some purposeful changes were applied in the sequence of each gene and the sequences were placed next to each other and the construct was designed. Specific primers designed for each region using Oligo7, BioEdit, GeneRunner softwares, Oligo analyzer website and NCBI database. Finally, the construct cloned in PUC57 in SnapGene and PCRsimulated on hybrid vector using designed primers. Results: Analysis confirmed that conserved region for each gene is located on hybrid vector for each pathogen, and simulation of PCR proved the accuracy of designed primers. Conclusion: Hybrid vectors design contain similar sequence of pathogens genome but they are none-pathogenic. We can use these hybrid vectors as positive control, without any concern.

Background: Riboswitches are cis-acting, non-coding RNA elements found in the 5’UTR of bacterial mRNA and 3’ UTR of eukaryotic mRNA, that fold in a complex manner to act as receptors for specific metabolites hence altering their conformation in response to the change in concentrations of a ligand or metabolite. Riboswitches function as gene regulators in numerous bacteria, archaea, fungi, algae and plants. Aim and Objective: This study identifies different classes of riboswitches in the Archaeal domain of life. Previous studies have suggested that riboswitches carry a conserved aptameric domain in different domains of life. Since Archaea are considered to be the most idiosyncratic organisms it was interesting to look for the conservation pattern of riboswitches in these obviously strange microorganisms. Materials and Methods: Completely sequenced Archaeal Genomes present in the NCBI repository were used for studying riboswitches and other ncRNAs. The sequence files in FASTA format were downloaded from NCBI Genome database and information related to these genomes was retrieved from GenBank. Three bioinformatics approaches were used namely, ab initio, consensus structure prediction and statistical model-based prediction for identifying riboswitches. Results: Archaeal genomes have a sporadic distribution of putative riboswitches like the TPP, FMN, Guanidine, Lysine and c-di-AMP riboswitches, which are known to occur in bacteria. Also, a class of riboswitch sensing c-di-GMP, a second messenger, has been identified in a few Archaeal organisms. Conclusion: This study clearly reveals that bioinformatics methods are likely to play a major role in identifying conserved riboswitches and in establishing how widespread these classes are in all domains of life, even though the final confirmation may come from wet lab methods.

Experiments in which complex virome sequencing data is generated remain difficult to explore and unpack for scientists without a background in data science. The processing of raw sequencing data by high throughput sequencing workflows usually results in contigs in FASTA format coupled to an annotation file linking the contigs to a reference sequence or taxonomic identifier. The next step is to compare the virome of different samples based on the metadata of the experimental setup and extract sequences of interest that can be used in subsequent analyses. The viromeBrowser is an application written in the opensource R shiny framework that was developed in collaboration with end-users and is focused on three common data analysis steps. First, the application allows interactive filtering of annotations by default or custom quality thresholds. Next, multiple samples can be visualized to facilitate comparison of contig annotations based on sample specific metadata values. Last, the application makes it easy for users to extract sequences of interest in FASTA format. With the interactive features in the viromeBrowser we aim to enable scientists without a data science background to compare and extract annotation data and sequences from virome sequencing analysis results.

sangeranalyseR is feature-rich, free, and open-source R package for processing Sanger sequencing data. It allows users to go from loading reads to saving aligned contigs in a few lines of R code by using sensible defaults for most actions. It also provides complete flexibility for determining how individual reads and contigs are processed, both at the command-line in R and via interactive Shiny applications. sangeranalyseR provides a wide range of options for all steps in Sanger processing pipelines including trimming reads, detecting secondary peaks, viewing chromatograms, detecting indels and stop codons, aligning contigs, estimating phylogenetic trees, and more. Input data can be in either ABIF or FASTA format. sangeranalyseR comes with extensive online documentation and outputs aligned and unaligned reads and contigs in FASTA format, along with detailed interactive HTML reports. sangeranalyseR supports the use of colorblind-friendly palettes for viewing alignments and chromatograms. It is released under an MIT licence and available for all platforms on Bioconductor (https://bioconductor.org/packages/sangeranalyseR, last accessed February 22, 2021) and on Github (https://github.com/roblanf/sangeranalyseR, last accessed February 22, 2021).

Introduction MSX1 protein, a homeobox transcriptional regulator plays a significant role in various developmental processes of the mammalian system such as limb-pattern formation, craniofacial development, in particular, odontogenesis, and tumor growth inhibition. Several studies have been performed on MSX1 at the genomic and transcriptomic levels. However, there is a lack of information on its structural and conformational aspects. Objective For better understanding of the molecular mechanism of MSX1, the present study aims to conduct a detailed in-silico analysis of this protein in terms of its physicochemical properties, secondary and tertiary structure predictions, interacting partners, and phylogenetic relationship with other orthologs. Methods The sequence of the MSX1 protein from Homo sapiens was retrieved in the FASTA format from the National Center for Biotechnology Information (NCBI). The standard bioinformatic tools were further used to characterize and model the structure of this protein. Results The in-silico characterization of MSX1 revealed that it is a basic, non-polar, and thermostable globular protein mainly localized in the nucleus. This protein is extremely rigid due to the presence of high proline content. The phylogenetic and synteny analysis revealed that the gene is highly conserved at the level of the amino acid sequences, but underwent several modifications at the genomic level in the course of evolution possibly to attain the diverse function. Major part of this protein is a random coil, making it suitable for interaction with other proteins. Subcellular localization and protein-protein interaction suggested that the protein may act as a secretory protein and play a crucial role in regulating several developmental processes. Docking analysis suggested that the MSX1 protein may interact with other proteins and form complexes to carry out its function. Conclusion The structural characterization of this protein will help to better understand its molecular mechanism of action. In addition, the predicted 3-D model would act as a base for further understanding of the protein's other functional potential.

