Project description:We sequenced the complete genome of Felis catus gammaherpesvirus 1 (FcaGHV1) from lymph node DNA of an infected cat. The genome includes a 121,556-nucleotide unique region with 87 predicted open reading frames (61 gammaherpesvirus conserved and 26 unique) flanked by multiple copies of a 966-nucleotide terminal repeat.
Project description:The sourmash software package uses MinHash-based sketching to create "signatures", compressed representations of DNA, RNA, and protein sequences, that can be stored, searched, explored, and taxonomically annotated. sourmash signatures can be used to estimate sequence similarity between very large data sets quickly and in low memory, and can be used to search large databases of genomes for matches to query genomes and metagenomes. sourmash is implemented in C++, Rust, and Python, and is freely available under the BSD license at http://github.com/dib-lab/sourmash.
Project description:Analysis of sequence variation among members of a single species offers a potential approach to identify functional DNA elements responsible for biological features unique to that species. Due to its high rate of allelic polymorphism and ease of genetic manipulability, we chose the sea squirt, Ciona intestinalis, to explore intraspecies sequence comparisons for genome annotation. A large number of C. intestinalis specimens were collected from four continents, and a set of genomic intervals were amplified, resequenced, and analyzed to determine the mutation rates at each nucleotide in the sequence. We found that regions with low mutation rates efficiently demarcated functionally constrained sequences: these include a set of noncoding elements, which we showed in C. intestinalis transgenic assays to act as tissue-specific enhancers, as well as the location of coding sequences. This illustrates that comparisons of multiple members of a species can be used for genome annotation, suggesting a path for the annotation of the sequenced genomes of organisms occupying uncharacterized phylogenetic branches of the animal kingdom. It also raises the possibility that the resequencing of a large number of Homo sapiens individuals might be used to annotate the human genome and identify sequences defining traits unique to our species.
Project description:The complete genome sequence of the alcelaphine gammaherpesvirus 1 (AIHV-1) attenuated laboratory strain WC11 was determined from purified virion DNA. The viral light DNA (L-DNA) genome of 127,215 bp is mostly conserved compared to the pathogenic strain C500; however, 3.3 kb is deleted in two regions, affecting 4 of 10 AIHV-1-specific open reading frames.
Project description:BackgroundIn this work a simple method for the computation of relative similarities between homologous metabolic network modules is presented. The method is similar to classical sequence alignment and allows for the generation of phenotypic trees amenable to be compared with correspondent sequence based trees. The procedure can be applied to both single metabolic modules and whole metabolic network data without the need of any specific assumption.ResultsWe demonstrate both the ability of the proposed method to build reliable biological classification of a set of microorganisms and the strong correlation between the metabolic network wiring and involved enzymes sequence space.ConclusionThe method represents a valuable tool for the investigation of genotype/phenotype correlations allowing for a direct comparison of different species as for their metabolic machinery. In addition the detection of enzymes whose sequence space is maximally correlated with the metabolic network space gives an indication of the most crucial (on an evolutionary viewpoint) steps of the metabolic process.
Project description:Murine gammaherpesvirus 68 (gammaHV68) infects mice, thus providing a tractable small-animal model for analysis of the acute and chronic pathogenesis of gammaherpesviruses. To facilitate molecular analysis of gammaHV68 pathogenesis, we have sequenced the gammaHV68 genome. The genome contains 118,237 bp of unique sequence flanked by multiple copies of a 1,213-bp terminal repeat. The GC content of the unique portion of the genome is 46%, while the GC content of the terminal repeat is 78%. The unique portion of the genome is estimated to encode at least 80 genes and is largely colinear with the genomes of Kaposi's sarcoma herpesvirus (KSHV; also known as human herpesvirus 8), herpesvirus saimiri (HVS), and Epstein-Barr virus (EBV). We detected 63 open reading frames (ORFs) homologous to HVS and KSHV ORFs and used the HVS/KSHV numbering system to designate these ORFs. gammaHV68 shares with HVS and KSHV ORFs homologous to a complement regulatory protein (ORF 4), a D-type cyclin (ORF 72), and a G-protein-coupled receptor with close homology to the interleukin-8 receptor (ORF 74). One ORF (K3) was identified in gammaHV68 as homologous to both ORFs K3 and K5 of KSHV and contains a domain found in a bovine herpesvirus 4 major immediate-early protein. We also detected 16 methionine-initiated ORFs predicted to encode proteins at least 100 amino acids in length that are unique to gammaHV68 (ORFs M1 to 14). ORF M1 has striking homology to poxvirus serpins, while ORF M11 encodes a potential homolog of Bcl-2-like molecules encoded by other gammaherpesviruses (gene 16 of HVS and KSHV and the BHRF1 gene of EBV). In addition, clustered at the left end of the unique region are eight sequences with significant homology to bacterial tRNAs. The unique region of the genome contains two internal repeats: a 40-bp repeat located between bp 26778 and 28191 in the genome and a 100-bp repeat located between bp 98981 and 101170. Analysis of the gammaHV68, HVS, EBV, and KSHV genomes demonstrated that each of these viruses have large colinear gene blocks interspersed by regions containing virus-specific ORFs. Interestingly, genes associated with EBV cell tropism, latency, and transformation are all contained within these regions encoding virus-specific genes. This finding suggests that pathogenesis-associated genes of gammaherpesviruses, including gammaHV68, may be contained in similarly positioned genome regions. The availability of the gammaHV68 genomic sequence will facilitate analysis of critical issues in gammaherpesvirus biology via integration of molecular and pathogenetic studies in a small-animal model.
