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Computational methods for assessing the likely impacts of mutations, known as variant effect predictors (VEPs), are widely used in the assessment and interpretation of human genetic variation, as well as in other applications like protein engineering. Many different VEPs have been released, and there is tremendous variability in their underlying algorithms, outputs, and the ways in which the methodologies and predictions are shared. This leads to considerable difficulties for users trying to navigate the selection and application of VEPs. Here, to address these issues, we provide guidelines and recommendations for the release of novel VEPs.

Given the complex relationship between gene expression and phenotypic outcomes, computationally efficient approaches are needed to sift through large high-dimensional datasets in order to identify biologically relevant biomarkers. In this report, we describe a method of identifying the most salient biomarker genes in a dataset, which we call “candidate genes”, by evaluating the ability of gene combinations to classify samples from a dataset, which we call “classification potential”. Our algorithm, Gene Oracle, uses a neural network to test user defined gene sets for polygenic classification potential and then uses a combinatorial approach to further decompose selected gene sets into candidate and non-candidate biomarker genes. We tested this algorithm on curated gene sets from the Molecular Signatures Database (MSigDB) quantified in RNAseq gene expression matrices obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) data repositories. First, we identified which MSigDB Hallmark subsets have significant classification potential for both the TCGA and GTEx datasets. Then, we identified the most discriminatory candidate biomarker genes in each Hallmark gene set and provide evidence that the improved biomarker potential of these genes may be due to reduced functional complexity.

Computational methods for assessing the likely impacts of mutations, known as variant effect predictors (VEPs), are widely used in the assessment and interpretation of human genetic variation, as well as in other applications like protein engineering. Many different VEPs have been released to date, and there is tremendous variability in their underlying algorithms and outputs, and in the ways in which the methodologies and predictions are shared. This leads to considerable challenges for end users in knowing which VEPs to use and how to use them. Here, to address these issues, we provide guidelines and recommendations for the release of novel VEPs. Emphasising open-source availability, transparent methodologies, clear variant effect score interpretations, standardised scales, accessible predictions, and rigorous training data disclosure, we aim to improve the usability and interpretability of VEPs, and promote their integration into analysis and evaluation pipelines. We also provide a large, categorised list of currently available VEPs, aiming to facilitate the discovery and encourage the usage of novel methods within the scientific community.

Disease-associated genetic variants occur extensively in noncoding regions like promoters, but current methods focus primarily on single nucleotide variants (SNVs) that typically have small regulatory effect sizes. Expanding beyond single nucleotide events is essential with insertions and deletions (indels) representing the logical next step as they are readily identifiable in population data and more likely to disrupt regulatory elements. However, existing methods struggle with indel prediction, and clinical interpretation often requires assessing complete promoter haplotypes rather than individual variants. We present LOL-EVE (Language Of Life for Evolutionary Variant Effects), a conditional autoregressive transformer trained on 13.6 million mammalian promoter sequences that enables both zero-shot indel prediction and complete promoter sequence scoring. We introduce three benchmarks for promoter indel prediction: ultra rare variant prioritization, causal eQTL identification, and transcription factor binding site disruption analysis. LOL-EVE’s superior performance demonstrates that evolutionary patterns learned from indels enable accurate assessment of broader promoter function. Application to Genomics England clinical data shows that LOL-EVE can prioritize promoter haplotypes in known developmental disorder genes, suggesting potential utility for clinical variant assessment. LOL-EVE bridges individual variant prediction with haplotype-level analysis, demonstrating how evolution-based genomic language models may assist in evaluating regulatory variants in complex genetic cases.

Computational methods for assessing the likely impacts of mutations, known as variant effect predictors (VEPs), are widely used in the assessment and interpretation of human genetic variation, as well as in other applications like protein engineering. Many different VEPs have been released to date, and there is tremendous variability in their underlying algorithms and outputs, and in the ways in which the methodologies and predictions are shared. This leads to considerable challenges for end users in knowing which VEPs to use and how to use them. Here, to address these issues, we provide guidelines and recommendations for the release of novel VEPs. Emphasising open-source availability, transparent methodologies, clear variant effect score interpretations, standardised scales, accessible predictions, and rigorous training data disclosure, we aim to improve the usability and interpretability of VEPs, and promote their integration into analysis and evaluation pipelines. We also provide a large, categorised list of currently available VEPs, aiming to facilitate the discovery and encourage the usage of novel methods within the scientific community.

Structural variation includes a change in copy number, orientation, or location of a part of the genome. Copy number variants (CNVs) are a common cause of genetic hearing loss, comprising nearly 20% of diagnosed cases. While large deletions involving the gene STRC are the most common pathogenic CNVs, a significant proportion of known hearing loss genes also contain pathogenic CNVs. In this review, we provide an overview of currently used methods for detection of CNVs in genes known to cause hearing loss including molecular techniques such as multiplex ligation probe amplification (MLPA) and digital droplet polymerase chain reaction (ddPCR), array-CGH and single-nucleotide polymorphism (SNP) arrays, as well as techniques for detection of CNVs using next-generation sequencing data analysis including targeted gene panel, exome, and genome sequencing data. In addition, in this review, we compile published data on pathogenic hearing loss CNVs to provide an up-to-date overview. We show that CNVs have been identified in 29 different non-syndromic hearing loss genes. An understanding of the contribution of CNVs to genetic hearing loss is critical to the current diagnosis of hearing loss and is crucial for future gene therapies. Thus, evaluation for CNVs is required in any modern pipeline for genetic diagnosis of hearing loss.