Transposable elements (TEs) are mobile genetic elements capable of rapidly altering the genome through their movements. The importance of TE activity has been documented in many biological processes, such as introducing genetic instability, altering patterns of gene expression, and accelerating genome evolution. Increasing appreciation of TEs results in the growing number of bioinformatics software to identify insertion events. However, the application of existing TE finding tools is limited by either narrow-focused design of the package, too many dependencies on other tools, or prior knowledge required as input files that may not be readily available to all users. Here, we report a simple pipeline, TEfinder, developed for the detection of new TE insertions with minimal software dependencies using four inputs that can be easily generated with popular variant calling pipelines. The external software requirements are BEDTools, SAMtools, and Picard. Necessary inputs include TEs present in the reference genome, binary paired-end alignment, reference genome index, and a list of TE names. We tested TEfinder pipeline among several evolving populations of Fusarium oxysporum generated through a short-term adaptation study. Our results demonstrate that this easy-to-use tool can effectively detect new TE insertion events, making it accessible and practical for TE analysis.

Summary Genome detective is a web-based, user-friendly software application to quickly and accurately assemble all known virus genomes from next-generation sequencing datasets. This application allows the identification of phylogenetic clusters and genotypes from assembled genomes in FASTA format. Since its release in 2019, we have produced a number of typing tools for emergent viruses that have caused large outbreaks, such as Zika and Yellow Fever Virus in Brazil. Here, we present the Genome Detective Coronavirus Typing Tool that can accurately identify the novel severe acute respiratory syndrome (SARS)-related coronavirus (SARS-CoV-2) sequences isolated in China and around the world. The tool can accept up to 2000 sequences per submission and the analysis of a new whole-genome sequence will take approximately 1 min. The tool has been tested and validated with hundreds of whole genomes from 10 coronavirus species, and correctly classified all of the SARS-related coronavirus (SARSr-CoV) and all of the available public data for SARS-CoV-2. The tool also allows tracking of new viral mutations as the outbreak expands globally, which may help to accelerate the development of novel diagnostics, drugs and vaccines to stop the COVID-19 disease. Availability and implementation https://www.genomedetective.com/app/typingtool/cov Contact koen@emweb.be or deoliveira@ukzn.ac.za Supplementary information Supplementary data are available at Bioinformatics online.

Protein identification by tandem mass spectrometry sequence database searching is a standard practice in many proteomics laboratories. The de facto standard for the representation of sequence databases used as input to sequence database search tools is the FASTA format. The Human Proteome Organization's Proteomics Standards Initiative has developed an extension to the FASTA format termed the proteomics standards initiative extended FASTA format or PSI extended FASTA format (PEFF) where additional information such as structural annotations are encoded in the protein description lines. Comet has been extended to automatically analyze the post translational modifications and amino acid substitutions encoded in PEFF databases. Comet's PEFF implementation and example analysis results searching a HEK293 dataset against the neXtProt PEFF database are presented.

Since the identification of the plant species is an important event, the present work was carried out to identify the medicinal plant Phyllanthus amarus taking the genetic variability among the species into consideration. The chloroplast genomic DNA was isolated and tested for quantity and quality. 2 μl of the template DNA, master mix, molecular-grade water and the forward and reverse primer were added and the final volume was made to 50 μl, and the amplification was performed in a gradient thermo-cycler and tested for amplification. The product was purified and sequenced and the sequence was submitted to GenBank. The sequence was subjected to multiple sequence alignment, and the phylogenic tree was constructed using MEGA 4 bioinformatics tools. The genomic DNA of the chloroplast was isolated, the highly conserved non-coding intron region of t-RNA L of the chloroplast was amplified, the amplified fragment was sequenced and the sequence in FASTA format was subjected to Multiple Sequence Alignment. The sequence showed no homology with any other sequence reported in the GenBank and this was the first highly conserved non-coding intron sequence of the plant Phyllanthus amarus that was deposited and the phylogenic tree was constructed. The identification of plant species based on the morphology is acceptable to an extent but the genetic blueprint gives you the exact information for the identification of the plant species. DNA barcode is a standardized and cost-effective molecular identification system used for plant identification. Hence the medicinally valuable plants needs the accurate identification among the species belonging to the same genus.

The phylogenetic tree plays a major role in identifying the evolution of specie or evolutionary relationships. The aim of this study was to reconstruct phylogenetic tree for cyclooxygenase (COX) gene with DNA sequences. The COX gene (P22437.1) data is taken from the NCBI (National Centre for Biotechnology Information) repository. During the process of reconstruction, the data is always considered in FASTA format. Blast function is used on this data which helps in calculating optimal alignments in faster process and also searches for similarity scores in known DNA sequences or other organisms. Then, the ClustalW approach is used for reconstruction of the tree as it uses progressive alignment methods and also helps in reducing duplicate sequences.

