At a glance: the largest Niemann-Pick type C1 cohort with 602 patients diagnosed over 15 years.

Niemann-Pick type C1 disease (NPC1 [OMIM 257220]) is a rare and severe autosomal recessive disorder, characterized by a multitude of neurovisceral clinical manifestations and a fatal outcome with no effective treatment to date. Aiming to gain insights into the genetic aspects of the disease, clinical, genetic, and biomarker PPCS data from 602 patients referred from 47 countries and diagnosed with NPC1 in our laboratory were analyzed. Patients' clinical data were dissected using Human Phenotype Ontology (HPO) terms, and genotype-phenotype analysis was performed.

Novel NAXE variants as a cause for neurometabolic disorder: implications for treatment.

Neurometabolic disorders are often inherited and complex disorders that result from abnormalities of enzymes important for development and function of the nervous system. Recently, biallelic mutations in NAXE (APOA1BP) were found in patients with an infantile, lethal, neurometabolic disease. Here, exome sequencing was performed in two affected sisters and their healthy parents.

Novel pathogenic variants and multiple molecular diagnoses in neurodevelopmental disorders.

Background: Rare denovo variants represent a significant cause of neurodevelopmental delay and intellectual disability (ID).

Methods: Exome sequencing was performed on 4351 patients with global developmental delay, seizures, microcephaly, macrocephaly, motor delay, delayed speech and language development, or ID according to Human Phenotype Ontology (HPO) terms. All patients had previously undergone whole exome sequencing as part of diagnostic genetic testing with a focus on variants in genes implicated in neurodevelopmental disorders up to January 2017.

Biallelic variants in the transcription factor PAX7 are a new genetic cause of myopathy.

Purpose: Skeletal muscle growth and regeneration rely on muscle stem cells, called satellite cells. Specific transcription factors, particularly PAX7, are key regulators of the function of these cells. Knockout of this factor in mice leads to poor postnatal survival; however, the consequences of a lack of PAX7 in humans have not been established.

Development of an evidence-based algorithm that optimizes sensitivity and specificity in ES-based diagnostics of a clinically heterogeneous patient population.

Purpose: Next-generation sequencing (NGS) is rapidly replacing Sanger sequencing in genetic diagnostics. Sensitivity and specificity of NGS approaches are not well-defined, but can be estimated from applying NGS and Sanger sequencing in parallel. Utilizing this strategy, we aimed at optimizing exome sequencing (ES)-based diagnostics of a clinically diverse patient population.

Clinical exome sequencing: results from 2819 samples reflecting 1000 families.

We report our results of 1000 diagnostic WES cases based on 2819 sequenced samples from 54 countries with a wide phenotypic spectrum. Clinical information given by the requesting physicians was translated to HPO terms. WES processes were performed according to standardized settings.

Validation of a semiconductor next-generation sequencing assay for the clinical genetic screening of CFTR.

Genetic testing for cystic fibrosis and CFTR-related disorders mostly relies on laborious molecular tools that use Sanger sequencing to scan for mutations in the CFTR gene. We have explored a more efficient genetic screening strategy based on next-generation sequencing (NGS) of the CFTR gene. We validated this approach in a cohort of 177 patients with previously known CFTR mutations and polymorphisms.

EcmPred: prediction of extracellular matrix proteins based on random forest with maximum relevance minimum redundancy feature selection.

The extracellular matrix (ECM) is a major component of tissues of multicellular organisms. It consists of secreted macromolecules, mainly polysaccharides and glycoproteins. Malfunctions of ECM proteins lead to severe disorders such as marfan syndrome, osteogenesis imperfecta, numerous chondrodysplasias, and skin diseases.

RSARF: prediction of residue solvent accessibility from protein sequence using random forest method.

Prediction of protein structure from its amino acid sequence is still a challenging problem. The complete physicochemical understanding of protein folding is essential for the accurate structure prediction. Knowledge of residue solvent accessibility gives useful insights into protein structure prediction and function prediction.

