DNAscan2: a versatile, scalable, and user-friendly analysis pipeline for human next-generation sequencing data.

SUMMARY: The current widespread adoption of next-generation sequencing (NGS) in all branches of basic research and clinical genetics fields means that users with highly variable informatics skills, computing facilities and application purposes need to process, analyse, and interpret NGS data. In this landscape, versatility, scalability, and user-friendliness are key characteristics for an NGS analysis software. We developed DNAscan2, a highly flexible, end-to-end pipeline for the analysis of NGS data, which (i) can be used for the detection of multiple variant types, including SNVs, small indels, transposable elements, short tandem repeats, and other large structural variants; (ii) covers all standard steps of NGS analysis, from quality control of raw data and genome alignment to variant calling, annotation, and generation of reports for the interpretation and prioritization of results; (iii) is highly adaptable as it can be deployed and run via either a graphic user interface for non-bioinformaticians and a command line tool for personal computer usage; (iv) is scalable as it can be executed in parallel as a Snakemake workflow, and; (v) is computationally efficient by minimizing RAM and CPU time requirements. AVAILABILITY AND IMPLEMENTATION: DNAscan2 is implemented in Python3 and is available at https://github.com/KHP-Informatics/DNAscanv2.

GEOexplorer: a webserver for gene expression analysis and visualisation.

Gene Expression Omnibus (GEO) is a database repository hosting a substantial proportion of publicly available high throughput gene expression data. Gene expression analysis is a powerful tool to gain insight into the mechanisms and processes underlying the biological and phenotypic differences between sample groups. Despite the wide availability of gene expression datasets, their access, analysis, and integration are not trivial and require specific expertise and programming proficiency. We developed the GEOexplorer webserver to allow scientists to access, integrate and analyse gene expression datasets without requiring programming proficiency. Via its user-friendly graphic interface, users can easily apply GEOexplorer to perform interactive and reproducible gene expression analysis of microarray and RNA-seq datasets, while producing a wealth of interactive visualisations to facilitate data exploration and interpretation, and generating a range of publication ready figures. The webserver allows users to search and retrieve datasets from GEO as well as to upload user-generated data and combine and harmonise two datasets to perform joint analyses. GEOexplorer, available at https://geoexplorer.rosalind.kcl.ac.uk, provides a solution for performing interactive and reproducible analyses of microarray and RNA-seq gene expression data, empowering life scientists to perform exploratory data analysis and differential gene expression analysis on-the-fly without informatics proficiency.

Cyclic alternating pattern in obstructive sleep apnea: A preliminary study.

Obstructive sleep apnea is linked to cardiovascular disease, metabolic disorders and dementia. The precise nature of the association between respiratory events in obstructive sleep apnea, cortical or subcortical arousals, and cognitive, autonomic and oxidative stress consequences remains incompletely elucidated. Previous studies have aimed to understand the relationship between obstructive sleep apnea and arousal patterns, as defined by the cyclic alternating pattern, but results have been inconsistent, in part likely due to the presence of associated comorbidities. To better define this relationship, we analysed cyclic alternating patterns in patients with obstructive sleep apnea without any additional comorbidities. We identified 18 adult male, non-obese subjects with obstructive sleep apnea and no other comorbidities or medication history, who underwent whole-night electroencephalography and polysomnography. Cyclic alternating pattern analysis was performed and verified by certified somnologists. Pairwise linear regression analysis demonstrated an inverse relationship between obstructive sleep apnea severity and cyclic alternating pattern subtype A1, and a direct correlation with cyclic alternating pattern subtype A3. Cyclic alternating pattern subtypes A1 prevail in milder obstructive sleep apnea phenotype, whilst cyclic alternating pattern subtypes A2 and A3 overcome among moderate-to-severe obstructive sleep apnea patients. The milder obstructive sleep apnea group also presented higher sleep efficiency, and increased percentages of non-rapid eye movement stage 3 and rapid eye movement sleep, as well as longer cyclic alternating pattern sequences in N3, while severe obstructive sleep apnea patients spent more time in lighter sleep stages. These results imply/suggest a balance between cyclic alternating pattern's adaptive and maladaptive arousal processes in obstructive sleep apnea of differing severities. In milder obstructive sleep apnea (apnea-hypopnea index < 20), sleep continuity may be reinforced by cyclic alternating pattern subtype A1, whereas in more severe obstructive sleep apnea, decompensation of these sleep-stabilizing mechanisms may occur and more intrusive cyclic alternating pattern fluctuations disrupt sleep circuitry.