Chemical substances not showing any importance in existence of biological systems and causing serious health hazards may be designated as Xenobiotic compound. Elimination or degradation of these unwanted substances is a major issue of concern for current time research. Process of biodegradation is a very important aspect of current research as discussed in current manuscript. Current study focuses on the detailed mining of data for the construction of microbial consortia for wide range of xenobiotics compounds. Intensive literature search was done for the construction of this library. Desired data was retrieved from NCBI in fasta format. Data was analysed through homology approaches by using BLAST. This homology based searched enriched with a great vision that not only bacterial population but many other cheap and potential sources are available for different xenobiotic degradation. Though it was focused that bacterial population covers a major part of biodegradation which is near about 90.6% but algae and fungi are also showing promising future in degradation of some important xenobiotic compounds. Analysis of data reveals that Pseudomonas putida has potential for degrading maximum compounds. Establishment of correlation through cluster analysis signifies that Pseudomonas putida, Aspergillus niger and Skeletonema costatum can have combined traits that can be used in finding out actual evolutionary relationship between these species. These findings may also givea new outcome in terms of much cheaper and eco-friendly source in the area of biodegradation of specified xenobiotic compounds.

Recently, there has been an increase in the number of whole bacterial genomes sequenced, mainly due to the advancing of next-generation sequencing technologies. In face of this, there is a need to provide new analytical alternatives that can follow this advance. Given our current knowledge about the genomic plasticity of bacteria and that those genomic regions can uncover important features about this microorganism, our goal was to develop a fast methodology based on maximum entropy (ME) to guide the researcher to regions that could be prioritized during the analysis. This methodology was compared with other available methods. In addition, ME was applied to eight different bacterial genera. The methodology consists of two main steps: processing the nucleotide sequence and ME calculation. We applied ME to Xanthomonas axonopodis pv. citri 306 (XAC) and Xanthomonas campestris pv. campestris ATCC 33913 (XCC), both of which have their anomalous regions well documented. We then compared our results against those from Alien Hunter, HGT-DB, Islander, IslandPath, and SIGI-HMM. ME was shown to be superior in terms of efficiency and analysis duration. Besides, ME only needs the genome sequence in FASTA format as input. The proposed strategy based on ME is able to help in bacterial genome exploration. This is a simple and fast strategy for individual genomes in comparison with other available methods, without relying on previous annotation and alignments. This methodology can also be a new option in the early stages of analysis of newly sequenced bacterial genomes.

Mining and characterization of Simple Sequence Repeat (SSR) markers from whole genomes provide valuable information about biological significance of SSR distribution and also facilitate development of markers for genetic analysis. Whole genome sequencing (WGS)-SSR Annotation Tool (WGSSAT) is a graphical user interface pipeline developed using Java Netbeans and Perl scripts which facilitates in simplifying the process of SSR mining and characterization. WGSSAT takes input in FASTA format and automates the prediction of genes, noncoding RNA (ncRNA), core genes, repeats and SSRs from whole genomes followed by mapping of the predicted SSRs onto a genome (classified according to genes, ncRNA, repeats, exonic, intronic, and core gene region) along with primer identification and mining of cross-species markers. The program also generates a detailed statistical report along with visualization of mapped SSRs, genes, core genes, and RNAs. The features of WGSSAT were demonstrated using Takifugu rubripes data. This yielded a total of 139 057 SSR, out of which 113 703 SSR primer pairs were uniquely amplified in silico onto a T. rubripes (fugu) genome. Out of 113 703 mined SSRs, 81 463 were from coding region (including 4286 exonic and 77 177 intronic), 7 from RNA, 267 from core genes of fugu, whereas 105 641 SSR and 601 SSR primer pairs were uniquely mapped onto the medaka genome. WGSSAT is tested under Ubuntu Linux. The source code, documentation, user manual, example dataset and scripts are available online at https://sourceforge.net/projects/wgssat-nbfgr.

Background Constructing alignments and phylogenies for a given locus from large genome sequencing studies with relevant outgroups allow novel evolutionary and anthropological insights. However, no user-friendly tool has been developed to integrate thousands of recently available and anthropologically relevant genome sequences to construct complete sequence alignments and phylogenies. Results Here, we provide VCFtoTree, a user friendly tool with a graphical user interface that directly accesses online databases to download, parse and analyze genome variation data for regions of interest. Our pipeline combines popular sequence datasets and tree building algorithms with custom data parsing to generate accurate alignments and phylogenies using all the individuals from the 1000 Genomes Project, Neanderthal and Denisovan genomes, as well as reference genomes of Chimpanzee and Rhesus Macaque. It can also be applied to other phased human genomes, as well as genomes from other species. The output of our pipeline includes an alignment in FASTA format and a tree file in newick format. Conclusion VCFtoTree fulfills the increasing demand for constructing alignments and phylogenies for a given loci from thousands of available genomes. Our software provides a user friendly interface for a wider audience without prerequisite knowledge in programming. VCFtoTree can be accessed from https://github.com/duoduoo/VCFtoTree_3.0.0. Electronic supplementary material The online version of this article (10.1186/s12859-017-1844-0) contains supplementary material, which is available to authorized users.

Motivation The data generation capabilities of high throughput sequencing (HTS) instruments have exponentially increased over the last few years, while the cost of sequencing has dramatically decreased allowing this technology to become widely used in biomedical studies. For small labs and individual researchers, however, storage and transfer of large amounts of HTS data present a significant challenge. The recent trends in increased sequencing quality and genome coverage can be used to reconsider HTS data storage strategies. Results We present Broom, a stand-alone application designed to select and store only high-quality sequencing reads at extremely high compression rates. Written in C++, the application accepts single and paired-end reads in FASTQ and FASTA formats and decompresses data in FASTA format. Availability and implementation C++ code available at https://scsb.utmb.edu/labgroups/fofanov/broom.asp. Supplementary information Supplementary data are available at Bioinformatics online.

Many machine learning methods have been used to predict prokaryotic and eukaryotic protein subcellular localization. As most algorithms involve specific feature engineering, we carry out prediction using the feature-free property of deep learning methods. We present PSLCNN, a model using deep neural networks to predict protein subcellular localization for eukaryotes and prokaryotes. Only sequence information is needed (FASTA format). The model uses 1D convolution and predicts where the query localizes. It was trained and tested on an un-redundant dataset from the latest UniProt release, only for data with experimental annotation. Compared with the state-of-the-art tools, PSLCNN achieves the best performance for prokaryotes and is comparable for eukaryotes. We have also implemented a free PSLCNN web service available at https://github.com/changlabtw/PSLCNN.

BackgroundProphages play a significant role in prokaryotic evolution, often altering the function of the cell that they infect via transfer of new genes e.g., virulence or antibiotic resistance factors, inactivation of existing genes or by modifying gene expression. Recently, phage therapy has gathered renewed interest as a promising alternative to control bacterial infections. Cataloging the repertoire of prophages in large collections of species' genomes is an important initial step in understanding their evolution and potential therapeutic utility. However, current widely-used tools for identifying prophages within bacterial genome sequences are mainly web-based, can have long response times, and do not scale to keep pace with the many thousands of genomes currently being sequenced routinely.MethodologyIn this work, we present ProphET, an easy to install prophage predictor to be used in Linux operation system, without the constraints associated with a web-based tool. ProphET predictions rely on similarity searches against a database of prophage genes, taking as input a bacterial genome sequence in FASTA format and its corresponding gene annotation in GFF. ProphET identifies prophages in three steps: similarity search, calculation of the density of prophage genes, and edge refinement. ProphET performance was evaluated and compared with other phage predictors based on a set of 54 bacterial genomes containing 267 manually annotated prophages.Findings and conclusionsProphET identifies prophages in bacterial genomes with high precision and offers a fast, highly scalable alternative to widely-used web-based applications for prophage detection.

RNA interference (RNAi) is a technique used for transgene-mediated gene silencing based on the mechanism of posttranscriptional gene silencing (PTGS). PTGS is an ubiquitous basic biological phenomenon involved in the regulation of transcript abundance and plants’ immune response to viruses. PTGS also mediates genomic stability by silencing of retroelements. RNAi has become an important research tool for studying gene function by strong and selective suppression of target genes. Here, we present si-Fi, a software tool for design optimization of RNAi constructs necessary for specific target gene knock-down. It offers efficiency prediction of RNAi sequences and off-target search, required for the practical application of RNAi. si-Fi is an open-source (CC BY-SA license) desktop software that works in Microsoft Windows environment and can use custom sequence databases in standard FASTA format.

MyDGR is a web server providing integrated prediction and visualization of Diversity-Generating Retroelements (DGR) systems in query nucleotide sequences. It is built upon an enhanced version of DGRscan, a tool we previously developed for identification of DGR systems. DGR systems are remarkable genetic elements that use error-prone reverse transcriptases to generate vast sequence variants in specific target genes, which have been shown to benefit their hosts (bacteria, archaea or phages). As the first web server for annotation of DGR systems, myDGR is freely available on the web at http://omics.informatics.indiana.edu/myDGR with all major browsers supported. MyDGR accepts query nucleotide sequences in FASTA format, and outputs all the important features of a predicted DGR system, including a reverse transcriptase, a template repeat and one (or more) variable repeats and their alignment featuring A-to-N (N can be C, T or G) substitutions, and VR-containing target gene(s). In addition to providing the results as text files for download, myDGR generates a visual summary of the results for users to explore the predicted DGR systems. Users can also directly access pre-calculated, putative DGR systems identified in currently available reference bacterial genomes and a few other collections of sequences (including human microbiomes).

Background Identifying immunogenic proteins is the first stage in vaccine design and development. VaxiJen is the most widely used and highly cited server for immunogenicity prediction. As the developers of VaxiJen, we are obliged to update and improve it regularly. Here, we present an updated dataset of bacterial immunogens containing 317 experimentally proven immunogenic proteins of bacterial origin, of which 60% have been reported during the last 10 years. Methods PubMed was searched for papers containing data for novel immunogenic proteins tested on humans till March 2017. Corresponding protein sequences were collected from NCBI and UniProtKB. The set was curated manually for multiple protein fragments, isoforms, and duplicates. Results The final curated dataset consists of 306 immunogenic proteins tested on humans derived from 47 bacterial microorganisms. Certain proteins have several isoforms. All were considered, and the total protein sequences in the set are 317. The updated set contains 206 new immunogens, compared to the previous VaxiJen bacterial dataset. The average number of immunogens per species is 6.7. The set also contains 12 fusion proteins and 41 peptide fragments and epitopes. The dataset includes the names of bacterial microorganisms, protein names, and protein sequences in FASTA format. Conclusion Currently, the updated VaxiJen bacterial dataset is the best known manually-curated compilation of bacterial immunogens. It is freely available at http://www.ddg-pharmfac.net/vaxi jen/dataset. It can easily be downloaded, searched, and processed. When combined with an appropriate negative dataset, this update could also serve as a training set, allowing enhanced prediction of the potential immunogenicity of unknown protein sequences.