Background Musculoskeletal disorders (MSDs) of the upper and lower extremities are common in the general population and place a significant burden on the health care system. Manual therapy is recommended by clinical practice guidelines for the management of these injuries; however, there is limited evidence to support its effectiveness. The purpose of our review was to investigate the effectiveness of manual therapy in adults or children with MSDs of the upper or lower extremity. Methods Randomized controlled trials (RCTs), cohort studies, and case–control studies evaluating the effectiveness of manual therapy were eligible. We searched MEDLINE, EMBASE, PsycINFO, CINAHL, and the Cochrane Central Register of Controlled Trials from 1990 to 2015. Paired reviewers screened studies for relevance and critically appraised relevant studies using the Scottish Intercollegiate Guidelines Network criteria. Studies with low risk of bias were synthesized following best-evidence synthesis principles. Where available, we computed mean changes between groups, relative risks and 95 % CI. Results We screened 6047 articles. Seven RCTs were critically appraised and three had low risk of bias. For adults with nonspecific shoulder pain of variable duration, cervicothoracic spinal manipulation and mobilization in addition to usual care may improve self-perceived recovery compared to usual care alone. For adults with subacromial impingement syndrome of variable duration, neck mobilization in addition to a multimodal shoulder program of care provides no added benefit. Finally, for adults with grade I-II ankle sprains of variable duration, lower extremity mobilization in addition to home exercise and advice provides greater short-term improvements in activities and function over home exercise and advice alone. No studies were included that evaluated the effectiveness of manual therapy in children or for the management of other extremity injuries in adults. Conclusions The current evidence on the effectiveness of manual therapy for MSDs of the upper and lower extremities is limited. The available evidence supports the use of manual therapy for non-specific shoulder pain and ankle sprains, but not for subacromial impingement syndrome in adults. Future research is needed to determine the effectiveness of manual therapy and guide clinical practice. Systematic review registration number CRD42014009899

Boston Children’s Hospital has established a genomic sequencing and analysis research initiative to improve clinical care for pediatric rare disease patients. Through the Children’s Rare Disease Collaborative (CRDC), the hospital offers CLIA-grade exome and genome sequencing, along with other sequencing types, to patients enrolled in specialized rare disease research studies. The data, consented for broad research use, are harmonized and analyzed with CRDC-supported variant interpretation tools. Since its launch, 66 investigators representing 26 divisions and 45 phenotype-based cohorts have joined the CRDC. These studies enrolled 4653 families, with 35% of analyzed cases having a finding either confirmed or under further investigation. This accessible and harmonized genomics platform also supports additional institutional data collections, research and clinical, and now encompasses 13,800+ patients and their families. This has fostered new research projects and collaborations, increased genetic diagnoses and accelerated innovative research via integration of genomics research with clinical care.

Ruminant animals have a symbiotic relationship with the microorganisms in their rumens. In this relationship, rumen microbes efficiently degrade complex plant-derived compounds into smaller digestible compounds, a process that is very likely associated with host animal feed efficiency. The resulting simpler metabolites can then be absorbed by the host and converted into other compounds by host enzymes. We used a microbial community metabolic network inferred from shotgun metagenomics data to assess how this metabolic system differs between animals that are able to turn ingested feedstuffs into body mass with high efficiency and those that are not. We conducted shotgun sequencing of microbial DNA from the rumen contents of 16 sheep that differed in their residual feed intake (RFI), a measure of feed efficiency. Metagenomic reads from each sheep were mapped onto a database-derived microbial metabolic network, which was linked to the sheep metabolic network by interface metabolites (metabolites transferred from microbes to host). No single enzyme was identified as being significantly different in abundance between the low and high RFI animals (P > 0.05, Wilcoxon test). However, when we analyzed the metabolic network as a whole, we found several differences between efficient and inefficient animals. Microbes from low RFI (efficient) animals use a suite of enzymes closer in network space to the host's reactions than those of the high RFI (inefficient) animals. Similarly, low RFI animals have microbial metabolic networks that, on average, contain reactions using shorter carbon chains than do those of high RFI animals, potentially allowing the host animals to extract metabolites more efficiently. Finally, the efficient animals possess community networks with greater Shannon diversity among their enzymes than do inefficient ones. Thus, our system approach to the ruminal microbiome identified differences attributable to feed efficiency in the structure of the microbes' community metabolic network that were undetected at the level of individual microbial taxa or reactions.

The use of recombinant CD4-IgG2 in pediatrie human immunodeficiency virus type 1 (HIV-1) infection was evaluated by single and multidose intravenous infusions in 18 children in a phase 1/2 study. The study drug was well tolerated, and dose proportionality was observed in terms of area under time-concentration curve and peak serum concentration. Acute decreases of >0.7 log10 copies/mL in serum HIV-1 RNA concentration were seen in 4 of the 6 children treated with 4 weekly 10 mg/kg doses. At 14 days after treatment, 3 children had sustained reductions in serum HIV-1 RNA; the other children had rebounded to baseline levels or above. By 28 days after therapy, the peak HIV-1 cellular infectious units was reduced in all 6 children, including the 2 who had experienced an earlier transient increase in values. Thus, recombinant CD4-IgG2 treatment of HIV-1—infected children appears to be well tolerated and capable of reducing HIV-1 burden.