scholarly journals Exploiting transcriptomic data in genome-scale metabolic networks: new insights into obesity

2017 ◽  
Author(s):  
Ilaria Granata ◽  
Enrico Troiano ◽  
Mara Sangiovanni ◽  
Mario R Guarracino
Keyword(s):  
High Throughput Sequencing ◽  
Structural Information ◽  
Growth Function ◽  
Mathematical Representation ◽  
Sequencing Data ◽  
Mathematical Methods ◽  
Stoichiometric Matrix ◽  
Transcriptomic Data ◽  
Increased Risk ◽  
Genome Scale

Systems Biology is a holistic approach, based on the integration of multiscale models and different kinds of data, aimed at studying the underlying mechanisms of complex biological systems. A GEnome-scale metabolic Model (GEM) is the representation of the metabolic structure of a cell in terms of chemical reactions, involved metabolites, and associated genes. GEMs provide a functional scaffold for constraint-based mathematical methods aimed at simulating and predicting metabolic fluxes in living organisms. The most widely used constraint-based method is the Flux Balance Analysis (FBA), that exploits the stoichiometric matrix, a mathematical representation of the relations between substrates and products of all the reactions in the GEM. Recently, the increasing availability of large amounts of high-throughput sequencing data has fostered the research of new approaches in which the structural information described by GEMs is integrated with the knowledge coming from omics data, with the aim to build more accurate descriptions of metabolic states. Here we propose to use a recently published method, in which transcriptomic data are integrated into genome-scale metabolic models through the maximization of the correlation between the steady-state pattern of the predicted fluxes and the corresponding absolute gene expression data generated under the condition of interest. This approach has the interesting property that no cell growth function must be minimized to execute the model. We used this methodology to simulate a novel GEM of the human adipocyte (iAdipocytes1809), with the aim of getting new insights into the metabolic mechanisms underlying obesity and its relationships with cancer. Obesity is a complex disorder associated with an increased risk of developing several comorbid chronic diseases, ranging from cardiovascular alterations to diabetes, hypertension and cancer. In particular, weight increase and obesity have been identified as the most important risk and prognostic factors for breast cancer, especially in postmenopausal women. We discuss some preliminary results obtained with this approach, hilighting the importance of data integration, and the need for developing new methods that could help in improving our interpretation of biological phenomena.

scholarly journals Exploiting transcriptomic data in genome-scale metabolic networks: new insights into obesity

2017 ◽  
Author(s):  
Ilaria Granata ◽  
Enrico Troiano ◽  
Mara Sangiovanni ◽  
Mario R Guarracino
Keyword(s):  
High Throughput Sequencing ◽  
Structural Information ◽  
Growth Function ◽  
Mathematical Representation ◽  
Sequencing Data ◽  
Mathematical Methods ◽  
Stoichiometric Matrix ◽  
Transcriptomic Data ◽  
Increased Risk ◽  
Genome Scale

Systems Biology is a holistic approach, based on the integration of multiscale models and different kinds of data, aimed at studying the underlying mechanisms of complex biological systems. A GEnome-scale metabolic Model (GEM) is the representation of the metabolic structure of a cell in terms of chemical reactions, involved metabolites, and associated genes. GEMs provide a functional scaffold for constraint-based mathematical methods aimed at simulating and predicting metabolic fluxes in living organisms. The most widely used constraint-based method is the Flux Balance Analysis (FBA), that exploits the stoichiometric matrix, a mathematical representation of the relations between substrates and products of all the reactions in the GEM. Recently, the increasing availability of large amounts of high-throughput sequencing data has fostered the research of new approaches in which the structural information described by GEMs is integrated with the knowledge coming from omics data, with the aim to build more accurate descriptions of metabolic states. Here we propose to use a recently published method, in which transcriptomic data are integrated into genome-scale metabolic models through the maximization of the correlation between the steady-state pattern of the predicted fluxes and the corresponding absolute gene expression data generated under the condition of interest. This approach has the interesting property that no cell growth function must be minimized to execute the model. We used this methodology to simulate a novel GEM of the human adipocyte (iAdipocytes1809), with the aim of getting new insights into the metabolic mechanisms underlying obesity and its relationships with cancer. Obesity is a complex disorder associated with an increased risk of developing several comorbid chronic diseases, ranging from cardiovascular alterations to diabetes, hypertension and cancer. In particular, weight increase and obesity have been identified as the most important risk and prognostic factors for breast cancer, especially in postmenopausal women. We discuss some preliminary results obtained with this approach, hilighting the importance of data integration, and the need for developing new methods that could help in improving our interpretation of biological phenomena.