Motivation The existence of complex subpopulations of miRNA isoforms, or isomiRs, is well established. While many tools exist for investigating isomiR populations, they differ in how they characterize an isomiR, making it difficult to compare results across different tools. Thus, there is a need for a more comprehensive and systematic standard for defining isomiRs. Such a standard would allow investigation of isomiR population structure in progressively more refined sub-populations, permitting the identification of more subtle changes between conditions and leading to an improved understanding of the processes that generate these differences. Results We developed Jasmine, a software tool that incorporates a hierarchal framework for characterizing isomiR populations. Jasmine is a Java application that can process raw read data in fastq/fasta format, or mapped reads in SAM format to produce a detailed characterization of isomiR populations. Thus, Jasmine can reveal structure not apparent in a standard miRNA-Seq analysis pipeline. Availability and implementation Jasmine is implemented in Java and R and freely available at bitbucket https://bitbucket.org/bipous/jasmine/src/master/. Contact simon.rayner@medisin.uio.no Supplementary information Supplementary data are available at Bioinformatics online.

Microbiologists are routinely engaged isolation, identification and comparison of isolated bacteria for their novelty. 16S rRNA sequences of Bacillus pumilus were retrieved from NCBI repository and generated QR codes for sequences (FASTA format and full Gene Bank information). 16SrRNA were used to generate quick response (QR) codes of Bacillus pumilus isolated from Lonar Crator Lake (19° 58′ N; 76° 31′ E), India. Bacillus pumilus 16S rRNA gene sequences were used to generate CGR, FCGR and PCA. These can be used for visual comparison and evaluation respectively. The hyperlinked QR codes, CGR, FCGR and PCA of all the isolates are made available to the users on a portal https://sites.google.com/site/bhagwanrekadwad/. This generated digital data helps to evaluate and compare any Bacillus pumilus strain, minimizes laboratory efforts and avoid misinterpretation of the species. Keywords: Alkalophiles, Alkaline environment, Bacillus signatures, Lonar Crator Lake, Soda Lake

Phylogenetic analyses can provide a wealth of information about the past demography of a population and the level of genetic diversity within and between species. By using special computer programs developed in recent years, large amounts of data have been produced in the molecular genetics area. To analyze these data, powerful new methods based on large computations have been applied in various software packages and programs. But these programs have their own specific input and output formats, and users need to create different input formats for almost every program. R is an open source software environment, and it supports open contribution and modification to its libraries. Furthermore, it is also possible to perform several analyses using a single input file format. In this article, by using the multiple sequences FASTA format file (.fas extension) we demonstrate and share a workflow of how to extract haplotypes and perform phylogenetic analyses and visualizations in R. As an example dataset, we used 120 Bombus terrestris dalmatinus mitochondrial cytochrome b gene (cyt b) sequences (373 bp) collected from eight different beehives in Antalya. This article presents a short guide on how to perform phylogenetic analyses using R and RStudio.

The complete human genome has many genes of which few genes are associated with no known function but are expressed in normal as well as disease states. These proteins of unknown function have been reported in database and few structures of these sequences are also deposited in Protein Data Bank. Therefore performing computer-aided in silico analysis would enable to predict possible role using bioinformatics tools and software’s. An example of such protein structure deposited in PDB, (id: 1u5w) was selected to assign probable role in biological process. The protein sequence in fasta format was subjected to BLAST search against various protein sequence and structure databases. Analysis resulted in several similarities with other proteins/enzymes, of which Nucleoside triphosphatases were observed with reasonable similarity with the queried protein.

MotivationPost-translational modifications (PTMs) of proteins are associated with many significant biological functions and can be identified in high throughput using tandem mass spectrometry. Many PTMs are associated with short sequence patterns called “motifs” that help localize the modifying enzyme. Accordingly, many algorithms have been designed to identify these motifs from mass spectrometry data.ResultsMoMo is a software tool for identifying motifs among sets of PTMs. The program re-implements two previously described algorithms, Motif-X and MoDL, packaging them in a web-accessible user interface. In addition to reading sequence files in FASTA format, MoMo is capable of directly parsing output files produced by commonly used mass spectrometry search engines. The resulting motifs are presented to the user in an HTML summary with motif logos and linked text files in MEME motif format.AvailabilitySource code and web server available at http://meme-suite.orgContacttimothybailey@unr.edu and william-noble@uw.eduSupplementary informationSupplementary figures are available at Bioinformatics online.