BLProt: prediction of bioluminescent proteins based on support vector machine and relieff feature selection.

Background: Bioluminescence is a process in which light is emitted by a living organism. Most creatures that emit light are sea creatures, but some insects, plants, fungi etc, also emit light. The biotechnological application of bioluminescence has become routine and is considered essential for many medical and general technological advances.

3dswap-pred: prediction of 3D domain swapping from protein sequence using Random Forest approach.

3D domain swapping is a protein structural phenomenon that mediates the formation of the higher order oligomers in a variety of proteins with different structural and functional properties. 3D domain swapping is associated with a variety of biological functions ranging from oligomerization to pathological conformational diseases. 3D domain swapping is realised subsequent to structure determination where the protein is observed in the swapped conformation in the oligomeric state.

AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties.

Some creatures living in extremely low temperatures can produce some special materials called "antifreeze proteins" (AFPs), which can prevent the cell and body fluids from freezing. AFPs are present in vertebrates, invertebrates, plants, bacteria, fungi, etc. Although AFPs have a common function, they show a high degree of diversity in sequences and structures.

SMpred: a support vector machine approach to identify structural motifs in protein structure without using evolutionary information.

Knowledge of three dimensional structure is essential to understand the function of a protein. Although the overall fold is made from the whole details of its sequence, a small group of residues, often called as structural motifs, play a crucial role in determining the protein fold and its stability. Identification of such structural motifs requires sufficient number of sequence and structural homologs to define conservation and evolutionary information.

Prediction of apoptosis protein locations with genetic algorithms and support vector machines through a new mode of pseudo amino acid composition.

Apoptosis is an essential process for controlling tissue homeostasis by regulating a physiological balance between cell proliferation and cell death. The subcellular locations of proteins performing the cell death are determined by mostly independent cellular mechanisms. The regular bioinformatics tools to predict the subcellular locations of such apoptotic proteins do often fail.

Insights into Protein Sequence and Structure-Derived Features Mediating 3D Domain Swapping Mechanism using Support Vector Machine Based Approach.

3-dimensional domain swapping is a mechanism where two or more protein molecules form higher order oligomers by exchanging identical or similar subunits. Recently, this phenomenon has received much attention in the context of prions and neurodegenerative diseases, due to its role in the functional regulation, formation of higher oligomers, protein misfolding, aggregation etc. While 3-dimensional domain swap mechanism can be detected from three-dimensional structures, it remains a formidable challenge to derive common sequence or structural patterns from proteins involved in swapping.

Identification of functionally diverse lipocalin proteins from sequence information using support vector machine.

Lipocalins are functionally diverse proteins that are composed of 120-180 amino acid residues. Members of this family have several important biological functions including ligand transport, cryptic coloration, sensory transduction, endonuclease activity, stress response activity in plants, odorant binding, prostaglandin biosynthesis, cellular homeostasis regulation, immunity, immunotherapy and so on. Identification of lipocalins from protein sequence is more challenging due to the poor sequence identity which often falls below the twilight zone.

SVMCRYS: an SVM approach for the prediction of protein crystallization propensity from protein sequence.

X-ray crystallography is the most widely used method for protein 3-dimensional structure determination. Selection of target protein that can yield high quality crystal for X-ray crystallography is a challenging task. Prediction of protein crystallization propensity from sequence information is useful for the selection of target protein for crystallization.

SPRED: A machine learning approach for the identification of classical and non-classical secretory proteins in mammalian genomes.

Eukaryotic protein secretion generally occurs via the classical secretory pathway that traverses the ER and Golgi apparatus. Secreted proteins usually contain a signal sequence with all the essential information required to target them for secretion. However, some proteins like fibroblast growth factors (FGF-1, FGF-2), interleukins (IL-1 alpha, IL-1 beta), galectins and thioredoxin are exported by an alternative pathway.