Use of artificial genomes in assessing methods for atypical gene detection.
Ontology highlight
ABSTRACT:
SUBMITTER: Azad RK
PROVIDER: S-EPMC1282332 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundThe increasing abundance of sequence data has exacerbated a long known problem: gene trees and species trees for the same terminal taxa are often incongruent. Indeed, genes within a genome have not all followed the same evolutionary path due to events such as incomplete lineage sorting, horizontal gene transfer, gene duplication and deletion, or recombination. Considering conflicts between gene trees as an obstacle, numerous methods have been developed to deal with these incongruences and to reconstruct consensus evolutionary histories of species despite the heterogeneity in the history of their genes. However, inconsistencies can also be seen as a source of information about the specific evolutionary processes that have shaped genomes.ResultsThe goal of the approach here proposed is to exploit this conflicting information: we have compiled eleven variables describing phylogenetic relationships and evolutionary pressures and submitted them to dimensionality reduction techniques to identify genes with similar evolutionary histories. To illustrate the applicability of the method, we have chosen two viral datasets, namely papillomaviruses and Turnip mosaic virus (TuMV) isolates, largely dissimilar in genome, evolutionary distance and biology. Our method pinpoints viral genes with common evolutionary patterns. In the case of papillomaviruses, gene clusters match well our knowledge on viral biology and life cycle, illustrating the potential of our approach. For the less known TuMV, our results trigger new hypotheses about viral evolution and gene interaction.ConclusionsThe approach here presented allows turning phylogenetic inconsistencies into evolutionary information, detecting gene assemblies with similar histories, and could be a powerful tool for comparative pathogenomics.
| S-EPMC4743424 | biostudies-literature
Project description:Genome sequencing projects have been initiated for a wide range of eukaryotes. A few projects have reached completion, but most exist as draft assemblies. As one of the main reasons to sequence a genome is to obtain its catalog of genes, an important question is how complete or completable the catalog is in unfinished genomes. To answer this question, we have identified a set of core eukaryotic genes (CEGs), that are extremely highly conserved and which we believe are present in low copy numbers in higher eukaryotes. From an analysis of a phylogenetically diverse set of eukaryotic genome assemblies, we found that the proportion of CEGs mapped in draft genomes provides a useful metric for describing the gene space, and complements the commonly used N50 length and x-fold coverage values.
| S-EPMC2615622 | biostudies-literature
Project description:We tested four gene enrichment and complexity reduction target preparation methods for scoring SFPs on the Affymetrix GeneChip 18k Maize Genome Array (Maize GeneChip). Methylation filtration (MF), Cot filtration (CF), mRNA-derived cRNA, and amplified fragment length polymorphism (AFLP) methods were applied to three diverse maize inbred lines (B73, Mo17, and CML69) with three replications per line (36 Maize GeneChips). Due to large amounts of repetitive, mobile DNA, the maize genome requires a target preparation method that offers both a high level of gene enrichment and accurate scoring of SFPs. The objectives of this research are (i) to determine which target preparation method (CF, MF, mRNA, or AFLP) optimally enriches for gene sequences complementary to probe sequences on the Affymetrix GeneChip Maize Genome Array and (ii) to estimate SFP detection power for each target method. The AFLP technology is covered by patents, and patent applications owned by Keygene N.V. AFLP is a registered trademark of Keygene N.V. GeneChip. This work was supported in part by U.S. National Science Foundation grant DBI-0321467 and USDA-ARS. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA. ****[PLEXdb(http://www.plexdb.org) has submitted this series at GEO on behalf of the original contributor, Michael Gore. The equivalent experiment is ZM10 at PLEXdb.]
2008-09-16 | GSE12770 | GEO
Project description:Orthology detection is critically important for accurate functional annotation, and has been widely used to facilitate studies on comparative and evolutionary genomics. Although various methods are now available, there has been no comprehensive analysis of performance, due to the lack of a genomic-scale 'gold standard' orthology dataset. Even in the absence of such datasets, the comparison of results from alternative methodologies contains useful information, as agreement enhances confidence and disagreement indicates possible errors. Latent Class Analysis (LCA) is a statistical technique that can exploit this information to reasonably infer sensitivities and specificities, and is applied here to evaluate the performance of various orthology detection methods on a eukaryotic dataset. Overall, we observe a trade-off between sensitivity and specificity in orthology detection, with BLAST-based methods characterized by high sensitivity, and tree-based methods by high specificity. Two algorithms exhibit the best overall balance, with both sensitivity and specificity>80%: INPARANOID identifies orthologs across two species while OrthoMCL clusters orthologs from multiple species. Among methods that permit clustering of ortholog groups spanning multiple genomes, the (automated) OrthoMCL algorithm exhibits better within-group consistency with respect to protein function and domain architecture than the (manually curated) KOG database, and the homolog clustering algorithm TribeMCL as well. By way of using LCA, we are also able to comprehensively assess similarities and statistical dependence between various strategies, and evaluate the effects of parameter settings on performance. In summary, we present a comprehensive evaluation of orthology detection on a divergent set of eukaryotic genomes, thus providing insights and guides for method selection, tuning and development for different applications. Many biological questions have been addressed by multiple tests yielding binary (yes/no) outcomes but no clear definition of truth, making LCA an attractive approach for computational biology.