In this paper, the Anopheles gambiae ABCC12 MRP protein domain sequence which contained 216 residues was obtained from the NCBI database in its fasta format (NCBI entry EAA12438.4). This MRP protein sequence was Gapped Blast using BLOSUM 62 matrix with an E-value cut-off of 0.000001 to identify the closest homologous structure as at the date of the study (Nov 2016). Additionally, the sequence was aligned with three prediction modelers, which are the Modeller v9.15 alignment script, the Swiss Model Server and the Raptor-X server for modelling based on the server’s automated choice for a suitable template. The structure predicted by Raptor-X has a higher percentage (90.1%) of residues in the most favored regions as compared to Modeller and Swiss-Model (86.5%). This paper further unveils the quality of structure predicted during homology modeling and the diverse correlation as well as the significance of ABCC12 in drug design for malaria vector.

Phosphorus is one of the primary macronutrient of plants, which is present in soil. It is essential for normal growth and development of plants. Plants use inorganic form of phosphate but organic form can also be assimilated with the help of soil inhabiting bacteria. Alkaline phosphatase is an enzyme present in Rizobium bacteria. This enzyme is responsible for solubilization and mineralization of organic phosphate and makes it readily available for plants. In the present study, nine different strains of Rhizobium leguminosarum were selected for a detailed computational structural and functional characterization and phylogenetic studies of alkaline phosphatase. Amino acid sequences were retrieved from UniProt and saved in FASTA format for use in analysis. Phylogenetic analysis of these strains was done by using MEGA7. 3D structure prediction was performed by using online server I-Tasser. Galaxy Web and 3D Refine were used for structure refinement. The refined structures were evaluated using two validation servers, QMEAN and SAVES. Protein-protein interaction analysis was done by using STRING. For detailed functional characterization, Cofactor, Coach, RaptorX, PSORT and MEME were used. Overall quality of predicted protein models was above 80%. Refined and validated models were submitted into PMDB. Seven out of nine strains were closely related and other two were distantly related. Protein-Protein interaction showed no significant co-expression among the interaction partners.

Clustered regularly interspaced short palindromic repeats (CRISPRs) are important genetic elements in many bacterial and archaeal genomes, and play a key role in prokaryote immune systems’ fight against invasive foreign elements. The CRISPR system has also been engineered to facilitate target gene editing in eukaryotic genomes. Using the common features of mis-annotated CRISPRs in prokaryotic genomes, this study proposed an accurate de novo CRISPR annotation program CRISPRdigger, which can take a partially assembled genome as its input. A comprehensive comparison with the three existing programs demonstrated that CRISPRdigger can recover more Direct Repeats (DRs) for CRISPRs and achieve a higher accuracy for a query genome. The program was implemented by Perl and all the parameters had default values, so that a user could annotate CRISPRs in a query genome by supplying only a genome sequence in the FASTA format. All the supplementary data are available at http://www.healthinformaticslab.org/supp/.

Given the huge potential impact of the growing number of complete genome-scale metabolic network reconstructions of organisms, it is necessary to use bioinformatics tools to simplify and accelerate the course of knowledge in this field. One essential component of genome-scale metabolic model is biomass equation, whose maximization is one of the most common objective functions used in Flux Balance Analysis formulations. Some components of biomass, such as amino acids and nucleotides, can be estimated from genome information. In this work it is proposed a Java tool that estimates biomass composition in amino acids and nucleotides, from genome and transcriptomic information, using as input files sequences in FASTA format and files with transcriptomic data in csv format. This application allows to obtain the results rapidly and is also a user-friendly tool since facilitates its use by users with any or little background in informatics., Project DeYeastLibrary - Designer yeast strain library optimized for metabolic engineering applications, Ref. ERA-IB-2/0003/2013, funded by national funds through FCT/MCTES

FASTA and FASTQ are basic and ubiquitous formats for storing nucleotide and protein sequences. Common manipulations of FASTA/Q file include converting, searching, filtering, deduplication, splitting, shuffling, and sampling. Existing tools only implement some of these manipulations, and not particularly efficiently, and some are only available for certain operating systems. Furthermore, the complicated installation process of required packages and running environments can render these programs less user friendly. This paper describes a cross-platform ultrafast comprehensive toolkit for FASTA/Q processing. SeqKit provides executable binary files for all major operating systems, including Windows, Linux, and Mac OSX, and can be directly used without any dependencies or pre-configurations. SeqKit demonstrates competitive performance in execution time and memory usage compared to similar tools. The efficiency and usability of SeqKit enable researchers to rapidly accomplish common FASTA/Q file manipulations. SeqKit is open source and available on Github at https://github.com/shenwei356/seqkit.

Prokaryotic and viral genomes are often altered by recombination and horizontal gene transfer. The existing methods for detecting recombination are primarily aimed at viral genomes or sets of loci, since the expensive computation of underlying statistical models often hinders the comparison of complete prokaryotic genomes. As an alternative, alignment-free solutions are more efficient, but cannot map (align) a query to subject genomes. To address this problem, we have developed gmos (Genome MOsaic Structure), a new program that determines the mosaic structure of query genomes when compared to a set of closely related subject genomes. The program first computes local alignments between query and subject genomes and then reconstructs the query mosaic structure by choosing the best local alignment for each query region. To accomplish the analysis quickly, the program mostly relies on pairwise alignments and constructs multiple sequence alignments over short overlapping subject regions only when necessary. This fine-tuned implementation achieves an efficiency comparable to an alignment-free tool. The program performs well for simulated and real data sets of closely related genomes and can be used for fast recombination detection; for instance, when a new prokaryotic pathogen is discovered. As an example, gmos was used to detect genome mosaicism in a pathogenic Enterococcus faecium strain compared to seven closely related genomes. The analysis took less than two minutes on a single 2.1 GHz processor. The output is available in fasta format and can be visualized using an accessory program, gmosDraw (freely available with gmos).

