Project description:Functional protein-coding small open reading frames (smORFs) are emerging as an important class of genes. However, the number of translated smORFs in the human genome is unclear because proteogenomic methods are not sensitive enough, and, as we show, Ribo-seq strategies require additional measures to ensure comprehensive and accurate smORF annotation. Here, we integrate de novo transcriptome assembly and Ribo-seq into an improved workflow that overcomes obstacles with previous methods, to more confidently annotate thousands of smORFs. Evolutionary conservation analyses suggest that hundreds of smORF-encoded microproteins are likely functional. Additionally, many smORFs are regulated during fundamental biological processes, such as cell stress. Peptides derived from smORFs are also detectable on human leukocyte antigen complexes, revealing smORFs as a source of antigens. Thus, by including additional validation into our smORF annotation workflow, we accurately identify thousands of unannotated translated smORFs that will provide a rich pool of unexplored, functional human genes.
Project description:Genome-wide tiling array experiments are increasingly used for the analysis of DNA methylation. Because DNA methylation patterns are tissue and cell type specific, the detection of differentially methylated regions (DMRs) with small effect size is a necessary feature of tiling microarray 'peak' finding algorithms, as cellular heterogeneity within a studied tissue may lead to a dilution of the phenotypically relevant effects. Additionally, the ability to detect short length DMRs is necessary as biologically relevant signal may occur in focused regions throughout the genome.We present a free open-source Perl application, Binding Intensity Only Tile array analysis or "BioTile", for the identification of differentially enriched regions (DERs) in tiling array data. The application of BioTile to non-smoothed data allows for the identification of shorter length and smaller effect-size DERs, while correcting for probe specific variation by inversely weighting on probe variance through a permutation corrected meta-analysis procedure employed at identified regions. BioTile exhibits higher power to identify significant DERs of low effect size and across shorter genomic stretches as compared to other peak finding algorithms, while not sacrificing power to detect longer DERs.BioTile represents an easy to use analysis option applicable to multiple microarray platforms, allowing for its integration into the analysis workflow of array data analysis.
Project description:With ever-increasing numbers of microbial genomes being sequenced, efficient tools are needed to perform strain-level identification of any newly sequenced genome. Here, we present the SNP identification for strain typing (SNIT) pipeline, a fast and accurate software system that compares a newly sequenced bacterial genome with other genomes of the same species to identify single nucleotide polymorphisms (SNPs) and small insertions/deletions (indels). Based on this information, the pipeline analyzes the polymorphic loci present in all input genomes to identify the genome that has the fewest differences with the newly sequenced genome. Similarly, for each of the other genomes, SNIT identifies the input genome with the fewest differences. Results from five bacterial species show that the SNIT pipeline identifies the correct closest neighbor with 75% to 100% accuracy. The SNIT pipeline is available for download at http://www.bhsai.org/snit.html.
Project description:Inducible gene expression systems are vital tools for the advancement of synthetic biology. Their application as genetically encoded biosensors has the potential to contribute to diagnostics and to revolutionise the field of microbial cell factory development. Currently, the number of compounds of biological interest by far exceeds the number of available biosensors. Here, we address this limitation by developing a generic genome-wide approach to identify transcription factor-based inducible gene expression systems. We construct and validate 15 functional biosensors, provide a characterisation workflow to facilitate forward engineering efforts, exemplify their broad-host-range applicability, and demonstrate their utility in enzyme screening. Previously uncharacterised interactions between sensors and compounds of biological relevance are identified by employing the largest reported library of metabolite-responsive biosensors in an automated high-throughput screen. With the rapidly growing genomic data these innovative capabilities offer a platform to vastly increase the number of biologically detectable molecules.
Project description:Streptococcus mutans UA159, the genome sequence reference strain, exhibits nonlantibiotic mutacin activity. In this study, bioinformatic and mutational analyses were employed to demonstrate that the antimicrobial repertoire of strain UA159 includes mutacin IV (specified by the nlm locus) and a newly identified bacteriocin, mutacin V (encoded by SMU.1914c).
Project description:We have designed and developed a data integration and visualization platform that provides evidence about the association of known and potential drug targets with diseases. The platform is designed to support identification and prioritization of biological targets for follow-up. Each drug target is linked to a disease using integrated genome-wide data from a broad range of data sources. The platform provides either a target-centric workflow to identify diseases that may be associated with a specific target, or a disease-centric workflow to identify targets that may be associated with a specific disease. Users can easily transition between these target- and disease-centric workflows. The Open Targets Validation Platform is accessible at https://www.targetvalidation.org.