| S-EPMC1849888 | biostudies-literature
Project description:Background and purposeIn comparison to conventional medical imaging diagnostic modalities, the aim of this overview article is to analyze the accuracy of the application of Artificial Intelligence (AI) techniques in the identification and diagnosis of malignant tumors in adult patients.Data sourcesThe acronym PIRDs was used and a comprehensive literature search was conducted on PubMed, Cochrane, Scopus, Web of Science, LILACS, Embase, Scielo, EBSCOhost, and grey literature through Proquest, Google Scholar, and JSTOR for systematic reviews of AI as a diagnostic model and/or detection tool for any cancer type in adult patients, compared to the traditional diagnostic radiographic imaging model. There were no limits on publishing status, publication time, or language. For study selection and risk of bias evaluation, pairs of reviewers worked separately.ResultsIn total, 382 records were retrieved in the databases, 364 after removing duplicates, 32 satisfied the full-text reading criterion, and 09 papers were considered for qualitative synthesis. Although there was heterogeneity in terms of methodological aspects, patient differences, and techniques used, the studies found that several AI approaches are promising in terms of specificity, sensitivity, and diagnostic accuracy in the detection and diagnosis of malignant tumors. When compared to other machine learning algorithms, the Super Vector Machine method performed better in cancer detection and diagnosis. Computer-assisted detection (CAD) has shown promising in terms of aiding cancer detection, when compared to the traditional method of diagnosis.ConclusionsThe detection and diagnosis of malignant tumors with the help of AI seems to be feasible and accurate with the use of different technologies, such as CAD systems, deep and machine learning algorithms and radiomic analysis when compared with the traditional model, although these technologies are not capable of to replace the professional radiologist in the analysis of medical images. Although there are limitations regarding the generalization for all types of cancer, these AI tools might aid professionals, serving as an auxiliary and teaching tool, especially for less trained professionals. Therefore, further longitudinal studies with a longer follow-up duration are required for a better understanding of the clinical application of these artificial intelligence systems.Trial registrationSystematic review registration. Prospero registration number: CRD42022307403.
| S-EPMC10553229 | biostudies-literature
Project description:We tested four gene enrichment and complexity reduction target preparation methods for scoring SFPs on the Affymetrix GeneChip 18k Maize Genome Array (âMaize GeneChipâ). Methylation filtration (MF), Cot filtration (CF), mRNA-derived cRNA, and amplified fragment length polymorphism (AFLP) methods were applied to three diverse maize inbred lines (B73, Mo17, and CML69) with three replications per line (36 Maize GeneChips). Due to large amounts of repetitive, mobile DNA, the maize genome requires a target preparation method that offers both a high level of gene enrichment and accurate scoring of SFPs. The objectives of this research are (i) to determine which target preparation method (CF, MF, mRNA, or AFLP) optimally enriches for gene sequences complementary to probe sequences on the Affymetrix GeneChip Maize Genome Array and (ii) to estimate SFP detection power for each target method. The AFLP technology is covered by patents, and patent applications owned by Keygene N.V. AFLP is a registered trademark of Keygene N.V. GeneChip. This work was supported in part by U.S. National Science Foundation grant DBI-0321467 and USDA-ARS. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA. ****[PLEXdb(http://www.plexdb.org) has submitted this series at GEO on behalf of the original contributor, Michael Gore. The equivalent experiment is ZM10 at PLEXdb.] Experiment Overall Design: protocol: mRNA - genotype: B73 maize inbred(3-replications) Experiment Overall Design: protocol: mRNA - genotype: Mo17 maize inbred(3-replications) Experiment Overall Design: protocol: mRNA - genotype: CML69 maize inbred(3-replications) Experiment Overall Design: protocol: Cot filtration (CF) - genotype: B73 maize inbred(3-replications) Experiment Overall Design: protocol: Cot filtration (CF) - genotype: Mo17 maize inbred(3-replications) Experiment Overall Design: protocol: Cot filtration (CF) - genotype: CML69 maize inbred(3-replications) Experiment Overall Design: protocol: Methylation filtration (MF) - genotype: B73 maize inbred(3-replications) Experiment Overall Design: protocol: Methylation filtration (MF) - genotype: Mo17 maize inbred(3-replications) Experiment Overall Design: protocol: Methylation filtration (MF) - genotype: CML69 maize inbred(3-replications) Experiment Overall Design: protocol: Amplified fragment restriction polymorphism (AFLP) - genotype: B73 maize inbred(3-replications) Experiment Overall Design: protocol: Amplified fragment restriction polymorphism (AFLP) - genotype: Mo17 maize inbred(3-replications) Experiment Overall Design: protocol: Amplified fragment restriction polymorphism (AFLP) - genotype: CML69 maize inbred(3-replications)
