Systems approach to refining genome annotation.
Ontology highlight
ABSTRACT:
SUBMITTER: Reed JL
PROVIDER: S-EPMC1859954 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundAccurate bacterial genome annotations provide a framework to understanding cellular functions, behavior and pathogenicity and are essential for metabolic engineering. Annotations based only on in silico predictions are inaccurate, particularly for large, high G + C content genomes due to the lack of similarities in gene length and gene organization to model organisms.ResultsHere we describe a 2D systems biology driven re-annotation of the Saccharopolyspora erythraea genome using proteogenomics, a genome-scale metabolic reconstruction, RNA-sequencing and small-RNA-sequencing. We observed transcription of more than 300 intergenic regions, detected 59 peptides in intergenic regions, confirmed 164 open reading frames previously annotated as hypothetical proteins and reassigned function to open reading frames using the genome-scale metabolic reconstruction. Finally, we present a novel way of mapping ribosomal binding sites across the genome by sequencing small RNAs.ConclusionsThe work presented here describes a novel framework for annotation of the Saccharopolyspora erythraea genome. Based on experimental observations, the 2D annotation framework greatly reduces errors that are commonly made when annotating large-high G + C content genomes using computational prediction algorithms.
| S-EPMC4008361 | biostudies-literature
Project description:We collected a total of 9.8 million mass spectra generated in the laboratory from the proteomics analyses of different silkworm tissues, including the posterior silk gland (PSG) 30, middle silk gland 31, ovary and testis 32, head 33, brain, prothoracic glands, subesophageal ganglion 34, hemolymph 35, fat body 36 and embryo 37,38 of domestic silkworm, and the posterior silk gland of wild silkworm 39.
2019-07-01 | PXD009697 | Pride
Project description:We collected a total of 9.8 million mass spectra data generated in the laboratory from the proteomics analyses of different silkworm tissues, including the posterior silk gland 29, middle silk gland 30, ovary and testis 31, head 32, brain, prothoracic glands, subesophageal ganglion 33, hemolymph 34, fat body 35 and embryo 36,37 of domestic silkworm, and the posterior silk gland of wild silkworm 38.
2019-07-01 | PXD009672 | Pride
Project description:Cellular signalling pathways consolidate multiple molecular interactions into working models of signal propagation, amplification, and modulation. They are described and visualized as networks. Adjusting network topologies to experimental data is a key goal of systems biology. While network reconstruction algorithms like nested effects models are well established tools of computational biology, their data requirements can be prohibitive for their practical use. In this paper we suggest focussing on well defined aspects of a pathway and develop the computational tools to do so. We adapt the framework of nested effect models to focus on a specific aspect of activated Wnt signalling in HCT116 colon cancer cells: Does the activation of Wnt target genes depend on the secretion of Wnt ligands or do mutations in the signalling molecule ?-catenin make this activation independent from them? We framed this question into two competing classes of models: Models that depend on Wnt ligands secretion versus those that do not. The model classes translate into restrictions of the pathways in the network topology. Wnt dependent models are more flexible than Wnt independent models. Bayes factors are the standard Bayesian tool to compare different models fairly on the data evidence. In our analysis, the Bayes factors depend on the number of potential Wnt signalling target genes included in the models. Stability analysis with respect to this number showed that the data strongly favours Wnt ligands dependent models for all realistic numbers of target genes.
| S-EPMC4889067 | biostudies-literature
Project description:We have developed a systematic approach to generating cDNA clones containing full-length open reading frames (ORFs), exploiting knowledge of gene structure from genomic sequence. Each ORF was amplified by PCR from a pool of primary cDNAs, cloned and confirmed by sequencing. We obtained clones representing 70% of genes on human chromosome 22, whereas searching available cDNA clone collections found at best 48% from a single collection and 60% for all collections combined.
| S-EPMC545604 | biostudies-literature
Project description:BACKGROUND:Today large scale genome sequencing technologies are uncovering an increasing amount of new genes and proteins, which remain uncharacterized. Experimental procedures for protein function prediction are low throughput by nature and thus can't be used to keep up with the rate at which new proteins are discovered. On the other hand, proteins are the prominent stakeholders in almost all biological processes, and therefore the need to precisely know their functions for a better understanding of the underlying biological mechanism is inevitable. The challenge of annotating uncharacterized proteins in functional genomics and biology in general motivates the use of computational techniques well orchestrated to accurately predict their functions. METHODS:We propose a computational flow for the functional annotation of a protein able to assign the most probable functions to a protein by aggregating heterogeneous information. Considered information include: protein motifs, protein sequence similarity, and protein homology data gathered from interacting proteins, combined with data from highly similar non-interacting proteins (hereinafter called Similactors). Moreover, to increase the predictive power of our model we also compute and integrate term specific relationships among functional terms based on Gene Ontology (GO). RESULTS:We tested our method on Saccharomyces Cerevisiae and Homo sapiens species proteins. The aggregation of different structural and functional evidence with GO relationships outperforms, in terms of precision and accuracy of prediction than the other methods reported in literature. The predicted precision and accuracy is 100% for more than half of the input set for both species; overall, we obtained 85.38% precision and 81.95% accuracy for Homo sapiens and 79.73% precision and 80.06% accuracy for Saccharomyces Cerevisiae species proteins.
