Biology subjects

Volders, P.-J.

Publications and source records attributed to Volders, P.-J..

11 recordsLinked to original sources

bioRxiv2023

Multi-omic profiling of pathogen-stimulated primary immune cells

We performed long-read transcriptome and proteome profiling of pathogen-stimulated peripheral blood mononuclear cells (PBMCs) from healthy donors to discover new transcript and protein isoforms expressed during immune responses to diverse pathogens. Long-read transcriptome profiling reveals novel sequences and isoform switching induced upon pathogen stimulation, including transcripts that are difficult to detect using traditional short-read sequencing. Widespread loss of intron retention occurs as a common result of all pathogen stimulations. We highlight novel transcripts of NFKB1 and CASP1 that may indicate novel immunological mechanisms. RNA expression differences did not result in differences in the amounts of secreted proteins. Clustering analysis of secreted proteins revealed a correlation between chemokine (receptor) expression on the RNA and protein levels in C. albicans- and Poly(I:C)-stimulated PBMCs. Isoform aware long-read sequencing of pathogen-stimulated immune cells highlights the potential of these methods to identify novel transcripts, revealing a more complex transcriptome landscape than previously appreciated.

immunology↗

bioRxiv2022

Large-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precision

The detection of circular RNA molecules (circRNAs) is typically based on short-read RNA sequencing data processed by computational detection tools. During the last decade, a plethora of such tools have been developed, but a systematic comparison with orthogonal validation is missing. Here, we set up a circRNA detection tool benchmarking study, in which 16 tools were used and detected over 315,000 unique circRNAs in three deeply sequenced human cell types. Next, 1,516 predicted circRNAs were empirically validated using three orthogonal methods. Generally, tool-specific precision values are high and similar (median of 98.8%, 96.3%, and 95.5% for qPCR, RNase R, and amplicon sequencing, respectively) whereas the sensitivity and number of predicted circRNAs (ranging from 1,372 to 58,032) are the most significant tool differentiators. Furthermore, we demonstrate the complementarity of tools through the increase in detection sensitivity by considering the union of highly-precise tool combinations while keeping the number of false discoveries low. Finally, based on the benchmarking results, recommendations are put forward for circRNA detection and validation.

genomics↗

bioRxiv2022

SUsPECT: A pipeline for variant effect prediction based on custom long-read transcriptomes for improved clinical variant annotation

Our incomplete knowledge of the human transcriptome impairs the detection of disease-causing variants, in particular in transcripts only expressed under certain conditions. These transcripts are often lacking from reference transcript sets, such as Ensembl/GENCODE and RefSeq, and could be relevant for establishing genetic diagnoses. We present SUsPECT (Solving Unsolved Patient Exomes/gEnomes using Custom Transcriptomes), a pipeline based on the Ensembl Variant Effect Predictor (VEP) to predict variant impact on custom transcript sets, such as those generated by long-read RNA-sequencing, for downstream prioritization. Our pipeline predicts the functional consequence and likely deleteriousness scores for missense variants in the context of novel open reading frames predicted from any transcriptome. We demonstrate the utility of SUsPECT by uncovering potential mutational mechanisms of pathogenic variants in ClinVar that are predicted to be benign using the reference transcript annotation. In further support of SUsPECTs utility, we identified an enrichment of immune-related variants predicted to have a more severe molecular consequence when annotating with a newly generated transcriptome from stimulated immune cells instead of the reference transcriptome. Our pipeline outputs crucial information for further prioritization of potentially disease-causing variants for any disease and will become increasingly useful as more long-read RNA sequencing datasets become available.

bioinformatics↗

bioRxiv2022

Performance evaluation of three DNA sample tracking tools in a whole exome sequencing workflow