Bacterial Autotransporters is a huge and assorted super family of proteins present in external boundary of the gram negative bacteria. Autotransporters consists of C-terminal beta domain, N-terminal passenger domain and a pathogenic function. The beta domain of the bacterial autotransporter is associated with pathogenicity with three types of functions namely adhesions, esterase and proteases. Identification and classification of a bacterial autotransporter is important in order to target it and hinder its ability to cause disease. Identification and classification therefore has become a challenging problem due to its homology sequence. In this paper, we ought to establish a web based tool that could identify and classify bacterial autotransporters on basis of homology with already reported bacterial autotransporters. The input required for tool amino acid sequence in FASTA format. The tool will analyze the query based on similarity with already reported sequence in database and generate output with information in terms of function, organism, class, length, protein ID and symbol. Further, this user friendly and freely accessible tool is integrated with NCBI, InterPro, Pfam, pubmed. Moreover, newly detected bacterial autotransporter can be easily added in the Bacterial Autotransporter Detector tool database with authentic reference

This report describes the miRQuest - a novel middleware available in a Web server that allows the end user to do the miRNA research in a user-friendly way. It is known that there are many prediction tools for microRNA (miRNA) identification that use different programming languages and methods to realize this task. It is difficult to understand each tool and apply it to diverse datasets and organisms available for miRNA analysis. miRQuest can easily be used by biologists and researchers with limited experience with bioinformatics. We built it using the middleware architecture on a Web platform for miRNA research that performs two main functions: i) integration of different miRNA prediction tools for miRNA identification in a user-friendly environment; and ii) comparison of these prediction tools. In both cases, the user provides sequences (in FASTA format) as an input set for the analysis and comparisons. All the tools were selected on the basis of a survey of the literature on the available tools for miRNA prediction. As results, three different cases of use of the tools are also described, where one is the miRNA identification analysis in 30 different species. Finally, miRQuest seems to be a novel and useful tool; and it is freely available for both benchmarking and miRNA identification at http://mirquest.integrativebioinformatics.me/.

Approaches in molecular biology, particularly those that deal with high-throughput sequencing of entire microbial communities (the field of metagenomics), are rapidly advancing our understanding of the composition and functional content of microbial communities involved in climate change, environmental pollution, human health, biotechnology, etc. Metagenomics provides researchers with the most complete picture of the taxonomic (i.e., what organisms are there) and functional (i.e., what are those organisms doing) composition of natively sampled microbial communities, making it possible to perform investigations that include organisms that were previously intractable to laboratory-controlled culturing; currently, these constitute the vast majority of all microbes on the planet. All organisms contained in environmental samples are sequenced in a culture-independent manner, most often with 16S ribosomal amplicon methods to investigate the taxonomic or whole-genome shotgun-based methods to investigate the functional content of sampled communities. Metagenomics allows researchers to characterize the community composition and functional content of microbial communities, but it cannot show which functional processes are active; however, near parallel developments in transcriptomics promise a dramatic increase in our knowledge in this area as well. Since 2008, MG-RAST (Meyer et al., BMC Bioinformatics 9:386, 2008) has served as a public resource for annotation and analysis of metagenomic sequence data, providing a repository that currently houses more than 150,000 data sets (containing 60+ tera-base-pairs) with more than 23,000 publically available. MG-RAST, or the metagenomics RAST (rapid annotation using subsystems technology) server makes it possible for users to upload raw metagenomic sequence data in (preferably) fastq or fasta format. Assessments of sequence quality, annotation with respect to multiple reference databases, are performed automatically with minimal input from the user (see Subheading 4 at the end of this chapter for more details). Post-annotation analysis and visualization are also possible, directly through the web interface, or with tools like matR (metagenomic analysis tools for R, covered later in this chapter) that utilize the MG-RAST API ( http://api.metagenomics.anl.gov/api.html ) to easily download data from any stage in the MG-RAST processing pipeline. Over the years, MG-RAST has undergone substantial revisions to keep pace with the dramatic growth in the number, size, and types of sequence data that accompany constantly evolving developments in metagenomics and related -omic sciences (e.g., metatranscriptomics).

paper, we present a three-layered predictor, Profinder, for identification and analysis of protein enzyme "Protease". This predictor is shaped by collecting the protease family domains represented by multiple sequence alignments and hidden morkov modeling techniques. Present study here is an attempt to develop a specific algorithm for searching particular domains in the genome sequences of these protein enzymes. Therefore, it is important for both basic research and drug discovery to consider the following two problems. Given the sequence of a protein, determine whether the protein is a protease or not? And if so, then which class of proteases? It is only on the basis of their sequence analysis, one can identify their types and also can predict their secondary or tertiary structures. User can test their sequences in fasta format for identification of proteases domain and therefore can get some insights on their fuctions and secondary structures. Besides, analysis based on phylogenetic relation of these proteases by constructing their phylogenetic trees in the light of evolution can be done. Storing all the information extracted from these sequences in a new database is another perspective of this present in-silico study.

