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An estimated 3.5%-5.9% of the global population live with rare diseases, and approximately 80% of these diseases have a genetic cause. Rare genetic diseases are difficult to diagnose, with some affected individuals experiencing diagnostic delays of 5-30 years. Next-generation sequencing has improved clinical diagnostic rates to 33%-48%. In a majority of cases, novel variants potentially causing the disease are discovered.
Advances in screening and diagnostics have changed the way in which we identify and diagnose congenital anomalies.
SETBP1 Haploinsufficiency Disorder (SETBD) is characterised by mild to moderate intellectual disability, speech and language impairment, mild motor developmental delay, behavioural issues, hypotonia, mild facial dysmorphisms, and vision impairment. Despite a clear link between SETBP1 mutations and neurodevelopmental disorders the precise role of SETBP1 in neural development remains elusive.
Immune checkpoint therapy (ICT) causes durable tumour responses in a subgroup of patients, but it is not well known how T cell receptor beta (TCRβ) repertoire dynamics contribute to the therapeutic response.
The aim of the Translational Intelligence team is to understand how individual bases in our genome predispose, alter and interact in normal and disease contexts.
To describe trends, age-specific patterns, and factors influencing hospitalizations for 5 rare craniofacial anomalies.
While chemotherapy remains the first-line treatment for many cancers, it is still unclear what distinguishes responders from non-responders. Here, we characterize the chemotherapy-responsive tumor microenvironment in mice, using RNA sequencing on tumors before and after cyclophosphamide, and compare the gene expression profiles of responders with progressors.
SAMStat is an efficient program to extract quality control metrics from fastq and SAM/BAM files. A distinguishing feature is that it displays sequence composition, base quality composition and mapping error profiles split by mapping quality. This allows users to rapidly identify reasons for poor mapping including the presence of untrimmed adapters or poor sequencing quality at individual read positions.
Accurate knowledge of the relationship between craniofacial anomalies (CFA), intellectual disability (ID) and autism spectrum disorder (ASD) is essential to improve services and outcomes. The aim is to describe the association between CFA, ID and ASD using linked population data.
Identifying the causal variant for diagnosis of genetic diseases is challenging when using next-generation sequencing approaches and variant prioritization tools can assist in this task. These tools provide in silico predictions of variant pathogenicity, however they are agnostic to the disease under study. We previously performed a disease-specific benchmark of 24 such tools to assess how they perform in different disease contexts.