IntroductionNext-generation sequencing applications are becoming indispensable for clinical diagnostics. These experiments require numerous wet and dry lab steps, each one increasing the probability of a sample swap or contamination. Therefore, an identity confirmation at the end of the process is recommended to ensure the right data is used for each patient. MethodsWe tested three commercially available, SNP based sample tracking kits in a diagnostic workflow to evaluate their ease of use and performance. The coverage uniformity, on-target specificity, sample identification and genotyping performance were determined to assess the reliability and the cost-effectiveness of each kit. Results and discussionHands-on time and manual steps are almost identical for the kits from pxlence and Nimagen. The Swift kit has an extra purification step, making it the longest and most demanding protocol. Furthermore, the Swift kit failed to correctly genotype 26 out of the 46 samples. The Nimagen kit identified all but one sample and the pxlence kit unambiguously identified all samples, making it the most reliable and robust kit of this evaluation. The Nimagen kit showed poor on-target mapping rates, resulting in deeper sequencing needs and higher sequencing costs compared to the other two kits. Our conclusion is that the Human Sample ID kit from pxlence is the most cost-effective of the three tested tools for DNA sample tracking and identification. Key pointsO_LIKits from pxlence and Nimagen are easy to use. C_LIO_LIUnambiguous identification of all samples possible with the pxlence kit. C_LIO_LIOnly 20 out of 46 samples were correctly identified with the Swift kit. C_LIO_LIPoor on-target rates for the Nimagen kit results in higher sequencing costs. C_LI

genomics↗

bioRxiv2021

Sensitive and specific spectral library searching with COSS and Percolator

Maintaining high sensitivity while limiting false positives is a key challenge in peptide identification from mass spectrometry data. Here, we therefore investigate the effects of integrating the machine learning-based post-processor Percolator into our spectral library searching tool COSS. To evaluate the effects of this post-processing, we have used forty data sets from two different projects and have searched these against the NIST and MassIVE spectral libraries. The searching is carried out using two spectral library search tools, COSS and MSPepSearch with and without Percolator post-processing, and using sequence database search engine MS-GF+ as a baseline comparator. The addition of the Percolator rescoring step to COSS is effective and results in a substantial improvement in sensitivity and specificity of the identifications. COSS is freely available as open source under the permissive Apache2 license, and binaries and source code are found at https://github.com/compomics/COSS

bioinformatics↗

bioRxiv2020

The Personalized Proteome: Comparing Proteogenomics and Open Variant Search Approaches for Single Amino Acid Variant Detection

Discovery of variant peptides such as single amino acid variant (SAAV) in shotgun proteomics data is essential for personalized proteomics. Both the resolution of shotgun proteomics methods and the search engines have improved dramatically, allowing for confident identification of SAAV peptides. However, it is not yet known if these methods are truly successful in accurately identifying SAAV peptides without prior genomic information in the search database. We studied this in unprecedented detail by exploiting publicly available long-read RNA seq and shotgun proteomics data from the gold standard reference cell line NA12878. Searching spectra from this cell line with the state-of-the-art open modification search engine ionbot against carefully curated search databases resulted in 96.7% false positive SAAVs and an 85% lower true positive rate than searching with peptide search databases that incorporate prior genetic information. While adding genetic variants to the search database remains indispensable for correct peptide identification, inclusion of long-read RNA sequences in the search database contributes only 0.3% new peptide identifications. These findings reveal the differences in SAAV detection that result from various approaches, providing guidance to researchers studying SAAV peptides and developers of peptide spectrum identification tools.

bioinformatics↗

bioRxiv2019

The RNA Atlas, a single nucleotide resolution map of the human transcriptome

The human transcriptome consists of various RNA biotypes including multiple types of non-coding RNAs (ncRNAs). Current ncRNA compendia remain incomplete partially because they are almost exclusively derived from the interrogation of small- and polyadenylated RNAs. Here, we present a more comprehensive atlas of the human transcriptome that is derived from matching polyA-, total-, and small-RNA profiles of a heterogeneous collection of nearly 300 human tissues and cell lines. We report on thousands of novel RNA species across all major RNA biotypes, including a hitherto poorly-cataloged class of non-polyadenylated single-exon long non-coding RNAs. In addition, we exploit intron abundance estimates from total RNA-sequencing to test and verify functional regulation by novel non-coding RNAs. Our study represents a substantial expansion of the current catalogue of human ncRNAs and their regulatory interactions. All data, analyses, and results are available in the R2 web portal and serve as a basis to further explore RNA biology and function.

