PattRec: An easy-to-use CNV detection tool optimized for targeted NGS assays with diagnostic purposes.

Genetic laboratories use custom-commercial targeted next-generation sequencing (tg-NGS) assays to identify disease-causing variants. Although the high coverage achieved with these tests allows for the detection of copy number variants (CNVs), which account for an important proportion of the genetic burden in human diseases, an easy-to-use tool for automatic CNV detection is still lacking. This article presents a new CNV detection tool optimized for tg-NGS data: PattRec. PattRec was evaluated using a wide range of data, and its performance compared with those of other CNV detection tools. The software includes features for selecting optimal controls, discarding polymorphic CNVs prior to analysis, and filtering out deletions based on SNV zygosity, and automatically creates an in-house CNV database. There is no need for high level bioinformatic expertise and users can choose color-coded xlsx output that helps to prioritize potentially pathogenic CNVs. PattRec is presented as a Java based GUI, freely available online: https://github.com/irotero/PattRec.

The Increasing Impact of Translational Research in the Molecular Diagnostics of Neuromuscular Diseases.

The diagnosis of neuromuscular diseases (NMDs) has been progressively evolving from the grouping of clinical symptoms and signs towards the molecular definition. Optimal clinical, biochemical, electrophysiological, electrophysiological, and histopathological characterization is very helpful to achieve molecular diagnosis, which is essential for establishing prognosis, treatment and genetic counselling. Currently, the genetic approach includes both the gene-targeted analysis in specific clinically recognizable diseases, as well as genomic analysis based on next-generation sequencing, analyzing either the clinical exome/genome or the whole exome or genome. However, as of today, there are still many patients in whom the causative genetic variant cannot be definitely established and variants of uncertain significance are often found. In this review, we address these drawbacks by incorporating two additional biological omics approaches into the molecular diagnostic process of NMDs. First, functional genomics by introducing experimental cell and molecular biology to analyze and validate the variant for its biological effect in an in-house translational diagnostic program, and second, incorporating a multi-omics approach including RNA-seq, metabolomics, and proteomics in the molecular diagnosis of neuromuscular disease. Both translational diagnostics programs and omics are being implemented as part of the diagnostic process in academic centers and referral hospitals and, therefore, an increase in the proportion of neuromuscular patients with a molecular diagnosis is expected. This improvement in the process and diagnostic performance of patients will allow solving aspects of their health problems in a precise way and will allow them and their families to take a step forward in their lives.

Sequence information gain based motif analysis.

BACKGROUND: The detection of regulatory regions in candidate sequences is essential for the understanding of the regulation of a particular gene and the mechanisms involved. This paper proposes a novel methodology based on information theoretic metrics for finding regulatory sequences in promoter regions. RESULTS: This methodology (SIGMA) has been tested on genomic sequence data for Homo sapiens and Mus musculus. SIGMA has been compared with different publicly available alternatives for motif detection, such as MEME/MAST, Biostrings (Bioconductor package), MotifRegressor, and previous work such Qresiduals projections or information theoretic based detectors. Comparative results, in the form of Receiver Operating Characteristic curves, show how, in 70% of the studied Transcription Factor Binding Sites, the SIGMA detector has a better performance and behaves more robustly than the methods compared, while having a similar computational time. The performance of SIGMA can be explained by its parametric simplicity in the modelling of the non-linear co-variability in the binding motif positions. CONCLUSIONS: Sequence Information Gain based Motif Analysis is a generalisation of a non-linear model of the cis-regulatory sequences detection based on Information Theory. This generalisation allows us to detect transcription factor binding sites with maximum performance disregarding the covariability observed in the positions of the training set of sequences. SIGMA is freely available to the public at http://b2slab.upc.edu.

A subspace method for the detection of transcription factor binding sites.

MOTIVATION: The identification of the sites at which transcription factors (TFs) bind to Deoxyribonucleic acid (DNA) is an important problem in molecular biology. Many computational methods have been developed for motif finding, most of them based on position-specific scoring matrices (PSSMs) which assume the independence of positions within a binding site. However, some experimental and computational studies demonstrate that interdependences within the positions exist. RESULTS: In this article, we introduce a novel motif finding method which constructs a subspace based on the covariance of numerical DNA sequences. When a candidate sequence is projected into the modeled subspace, a threshold in the Q-residuals confidence allows us to predict whether this sequence is a binding site. Using the TRANSFAC and JASPAR databases, we compared our Q-residuals detector with existing PSSM methods. In most of the studied TF binding sites, the Q-residuals detector performs significantly better and faster than MATCH and MAST. As compared with Motifscan, a method which takes into account interdependences, the performance of the Q-residuals detector is better when the number of available sequences is small.

Fusion InPipe, an integrative pipeline for gene fusion detection from RNA-seq data in acute pediatric leukemia.