Mistimed sleep disrupts circadian regulation of the human transcriptome.

Circadian organization of the mammalian transcriptome is achieved by rhythmic recruitment of key modifiers of chromatin structure and transcriptional and translational processes. These rhythmic processes, together with posttranslational modification, constitute circadian oscillators in the brain and peripheral tissues, which drive rhythms in physiology and behavior, including the sleep-wake cycle. In humans, sleep is normally timed to occur during the biological night, when body temperature is low and melatonin is synthesized. Desynchrony of sleep-wake timing and other circadian rhythms, such as occurs in shift work and jet lag, is associated with disruption of rhythmicity in physiology and endocrinology. However, to what extent mistimed sleep affects the molecular regulators of circadian rhythmicity remains to be established. Here, we show that mistimed sleep leads to a reduction of rhythmic transcripts in the human blood transcriptome from 6.4% at baseline to 1.0% during forced desynchrony of sleep and centrally driven circadian rhythms. Transcripts affected are key regulators of gene expression, including those associated with chromatin modification (methylases and acetylases), transcription (RNA polymerase II), translation (ribosomal proteins, initiation, and elongation factors), temperature-regulated transcription (cold inducible RNA-binding proteins), and core clock genes including CLOCK and ARNTL (BMAL1). We also estimated the separate contribution of sleep and circadian rhythmicity and found that the sleep-wake cycle coordinates the timing of transcription and translation in particular. The data show that mistimed sleep affects molecular processes at the core of circadian rhythm generation and imply that appropriate timing of sleep contributes significantly to the overall temporal organization of the human transcriptome.

Effects of insufficient sleep on circadian rhythmicity and expression amplitude of the human blood transcriptome.

Insufficient sleep and circadian rhythm disruption are associated with negative health outcomes, including obesity, cardiovascular disease, and cognitive impairment, but the mechanisms involved remain largely unexplored. Twenty-six participants were exposed to 1 wk of insufficient sleep (sleep-restriction condition 5.70 h, SEM = 0.03 sleep per 24 h) and 1 wk of sufficient sleep (control condition 8.50 h sleep, SEM = 0.11). Immediately following each condition, 10 whole-blood RNA samples were collected from each participant, while controlling for the effects of light, activity, and food, during a period of total sleep deprivation. Transcriptome analysis revealed that 711 genes were up- or down-regulated by insufficient sleep. Insufficient sleep also reduced the number of genes with a circadian expression profile from 1,855 to 1,481, reduced the circadian amplitude of these genes, and led to an increase in the number of genes that responded to subsequent total sleep deprivation from 122 to 856. Genes affected by insufficient sleep were associated with circadian rhythms (PER1, PER2, PER3, CRY2, CLOCK, NR1D1, NR1D2, RORA, DEC1, CSNK1E), sleep homeostasis (IL6, STAT3, KCNV2, CAMK2D), oxidative stress (PRDX2, PRDX5), and metabolism (SLC2A3, SLC2A5, GHRL, ABCA1). Biological processes affected included chromatin modification, gene-expression regulation, macromolecular metabolism, and inflammatory, immune and stress responses. Thus, insufficient sleep affects the human blood transcriptome, disrupts its circadian regulation, and intensifies the effects of acute total sleep deprivation. The identified biological processes may be involved with the negative effects of sleep loss on health, and highlight the interrelatedness of sleep homeostasis, circadian rhythmicity, and metabolism.

Profile of sleep disturbances in patients with recurrent depressive disorder or bipolar affective disorder in a tertiary sleep disorders service.