| S-EPMC3909112 | biostudies-literature
Project description:Viral metagenomics, also known as virome studies, have yielded an unprecedented number of novel sequences, essential in recognizing and characterizing the etiological agent and the origin of emerging infectious diseases. Several tools and pipelines have been developed, to date, for the identification and assembly of viral genomes. Assembly pipelines often result in viral genomes contaminated with host genetic material, some of which are currently deposited into public databases. In the current report, we present a group of deposited sequences that encompass ribosomal RNA (rRNA) contamination. We highlight the detrimental role of chimeric next generation sequencing reads, between host rRNA sequences and viral sequences, in virus genome assembly and we present the hindrances these reads may pose to current methodologies. We have further developed a refining pipeline, the Zero Waste Algorithm (ZWA) that assists in the assembly of low abundance viral genomes. ZWA performs context-depended trimming of chimeric reads, precisely removing their rRNA moiety. These, otherwise discarded, reads were fed to the assembly pipeline and assisted in the construction of larger and cleaner contigs making a substantial impact on current assembly methodologies. ZWA pipeline may significantly enhance virus genome assembly from low abundance samples and virus metagenomics approaches in which a small number of reads determine genome quality and integrity.
| S-EPMC8376068 | biostudies-literature
Project description:Functionality of the non-coding transcripts encoded by the human genome is the coveted goal of the modern genomics research. While commonly relied on the classical methods of forward genetics, integration of different genomics datasets in a global Systems Biology fashion presents a more productive avenue of achieving this very complex aim. Here we report application of a Systems Biology-based approach to dissect functionality of a newly identified vast class of very long intergenic non-coding (vlinc) RNAs. Using highly quantitative FANTOM5 CAGE dataset, we show that these RNAs could be grouped into 1542 novel human genes based on analysis of insulators that we show here indeed function as genomic barrier elements. We show that vlinc RNAs genes likely function in cisto activate nearby genes. This effect while most pronounced in closely spaced vlinc RNA-gene pairs can be detected over relatively large genomic distances. Furthermore, we identified 101 vlinc RNA genes likely involved in early embryogenesis based on patterns of their expression and regulation. We also found another 109 such genes potentially involved in cellular functions also happening at early stages of development such as proliferation, migration and apoptosis. Overall, we show that Systems Biology-based methods have great promise for functional annotation of non-coding RNAs.
| S-EPMC4838384 | biostudies-literature
Project description:The thermophilic fungus Chaetomium thermophilum holds great promise for structural biology. To increase the efficiency of its biochemical and structural characterization and to explore its thermophilic properties beyond those of individual proteins, we obtained transcriptomics and proteomics data, and integrated them with computational annotation methods and a multitude of biochemical experiments conducted by the structural biology community. We considerably improved the genome annotation of Chaetomium thermophilum and characterized the transcripts and expression of thousands of genes. We furthermore show that the composition and structure of the expressed proteome of Chaetomium thermophilum is similar to its mesophilic relatives. Data were deposited in a publicly available repository and provide a rich source to the structural biology community.
| S-EPMC4267624 | biostudies-literature
Project description:BackgroundDNA rearrangement events have been widely studied in comparative genomic for many years. The importance of these events resides not only in the study about relatedness among different species, but also to determine the mechanisms behind evolution. Although there are many methods to identify genome-rearrangements (GR), the refinement of their borders has become a huge challenge. Until now no accepted method exists to achieve accurate fine-tuning: i.e. the notion of breakpoint (BP) is still an open issue, and despite repeated regions are vital to understand evolution they are not taken into account in most of the GR detection and refinement methods.Methods and resultsWe propose a method to refine the borders of GR including repeated regions. Instead of removing these repetitions to facilitate computation, we take advantage of them using a consensus alignment sequence of the repeated region in between two blocks. Using the concept of identity vectors for Synteny Blocks (SB) and repetitions, a Finite State Machine is designed to detect transition points in the difference between such vectors. The method does not force the BP to be a region or a point but depends on the alignment transitions within the SBs and repetitions.ConclusionThe accurate definition of the borders of SB and repeated genomic regions and consequently the detection of BP might help to understand the evolutionary model of species. In this manuscript we present a new proposal for such a refinement. Features of the SBs borders and BPs are different and fit with what is expected. SBs with more diversity in annotations and BPs short and richer in DNA replication and stress response, which are strongly linked with rearrangements.
| S-EPMC5088515 | biostudies-literature