RNA sequencing (RNA-seq) is a reliable tool for detecting gene fusions in acute leukemia. Multiple bioinformatics pipelines have been developed to analyze RNA-seq data, but an agreed gold standard has not been established. This study aimed to compare the applicability of 5 fusion calling pipelines (Arriba, deFuse, CICERO, FusionCatcher, and STAR-Fusion), as well as to define and develop an integrative bioinformatics pipeline (Fusion InPipe) to detect clinically relevant gene fusions in acute pediatric leukemia. We analyzed RNA-seq data by each pipeline individually and by Fusion InPipe. Each algorithm individually called most of the fusions with similar sensitivity and precision. However, not all rearrangements were called, suggesting that choosing a single pipeline might cause missing important fusions. To improve this, we integrated the results of the five algorithms in just one pipeline, Fusion InPipe, comparing the output from the agreement of 5/5, 4/5, and 3/5 algorithms. The maximum sensitivity was achieved with the agreement of 3/5 algorithms, with a global sensitivity of 95%, achieving a 100% in patients' data. Furthermore, we showed the necessity of filtering steps to reduce the false positive detection rate. Here, we demonstrate that Fusion InPipe is an excellent tool for fusion detection in pediatric acute leukemia with the best performance when selecting those fusions called by at least 3/5 pipelines.

Coenzyme Q10 Treatment Monitoring in Different Human Biological Samples.

Coenzyme Q10 (CoQ) treatment monitoring is a matter of debate since CoQ distribution from plasma to blood cells and tissues is not fully understood. We aimed to analyze the CoQ levels in a wide set of human biological samples (plasma, blood mononuclear cells (BMCs), platelets, urinary cells, and skeletal muscle) from a group of 11 healthy male runners before and after CoQ supplementation. The CoQ content in the different samples was analyzed by HPLC coupled to electrochemical detection. No significant differences were observed in the CoQ levels measured in the BMCs, platelets, and urine after the one-month treatment period. Plasma CoQ (expressed in absolute values and values relative to total cholesterol) significantly increased after CoQ supplementation (p = 0.003 in both cases), and the increase in CoQ in muscle approached significance (p = 0.074). CoQ levels were increased in the plasma of all supplemented subjects, and muscle CoQ levels were increased in 8 out of 10 supplemented subjects. In conclusion, the analysis of CoQ in plasma samples seems to be the best surrogate biomarker for CoQ treatment monitoring. Moreover, oral CoQ administration was effective for increasing muscle CoQ concentrations in most subjects.

Antigen-stimulated PBMC transcriptional protective signatures for malaria immunization.

Identifying immune correlates of protection and mechanisms of immunity accelerates and streamlines the development of vaccines. RTS,S/AS01E, the most clinically advanced malaria vaccine, has moderate efficacy in African children. In contrast, immunization with sporozoites under antimalarial chemoprophylaxis (CPS immunization) can provide 100% sterile protection in naïve adults. We used systems biology approaches to identifying correlates of vaccine-induced immunity based on transcriptomes of peripheral blood mononuclear cells from individuals immunized with RTS,S/AS01E or chemoattenuated sporozoites stimulated with parasite antigens in vitro. Specifically, we used samples of individuals from two age cohorts and three African countries participating in an RTS,S/AS01E pediatric phase 3 trial and malaria-naïve individuals participating in a CPS trial. We identified both preimmunization and postimmunization transcriptomic signatures correlating with protection. Signatures were validated in independent children and infants from the RTS,S/AS01E phase 3 trial and individuals from an independent CPS trial with high accuracies (>70%). Transcription modules revealed interferon, NF-κB, Toll-like receptor (TLR), and monocyte-related signatures associated with protection. Preimmunization signatures suggest that priming the immune system before vaccination could potentially improve vaccine immunogenicity and efficacy. Last, signatures of protection could be useful to determine efficacy in clinical trials, accelerating vaccine candidate testing. Nevertheless, signatures should be tested more extensively across multiple cohorts and trials to demonstrate their universal predictive capacity.

Helpful Criteria When Implementing NGS Panels in Childhood Lymphoblastic Leukemia.

The development of Next-Generation Sequencing (NGS) has provided useful diagnostic, prognostic, and therapeutic strategies for individualized management of B-cell precursor acute lymphoblastic leukemia (BCP-ALL) patients. Consequently, NGS is rapidly being established in clinical practice. However, the technology's complexity, bioinformatics analysis, and the different available options difficult a broad consensus between different laboratories in its daily routine introduction. This collaborative study among Spanish centers was aimed to assess the feasibility, pros, and cons of our customized panel and other commercial alternatives of NGS-targeted approaches. The custom panel was tested in three different sequencing centers. We used the same samples to assess other commercial panels (OncomineTM Childhood Cancer Research Assay; Archer®FusionPlex® ALL, and Human Comprehensive Cancer Panel GeneRead Panel v2®). Overall, the panels showed a good performance in different centers and platforms, but each NGS approach presented some issues, as well as pros and cons. Moreover, a previous consensus on the analysis and reporting following international guidelines would be preferable to improve the concordance in results among centers. Our study shows the challenges posed by NGS methodology and the need to consider several aspects of the chosen NGS-targeted approach and reach a consensus before implementing it in daily practice.