Bidirectional relationship between sleep disturbances and affective disorders is increasingly recognised, but its underlying mechanisms are far from clear, and there is a scarcity of studies that report on sleep disturbances in recurrent depressive disorder (RDD) and bipolar affective disorder (BPAD). To address this, we conducted a retrospective study of polysomnographic and clinical records of patients presenting to a tertiary sleep disorders clinic with affective disorders. Sixty-three BPAD patients (32 female; mean age ± S.D.: 41.8 ± 12.4 years) and 126 age- and gender-matched RDD patients (62 female; 41.5 ± 12.8) were studied. Whilst no significant differences were observed in sleep macrostructure parameters between BPAD and RDD patients, major differences were observed in comorbid sleep and physical disorders, both of which were higher in BPAD patients. Two most prevalent sleep disorders, namely obstructive sleep apnoea (OSA) (BPAD 50.8.0% vs RDD 29.3%, P = 0.006) and insomnia (BPAD 34.9% vs RDD 15.0%, P = 0.005) were found to be strongly linked with BPAD. In summary, in our tertiary sleep clinic cohort, no overt differences in the sleep macrostructure between BPAD and RDD patients were demonstrated. However, OSA and insomnia, two most prevalent sleep disorders, were found significantly more prevalent in patients with BPAD, by comparison to RDD patients. Also, BPAD patients presented with significantly more severe OSA, and with higher overall physical co-morbidity. Thus, our findings suggest an unmet/hidden need for earlier diagnosis of those with BPAD.

Unsupervised machine learning identifies distinct ALS molecular subtypes in post-mortem motor cortex and blood expression data.

Amyotrophic lateral sclerosis (ALS) displays considerable clinical and genetic heterogeneity. Machine learning approaches have previously been utilised for patient stratification in ALS as they can disentangle complex disease landscapes. However, lack of independent validation in different populations and tissue samples have greatly limited their use in clinical and research settings. We overcame these issues by performing hierarchical clustering on the 5000 most variably expressed autosomal genes from motor cortex expression data of people with sporadic ALS from the KCL BrainBank (N = 112). Three molecular phenotypes linked to ALS pathogenesis were identified: synaptic and neuropeptide signalling, oxidative stress and apoptosis, and neuroinflammation. Cluster validation was achieved by applying linear discriminant analysis models to cases from TargetALS US motor cortex (N = 93), as well as Italian (N = 15) and Dutch (N = 397) blood expression datasets, for which there was a high assignment probability (80-90%) for each molecular subtype. The ALS and motor cortex specificity of the expression signatures were tested by mapping KCL BrainBank controls (N = 59), and occipital cortex (N = 45) and cerebellum (N = 123) samples from TargetALS to each cluster, before constructing case-control and motor cortex-region logistic regression classifiers. We found that the signatures were not only able to distinguish people with ALS from controls (AUC 0.88 ± 0.10), but also reflect the motor cortex-based disease process, as there was perfect discrimination between motor cortex and the other brain regions. Cell types known to be involved in the biological processes of each molecular phenotype were found in higher proportions, reinforcing their biological interpretation. Phenotype analysis revealed distinct cluster-related outcomes in both motor cortex datasets, relating to disease onset and progression-related measures. Our results support the hypothesis that different mechanisms underpin ALS pathogenesis in subgroups of patients and demonstrate potential for the development of personalised treatment approaches. Our method is available for the scientific and clinical community at https://alsgeclustering.er.kcl.ac.uk .

Molecular dynamics analysis of superoxide dismutase 1 mutations suggests decoupling between mechanisms underlying ALS onset and progression.

Mutations in the superoxide dismutase 1 (SOD1) gene are the second most common known cause of ALS. SOD1 variants express high phenotypic variability and over 200 have been reported in people with ALS. It was previously proposed that variants can be broadly classified in two groups, 'wild-type like' (WTL) and 'metal binding region' (MBR) variants, based on their structural location and biophysical properties. MBR variants, but not WTL variants, were associated with a reduction of SOD1 enzymatic activity. In this study we used molecular dynamics and large clinical datasets to characterise the differences in the structural and dynamic behaviour of WTL and MBR variants with respect to the wild-type SOD1, and how such differences influence the ALS clinical phenotype. Our study identified marked structural differences, some of which are observed in both variant groups, while others are group specific. Moreover, collecting clinical data of approximately 500 SOD1 ALS patients carrying variants, we showed that the survival time of patients carrying an MBR variant is generally longer (∼6 years median difference, p < 0.001) with respect to patients with a WTL variant. In conclusion, our study highlighted key differences in the dynamic behaviour between WTL and MBR SOD1 variants, and between variants and wild-type SOD1 at an atomic and molecular level, that could be further investigated to explain the associated phenotypic variability. Our results support the hypothesis of a decoupling between mechanisms of onset and progression of SOD1 ALS, and an involvement of loss-of-function of SOD1 with the disease progression.

