8,539 research outputs found
NETTAB 2012 on “Integrated Bio-Search”
The NETTAB 2012 workshop, held in Como on November 14-16, 2012, was devoted to "Integrated Bio-Search", that is to technologies, methods, architectures, systems and applications for searching, retrieving, integrating and analyzing data, information, and knowledge with the aim of answering complex bio-medical-molecular questions, i.e. some of the most challenging issues in bioinformatics today. It brought together about 80 researchers working in the field of Bioinformatics, Computational Biology, Biology, Computer Science and Engineering. More than 50 scientific contributions, including keynote and tutorial talks, oral communications, posters and software demonstrations, were presented at the workshop. This preface provides a brief overview of the workshop and shortly introduces the peer-reviewed manuscripts that were accepted for publication in this Supplement
Preface : in silico pipeline for accurate cell-free fetal DNA fraction prediction
Objective During routine noninvasive prenatal testing (NIPT), cell-free fetal DNA fraction is ideally derived from shallow-depth whole-genome sequencing data, preventing the need for additional experimental assays. The fraction of aligned reads to chromosome Y enables proper quantification for male fetuses, unlike for females, where advanced predictive procedures are required. This study introduces PREdict FetAl ComponEnt (PREFACE), a novel bioinformatics pipeline to establish fetal fraction in a gender-independent manner. Methods PREFACE combines the strengths of principal component analysis and neural networks to model copy number profiles. Results For sets of roughly 1100 male NIPT samples, a cross-validated Pearson correlation of 0.9 between predictions and fetal fractions according to Y chromosomal read counts was noted. PREFACE enables training with both male and unlabeled female fetuses. Using our complete cohort (n(female) = 2468, n(male) = 2723), the correlation metric reached 0.94. Conclusions Allowing individual institutions to generate optimized models sidelines between-laboratory bias, as PREFACE enables user-friendly training with a limited amount of retrospective data. In addition, our software provides the fetal fraction based on the copy number state of chromosome X. We show that these measures can predict mixed multiple pregnancies, sex chromosomal aneuploidies, and the source of observed aberrations
Data Resources for Structural Bioinformatics
While many good textbooks are available on Protein Structure, Molecular
Simulations, Thermodynamics and Bioinformatics methods in general, there is no
good introductory level book for the field of Structural Bioinformatics. This
book aims to give an introduction into Structural Bioinformatics, which is
where the previous topics meet to explore three dimensional protein structures
through computational analysis. We provide an overview of existing
computational techniques, to validate, simulate, predict and analyse protein
structures. More importantly, it will aim to provide practical knowledge about
how and when to use such techniques. We will consider proteins from three major
vantage points: Protein structure quantification, Protein structure prediction,
and Protein simulation & dynamics.
Structural bioinformatics involves a variety of computational methods, all of
which require input data. Typical inputs include protein structures and
sequences, which are usually retrieved from a public or private database. This
chapter introduces several key resources that make such data available, as well
as a handful of tools that derive additional information from experimentally
determined or computationally predicted protein structures and sequences.Comment: editorial responsability: Sanne Abeln, K. Anton Feenstra, Halima
Mouhib. This chapter is part of the book "Introduction to Protein Structural
Bioinformatics". The Preface arXiv:1801.09442 contains links to all the
(published) chapters. The update adds available arxiv hyperlinks for the
chapter
Structure Alignment
While many good textbooks are available on Protein Structure, Molecular
Simulations, Thermodynamics and Bioinformatics methods in general, there is no
good introductory level book for the field of Structural Bioinformatics. This
book aims to give an introduction into Structural Bioinformatics, which is
where the previous topics meet to explore three dimensional protein structures
through computational analysis. We provide an overview of existing
computational techniques, to validate, simulate, predict and analyse protein
structures. More importantly, it will aim to provide practical knowledge about
how and when to use such techniques. We will consider proteins from three major
vantage points: Protein structure quantification, Protein structure prediction,
and Protein simulation & dynamics.
The Protein DataBank (PDB) contains a wealth of structural information. In
order to investigate the similarity between different proteins in this
database, one can compare the primary sequence through pairwise alignment and
calculate the sequence identity (or similarity) over the two sequences. This
strategy will work particularly well if the proteins you want to compare are
close homologs. However, in this chapter we will explain that a structural
comparison through structural alignment will give you much more valuable
information, that allows you to investigate similarities between proteins that
cannot be discovered by comparing the sequences alone.Comment: editorial responsability: K. Anton Feenstra, Sanne Abeln. This
chapter is part of the book "Introduction to Protein Structural
Bioinformatics". The Preface arXiv:1801.09442 contains links to all the
(published) chapters. The update adds available arxiv hyperlinks for the
chapter
Molecular Dynamics
While many good textbooks are available on Protein Structure, Molecular
Simulations, Thermodynamics and Bioinformatics methods in general, there is no
good introductory level book for the field of Structural Bioinformatics. This
book aims to give an introduction into Structural Bioinformatics, which is
where the previous topics meet to explore three dimensional protein structures
through computational analysis. We provide an overview of existing
computational techniques, to validate, simulate, predict and analyse protein
structures. More importantly, it will aim to provide practical knowledge about
how and when to use such techniques. We will consider proteins from three major
vantage points: Protein structure quantification, Protein structure prediction,
and Protein simulation & dynamics.
