Unique features of Plasmids among different Citrobacter species

The _Citrobacter_ plasmids are supposed to represent the host genetic association within the living bacterial cell. The plasmids impart various beneficial characteristics to the host, helping it to retain suitable characteristics for adaptation as well as evolution. The study aims at understanding the role of prophage in influencing host functional characteristics by horizontal gene transfer or as whole plasmids. The _Citrobacter_ plasmid can be understood by analyzing many hypothetical protein sequences within its genome. Our study included 82 hypothetical proteins in 5 _Citrobacter_ plasmids genomes. The function predictions in 31 hypothetical proteins and 3-D structures were predicted for 11 protein sequences using PS2 server. The probable function prediction was done by using Bioinformatics web tools like CDD-BLAST, INTERPROSCAN, PFAM and COGs by searching sequence databases for the presence of orthologous enzymatic conserved domains in the hypothetical sequences. This study identified many uncharacterized proteins, whose roles are yet to be discovered in _Citrobacter_ plasmids. These results for unknown proteins within plasmids can be used in linking the genetic interactions of _Citrobacter_ species and their functions in different environmental conditions

Predicting protein-protein interactions as a one-class classification problem

Protein-protein interactions represent a key step in understanding proteins functions. This is due to the fact that proteins usually work in context of other proteins and rarely function alone. Machine learning techniques have been used to predict protein-protein interactions. However, most of these techniques address this problem as a binary classification problem. While it is easy to get a dataset of interacting protein as positive example, there is no experimentally confirmed non-interacting protein to be considered as a negative set. Therefore, in this paper we solve this problem as a one-class classification problem using One-Class SVM (OCSVM). Using only positive examples (interacting protein pairs) for training, the OCSVM achieves accuracy of 80%. These results imply that protein-protein interaction can be predicted using one-class classifier with reliable accuracy

A molecular insight into algal-oomycete warfare : cDNA analysis of Ectocarpus siliculosus infected with the basal oomycete Eurychasma dicksonii

Peer reviewedPublisher PD

Curated genome annotation of Oryza sativa ssp. japonica and comparative genome analysis with Arabidopsis thaliana

We present here the annotation of the complete genome of rice Oryza sativa L. ssp. japonica cultivar Nipponbare. All functional annotations for proteins and non-protein-coding RNA (npRNA) candidates were manually curated. Functions were identified or inferred in 19,969 (70%) of the proteins, and 131 possible npRNAs (including 58 antisense transcripts) were found. Almost 5000 annotated protein-coding genes were found to be disrupted in insertional mutant lines, which will accelerate future experimental validation of the annotations. The rice loci were determined by using cDNA sequences obtained from rice and other representative cereals. Our conservative estimate based on these loci and an extrapolation suggested that the gene number of rice is ~32,000, which is smaller than previous estimates. We conducted comparative analyses between rice and Arabidopsis thaliana and found that both genomes possessed several lineage-specific genes, which might account for the observed differences between these species, while they had similar sets of predicted functional domains among the protein sequences. A system to control translational efficiency seems to be conserved across large evolutionary distances. Moreover, the evolutionary process of protein-coding genes was examined. Our results suggest that natural selection may have played a role for duplicated genes in both species, so that duplication was suppressed or favored in a manner that depended on the function of a gene

Transcriptome analysis of Taenia solium cysticerci using Open reading Frame ESTS (ORESTES)

<p>Abstract</p> <p>Background</p> <p>Human infection by the pork tapeworm <it>Taenia solium </it>affects more than 50 million people worldwide, particularly in underdeveloped and developing countries. Cysticercosis which arises from larval encystation can be life threatening and difficult to treat. Here, we investigate for the first time the transcriptome of the clinically relevant cysticerci larval form.</p> <p>Results</p> <p>Using Expressed Sequence Tags (ESTs) produced by the ORESTES method, a total of 1,520 high quality ESTs were generated from 20 ORESTES cDNA mini-libraries and its analysis revealed fragments of genes with promising applications including 51 ESTs matching antigens previously described in other species, as well as 113 sequences representing proteins with potential extracellular localization, with obvious applications for immune-diagnosis or vaccine development.</p> <p>Conclusion</p> <p>The set of sequences described here will contribute to deciphering the expression profile of this important parasite and will be informative for the genome assembly and annotation, as well as for studies of intra- and inter-specific sequence variability. Genes of interest for developing new diagnostic and therapeutic tools are described and discussed.</p

MorphDB : prioritizing genes for specialized metabolism pathways and gene ontology categories in plants

Recent times have seen an enormous growth of "omics" data, of which high-throughput gene expression data are arguably the most important from a functional perspective. Despite huge improvements in computational techniques for the functional classification of gene sequences, common similarity-based methods often fall short of providing full and reliable functional information. Recently, the combination of comparative genomics with approaches in functional genomics has received considerable interest for gene function analysis, leveraging both gene expression based guilt-by-association methods and annotation efforts in closely related model organisms. Besides the identification of missing genes in pathways, these methods also typically enable the discovery of biological regulators (i.e., transcription factors or signaling genes). A previously built guilt-by-association method is MORPH, which was proven to be an efficient algorithm that performs particularly well in identifying and prioritizing missing genes in plant metabolic pathways. Here, we present MorphDB, a resource where MORPH-based candidate genes for large-scale functional annotations (Gene Ontology, MapMan bins) are integrated across multiple plant species. Besides a gene centric query utility, we present a comparative network approach that enables researchers to efficiently browse MORPH predictions across functional gene sets and species, facilitating efficient gene discovery and candidate gene prioritization. MorphDB is available at http://bioinformatics.psb.ugent.be/webtools/morphdb/morphDB/index/. We also provide a toolkit, named "MORPH bulk" (https://github.com/arzwa/morph-bulk), for running MORPH in bulk mode on novel data sets, enabling researchers to apply MORPH to their own species of interest

Dramatic expansion of the black widow toxin arsenal uncovered by multi-tissue transcriptomics and venom proteomics.

