Top-down approach in protein RDC data analysis: de novo estimation of the alignment tensor

2007 ◽  
Vol 38 (4) ◽  
pp. 303-313 ◽  
Author(s):  
Kang Chen ◽  
Nico Tjandra
2010 ◽  
Vol 14 (2) ◽  
pp. 369-382 ◽  
Author(s):  
M. G. Kleinhans ◽  
M. F. P. Bierkens ◽  
M. van der Perk

Abstract. From an outsider's perspective, hydrology combines field work with modelling, but mostly ignores the potential for gaining understanding and conceiving new hypotheses from controlled laboratory experiments. Sivapalan (2009) pleaded for a question- and hypothesis-driven hydrology where data analysis and top-down modelling approaches lead to general explanations and understanding of general trends and patterns. We discuss why and how such understanding is gained very effectively from controlled experimentation in comparison to field work and modelling. We argue that many major issues in hydrology are open to experimental investigations. Though experiments may have scale problems, these are of similar gravity as the well-known problems of fieldwork and modelling and have not impeded spectacular progress through experimentation in other geosciences.


2016 ◽  
Vol 7 ◽  
Author(s):  
Li Guo ◽  
Kelly S. Allen ◽  
Greg Deiulio ◽  
Yong Zhang ◽  
Angela M. Madeiras ◽  
...  

2013 ◽  
pp. 1494-1521
Author(s):  
Jose M. Garcia-Manteiga

Metabolomics represents the new ‘omics’ approach of the functional genomics era. It consists in the identification and quantification of all small molecules, namely metabolites, in a given biological system. While metabolomics refers to the analysis of any possible biological system, metabonomics is specifically applied to disease and physiopathological situations. The data collected within these approaches is highly integrative of the other higher levels and is hence amenable to be explored with a top-down systems biology point of view. The aim of this chapter is to give a global view of the state of the art in metabolomics describing the two analytical techniques usually used to give rise to this kind of data, nuclear magnetic resonance, NMR, and mass spectrometry. In addition, the author will focus on the different data analysis tools that can be applied to such studies to extract information with special interest at the attempts to integrate metabolomics with other ‘omics’ approaches and its relevance in systems biology modeling.


Author(s):  
Jose M. Garcia-Manteiga

Metabolomics represents the new ‘omics’ approach of the functional genomics era. It consists in the identification and quantification of all small molecules, namely metabolites, in a given biological system. While metabolomics refers to the analysis of any possible biological system, metabonomics is specifically applied to disease and physiopathological situations. The data collected within these approaches is highly integrative of the other higher levels and is hence amenable to be explored with a top-down systems biology point of view. The aim of this chapter is to give a global view of the state of the art in metabolomics describing the two analytical techniques usually used to give rise to this kind of data, nuclear magnetic resonance, NMR, and mass spectrometry. In addition, the author will focus on the different data analysis tools that can be applied to such studies to extract information with special interest at the attempts to integrate metabolomics with other ‘omics’ approaches and its relevance in systems biology modeling.


2020 ◽  
Author(s):  
Albert T. Lebedev ◽  
Irina D. Vasileva ◽  
Tatiana Y. Samgina
Keyword(s):  
De Novo ◽  

2020 ◽  
Vol 37 (12) ◽  
pp. 3576-3600
Author(s):  
Di Chen ◽  
Marzia A Cremona ◽  
Zongtai Qi ◽  
Robi D Mitra ◽  
Francesca Chiaromonte ◽  
...  

Abstract Long INterspersed Elements-1 (L1s) constitute >17% of the human genome and still actively transpose in it. Characterizing L1 transposition across the genome is critical for understanding genome evolution and somatic mutations. However, to date, L1 insertion and fixation patterns have not been studied comprehensively. To fill this gap, we investigated three genome-wide data sets of L1s that integrated at different evolutionary times: 17,037 de novo L1s (from an L1 insertion cell-line experiment conducted in-house), and 1,212 polymorphic and 1,205 human-specific L1s (from public databases). We characterized 49 genomic features—proxying chromatin accessibility, transcriptional activity, replication, recombination, etc.—in the ±50 kb flanks of these elements. These features were contrasted between the three L1 data sets and L1-free regions using state-of-the-art Functional Data Analysis statistical methods, which treat high-resolution data as mathematical functions. Our results indicate that de novo, polymorphic, and human-specific L1s are surrounded by different genomic features acting at specific locations and scales. This led to an integrative model of L1 transposition, according to which L1s preferentially integrate into open-chromatin regions enriched in non-B DNA motifs, whereas they are fixed in regions largely free of purifying selection—depleted of genes and noncoding most conserved elements. Intriguingly, our results suggest that L1 insertions modify local genomic landscape by extending CpG methylation and increasing mononucleotide microsatellite density. Altogether, our findings substantially facilitate understanding of L1 integration and fixation preferences, pave the way for uncovering their role in aging and cancer, and inform their use as mutagenesis tools in genetic studies.


Author(s):  
Joseph Fong ◽  
Kamalakar Karlapalem ◽  
Qing Li ◽  
Irene Kwan

A practitioner’s approach to integrate databases and evolve them so as to support new database applications is presented. The approach consists of a joint bottom-up and top-down methodology; the bottom-up approach is taken to integrate existing database using standard schema integration techniques (B-Schema), the top-down approach is used to develop a database schema for the new applications (T-Schema). The T-Schema uses a joint functional-data analysis. The B-schema is evolved by comparing it with the generated T-schema. This facilitates an evolutionary approach to integrate existing databases to support new applications as and when needed. The mutual completeness check of the T-Schema against B-Schema derive the schema modification steps to be performed on B-Schema to meet the requirements of the new database applications. A case study is presented to illustrate the methodology.


PROTEOMICS ◽  
2017 ◽  
Vol 17 (23-24) ◽  
pp. 1600321 ◽  
Author(s):  
Kira Vyatkina ◽  
Lennard J. M. Dekker ◽  
Si Wu ◽  
Martijn M. VanDuijn ◽  
Xiaowen Liu ◽  
...  

2015 ◽  
Vol 14 (11) ◽  
pp. 4450-4462 ◽  
Author(s):  
Kira Vyatkina ◽  
Si Wu ◽  
Lennard J. M. Dekker ◽  
Martijn M. VanDuijn ◽  
Xiaowen Liu ◽  
...  

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