Project description:BACKGROUND: The piggyBac transposon system provides a powerful forward genetics tool to study gene function in Plasmodium parasites via random insertion mutagenesis and phenotypic screening. The identification of genotype of piggyBac mutants in the Plasmodium genome is thus an indispensable step in forward genetic analysis. Several PCR-based approaches have been used to identify the piggyBac insertion sites in Plasmodium falciparum and Plasmodium berghei, but all are tedious and inefficient. Next generation sequencing can produce large amounts of sequence data and is particularly suitable for genome-wide association studies. In this study, the Next generation sequencing technology was employed to efficiently identify piggyBac insertion sites in the genome of P. berghei. METHODS: Plasmodium berghei parasites were co-transfected with piggyBac donor and helper plasmids. Initially, the classical inverse PCR method was used to identify the existence of piggyBac insertions in the P. berghei genome. The whole genome of post-transfection parasites was subsequently sequenced with a PCR-free paired-end module using the Illumina HiSeq sequencing system. The two distinct methods ('BLAST method' and 'SOAP method') were employed to identify piggyBac insertion sites in the P. berghei genome with Illumina sequencing data. All the identified piggyBac insertions were further tested by half-nested PCR. RESULTS: The inverse PCR method resulted in a very low yield of ten individual insertions identified. Conversely, 47 piggyBac insertions were identified from about 1 Gb of Illumina sequencing data via the two distinct analysis methods. The majority of identified piggyBac insertions were confirmed by half-nested PCR. In addition, 1,850 single nucleotide polymorphisms were identified through alignment of the Illumina sequencing data of the P. berghei ANKA strain used in this study with the reference genome sequences. CONCLUSION: This study demonstrates that a high-throughput genome sequencing approach is an efficient tool for the identification of piggyBac-mediated insertions in Plasmodium parasites.
Project description:Advances in sequencing technology have drastically increased the depth and feasibility of bacterial genome sequencing. However, little information is available that details the specific techniques and procedures employed during genome sequencing despite the large numbers of published genomes. Shotgun approaches employed by second-generation sequencing platforms has necessitated the development of robust bioinformatics tools for in silico assembly, and complete assembly is limited by the presence of repetitive DNA sequences and multi-copy operons. Typically, re-sequencing with multiple platforms and laborious, targeted Sanger sequencing are employed to finish a draft bacterial genome. Here we describe a novel strategy based on the identification and targeted sequencing of repetitive rDNA operons to expedite bacterial genome assembly and finishing. Our strategy was validated by finishing the genome of Paenibacillus polymyxa strain CR1, a bacterium with potential in sustainable agriculture and bio-based processes. An analysis of the 38 contigs contained in the P. polymyxa strain CR1 draft genome revealed 12 repetitive rDNA operons with varied intragenic and flanking regions of variable length, unanimously located at contig boundaries and within contig gaps. These highly similar but not identical rDNA operons were experimentally verified and sequenced simultaneously with multiple, specially designed primer sets. This approach also identified and corrected significant sequence rearrangement generated during the initial in silico assembly of sequencing reads. Our approach reduces the required effort associated with blind primer walking for contig assembly, increasing both the speed and feasibility of genome finishing. Our study further reinforces the notion that repetitive DNA elements are major limiting factors for genome finishing. Moreover, we provided a step-by-step workflow for genome finishing, which may guide future bacterial genome finishing projects.
Project description:Predicting functions of proteins and alternatively spliced isoforms encoded in a genome is one of the important applications of bioinformatics in the post-genome era. Due to the practical limitation of experimental characterization of all proteins encoded in a genome using biochemical studies, bioinformatics methods provide powerful tools for function annotation and prediction. These methods also help minimize the growing sequence-to-function gap. Phylogenetic profiling is a bioinformatics approach to identify the influence of a trait across species and can be employed to infer the evolutionary history of proteins encoded in genomes. Here we propose an improved phylogenetic profile-based method which considers the co-evolution of the reference genome to derive the basic similarity measure, the background phylogeny of target genomes for profile generation and assigning weights to target genomes. The ordering of genomes and the runs of consecutive matches between the proteins were used to define phylogenetic relationships in the approach. We used Escherichia coli K12 genome as the reference genome and its 4195 proteins were used in the current analysis. We compared our approach with two existing methods and our initial results show that the predictions have outperformed two of the existing approaches. In addition, we have validated our method using a targeted protein-protein interaction network derived from protein-protein interaction database STRING. Our preliminary results indicates that improvement in function prediction can be attained by using coevolution-based similarity measures and the runs on to the same scale instead of computing them in different scales. Our method can be applied at the whole-genome level for annotating hypothetical proteins from prokaryotic genomes.
Project description:Cell wall-associated (CWA) proteins made by Gram-positive pathogens play a fundamental role in pathogenesis. Staphylococcus pseudintermedius is a major animal pathogen responsible for the canine skin disease bacterial pyoderma. Here, we describe the bioinformatic analysis of the family of 18 predicted CWA proteins encoded in the genome of S. pseudintermedius strain ED99 and determine their distribution among a phylogenetically diverse panel of S. pseudintermedius clinical isolates and closely related species of the Staphylococcus intermedius group. In parallel, we employed a proteomic approach to identify proteins presented on the surface of strain ED99 in vitro, revealing a total of 60 surface-localized proteins in one or more phases of growth, including 6 of the 18 genome-predicted CWA proteins. Based on these analyses, we selected two CWA proteins (SpsD and SpsL) encoded by all strains examined and investigated their capacity to mediate adherence to extracellular matrix proteins. We discovered that SpsD and SpsL mediated binding of a heterologous host, Lactococcus lactis, to fibrinogen and fibronectin and that SpsD mediated binding to cytokeratin 10, a major constituent of mammalian skin. Of note, the interaction with fibrinogen was host-species dependent, suggestive of a role for SpsD and SpsL in the host tropism of S. pseudintermedius. Finally, we identified IgG specific for SpsD and SpsL in sera from dogs with bacterial pyoderma, implying that both proteins are expressed during infection. The combined genomic and proteomic approach employed in the current study has revealed novel host-pathogen interactions which represent candidate therapeutic targets for the control of bacterial pyoderma.