scholarly journals Fantastic beasts and how to sequence them: genomic approaches for obscure model organisms

2017 ◽  
Author(s):  
Mikhail V. Matz
Keyword(s):  
Gene Expression ◽  
Frequency Spectrum ◽  
Gene Expression Analysis ◽  
Genomic Analysis ◽  
Reference Sequence ◽  
Model Organisms ◽  
List Type ◽  
Allele Frequency Spectrum ◽  
Specific Restriction ◽  
Genomic Basis

SummaryApplication of genomic approaches to “obscure model organisms” (OMOs), meaning species with little or no genomic resources, enables increasingly sophisticated studies of genomic basis of evolution, acclimatization and adaptation in real ecological contexts. Here, I highlight sequencing solutions and data handling techniques most suited for genomic analysis of OMOs.Glossary-Allele Frequency Spectrum, AFS(same as Site Frequency Spectrum, SFS): histogram of the number of segregating variants depending on their frequency in one or more populations.-Restriction site-Associated DNA (RAD) sequencing: family of diverse genotyping methods that sequence short fragments of the genome adjacent to recognition site(s) for specific restriction endonuclease(s).-Linkage Disequilibrium (LD): in this review, correlation of genotypes at a pair of markers across individuals.-LD block: typical distance between markers in the genome across which their genotypes remain correlated.-Genome scan:profiling of genotypes along the genome looking for unusual patterns. Often used to look for signatures of natural selection or introgression.-“Denser-than-LD” genotyping: genotyping of several polymorphic markers per LD block.-Highly contiguous reference: genome or transcriptome reference sequence containing the least amount of fragmentation.-Phased data: data showing which SNP alleles belong to the same homologous chromosome copy.-Cross-tissue gene expression analysis: looking for individual-specific shifts in gene expression detectable across multiple tissues. Such shifts are predominantly genetic in nature.

scholarly journals GADMA: Genetic algorithm for inferring demographic history of multiple populations from allele frequency spectrum data

GigaScience ◽  
2020 ◽  
Vol 9 (3) ◽  
Author(s):  
Ekaterina Noskova ◽  
Vladimir Ulyantsev ◽  
Klaus-Peter Koepfli ◽  
Stephen J O’Brien ◽  
Pavel Dobrynin
Keyword(s):  
Genetic Algorithm ◽  
Allele Frequency ◽  
Frequency Spectrum ◽  
Demographic History ◽  
Model Organisms ◽  
Demographic Model ◽  
Multiple Populations ◽  
Demographic Models ◽  
Allele Frequency Spectrum ◽  
History Of

Abstract Background The demographic history of any population is imprinted in the genomes of the individuals that make up the population. One of the most popular and convenient representations of genetic information is the allele frequency spectrum (AFS), the distribution of allele frequencies in populations. The joint AFS is commonly used to reconstruct the demographic history of multiple populations, and several methods based on diffusion approximation (e.g., ∂a∂i) and ordinary differential equations (e.g., moments) have been developed and applied for demographic inference. These methods provide an opportunity to simulate AFS under a variety of researcher-specified demographic models and to estimate the best model and associated parameters using likelihood-based local optimizations. However, there are no known algorithms to perform global searches of demographic models with a given AFS. Results Here, we introduce a new method that implements a global search using a genetic algorithm for the automatic and unsupervised inference of demographic history from joint AFS data. Our method is implemented in the software GADMA (Genetic Algorithm for Demographic Model Analysis, https://github.com/ctlab/GADMA). Conclusions We demonstrate the performance of GADMA by applying it to sequence data from humans and non-model organisms and show that it is able to automatically infer a demographic model close to or even better than the one that was previously obtained manually. Moreover, GADMA is able to infer multiple demographic models at different local optima close to the global one, providing a larger set of possible scenarios to further explore demographic history.

Comprehensive genomic analysis in metastatic pancreatic ductal adenocarcinoma (PDAC).

2016 ◽  
Vol 34 (4_suppl) ◽  
pp. 285-285
Author(s):  
Hui-Li Wong ◽  
Martin Jones ◽  
Peter Eirew ◽  
Joanna Karasinska ◽  
Kasmintan A Schrader ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
De Novo ◽  
Expression Patterns ◽  
Genomic Analysis ◽  
Ductal Adenocarcinoma ◽  
Tumor Biopsy ◽  
Significant Difference ◽  
Gene Expression Signatures

285 Background: In the absence of defined tumor molecular subtypes and validated predictive markers, PDAC has been largely treated as a single disease. Recent studies of molecular subtyping in PDAC reveal a complex mutational landscape with data suggesting the presence of genomic and gene expression signatures that may have prognostic and therapeutic significance. These studies predominantly focused on resected PDAC and lack data on metastatic tumors. We aim to explore the clinical utility of whole genome sequencing (WGS) and transcriptome analysis from metastatic biopsy samples in patients (pts) with advanced PDAC. Methods: Pts with incurable advanced cancers undergo tumor biopsy for in-depth WGS and RNA sequencing (RNASeq) as part of an ongoing prospective study (NCT02155621). Comprehensive bioinformatics analysis is performed to identify somatic cancer aberrations, gene expression changes and cellular pathway abnormalities. Here we describe clinical and molecular data on the subset of pts with advanced PDAC. Results: Sixteen PDAC pts have been enrolled; median age 59 years, 8 males (50%), 10 with de novo metastases (63%). Full WGS and RNASeq were completed in 11 pts (1 failed biopsy, 4 had insufficient tumor). KRAS codon 12 and TP53 mutations were present in all but one pt. CDKN2A and SMAD4 were also frequently altered (7 and 4 pts respectively). Gene expression analysis for classical and basal subtypes similar to those recently described (PMID 26343385) identified 3 and 6 pts with classical and basal expression patterns respectively, and 2 pts with mixed expression. Overall survival (OS) was significantly worse for the basal subtype vs all others (median OS 7 vs. 13.9 months (ms), p = 0.017). When separated into 3 subtypes a significant difference was still noted (median OS 7 ms in basal, 19.2 ms in classical and 11.8 ms in mixed subtype, p = 0.032). Conclusions: WGS analysis demonstrated a similar mutation pattern to that described in resectable PDAC, with no novel actionable mutations identified. Gene expression analysis demonstrated the presence of distinct gene expression signatures significantly associated with outcome, despite small pt numbers. These results need to be validated prospectively in larger cohorts. Clinical trial information: NCT02155621.