BackgroundAnimal venoms attract enormous interest given their potential for pharmacological discovery and understanding the evolution of natural chemistries. Next-generation transcriptomics and proteomics provide unparalleled, but underexploited, capabilities for venom characterization. We combined multi-tissue RNA-Seq with mass spectrometry and bioinformatic analyses to determine venom gland specific transcripts and venom proteins from the Western black widow spider (Latrodectus hesperus) and investigated their evolution.ResultsWe estimated expression of 97,217 L. hesperus transcripts in venom glands relative to silk and cephalothorax tissues. We identified 695 venom gland specific transcripts (VSTs), many of which BLAST and GO term analyses indicate may function as toxins or their delivery agents. ~38% of VSTs had BLAST hits, including latrotoxins, inhibitor cystine knot toxins, CRISPs, hyaluronidases, chitinase, and proteases, and 59% of VSTs had predicted protein domains. Latrotoxins are venom toxins that cause massive neurotransmitter release from vertebrate or invertebrate neurons. We discovered ≥ 20 divergent latrotoxin paralogs expressed in L. hesperus venom glands, significantly increasing this biomedically important family. Mass spectrometry of L. hesperus venom identified 49 proteins from VSTs, 24 of which BLAST to toxins. Phylogenetic analyses showed venom gland specific gene family expansions and shifts in tissue expression.ConclusionsQuantitative expression analyses comparing multiple tissues are necessary to identify venom gland specific transcripts. We present a black widow venom specific exome that uncovers a trove of diverse toxins and associated proteins, suggesting a dynamic evolutionary history. This justifies a reevaluation of the functional activities of black widow venom in light of its emerging complexity

Draft Genome Sequence of the Yeast Rhodotorula sp. Strain CCFEE 5036, Isolated from McMurdo Dry Valleys, Antarctica.

A draft genome sequence was assembled and annotated of the basidiomycetous yeast Rhodotorula sp. strain CCFEE 5036, isolated from Antarctic soil communities. The genome assembly is 19.07 megabases and encodes 6,434 protein-coding genes. The sequence will contribute to understanding the diversity of fungi inhabiting polar regions

AGMIAL: implementing an annotation strategy for prokaryote genomes as a distributed system

We have implemented a genome annotation system for prokaryotes called AGMIAL. Our approach embodies a number of key principles. First, expert manual annotators are seen as a critical component of the overall system; user interfaces were cyclically refined to satisfy their needs. Second, the overall process should be orchestrated in terms of a global annotation strategy; this facilitates coordination between a team of annotators and automatic data analysis. Third, the annotation strategy should allow progressive and incremental annotation from a time when only a few draft contigs are available, to when a final finished assembly is produced. The overall architecture employed is modular and extensible, being based on the W3 standard Web services framework. Specialized modules interact with two independent core modules that are used to annotate, respectively, genomic and protein sequences. AGMIAL is currently being used by several INRA laboratories to analyze genomes of bacteria relevant to the food-processing industry, and is distributed under an open source license

A practical, bioinformatic workflow system for large data sets generated by next-generation sequencing

Transcriptomics (at the level of single cells, tissues and/or whole organisms) underpins many fields of biomedical science, from understanding the basic cellular function in model organisms, to the elucidation of the biological events that govern the development and progression of human diseases, and the exploration of the mechanisms of survival, drug-resistance and virulence of pathogens. Next-generation sequencing (NGS) technologies are contributing to a massive expansion of transcriptomics in all fields and are reducing the cost, time and performance barriers presented by conventional approaches. However, bioinformatic tools for the analysis of the sequence data sets produced by these technologies can be daunting to researchers with limited or no expertise in bioinformatics. Here, we constructed a semi-automated, bioinformatic workflow system, and critically evaluated it for the analysis and annotation of large-scale sequence data sets generated by NGS. We demonstrated its utility for the exploration of differences in the transcriptomes among various stages and both sexes of an economically important parasitic worm (Oesophagostomum dentatum) as well as the prediction and prioritization of essential molecules (including GTPases, protein kinases and phosphatases) as novel drug target candidates. This workflow system provides a practical tool for the assembly, annotation and analysis of NGS data sets, also to researchers with a limited bioinformatic expertise. The custom-written Perl, Python and Unix shell computer scripts used can be readily modified or adapted to suit many different applications. This system is now utilized routinely for the analysis of data sets from pathogens of major socio-economic importance and can, in principle, be applied to transcriptomics data sets from any organism