scholarly journals Inherited platelet disorders: toward DNA-based diagnosis

Blood ◽  
2016 ◽  
Vol 127 (23) ◽  
pp. 2814-2823 ◽  
Author(s):  
Claire Lentaigne ◽  
Kathleen Freson ◽  
Michael A. Laffan ◽  
Ernest Turro ◽  
Willem H. Ouwehand
Keyword(s):  
High Throughput Sequencing ◽  
Rare Variants ◽  
Association Studies ◽  
Genetic Diagnosis ◽  
Whole Genome Sequencing Data ◽  
Genome Wide Association Studies ◽  
Sequencing Data ◽  
Increased Risk ◽  
Risk Of Malignancy ◽  
Platelet Disorders

Abstract Variations in platelet number, volume, and function are largely genetically controlled, and many loci associated with platelet traits have been identified by genome-wide association studies (GWASs).1 The genome also contains a large number of rare variants, of which a tiny fraction underlies the inherited diseases of humans. Research over the last 3 decades has led to the discovery of 51 genes harboring variants responsible for inherited platelet disorders (IPDs). However, the majority of patients with an IPD still do not receive a molecular diagnosis. Alongside the scientific interest, molecular or genetic diagnosis is important for patients. There is increasing recognition that a number of IPDs are associated with severe pathologies, including an increased risk of malignancy, and a definitive diagnosis can inform prognosis and care. In this review, we give an overview of these disorders grouped according to their effect on platelet biology and their clinical characteristics. We also discuss the challenge of identifying candidate genes and causal variants therein, how IPDs have been historically diagnosed, and how this is changing with the introduction of high-throughput sequencing. Finally, we describe how integration of large genomic, epigenomic, and phenotypic datasets, including whole genome sequencing data, GWASs, epigenomic profiling, protein–protein interaction networks, and standardized clinical phenotype coding, will drive the discovery of novel mechanisms of disease in the near future to improve patient diagnosis and management.

scholarly journals COGEM: A Toolbox for Computational Genomics in Matlab

2017 ◽  
Author(s):  
Theodore J. Perkins
Keyword(s):  
High Throughput Sequencing ◽  
Numerical Data ◽  
Computational Genomics ◽  
Sequencing Data ◽  
Binary File ◽  
High Throughput Sequencing Data ◽  
Initial Release ◽  
And Mathematics ◽  
Genome Scale ◽  
Matlab Programming

AbstractMotivationThe Matlab programming language is widely used for both teaching and research in engineering, computer science, and mathematics. Despite its many strengths, it has never been a dominant language in computational genomics or bioinformatics more generally.ResultsHere, we introduce COGEM, a long-term project to develop computational genomics functionality in Matlab. The initial release provides functions for manipulating genomic intervals, stranded or unstranded, with or without numerical data associated. It includes features for both text and binary file input and output, conversion between BAM, BED and BEDGRAPH formats, and numerous functions for manipulating intervals, including shifting, expanding, overlapping, intersecting, unioning, finding nearest intervals, piling up intervals, and performing unary and binary numerical and logical operations on sets of intervals. The toolbox is well-suited to the analysis of high-throughput sequencing data. We demonstrate its functionality by creating a ChIP-seq peak-calling algorithm by chaining together a series of commands, and find it capable of analyzing genome-scale data in reasonable time.AvailabilityThe current toolbox and reference manual is available as supplementary material, and updated versions will be maintained at www.perkinslab.ca online.