Project description:A herpesvirus genome was sequenced directly from a biopsy specimen of a rectal lesion from a female common bottlenose dolphin. This genome sequence comprises a unique region (161,235 bp) flanked by multiple copies of a terminal repeat (4,431 bp) and contains 72 putative genes. The virus was named common bottlenose dolphin gammaherpesvirus 1.
Project description:Similarity-based search of sequence collections is a core task in bioinformatics, one dominated for most of the genomic era by exact and heuristic alignment-based algorithms. However, even efficient heuristics such as BLAST may not scale to the data sets now emerging, motivating a range of alignment-free alternatives exploiting the underlying lexical structure of each sequence. In this paper, we introduce two supervised approaches-SuperVec and SuperVecX-to learn sequence embeddings. These methods extend earlier Representation Learning (RepL) based methods to include class-related information for each sequence during training. Including class information ensures that related sequence fragments have proximal representations in the target space, better reflecting the structure of the domain. We show the quality of the embeddings learned through these methods on (i) sequence retrieval and (ii) classification tasks. We also propose an hierarchical tree-based approach specifically designed for the sequence retrieval problem. The resulting methods, which we term H-SuperVec or H-SuperVecX, according to their respective use of SuperVec or SuperVecX, learn embeddings across a range of feature spaces based on exclusive and exhaustive subsets of the class labels. Experiments show that the proposed methods perform better for retrieval and classification tasks over existing (unsupervised) RepL-based approaches. Further, the new methods are an order of magnitude faster than BLAST for the database retrieval task, supporting hybrid approaches that rapidly filter the collection so that only potentially relevant records remain. Such filtering of the original database allows slower but more accurate methods to be executed quickly over a far smaller dataset. Thus, we may achieve faster query processing and higher precision than before.
Project description:The increasing incidence of opportunistic fungal infections necessitates rapid and accurate identification of the associated fungi to facilitate optimal patient treatment. Traditional phenotype-based identification methods utilized in clinical laboratories rely on the production and recognition of reproductive structures, making identification difficult or impossible when these structures are not observed. We hypothesized that DNA sequence analysis of multiple loci is useful for rapidly identifying medically important molds. Our study included the analysis of the D1/D2 hypervariable region of the 28S ribosomal gene and the internal transcribed spacer (ITS) regions 1 and 2 of the rRNA operon. Two hundred one strains, including 143 clinical isolates and 58 reference and type strains, representing 43 recognized species and one possible new species, were examined. We generated a phenotypically validated database of 118 diagnostic alleles. DNA length polymorphisms detected among ITS1 and ITS2 PCR products can differentiate 20 of 33 species of molds tested, and ITS DNA sequence analysis permits identification of all species tested. For 42 of 44 species tested, conspecific strains displayed >99% sequence identity at ITS1 and ITS2; sequevars were detected in two species. For all 44 species, identifications by genotypic and traditional phenotypic methods were 100% concordant. Because dendrograms based on ITS sequence analysis are similar in topology to 28S-based trees, we conclude that ITS sequences provide phylogenetically valid information and can be utilized to identify clinically important molds. Additionally, this phenotypically validated database of ITS sequences will be useful for identifying new species of pathogenic molds.
Project description:BackgroundThe amino acid sequence of a protein is the blueprint from which its structure and ultimately function can be derived. Therefore, sequence comparison methods remain essential for the determination of similarity between proteins. Traditional approaches for comparing two protein sequences begin with strings of letters (amino acids) that represent the sequences, before generating textual alignments between these strings and providing scores for each alignment. When the similitude between the two protein sequences to be compared is low however, the quality of the corresponding sequence alignment is usually poor, leading to poor performance for the recognition of similarity.ResultsIn this study, we develop an alignment free alternative to these methods that is based on the concept of string kernels. Starting from recently proposed kernels on the discrete space of protein sequences (Shen et al, Found. Comput. Math., 2013,14:951-984), we introduce our own version, SeqKernel. Its implementation depends on two parameters, a coefficient that tunes the substitution matrix and the maximum length of k-mers that it includes. We provide an exhaustive analysis of the impacts of these two parameters on the performance of SeqKernel for fold recognition. We show that with the right choice of parameters, use of the SeqKernel similarity measure improves fold recognition compared to the use of traditional alignment-based methods. We illustrate the application of SeqKernel to inferring phylogeny on RNA polymerases and show that it performs as well as methods based on multiple sequence alignments.ConclusionWe have presented and characterized a new alignment free method based on a mathematical kernel for scoring the similarity of protein sequences. We discuss possible improvements of this method, as well as an extension of its applications to other modeling methods that rely on sequence comparison.