Amyotrophic lateral sclerosis and cerebellum.

Amyotrophic lateral sclerosis (ALS) is a devastating, heterogeneous neurodegenerative neuromuscular disease that leads to a fatal outcome within 2-5 years, and yet, a precise nature of the association between its major phenotypes and the cerebellar role in ALS pathology remains unknown. Recently, repeat expansions in several genes in which variants appreciably contribute to cerebellar pathology, including C9orf72, NIPA1, ATXN2 and ATXN1, have been found to confer a significant risk for ALS. To better define this relationship, we performed MAGMA gene-based analysis and tissue enrichment analysis using genome-wide association study summary statistics based on a study of 27,205 people with ALS and 110,881 controls. Our preliminary results imply a striking cerebellar tissue specificity and further support increasing calls for re-evaluation of the cerebellar role in the ALS pathology.

RetroSnake: A modular pipeline to detect human endogenous retroviruses in genome sequencing data.

Human endogenous retroviruses (HERVs) integrated into the human genome as a result of ancient exogenous infections and currently comprise ∼8% of our genome. The members of the most recently acquired HERV family, HERV-Ks, still retain the potential to produce viral molecules and have been linked to a wide range of diseases including cancer and neurodegeneration. Although a range of tools for HERV detection in NGS data exist, most of them lack wet lab validation and they do not cover all steps of the analysis. Here, we describe RetroSnake, an end-to-end, modular, computationally efficient, and customizable pipeline for the discovery of HERVs in short-read NGS data. RetroSnake is based on an extensively wet-lab validated protocol, it covers all steps of the analysis from raw data to the generation of annotated results presented as an interactive html file, and it is easy to use by life scientists without substantial computational training. Availability and implementation: The Pipeline and an extensive documentation are available on GitHub.

An assessment of bioinformatics tools for the detection of human endogenous retroviral insertions in short-read genome sequencing data.

There is a growing interest in the study of human endogenous retroviruses (HERVs) given the substantial body of evidence that implicates them in many human diseases. Although their genomic characterization presents numerous technical challenges, next-generation sequencing (NGS) has shown potential to detect HERV insertions and their polymorphisms in humans. Currently, a number of computational tools to detect them in short-read NGS data exist. In order to design optimal analysis pipelines, an independent evaluation of the available tools is required. We evaluated the performance of a set of such tools using a variety of experimental designs and datasets. These included 50 human short-read whole-genome sequencing samples, matching long and short-read sequencing data, and simulated short-read NGS data. Our results highlight a great performance variability of the tools across the datasets and suggest that different tools might be suitable for different study designs. However, specialized tools designed to detect exclusively human endogenous retroviruses consistently outperformed generalist tools that detect a wider range of transposable elements. We suggest that, if sufficient computing resources are available, using multiple HERV detection tools to obtain a consensus set of insertion loci may be ideal. Furthermore, given that the false positive discovery rate of the tools varied between 8% and 55% across tools and datasets, we recommend the wet lab validation of predicted insertions if DNA samples are available.

A realistic assessment of methods for extracting gene/protein interactions from free text.

BACKGROUND: The automated extraction of gene and/or protein interactions from the literature is one of the most important targets of biomedical text mining research. In this paper we present a realistic evaluation of gene/protein interaction mining relevant to potential non-specialist users. Hence we have specifically avoided methods that are complex to install or require reimplementation, and we coupled our chosen extraction methods with a state-of-the-art biomedical named entity tagger. RESULTS: Our results show: that performance across different evaluation corpora is extremely variable; that the use of tagged (as opposed to gold standard) gene and protein names has a significant impact on performance, with a drop in F-score of over 20 percentage points being commonplace; and that a simple keyword-based benchmark algorithm when coupled with a named entity tagger outperforms two of the tools most widely used to extract gene/protein interactions. CONCLUSION: In terms of availability, ease of use and performance, the potential non-specialist user community interested in automatically extracting gene and/or protein interactions from free text is poorly served by current tools and systems. The public release of extraction tools that are easy to install and use, and that achieve state-of-art levels of performance should be treated as a high priority by the biomedical text mining community.