scholarly journals Improvement, identification, and target prediction for miRNAs in the porcine genome by using massive, public high-throughput sequencing data

2021 ◽  
Vol 99 (2) ◽  
Author(s):  
Yuhua Fu ◽  
Pengyu Fan ◽  
Lu Wang ◽  
Ziqiang Shu ◽  
Shilin Zhu ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Target Genes ◽  
Target Prediction ◽  
Large Data ◽  
Sequencing Data ◽  
Regulate Gene Expression ◽  
High Throughput Sequencing Data ◽  
Annotation Information ◽  
Public Data ◽  
Broad Variety

Abstract Despite the broad variety of available microRNA (miRNA) research tools and methods, their application to the identification, annotation, and target prediction of miRNAs in nonmodel organisms is still limited. In this study, we collected nearly all public sRNA-seq data to improve the annotation for known miRNAs and identify novel miRNAs that have not been annotated in pigs (Sus scrofa). We newly annotated 210 mature sequences in known miRNAs and found that 43 of the known miRNA precursors were problematic due to redundant/missing annotations or incorrect sequences. We also predicted 811 novel miRNAs with high confidence, which was twice the current number of known miRNAs for pigs in miRBase. In addition, we proposed a correlation-based strategy to predict target genes for miRNAs by using a large amount of sRNA-seq and RNA-seq data. We found that the correlation-based strategy provided additional evidence of expression compared with traditional target prediction methods. The correlation-based strategy also identified the regulatory pairs that were controlled by nonbinding sites with a particular pattern, which provided abundant complementarity for studying the mechanism of miRNAs that regulate gene expression. In summary, our study improved the annotation of known miRNAs, identified a large number of novel miRNAs, and predicted target genes for all pig miRNAs by using massive public data. This large data-based strategy is also applicable for other nonmodel organisms with incomplete annotation information.

scholarly journals High-precision and cost-efficient sequencing for real-time COVID-19 surveillance

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sung Yong Park ◽  
Gina Faraci ◽  
Pamela M. Ward ◽  
Jane F. Emerson ◽  
Ha Youn Lee
Keyword(s):  
Los Angeles ◽  
Whole Genome Sequencing ◽  
Real Time ◽  
Genome Sequencing ◽  
High Precision ◽  
High Throughput Sequencing ◽  
Whole Genome ◽  
Sequencing Data ◽  
Public Health Response ◽  
Cost Efficient

AbstractCOVID-19 global cases have climbed to more than 33 million, with over a million total deaths, as of September, 2020. Real-time massive SARS-CoV-2 whole genome sequencing is key to tracking chains of transmission and estimating the origin of disease outbreaks. Yet no methods have simultaneously achieved high precision, simple workflow, and low cost. We developed a high-precision, cost-efficient SARS-CoV-2 whole genome sequencing platform for COVID-19 genomic surveillance, CorvGenSurv (Coronavirus Genomic Surveillance). CorvGenSurv directly amplified viral RNA from COVID-19 patients’ Nasopharyngeal/Oropharyngeal (NP/OP) swab specimens and sequenced the SARS-CoV-2 whole genome in three segments by long-read, high-throughput sequencing. Sequencing of the whole genome in three segments significantly reduced sequencing data waste, thereby preventing dropouts in genome coverage. We validated the precision of our pipeline by both control genomic RNA sequencing and Sanger sequencing. We produced near full-length whole genome sequences from individuals who were COVID-19 test positive during April to June 2020 in Los Angeles County, California, USA. These sequences were highly diverse in the G clade with nine novel amino acid mutations including NSP12-M755I and ORF8-V117F. With its readily adaptable design, CorvGenSurv grants wide access to genomic surveillance, permitting immediate public health response to sudden threats.