We know that many proteins have functional motions, and in Chapter "Structure
Determination" we already introduced the famous example of the allosteric
cooperative binding of oxygen to the haem group in hemoglobin. However,
experimentally, such motions are hard to observe. Here, we will introduce MD
simulations to investigate the dynamic behaviour of proteins. In a simulation
the forces and interactions between particles are used to numerically derive
the resulting three-dimensional movement of these particles over a certain
time-scale. We will also highlight some applications, and will see how
simulation results may be interpreted.Comment: editorial responsability: Halima Mouhib, Sanne Abeln, K. Anton
Feenstra. This chapter is part of the book "Introduction to Protein
Structural Bioinformatics". The Preface arXiv:1801.09442 contains links to
all the (published) chapters. The update adds available arxiv hyperlinks for
the chapter
Thermodynamics of Protein Folding
While many good textbooks are available on Protein Structure, Molecular
Simulations, Thermodynamics and Bioinformatics methods in general, there is no
good introductory level book for the field of Structural Bioinformatics. This
book aims to give an introduction into Structural Bioinformatics, which is
where the previous topics meet to explore three dimensional protein structures
through computational analysis. We provide an overview of existing
computational techniques, to validate, simulate, predict and analyse protein
structures. More importantly, it will aim to provide practical knowledge about
how and when to use such techniques. We will consider proteins from three major
vantage points: Protein structure quantification, Protein structure prediction,
and Protein simulation & dynamics.
In the previous chapter, "Introduction to Protein Folding", we introduced the
concept of free energy and the protein folding landscape. Here, we provide a
deeper, more formal underpinning of free energy in terms of the entropy and
enthalpy; to this end, we will first need to better define the meaning of
equilibrium, entropy and enthalpy. When we understand these concepts, we will
come back for a more quantitative explanation of protein folding and dynamics.
We will discuss the influence of temperature on the free energy landscape, and
the difference between microstates and macrostates.Comment: editorial responsability: Juami H. M. van Gils, K. Anton Feenstra,
Sanne Abeln. This chapter is part of the book "Introduction to Protein
Structural Bioinformatics". The Preface arXiv:1801.09442 contains links to
all the (published) chapters. The update adds available arxiv hyperlinks for
the chapter
Introduction to Protein Structure
While many good textbooks are available on Protein Structure, Molecular
Simulations, Thermodynamics and Bioinformatics methods in general, there is no
good introductory level book for the field of Structural Bioinformatics. This
book aims to give an introduction into Structural Bioinformatics, which is
where the previous topics meet to explore three dimensional protein structures
through computational analysis. We provide an overview of existing
computational techniques, to validate, simulate, predict and analyse protein
structures. More importantly, it will aim to provide practical knowledge about
how and when to use such techniques. We will consider proteins from three major
vantage points: Protein structure quantification, Protein structure prediction,
and Protein simulation & dynamics.
Within the living cell, protein molecules perform specific functions,
typically by interacting with other proteins, DNA, RNA or small molecules. They
take on a specific three dimensional structure, encoded by its amino acid
sequence, which allows them to function within the cell. Hence, the
understanding of a protein's function is tightly coupled to its sequence and
its three dimensional structure. Before going into protein structure analysis
and prediction, and protein folding and dynamics, here, we give a short and
concise introduction into the basics of protein structures.Comment: editorial responsability: Laura Hoekstra, K. Anton Feenstra, Sanne
Abeln. This chapter is part of the book "Introduction to Protein Structural
Bioinformatics". The Preface arXiv:1801.09442 contains links to all the
(published) chapters. The update adds available arxiv hyperlinks for the
chapter
Monte Carlo for Protein Structures
While many good textbooks are available on Protein Structure, Molecular
Simulations, Thermodynamics and Bioinformatics methods in general, there is no
good introductory level book for the field of Structural Bioinformatics. This
book aims to give an introduction into Structural Bioinformatics, which is
where the previous topics meet to explore three dimensional protein structures
through computational analysis. We provide an overview of existing
computational techniques, to validate, simulate, predict and analyse protein
structures. More importantly, it will aim to provide practical knowledge about
how and when to use such techniques. We will consider proteins from three major
vantage points: Protein structure quantification, Protein structure prediction,
and Protein simulation & dynamics.
In the previous chapter "Molecular Dynamics" we have considered protein
simulations from a dynamical point of view, using Newton's laws. In the current
Chapter, we first take a step back and return to the bare minimum needed to
simulate proteins, and show that proteins may be simulated in a more simple
fashion, using the partition function directly. This means we do not have to
calculate explicit forces, velocities, moments and do not even consider time
explicitly. Instead, we will rely on the fact that for most systems we will
want to simulate, the system is in a dynamic equilibrium; and that we want to
find the most stable states in such systems by determining the relative
stabilities between those states.Comment: editorial responsability: Juami H. M. van Gils, K. Anton Feenstra,
Sanne Abeln. This chapter is part of the book "Introduction to Protein
Structural Bioinformatics". The Preface arXiv:1801.09442 contains links to
all the (published) chapters. The update adds available arxiv hyperlinks for
the chapter
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