2008-10-25 | E-GEOD-12770 | biostudies-arrayexpress
Project description:We present an improved version of DART-seq which utilizes a variant of the YTH domain engineered to achieve enhanced m6A recognition in APO1-YTH (D422N). In addition, we develop in vitro DART-seq and show that it performs similarly to cellular DART-seq and can map m6A in any sample of interest using nanogram amounts of total RNA.
2022-05-11 | GSE196416 | GEO
Project description:Almost all standard phylogenetic methods for reconstructing gene trees result in unrooted trees; yet, many of the most useful applications of gene trees require that the gene trees be correctly rooted. As a result, several computational methods have been developed for inferring the root of unrooted gene trees. However, the accuracy of such methods has never been systematically evaluated on prokaryotic gene families, where horizontal gene transfer is often one of the dominant evolutionary events driving gene family evolution. In this work, we address this gap by conducting a thorough comparative evaluation of five different rooting methods using large collections of both simulated and empirical prokaryotic gene trees. Our simulation study is based on 6000 true and reconstructed gene trees on 100 species and characterizes the rooting accuracy of the four methods under 36 different evolutionary conditions and 3 levels of gene tree reconstruction error. The empirical study is based on a large, carefully designed data set of 3098 gene trees from 504 bacterial species (406 Alphaproteobacteria and 98 Cyanobacteria) and reveals insights that supplement those gleaned from the simulation study. Overall, this work provides several valuable insights into the accuracy of the considered methods that will help inform the choice of rooting methods to use when studying microbial gene family evolution. Among other findings, this study identifies parsimonious Duplication-Transfer-Loss (DTL) rooting and Minimal Ancestor Deviation (MAD) rooting as two of the most accurate gene tree rooting methods for prokaryotes and specifies the evolutionary conditions under which these methods are most accurate, demonstrates that DTL rooting is highly sensitive to high evolutionary rates and gene tree error, and that rooting methods based on branch-lengths are generally robust to gene tree reconstruction error.
| S-EPMC7228096 | biostudies-literature
Project description:Because the properties of horizontally-transferred genes will reflect the mutational proclivities of their donor genomes, they often show atypical compositional properties relative to native genes. Parametric methods use these discrepancies to identify bacterial genes recently acquired by horizontal transfer. However, compositional patterns of native genes vary stochastically, leaving no clear boundary between typical and atypical genes. As a result, while strongly atypical genes are readily identified as alien, genes of ambiguous character are poorly classified when a single threshold separates typical and atypical genes. This limitation affects all parametric methods that examine genes independently, and escaping it requires the use of additional genomic information. We propose that the performance of all parametric methods can be improved by using a multiple-threshold approach. First, strongly atypical alien genes and strongly typical native genes would be identified using conservative thresholds. Genes with ambiguous compositional features would then be classified by examining gene context, including the class (native or alien) of flanking genes. By including additional genomic information in a multiple-threshold framework, we observed a remarkable improvement in the performance of several popular, but algorithmically distinct, methods for alien gene detection.
| S-EPMC3089488 | biostudies-literature
Project description:We conducted a large-scale control experiment to assess the transfer function of three scRNA-seq methods and factors modulating the function. Our approach was to dilute bulk total RNA (from a single source) to levels bracketing single-cell levels of total RNA (10 pg and 100 pg) in replicates and amplifying the RNA to levels sufficient for RNA sequencing.
2016-10-31 | GSE88850 | GEO