scholarly journals Experimental infection with the hookworm, Necator americanus, is associated with stable gut microbial diversity in human volunteers with relapsing multiple sclerosis

BMC Biology ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Timothy P. Jenkins ◽  
David I. Pritchard ◽  
Radu Tanasescu ◽  
Gary Telford ◽  
Marina Papaiakovou ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Experimental Infection ◽  
High Throughput Sequencing ◽  
Alpha Diversity ◽  
Placebo Treatment ◽  
Sequencing Data ◽  
Faecal Microbiota ◽  
Microbial Composition ◽  
Necator Americanus ◽  
Human Volunteers

Abstract Background Helminth-associated changes in gut microbiota composition have been hypothesised to contribute to the immune-suppressive properties of parasitic worms. Multiple sclerosis is an immune-mediated autoimmune disease of the central nervous system whose pathophysiology has been linked to imbalances in gut microbial communities. Results In the present study, we investigated, for the first time, qualitative and quantitative changes in the faecal bacterial composition of human volunteers with remitting multiple sclerosis (RMS) prior to and following experimental infection with the human hookworm, Necator americanus (N+), and following anthelmintic treatment, and compared the findings with data obtained from a cohort of RMS patients subjected to placebo treatment (PBO). Bacterial 16S rRNA high-throughput sequencing data revealed significantly decreased alpha diversity in the faecal microbiota of PBO compared to N+ subjects over the course of the trial; additionally, we observed significant differences in the abundances of several bacterial taxa with putative immune-modulatory functions between study cohorts. Parabacteroides were significantly expanded in the faecal microbiota of N+ individuals for which no clinical and/or radiological relapses were recorded at the end of the trial. Conclusions Overall, our data lend support to the hypothesis of a contributory role of parasite-associated alterations in gut microbial composition to the immune-modulatory properties of hookworm parasites.

scholarly journals deepBase v3.0: expression atlas and interactive analysis of ncRNAs from thousands of deep-sequencing data

2020 ◽  
Vol 49 (D1) ◽  
pp. D877-D883
Author(s):  
Fangzhou Xie ◽  
Shurong Liu ◽  
Junhao Wang ◽  
Jiajia Xuan ◽  
Xiaoqin Zhang ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Clinical Information ◽  
Sequencing Data ◽  
Normal Tissues ◽  
Interactive Analysis ◽  
High Throughput Sequencing Data ◽  
Expression Atlas ◽  
Expression Evolution ◽  
Noninvasive Biomarkers ◽  
Cancer Tissues

Abstract Eukaryotic genomes encode thousands of small and large non-coding RNAs (ncRNAs). However, the expression, functions and evolution of these ncRNAs are still largely unknown. In this study, we have updated deepBase to version 3.0 (deepBase v3.0, http://rna.sysu.edu.cn/deepbase3/index.html), an increasingly popular and openly licensed resource that facilitates integrative and interactive display and analysis of the expression, evolution, and functions of various ncRNAs by deeply mining thousands of high-throughput sequencing data from tissue, tumor and exosome samples. We updated deepBase v3.0 to provide the most comprehensive expression atlas of small RNAs and lncRNAs by integrating ∼67 620 data from 80 normal tissues and ∼50 cancer tissues. The extracellular patterns of various ncRNAs were profiled to explore their applications for discovery of noninvasive biomarkers. Moreover, we constructed survival maps of tRNA-derived RNA Fragments (tRFs), miRNAs, snoRNAs and lncRNAs by analyzing >45 000 cancer sample data and corresponding clinical information. We also developed interactive webs to analyze the differential expression and biological functions of various ncRNAs in ∼50 types of cancers. This update is expected to provide a variety of new modules and graphic visualizations to facilitate analyses and explorations of the functions and mechanisms of various types of ncRNAs.

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