scholarly journals Youthful and age-related matreotypes predict drugs promoting longevity

2021 ◽  
Author(s):  
Cyril Statzer ◽  
Elisabeth Jongsma ◽  
Sean X. Liu ◽  
Alexander Dakhovnik ◽  
Franziska Wandrey ◽  
...  
Keyword(s):  
Gene Expression ◽  
Extracellular Matrix ◽  
Gene Expression Pattern ◽  
Surrogate Marker ◽  
Model Organisms ◽  
List Type ◽  
C Elegans ◽  
Drug Candidates ◽  
Matrix Gene ◽  
Age Related

AbstractThe identification and validation of drugs that promote health during aging (‘geroprotectors’) is key to the retardation or prevention of chronic age-related diseases. Here we found that most of the established pro-longevity compounds shown to extend lifespan in model organisms also alter extracellular matrix gene expression (i.e., matrisome) in human cell lines. To harness this novel observation, we used age-stratified human transcriptomes to define the age-related matreotype, which represents the matrisome gene expression pattern associated with age. Using a ‘youthful’ matreotype, we screened in silico for geroprotective drug candidates. To validate drug candidates, we developed a novel tool using prolonged collagen expression as a non-invasive and in-vivo surrogate marker for C. elegans longevity. With this reporter, we were able to eliminate false positive drug candidates and determine the appropriate dose for extending the lifespan of C. elegans. We improved drug uptake for one of our predicted compounds, genistein, and reconciled previous contradictory reports of its effects on longevity. We identified and validated new compounds, tretinoin, chondroitin sulfate, and hyaluronic acid, for their ability to restore age-related decline of collagen homeostasis and increase lifespan. Thus, our innovative drug screening approach - employing extracellular matrix homeostasis - facilitates the discovery of pharmacological interventions promoting healthy aging.HighlightsMany geroprotective drugs alter extracellular matrix gene expressionDefined young and old human matreotype signatures can identify novel potential geroprotective compoundsProlonged collagen homeostasis as a surrogate marker for longevity

scholarly journals Gene expression analysis method integration and co-expression module detection applied to rare glucide metabolism disorders using ExpHunterSuite

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fernando M. Jabato ◽  
José Córdoba-Caballero ◽  
Elena Rojano ◽  
Carlos Romá-Mateo ◽  
Pascual Sanz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Immune Cell ◽  
Disease Model ◽  
Simulated Data ◽  
Model Organisms ◽  
Link Type ◽  
Module Detection ◽  
Expression Module

AbstractHigh-throughput gene expression analysis is widely used. However, analysis is not straightforward. Multiple approaches should be applied and methods to combine their results implemented and investigated. We present methodology for the comprehensive analysis of expression data, including co-expression module detection and result integration via data-fusion, threshold based methods, and a Naïve Bayes classifier trained on simulated data. Application to rare-disease model datasets confirms existing knowledge related to immune cell infiltration and suggest novel hypotheses including the role of calcium channels. Application to simulated and spike-in experiments shows that combining multiple methods using consensus and classifiers leads to optimal results. ExpHunter Suite is implemented as an R/Bioconductor package available from https://bioconductor.org/packages/ExpHunterSuite. It can be applied to model and non-model organisms and can be run modularly in R; it can also be run from the command line, allowing scalability with large datasets. Code and reports for the studies are available from https://github.com/fmjabato/ExpHunterSuiteExamples.

Gene expression analysis of metabolic markers during bone regeneration in a rat model

2014 ◽  
Vol 23 (03) ◽  
pp. 207-211
Author(s):  
C. Kasch ◽  
A. Osterberg ◽  
Thordis Granitzka ◽  
T. Lindner ◽  
M. Haenle ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Analysis ◽  
Saline Solution ◽  
Female Rats ◽  
Synthesis Rate ◽  
Metabolic Markers ◽  
Expression Levels ◽  
Fibrotic Tissue ◽  
Intermittent Pth ◽  
Lower Expression

SummaryThe RANK/RANKL/OPG system plays an important role in the regulation of bone metabolism and bony integration around implants. The aim of this study was to analyse gene expression of OPG, RANK, and RANKL in regenerating bone during implant integration. Additionally, the effect of intermittent para - thyroid hormone (PTH) treatment was analysed. A titanium chamber was implanted in the proximal tibiae of 48 female rats. The animals received either human PTH or saline solution (NaCl). After 21 and 42 days, RNA was isolated from tissue adjacent to the implant and expression of RANK, RANKL, and OPG was analysed. After 21 days, very low expression levels of all genes were shown. In contrast, increased gene expression after 42 days was determined. Expression of RANK and RANKL was lower than that for OPG. The lower expression levels after 21 days might be due to still ossifying, fibrotic tissue around the titanium chamber. An increased OPG synthesis rate associated with decreased RANKL expression after 42 days revealed bone-forming processes. Despite significant differences in gene expression between the time points, only slight differences were observed between application of intermittent PTH and NaCl after a period of 42 days.

Sign in / Sign up

Export Citation Format

Share Document