Molecular data

Ebook "Cell–Cell interactions: Methods and protocols" is a versatile collection of readily reproducible cell-cell interaction assays for uncovering cellular interactions at the molecular level, both in vitro and in vivo. The protocols cover a diverse set of cell-cell interaction models in both normal and pathological states, are readily adaptable to nearly any cell type and organ system, and include primary data and outcome analysis.

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Ebook "Dendritic cell protocols" provides chapter and verse for many useful practical approaches to the art of studying dendritic cells. The book gives information on the usual techniques for derivation of human dendritic cells from precursor stem cells, such as monocytes. In addition it provides data on the difficult tasks of isolating dendritic cells directly from different tissues; whether dendritic cells from precursor cells or from tissues of mouse or human are required, this book contributes practical information.

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In this continuity, the potentiality of D. citrea essential oil was subjected to screening research. The extract yield was obtained experimentally and its chemical characterization was acquired by gas chromatography-mass spectrometry (GC-MS). The determined data was subjected as the input for computational implementations, including density functional theory calculation, molecular docking simulation, and biopharma-potential predicting analyses.

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The molecular data of D2-D3 of 28S rRNA region, morphometrics, and morphological features of second-stage juveniles, males, and females in the present study have confirmed that the root-knot nematode recovered from pomelo in Vietnam belongs to M. enterolobii. To the best of our knowledge, this represents the first report of M. enterolobii infecting pomelo, providing new insight into the host status of this important pest.

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Ebook "Evolutionary transitions to multicellular life: Principles and mechanisms" integrates our understanding of the factors and processes underlying the evolution of multicellularity by providing several complementary perspectives (both theoretical and experimental) and using examples from various lineages in which multicellularity evolved.

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Ebook "Introduction to computational biology: An evolutionary approach" Analysis of molecular sequence data is the main subject of this introduction to computational biology. There are two closely connected aspects to biological sequences: (i) their relative position in the space of all other sequences, and (ii) their movement through this sequence space in evolutionary time. Accordingly, the first part of the book deals with classical methods of sequence analysis: pairwise alignment, exact string matching, multiple alignment, and hidden Markov models.

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Ebook "Models and algorithms for genome evolution" presents a review of the history, current status, and potential future directions of computational biology in molecular evolution. Gathering together the unique insights of an international selection of prestigious researchers, this must-read volume examines the latest developments in the field, the challenges that remain, and the new avenues emerging from the growing influx of sequence data.

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Ebook "Carbohydrate-based interactions at the molecular and the cellular level" offers a clearly written and highly accessible account of two different aspects of carbohydrate chemistry. Carbohydrates are an essential component of life and have many important biological functions, but the details of how carbohydrates interact with other biomolecules to mediate biological signalling remain unclear. Firstly, this thesis details innovative methods to mine protein structural data to uncover new features of carbohydrate-based interactions.

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Large-sequencing cancer genome projects have shown that tumors have thousands of molecular alterations and their frequency is highly heterogeneous. In such scenarios, physicians and oncologists routinely face lists of cancer genomic alterations where only a minority of them are relevant biomarkers to drive clinical decisionmaking.

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A comprehensive understanding of cancer has been furthered with technological improvements and decreasing costs of next-generation sequencing (NGS). However, the complexity of interpreting genomic data is hindering the implementation of high-throughput technologies in the clinical context: increasing evidence on gene–drug interactions complicates the task of assigning clinical significance to genomic variants.

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Recent studies have used genome-wide data to investigate evolutionary mechanisms related to behavioral phenotypes, identifying widespread signals of positive selection. Here, we conducted a genome-wide investigation to study whether the molecular mechanisms involved in these traits were affected by local adaptation.

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Comprehensive mutational profiling data now available on all major cancers have led to proposals of novel molecular tumor classifications that modify or replace the established organ- and tissue-based tumor typing. The rationale behind such molecular reclassifications is that genetic alterations underlying cancer pathology predict response to therapy and may therefore offer a more precise view on cancer than histology.

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Genomic medicine has considerable potential to provide novel diagnostic and therapeutic solutions for patients who have molecularly complex diseases and who are not responding to existing therapies. To bridge the gap between genomic medicine and clinical practice, integration of various data types, resources, and joint international initiatives will be required.

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Exome sequencing (ES) has been successfully applied in clinical detection of single nucleotide variants (SNVs) and small indels. However, identification of copy number variants (CNVs) using ES data remains challenging. The purpose of this study is to understand the contribution of CNVs and copy neutral runs of homozygosity (ROH) in molecular diagnosis of patients referred for ES.

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Variants of uncertain significance represent a massive challenge to medical genetics. Multiplexed functional assays, in which the functional effects of thousands of genomic variants are assessed simultaneously, are increasingly generating data that can be used as additional evidence for or against variant pathogenicity.

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Whole-exome sequencing (WES) has become an efficient diagnostic test for patients with likely monogenic conditions such as rare idiopathic diseases or sudden unexplained death. Yet, many cases remain undiagnosed. Here, we report the added diagnostic yield achieved for 101 WES cases re-analyzed 1 to 7 years after initial analysis.

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Next-generation sequencing has enabled patient selection for targeted drugs, some of which have shown remarkable efficacy in cancers that have the cognate molecular signatures. Intriguingly, rapidly emerging data indicate that altered genes representing oncogenic drivers can also be found in sporadic non-malignant conditions, some of which have negligible and/or low potential for transformation to cancer.

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Patient stratification based on molecular subtypes is an important strategy for cancer precision medicine. Deriving clinically informative cancer molecular subtypes from transcriptomic data generated on whole tumor tissue samples is a non-trivial task, especially given the various non-cancer cellular elements intertwined with cancer cells in the tumor microenvironment.

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Comprehensive molecular profiling has revealed somatic variations in cancer at genomic, epigenomic, transcriptomic, and proteomic levels. The accumulating data has shown clearly that molecular phenotypes of cancer are complex and influenced by a multitude of factors.

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Onco-proteogenomics aims to understand how changes in a cancer’s genome influences its proteome. One challenge in integrating these molecular data is the identification of aberrant protein products from massspectrometry (MS) datasets, as traditional proteomic analyses only identify proteins from a reference sequence database.

12p vioraclene 31-03-2024 6 2   Download