pyAmpli: an amplicon-based variant filter pipeline for targeted resequencing data

Investigation of Somatic Mutations in Human Brains Targeting Genes Associated With Parkinson's Disease

Background: Somatic single nucleotide variant (SNV) mutations occur in neurons but their role in synucleinopathies is unknown. Aim: We aimed to identify disease-relevant low-level somatic SNVs in brains from sporadic patients with synucleinopathies and a monozygotic twin carrying LRRK2 G2019S, whose penetrance could be explained by somatic variation. Methods and Results: We included different brain regions from 26 Parkinson's disease (PD), one Incidental Lewy body, three multiple system atrophy cases, and 12 controls. The whole SNCA locus and exons of other genes associated with PD and neurodegeneration were deeply sequenced using molecular barcodes to improve accuracy. We selected 21 variants at 0.33-5% allele frequencies for validation using accurate methods for somatic variant detection. Conclusions: We could not detect disease-relevant somatic SNVs, however we cannot exclude their presence at earlier stages of degeneration. Our results support that coding somatic SNVs in neurodegeneration are rare, but other types of somatic variants may hold pathological consequences in synucleinopathies.

Overrepresentation of genetic variation in the AnkyrinG interactome is related to a range of neurodevelopmental disorders

Upon the discovery of numerous genes involved in the pathogenesis of neurodevelopmental disorders, several studies showed that a significant proportion of these genes converge on common pathways and protein networks. Here, we used a reversed approach, by screening the AnkyrinG protein-protein interaction network for genetic variation in a large cohort of 1009 cases with neurodevelopmental disorders. We identified a significant enrichment of de novo potentially disease-causing variants in this network, confirming that this protein network plays an important role in the emergence of several neurodevelopmental disorders.

Hotspot DAXX, PTCH2 and CYFIP2 mutations in pancreatic neuroendocrine neoplasms

Mutations in DAXX/ATRX, MEN1 and genes involved in the phosphoinositide-3-kinase/Akt/mammalian target of rapamycin (PI3K/Akt/mTOR) pathway have been implicated in pancreatic neuroendocrine neoplasms (pNENs). However, mainly mutations present in the majority of tumor cells have been identified, while proliferation-driving mutations could be present only in small fractions of the tumor. This study aims to identify high- and low-abundance mutations in pNENs using ultra-deep targeted resequencing. Formalin-fixed paraffin-embedded matched tumor-normal tissue of 38 well-differentiated pNENs was sequenced using a HaloPlex targeted resequencing panel. Novel amplicon-based algorithms were used to identify both single nucleotide variants (SNVs) and insertion-deletions (indels) present in >10% of reads (high abundance) and in <10% of reads (low abundance). Found variants were validated by Sanger sequencing. Sequencing resulted in 416,711,794 reads with an average target base coverage of 2663 ± 1476. Across all samples, 32 high-abundance somatic, 3 germline and 30 low-abundance mutations were withheld after filtering and validation. Overall, 92% of high-abundance and 84% of low-abundance mutations were predicted to be protein damaging. Frequently, mutated genes were MEN1, DAXX, ATRX, TSC2, PI3K/Akt/mTOR and MAPK-ERK pathway-related genes. Additionally, recurrent alterations on the same genomic position, so-called hotspot mutations, were found in DAXX, PTCH2 and CYFIP2. This first ultra-deep sequencing study highlighted genetic intra-tumor heterogeneity in pNEN, by the presence of low-abundance mutations. The importance of the ATRX/DAXX pathway was confirmed by the first-ever pNEN-specific protein-damaging hotspot mutation in DAXX. In this study, both novel genes, including the pro-apoptotic CYFIP2 gene and hedgehog signaling PTCH2, and novel pathways, such as the MAPK-ERK pathway, were implicated in pNEN.

DiNAMO: highly sensitive DNA motif discovery in high-throughput sequencing data

Background

Discovering over-represented approximate motifs in DNA sequences is an essential part of bioinformatics. This topic has been studied extensively because of the increasing number of potential applications. However, it remains a difficult challenge, especially with the huge quantity of data generated by high throughput sequencing technologies. To overcome this problem, existing tools use greedy algorithms and probabilistic approaches to find motifs in reasonable time. Nevertheless these approaches lack sensitivity and have difficulties coping with rare and subtle motifs.

Results

We developed DiNAMO (for DNA MOtif), a new software based on an exhaustive and efficient algorithm for IUPAC motif discovery. We evaluated DiNAMO on synthetic and real datasets with two different applications, namely ChIP-seq peaks and Systematic Sequencing Error analysis. DiNAMO proves to compare favorably with other existing methods and is robust to noise.

Conclusions

We shown that DiNAMO software can serve as a tool to search for degenerate motifs in an exact manner using IUPAC models. DiNAMO can be used in scanning mode with sliding windows or in fixed position mode, which makes it suitable for numerous potential applications.

Availability

https://github.com/bonsai-team/DiNAMO.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2215-1) contains supplementary material, which is available to authorized users.