Summary Reading frame altering genomic variants can impact gene expression levels and the structure of protein products, thus potentially inducing disease phenotypes. Current annotation approaches report the impact of such variants in the context of altered DNA sequence only; attributes of the resulting transcript, reading frame and translated protein product are not reported. To remedy this shortcoming, we present a new genetic annotation approach termed Codon Consequence Scanner (COCOS). Implemented as an Ensembl variant effect predictor (VEP) plugin, COCOS captures amino acid sequence alterations stemming from variants that produce an altered reading frame, such as stop-lost variants and small insertions and deletions (InDels). To highlight its significance, COCOS was applied to data from the 1000 Genomes Project. Transcripts affected by stop-lost variants introduce a median of 15 amino acids, while InDels have a more extensive impact with a median of 66 amino acids being incorporated. Captured sequence alterations are written out in FASTA format and can be further analyzed for impact on the underlying protein structure. Availability and Implementation COCOS is available to all users on github: https://github.com/butkiem/COCOS

FASTR3D is a web-based search tool that allows the user to fast and accurately search the PDB database for structurally similar RNAs. Currently, it allows the user to input three types of queries: (i) a PDB code of an RNA tertiary structure (default), optionally with specified residue range, (ii) an RNA secondary structure, optionally with primary sequence, in the dot-bracket notation and (iii) an RNA primary sequence in the FASTA format. In addition, the user can run FASTR3D with specifying additional filtering options: (i) the released date of RNA structures in the PDB database, and (ii) the experimental methods used to determine RNA structures and their least resolutions. In the output page, FASTR3D will show the user-queried RNA molecule, as well as user-specified options, followed by a detailed list of identified structurally similar RNAs. Particularly, when queried with RNA tertiary structures, FASTR3D provides a graphical display to show the structural superposition of the query structure and each of identified structures. FASTR3D is now available online at http://bioalgorithm.life.nctu.edu.tw/FASTR3D/.

PrDOS is a server that predicts the disordered regions of a protein from its amino acid sequence (http://prdos.hgc.jp). The server accepts a single protein amino acid sequence, in either plain text or FASTA format. The prediction system is composed of two predictors: a predictor based on local amino acid sequence information and one based on template proteins. The server combines the results of the two predictors and returns a two-state prediction (order/disorder) and a disorder probability for each residue. The prediction results are sent by e-mail, and the server also provides a web-interface to check the results.

The M-Coffee server is a web server that makes it possible to compute multiple sequence alignments (MSAs) by running several MSA methods and combining their output into one single model. This allows the user to simultaneously run all his methods of choice without having to arbitrarily choose one of them. The MSA is delivered along with a local estimation of its consistency with the individual MSAs it was derived from. The computation of the consensus multiple alignment is carried out using a special mode of the T-Coffee package [Notredame, Higgins and Heringa (T-Coffee: a novel method for fast and accurate multiple sequence alignment. J. Mol. Biol. 2000; 302: 205–217); Wallace, O'Sullivan, Higgins and Notredame (M-Coffee: combining multiple sequence alignment methods with T-Coffee. Nucleic Acids Res. 2006; 34: 1692–1699)] Given a set of sequences (DNA or proteins) in FASTA format, M-Coffee delivers a multiple alignment in the most common formats. M-Coffee is a freeware open source package distributed under a GPL license and it is available either as a standalone package or as a web service from www.tcoffee.org.

The ever increasing number of sequences in protein databases usually turns out large numbers of homologs in sequence similarity searches. While information from homology can be very useful for functional prediction based on amino acid conservation, many of these homologs usually have high levels of identity among themselves, which hinders multiple sequence alignment computation and, especially, visualization. More generally, high redundancy reduces the usability of a protein set in machine learning applications and biases statistical analyses. We developed an algorithm to identify redundant sequence homologs that can be culled producing a streamlined FASTA file. As a difference from other automatic approaches that only aggregate sequences with high identity, our method clusters near-full length homologs allowing for lower sequence identity thresholds. Our method was fully tested and implemented in a web application called FASTA Herder, publicly available at http://fh.ogic.ca/.

Wood, which consists mainly of plant cell walls, is an extremely important resource in daily lives. Genes whose products participate in the processes of cell wall and wood formation are therefore major subjects of plant science research. The Wood-Formation Related Genes database (WFRGdb, http://me.lzu.edu.cn/woodformation/) serves as a data resource center for genes involved in wood formation. To create this database, we collected plant genome data published in other online databases and predicted all cell wall and wood formation related genes using BLAST and HMMER. To date, 47 gene families and 33 transcription factors from 57 genomes (28 herbaceous, 22 woody and 7 non-vascular plants) have been covered and more than 122,000 genes have been checked and recorded. To provide easy access to these data, we have developed several search methods, which make it easy to download targeted genes or groups of genes free of charge in FASTA format. Sequence and phylogenetic analyses are also available online. WFRGdb brings together cell wall and wood formation related genes from all available plant genomes and provides an integrative platform for gene inquiry, downloading and analysis. This database will therefore be extremely useful for those who focuses on cell wall and wood research.

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.

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.

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.

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.

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