Dataset Information

ProteinHistorian: tools for the comparative analysis of eukaryote protein origin.

ABSTRACT: The evolutionary history of a protein reflects the functional history of its ancestors. Recent phylogenetic studies identified distinct evolutionary signatures that characterize proteins involved in cancer, Mendelian disease, and different ontogenic stages. Despite the potential to yield insight into the cellular functions and interactions of proteins, such comparative phylogenetic analyses are rarely performed, because they require custom algorithms. We developed ProteinHistorian to make tools for performing analyses of protein origins widely available. Given a list of proteins of interest, ProteinHistorian estimates the phylogenetic age of each protein, quantifies enrichment for proteins of specific ages, and compares variation in protein age with other protein attributes. ProteinHistorian allows flexibility in the definition of protein age by including several algorithms for estimating ages from different databases of evolutionary relationships. We illustrate the use of ProteinHistorian with three example analyses. First, we demonstrate that proteins with high expression in human, compared to chimpanzee and rhesus macaque, are significantly younger than those with human-specific low expression. Next, we show that human proteins with annotated regulatory functions are significantly younger than proteins with catalytic functions. Finally, we compare protein length and age in many eukaryotic species and, as expected from previous studies, find a positive, though often weak, correlation between protein age and length. ProteinHistorian is available through a web server with an intuitive interface and as a set of command line tools; this allows biologists and bioinformaticians alike to integrate these approaches into their analysis pipelines. ProteinHistorian's modular, extensible design facilitates the integration of new datasets and algorithms. The ProteinHistorian web server, source code, and pre-computed ages for 32 eukaryotic genomes are freely available under the GNU public license at http://lighthouse.ucsf.edu/ProteinHistorian/.

SUBMITTER: Capra JA

PROVIDER: S-EPMC3386163 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

ProteinHistorian: tools for the comparative analysis of eukaryote protein origin.

Capra John A JA Williams Alexander G AG Pollard Katherine S KS

PLoS computational biology 20120628 6

The evolutionary history of a protein reflects the functional history of its ancestors. Recent phylogenetic studies identified distinct evolutionary signatures that characterize proteins involved in cancer, Mendelian disease, and different ontogenic stages. Despite the potential to yield insight into the cellular functions and interactions of proteins, such comparative phylogenetic analyses are rarely performed, because they require custom algorithms. We developed ProteinHistorian to make tools ...[more]

PMID: 22761559

Similar Datasets

Project description:UNLABELLED:Codon usage bias (CUB) is an omnipresent phenomenon, which occurs in nearly all organisms. Previous studies of codon bias in Plasmodium species were based on a limited dataset. This study uses whole genome datasets for comparative genome analysis of six Plasmodium species using CUB and other related methods for the first time. Codon usage bias, compositional variation in translated amino acid frequency, effective number of codons and optimal codons are analyzed for P.falciparum, P.vivax, P.knowlesi, P.berghei, P.chabaudii and P.yoelli. A plot of effective number of codons versus GC3 shows their differential codon usage pattern arises due to a combination of mutational and translational selection pressure. The increased relative usage of adenine and thymine ending optimal codons in highly expressed genes of P.falciparum is the result of higher composition biased pressure, and usage of guanine and cytosine bases at third codon position can be explained by translational selection pressure acting on them. While higher usage of adenine and thymine bases at third codon position in optimal codons of P.vivax highlights the role of translational selection pressure apart from composition biased mutation pressure in shaping their codon usage pattern. The frequency of those amino acids that are encoded by AT ending codons are significantly high in P.falciparum due to action of high composition biased mutational pressure compared with other Plasmodium species. The CUB variation in the three rodent parasites, P.berghei, P.chabaudii and P.yoelli is strikingly similar to that of P.falciparum. The simian and human malarial parasite, P.knowlesi shows a variation in codon usage bias similar to P.vivax but on closer study there are differences confirmed by the method of Principal Component Analysis (PCA). ABBREVIATIONS:CDS - Coding sequences, GC1 - GC composition at first site of codon, GC2 - GC composition at second site of codon, GC3 - GC composition at third site of codon, Ala - Alanine, Arg - Arginine, Asn - Asparagine, Asp - Aspartic acid, Cys - Cysteine, Gln - Glutamine Glu - Glutamic acid Gly - Glycine His - Histidine Ile - Isoleucine Leu - Leucine Lys - Lysine Met - Methionine Phe - Phenylalanine Pro - Proline Ser - Serine Thr - Threonine Trp - Tryptophan Tyr - Tyrosine Val - Valine.

Project description:BACKGROUND:The analysis of single-cell RNA sequencing (scRNAseq) data plays an important role in understanding the intrinsic and extrinsic cellular processes in biological and biomedical research. One significant effort in this area is the detection of differentially expressed (DE) genes. scRNAseq data, however, are highly heterogeneous and have a large number of zero counts, which introduces challenges in detecting DE genes. Addressing these challenges requires employing new approaches beyond the conventional ones, which are based on a nonzero difference in average expression. Several methods have been developed for differential gene expression analysis of scRNAseq data. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to evaluate and compare the performance of differential gene expression analysis methods for scRNAseq data. RESULTS:In this study, we conducted a comprehensive evaluation of the performance of eleven differential gene expression analysis software tools, which are designed for scRNAseq data or can be applied to them. We used simulated and real data to evaluate the accuracy and precision of detection. Using simulated data, we investigated the effect of sample size on the detection accuracy of the tools. Using real data, we examined the agreement among the tools in identifying DE genes, the run time of the tools, and the biological relevance of the detected DE genes. CONCLUSIONS:In general, agreement among the tools in calling DE genes is not high. There is a trade-off between true-positive rates and the precision of calling DE genes. Methods with higher true positive rates tend to show low precision due to their introducing false positives, whereas methods with high precision show low true positive rates due to identifying few DE genes. We observed that current methods designed for scRNAseq data do not tend to show better performance compared to methods designed for bulk RNAseq data. Data multimodality and abundance of zero read counts are the main characteristics of scRNAseq data, which play important roles in the performance of differential gene expression analysis methods and need to be considered in terms of the development of new methods.

Dataset Information

ProteinHistorian: tools for the comparative analysis of eukaryote protein origin.

Publications

ProteinHistorian: tools for the comparative analysis of eukaryote protein origin.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure