bluuebunny/crossref_metadata_2025_split huggingface dataset:
Structural Databases of Biological Macromolecules
Authors: Gabanyi Margaret J , Berman Helen M 
Published: April 2013

AbstractA biological macromolecule's function is determined by the chemical and physical characteristics of its three‐dimensional (3D) shape, or ‘structure’. For this reason, knowing the structure of a biomolecule is very helpful if we want to be able to understand living systems and disease. The Protein Data Bank (PDB) began as an archive of the structural data available about biological macromolecules. The advances made in all technologies have been mirrored in further development of the PDB and in the structural speciality and structural characteristic databases that have also evolved. New resource portals such as the Protein Structure Initiative (PSI) Structural Biology Knowledgebase (SBKB) also collect all available genomic, structural, and functional information together to reduce the time needed to obtain the latest information on structurally determined proteins. This article will describe selected structural databases and resources available to the public today.Key Concepts:Structural information about proteins at the atomic‐level can lead to an explanation of its role in living systems.The Protein Data Bank archive is the sole provider of primary structural data of biological macromolecules worldwide.The four members of the worldwide protein data bank consortium, the RCSB PDB, PDBe, PDBj and BMRB, maintain the PDB archive and provide tools for exploring and understanding the structural entries.Other value‐added databases further classify derived structural information by combining the structural and/or biological aspects of a biomolecule.Meta‐portals such as the Structural Biology Knowledgebase integrate all available genetic, structural, functional, and experimental information for all structurally determined proteins to enable a better understanding of sequence–structure–function relationships.
Hidden Markov Models
Authors: Przytycka Teresa M , Zheng Jie 
Published: April 2013

AbstractA hidden Markov model (HMM) is a statistical approach that is frequently used for modelling biological sequences. In applying it, a sequence is modelled as an output of a discrete stochastic process, which progresses through a series of states that are ‘hidden’ from the observer. Each such hidden state emits a symbol representing an elementary unit of the modelled data, for example, in case of a protein sequence – an amino acid. The parameters of a hidden Markov model can be estimated by learning from training data. Efficient algorithms are available to infer the most likely paths of states for given sequence data, which often lead to biological predictions and interpretations. Thanks to the well developed theories and algorithms, hidden Markov models have found wide applications in diverse areas of computational molecular biology.Key Concepts:Hidden Markov model is a statistical approach for modelling sequences with broad applications in computational biology.In an HMM, a biological sequence is modelled as being generated by a stochastic process moving from one state to the next state, where each state emits one element of the sequence according to some emission probability distribution which, in general, is different in different states.Training of an HMM is a process in which the parameters of the model are computed based on a training set of representative examples.Overfitting/overtraining the model occurs when model parameters correctly represent the training set but the model cannot generalise the training data to a larger set.Gene finding is a process of computational identification of genes, including exon/intron structure, in a genome.
Protein Homology Modelling
Authors: Peitsch Manuel C , Schwede Torsten 
Published: April 2013

Abstract

Protein structure prediction aims to model the three‐dimensional (3D) structure of so far structurally uncharacterised proteins from their amino acid sequence. Motivated by the observation that homologous proteins with related amino acid sequences have similar 3D structures, protein homology modelling uses comparative methods to generate models for a target protein based on one or more related proteins with known 3D structure. The coordinates of the model are generated based on alignments between the target's and template's amino acid sequences, which define the correspondence between residues in both proteins. Ultimately, the quality of a computational model determines its usefulness for specific biomedical applications. Therefore, model quality estimation methods are used to identify unreliable or erroneous regions in the resulting models, and to estimate the overall accuracy of a model. Homology modelling (or comparative modelling) is currently the most accurate computational method available to routinely generate models of sufficient quality for various applications in life science research. Comparative protein modelling methods have been completely automated in recent years, and several Internet servers offer protein modelling services which are reliable and easy to use – also for the nonexpert in computational biology.

Key Concepts:

Protein structure prediction aims to model the three‐dimensional structure of so far structurally uncharacterised proteins (‘target’) based on their amino acid sequence.

Homologous proteins with related amino acid sequences have similar three‐dimensional structures.

Protein homology modelling uses information from one or more related proteins with known three‐dimensional structure (‘template’) to generate models for the target protein.

Sensitive sequence searching methods are applied to identify template proteins with known structures in large databases.

An alignment between the target's and template's amino acid sequences describes the correspondence between residues in both proteins.

The coordinates of the model are constructed by extracting positional information from the corresponding structural template.

Segments of the target protein not covered by template information (e.g. insertions/deletion in the alignment) have to be constructed using
de novo
modelling methods.

Model quality estimation methods are used to identify unreliable or erroneous regions in the resulting models.

Ultimately, the quality of a structural model determines its usefulness for specific biomedical applications.
Genome Databases
Authors: Tramontano Anna 
Published: April 2013

Abstract

A genome comprises all the genetic material of an organism and contains all the instructions needed to direct its activities. Since a genome also provides a natural reference frame for mapping information about genes and proteins, genome databases, the official repositories of the data produced by the genomic initiatives, are rapidly becoming the primary entry point for accessing biological knowledge. They are continuously enriched with new information and with functional annotations about the genome products and are meant to serve many diverse communities, from biologists to geneticists, from clinical researchers to pharmacologists and more. This implies that a major effort is required for solving the complex issue of visualising the genomic and functional data in an integrated fashion whereas at the same time providing different views of the data, tailored to the many diverse user needs.

Key Concepts:

Genome databases are the official repositories of the ever growing amount of genomic sequences.

The genome represents a natural framework for mapping the biological data of an organism.

Genome browsers provide integrated and customizable views of the information.

Genome databases and their associated tools are becoming the primary entry points for accessing biological information.

As more data on individual genomes becomes available, genome databases are also the repositories of variation data and of their associated phenotype.

Future challenges are not only related to the sheer size of the data, but also to the need of protecting sensitive information without hampering the exploitation of the data for new discoveries.
Neanderthal Mitochondrial
DNA
Authors: Goodwin William , Ovchinnikov Igor 
Published: April 2013

Abstract

Mitochondrial deoxyribonucleic acid
(mtDNA) has been important in understanding the evolutionary histories of both modern humans and archaic hominins, especially the Neanderthals. mtDNA to date (2012) has been successfully extracted from 13 specimens recovered from the geographical range of the Neanderthals. Primer extension capture along with next generation sequencing has been used along with standard shotgun sequencing to determine the entire mtDNA genome of six specimens: two from Kleine Feldhofer Grotte in Germany, one from El Sidron cave in Spain, two from Vindija Cave in Croatia and one from Mezmaiskaya Cave in Russia. New mtDNA sequences have enhanced the data available from earlier studies that analysed the hypervariable regions of several specimens. The Neanderthal mtDNA pool is distinct from modern human mtDNA and forms a separate phylogenetic clade; genetic diversity of Neanderthals is estimated to be approximately one‐third of extant modern human populations. In addition to analysis of the mtDNA, next generation sequencing has allowed the first insights into the Neanderthal nuclear genome.

Key Concepts:

The mtDNA genomes of six Neanderthals from four countries have been fully sequenced. The hypervariable regions of several other specimens have also been analysed.

The Neanderthals form a distinct genetic clade from modern humans; the two lineages diverged approximately 500 000 years before the present.

Genetic diversity in the Neanderthal population appears to be considerably reduced in comparison to the modern human populations.

Based on the mtDNA analysis, the Eurasian Neanderthal population appeared to be fragmented into subpopulations with separate demographic histories.

The likelihood of admixture between Neanderthals and modern humans is so low that either behavioural or biological barriers to interbreeding may have existed.
Uniparental Disomy
Authors: Schinzel Albert , Baumer Alessandra 
Published: April 2013

Abstract

Uniparental disomy (UPD) is defined as the inheritance of both homologues of a given genome region from only one parent. In the majority of cases, a UPD concerns an entire chromosome; it may also be limited to part of a chromosome (segmental UPD) or may involve the entire genome (genome‐wide UPD: androgenetic or gynogenetic, if the two haplotypes derive from the father or the mother, respectively). The types of UPD are defined as ‘isodisomy’ (two identical copies of one of the parental alleles) or ‘heterodisomy’ (two distinct alleles derived from the same parent). A further issue relevant to uniparental disomies is mosaicism: certain uniparental disomies, for example, genome‐wide uniparental disomies, are viable only in the presence of normal biparental cells (mosaic state).
A UPD may cause abnormal development if it leads to abnormal expression of imprinted genes or to reduction to homozygosity of recessive mutations.

Key Concepts:

Uniparental disomy explains pedigrees in conflict with mendelian inheritance.

Uniparental disomy carries a risk for recessive diseases.

Segmental uniparental disomy is an excellent model to explain fundamental genetic rules.

Recombination errors around meiosis and early thereafter are very frequent as, among others, seen from (segmental) uniparental disomy cases.
Retrotransposons and Human Disease
Authors: Roy‐Engel Astrid M , Belancio Victoria P 
Published: April 2013

Abstract

Retrotransposition is a ‘copy‐and‐paste’ mechanism whereby a retrotransposable element is copied from one genomic location and inserted into another genomic location, using a ribonucleic acid intermediate. The consequences of the retrotransposon‐associated alterations of the genomic landscape range from silent events to changes contributing to species‐specific and individual differences as well as a broad spectrum of diseases. Retrotransposon‐induced genetic variation can lead to inactivation or alteration of expression of critical genes

via

insertional mutagenesis, loss of genomic sequences through nonallelic homologous recombination or in association with

de novo

integration. In addition to these well‐recognised mutagenic events, some retroelements such as LINE‐1 can induce deoxyribonucleic acid double‐strand breaks that, if repaired unfaithfully, may lead to mutations. Moreover, retroelements have also contributed to generation of novel genes or functions through pseudogene formation, exonisation or shuffling of genetic material during retrotransposition. Thus, a complete understanding of their cumulative effect on any given genome or on genome evolution remains elusive.

Key Concepts:

Retroelements are currently active in the human genome, but only a limited number of functional loci are retrotranspositionally competent.

Retrotransposition is a multistep process that requires LINE‐1 proteins and cellular factors.

Human retrotransposons (LINE‐1, Alu and SVA) contribute to disease through several mechanisms.

LINE‐1 ORF2 protein induces DNA double‐strand breaks, but their contribution to human disease is not yet characterised.

LINE‐1 expression varies among germ line, normal somatic tissues and cancer.

Retrotransposition can occur in the germ line, normal somatic tissues and cancer.

There is variation in the rate of LINE‐1, Alu and SVA retrotransposition.

The estimated frequency of LINE‐1‐, Alu‐ and SVA‐associated diseases is likely an underestimation of their contribution to human disease.
RNA
Editing and Human Disorders
Authors: Gott Jonatha M 
Published: April 2013

Abstract

Ribonucleic acid (RNA)
editing can be broadly defined as any process that changes the sequence of an RNA molecule in a way that could be encoded within the genome. These small, targeted changes at the RNA level can have profound effects on gene expression. Such programmed alterations can be controlled in a tissue‐specific or developmentally regulated fashion, providing an alternative means of augmenting the information encoded within the genome. In mammalian cells, cytidine to uridine (C to U) and adenosine to inosine (A to I) changes have been observed in both coding and noncoding sequences, including microRNAs, and dysregulation has been linked to disease states. Editing enzymes are often essential, and can act on viral, as well as cellular RNAs. Some human pathogens utilise additional forms of RNA editing for gene expression; these forms of editing provide potential therapeutic targets.

Key Concepts:

RNA editing is a means of generating genetic diversity that is far more flexible than simply encoding information at the genomic level.

Editing is regulated both developmentally and spatially, and dysregulation can have serious consequences.

Most editing events in humans fall outside of coding regions; the functional consequences of many of these changes are still being elucidated, but will likely be far‐reaching.

Editing can impact the function of noncoding RNAs, including those involved in RNA silencing.

RNA editing can have both antiviral and proviral effects.
Gastrointestinal Tract: Molecular Genetics of Hirschsprung Disease
Authors: Passarge Eberhard 
Published: April 2013

AbstractHirschsprung disease is a complex, heterogeneous disorder, resulting from absence of the intramural intestinal ganglion cells (congenital intestinal aganglionosis) owing to failure of migration from the neural crest during embryonic development. Therefore, Hirschsprung disease is considered a neural crest disorder (neurocristopathy). Nine genes and three signal pathways have been identified in relation to the aetiology of this group of disorders. In addition, disease‐related noncoding regions in theRETgene contribute to the causes by interacting with at least one of the other predisposing genes. The disease susceptibility between males and females differs by a factor of 5 (5:1 higher disease frequency in males than in females).Key Concepts:Hirschsprung disease is currently the best understood multifactorial, nonMendelian genetic disorder.Mutations in the receptor tyrosine kinase geneRETlocated at human chromosome 10q11.1 play a pivotal role in the etiology, in combination with other genes.A common noncoding variant in intron 1 of theRETgene within a conserved enhancer‐like DNA sequence markedly influences the susceptibility to congenital intestinal aganglionosis.This variant reduces enhancer activityin vitroand contributes to the disease risk 20‐fold more than other rare alleles, in spite of its low penetrance.The eight other known genes involved in the genetic aetiology account for less than 30% of patients.Three different signal pathways related to ganglion cell migration and intramural intestinal function are involved in the genetic pathogenesis of congenital intestinal aganglionosis: (1) the RET receptor kinase pathway with theRETgene and its ligand GDNF (glial cell line‐derived neurotrophic factor), (2) the endothelin type B receptor pathway with the EDNRB receptor and its ligand EDN3 (endothelin‐3) and (3) the transcription factor SOX10.A single genetic change in any of the known genes and chromosomal regions predisposing to congenital intestinal aganglionosis is neither sufficient nor necessary to cause the disease.The genetic effects differ in males and females. The frequency of transmission of predisposing factors differs between males and females, resulting in a 5.7‐fold and 2.1‐fold increase in susceptibility in males and females, respectively.The predisposingRETsusceptibility allele differs in world‐like frequency. It is virtually absent in Africa (less than 5%) in contrast to Asia (40% in China and Japan) and Europe (25%).In about 12% of patients, congenital intestinal aganglionosis occurs in syndromic form associated with other clinical manifestations outside the GI tract, usually in a monogenetic inheritance pattern.
Paroxysmal Nocturnal Hemoglobinuria
Authors: Kinoshita Taroh 
Published: April 2013

AbstractParoxysmal nocturnal hemoglobinuria is a peculiar acquired clonal genetic disease caused by somatic mutation of the X‐linkedPIGAgene in a hematopoietic stem cell. Loss‐of‐function mutation inPIGAgene causes a lack or a decreased biosynthesis of glycosylphosphatidylinositol (GPI), which in turn results in a complete or a partial deficiency in the cell surface expression of GPI‐anchored proteins, such as CD59 and CD55. ThePIGA‐mutant stem cell clone becomes dominant in hematopoiesis due to an autoimmune‐mediated decrease of normal hematopoietic stem cells and/or an acquisition of benign tumour characteristics after additional genetic change(s) such that it causes ectopic expression ofHMGA2gene known to cause benign tumours. Mutant red blood cells lacking CD59 and CD55 are highly sensitive to complement and are lysed upon activation of complement during infection and other events, resulting in haemolytic anemia and hemoglobinuria. A humanised monoclonal antibody against fifth component of complement C5 that inhibits activation of C5 is now in clinical use to prevent intravascular haemolysis.Key Concepts:Paroxysmal nocturnal hemoglobinuria is an acquired genetic disease due to somatic mutation in the X‐linkedPIGAgene that occurs in the hematopoietic stem cell.Loss‐of‐function somaticPIGAmutation causes a defective biosynthesis of glycosylphosphatidylinositol (GPI).The defective biosynthesis in GPI that anchors many proteins onto the plasma membrane, leads to defective cell surface expression of GPI‐anchored proteins.ThePIGA‐defective mutant clone expands and dominates hematopoietic cells in bone marrow and the peripheral blood.The clonal expansion is mediated by an autoimmune‐mediated decrease of normal hematopoietic stem cells and/or an acquisition of benign tumour characteristics.A lack of GPI‐anchored proteins makes red blood cells sensitive to homologous complement due to a loss of complement regulators, CD59 and CD55.Complement‐mediated haemolysis of GPI‐deficient red blood cells results in haemolytic anemia and hemoglobinuria.A humanised anti‐C5 monoclonal antibody is in clinical use to prevent intravascular haemolysis.
Microdeletion Syndromes
Authors: Slavotinek Anne 
Published: April 2013

AbstractA microdeletion syndrome is the clinical consequence of a submicroscopic chromosome deletion leading to monosomy for a small chromosomal segment. The clinical pictures of well‐known microdeletion syndromes are relatively specific and the phenotypic characteristics were often recognised before the causal microdeletions were identified. In recent years, the widespread use of array comparative genomic hybridisation (array CGH) has lead to the discovery of numerous novel microdeletion and microduplication syndromes that were clinically delineated only after the chromosome aberration was ascertained by array. The newer microdeletion syndromes are more likely to be rare and to have milder, less specific clinical features, such as developmental delays and behavioural differences. Many of the phenotypes are still evolving. It is also becoming clear for some of the smaller deletions that the same microdeletions can be present in normal individuals and that they thus can be considered to be susceptibility factors for the development of neurocognitive differences.Key Concepts:There are numerous novel microdeletion and microduplication syndromes that have been identified and characterised after the use of array comparative genomic hybridisation (array CGH) became widespread.Many microdeletion syndromes have a corresponding microduplication syndrome that is generally rarer and associated with a milder phenotype; the phenotypes of many of the newer microdeletion and microduplication syndromes are still evolving.Low copy repeats (LCRs) predispose to chromosome deletions and duplications through nonallelic homologous recombination (NAHR), and many of the copy number variable regions that result in microdeletion or microduplication syndromes are situated between blocks of LCRs.Most of the smaller microdeletions that were identified by array CGH have milder dysmorphic features that are less likely to be recognisable as a distinct phenotype; these microdeletions are also more likely to have been inherited from a normal or mildly affected parent.Individuals with small microdeletions can still have a ‘chromosomal’ presentation that is developmental differences and intellectual disability, growth differences, behavioural problems, feeding difficulties, low muscle tone, seizures, dysmorphic features and a pattern of varying malformations.
Multiple Sclerosis (
MS
): Genetics
Authors: Dyment DA , Ebers GC 
Published: April 2013

Abstract

Multiple sclerosis (MS) is a complex trait and highlights the interplay between nature, nurture and the stochastic events of development. Genetic‐epidemiological studies have shown the importance of the genetic contribution to the marked familial clustering of MS. Environmental factors also increase MS risk and act at a population‐based level. Climate and the subsequent effect on vitamin D levels are thought to play an important role. There is a clear association with MS and human leukocyte antigen (HLA) polymorphisms located at chromosome 6p21. Individuals who carry the at‐risk genotypes (DRB1
*
15/15) have a five‐ to sixfold increased chance of MS. Genome‐wide searches for other non‐HLA susceptibility genes by association have been successful in identifying many disease genes of mild effects. The genes implicated have an immune‐related function. There are likely additional genes contributing to MS risk and new technologies may prove fruitful for their identification.

Key Concepts:

Genetic‐epidemiological studies have shown the importance of genes to MS risk and studies such as twin, adoptive and half‐siblings are useful tools in the study of complex traits.

The search for genes in complex traits is difficult and often requires hundreds to thousands of cases and unaffected controls. This is due to the small effect of any given risk gene.

A risk‐related gene may be modified by its interaction with other genes and the environment and may be further modified by chance developmental processes.