Effect of blood contamination of cerebrospinal fluid on amino acids, biogenic amines, pterins and vitamins.

BACKGROUND: Cerebrospinal fluid (CSF) metabolomic investigations are a powerful tool for studying neurometabolic diseases. We aimed to assess the effect of CSF contamination with blood on the concentrations of selected biomarkers. METHODS: CSF samples were spiked in duplicate with increasing volumes of whole blood under two conditions: (A) pooled CSF spiked with fresh whole blood and frozen to cause red blood cell (RBC) lysis; (B) pooled CSF spiked with fresh blood and centrifuged (the supernatant with no RBCs was frozen until the moment of analysis). CSF concentrations of amino acids, biogenic amines, pterins, and vitamins were analysed by HPLC coupled with tandem mass spectrometry, electrochemical and fluorescence detection. RESULTS: Aspartate, glutamate, taurine, ornithine, glycine, citrulline, pyridoxal 5´-phosphate, 5-methyltetrahydrofolate, and thiamine showed higher values when RBCs were lysed when compared with those of CSF with no RBC, while arginine, 5-hydroxyindoleacetic and homovanillic acids showed lower values. When RBCs were removed from CSF, only some amino acids, thiamine and pyridoxal 5´-phosphate showed moderately higher values when compared with the non-spiked CSF sample. CONCLUSIONS: CSF-targeted metabolomic analysis is feasible even when substantial RBC contamination of CSF has occurred since CSF centrifugation to remove RBC prior to freezing eliminated most of the interferences observed.

Plasma coenzyme Q10 status is impaired in selected genetic conditions.

Identifying diseases displaying chronic low plasma Coenzyme Q10 (CoQ) values may be important to prevent possible cardiovascular dysfunction. The aim of this study was to retrospectively evaluate plasma CoQ concentrations in a large cohort of pediatric and young adult patients. We evaluated plasma CoQ values in 597 individuals (age range 1 month to 43 years, average 11 years), studied during the period 2005-2016. Patients were classified into 6 different groups: control group of healthy participants, phenylketonuric patients (PKU), patients with mucopolysaccharidoses (MPS), patients with other inborn errors of metabolism (IEM), patients with neurogenetic diseases, and individuals with neurological diseases with no genetic diagnosis. Plasma total CoQ was measured by reverse-phase high-performance liquid chromatography with electrochemical detection and ultraviolet detection at 275 nm. ANOVA with Bonferroni correction showed that plasma CoQ values were significantly lower in the PKU and MPS groups than in controls and neurological patients. The IEM group showed intermediate values that were not significantly different from those of the controls. In PKU patients, the Chi-Square test showed a significant association between having low plasma CoQ values and being classic PKU patients. The percentage of neurogenetic and other neurological patients with low CoQ values was low (below 8%). In conclusión, plasma CoQ monitoring in selected groups of patients with different IEM (especially in PKU and MPS patients, but also in IEM under protein-restricted diets) seems advisable to prevent the possibility of a chronic blood CoQ suboptimal status in such groups of patients.

MEET: motif elements estimation toolkit.

MEET is an R package that integrates a set of algorithms for the detection of transcription factor binding sites (TFBS). The MEET R package includes five motif searching algorithms: MEME/MAST(Multiple Expectation-Maximization for Motif Elicitation), Q-residuals, MDscan (Motif Discovery scan), ITEME (Information Theory Elements for Motif Estimation) and MATCH. In addition MEET allows the user to work with different alignment algorithms: MUSCLE (Multiple Sequence Comparison by Log-Expectation), ClustalW and MEME. The package can work in two modes, training and detection. The training mode allows the user to choose the best parameters of a detector. Once the parameters are chosen, the detection mode allows to analyze a genome looking for binding sites. Both modes can combine the different alignment and detection methods, offering multiple possibilities. Combining the alignments and the detection algorithms makes possible the comparison between detection models at the same level, without having to care about the differences produced during the alignment process. The MEET R package can be downloaded from http://sisbio.recerca.upc.edu/R/MEET_1.0. tar.gz.

Transcription factor binding site detection through position cross-mutual information variability analysis.

Regulatory sequence detection is a fundamental challenge in computational biology. One key process in protein synthesis starts with the binding of the transcription factor to its binding site. Different sites can show binding to the same factor. This variability found in binding sequences increases the difficulty of their detection using computational algorithms. In this manuscript, a method for the detection of binding sites is proposed, based on the correlation between binding sequence positions through information theoretical measures. Efficiency values of the method are reported in the form of Receiver Operating Characteristic curves on the detection of different transcription factors of the Saccharomyces cerevisiae organism. We compare our results with other known motif detection Motif Discovery scan (MDscan).