genetics↗

bioRxiv2019

SPECS: A non-parameteric method to identify tissue-specific molecular features for unbalanced sample groups

To understand biology and differences among various tissues or cell types, one typically searches for molecular features that display characteristic abundance patterns. Several specificity metrics have been introduced to identify tissue-specific molecular features, but these either require an equal number of replicates per tissue or they cant handle replicates at all. We describe a non-parametric specificity score that is compatible with unequal sample group sizes. To demonstrate its usefulness, the specificity score was calculated on all GTEx samples, detecting known and novel tissue-specific genes. A webtool was developed to browse these results for genes or tissues of interest. An example python implementation of SPECS is available at https://github.ugent.be/ceeverae/SPECs. The precalculated SPECS results on the GTEx data are available through a user-friendly browser at specs.cmgg.be.

bioinformatics↗

bioRxiv2019

COSS: A fast and user-friendly tool for spectral library searching

Spectral similarity searching to identify peptide-derived MS/MS spectra is a promising technique, and different spectrum similarity search tools have therefore been developed. Each of these tools, however, comes with some limitations, mainly due to low processing speed and issues with handling large databases. Furthermore, the number of spectral data formats supported is typically limited, which also creates a threshold to adoption. We have therefore developed COSS (CompOmics Spectral Searching), a new and user-friendly spectral library search tool that relies on a probabilistic scoring function, and that includes decoy spectra generation for result validation. We have benchmarked COSS on three different spectral libraries and compared the results with established spectral search and sequence database search tools. Our comparison showed that COSS identifies more peptides, and is faster than other tools. COSS binaries and source code can be freely downloaded from https://github.com/compomics/COSS. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=70 SRC="FIGDIR/small/640458v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@1b1bfacorg.highwire.dtl.DTLVardef@9ec990org.highwire.dtl.DTLVardef@c31055org.highwire.dtl.DTLVardef@654e47_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2018

High-throughput functional analysis of lncRNA core promoters elucidates rules governing tissue-specificity

Transcription initiates at both coding and non-coding genomic elements, including mRNA and long non-coding RNA (lncRNA) core promoters and enhancer RNAs (eRNAs). However, each class has different expression profiles with lncRNAs and eRNAs being the most tissue-specific. How these complex differences in expression profiles and tissue-specificities are encoded in a single DNA sequence, however, remains unresolved. Here, we address this question using computational approaches and massively parallel reporter assays (MPRA) surveying hundreds of promoters and enhancers. We find that both divergent lncRNA and mRNA core promoters have higher capacities to drive transcription than non-divergent lncRNA and mRNA core promoters, respectively. Conversely, lincRNAs and eRNAs have lower capacities to drive transcription and are more tissue-specific than divergent genes. This higher tissue-specificity is strongly associated with having less complex TF motif profiles at the core promoter. We experimentally validated these findings by testing both engineered single-nucleotide deletions and human single-nucleotide polymorphisms (SNPs) in MPRA. In both cases, we observe that single nucleotides associated with many motifs are important drivers of promoter activity. Thus, we suggest that high TF motif density serves as a robust mechanism to increase promoter activity at the expense of tissue-specificity. Moreover, we find that 22% of common SNPs in core promoter regions have significant regulatory effects. Collectively, our findings show that high TF motif density provides redundancy and increases promoter activity at the expense of tissue specificity, suggesting that specificity of expression may be regulated by simplicity of motif usage.

genomics↗

bioRxiv2017

Targeted genomic screen reveals focal long non-coding RNA copy number alterations in cancer

The landscape of somatic copy-number alterations (SCNAs) affecting long non-coding RNAs (lncRNAs) in human cancer remains largely unexplored. While the majority of lncRNAs remains to be functionally characterized, several have been implicated in cancer development and metastasis. Considering the plethora of lncRNAs genes that is currently reported, it is conceivable that several lncRNAs might function as oncogenes or tumor suppressor genes.\n\nWe devised a strategy to detect focal lncRNA SCNAs using a custom DNA microarray platform probing 20 418 lncRNA genes. By screening a panel of 80 cancer cell lines, we detected numerous focal aberrations targeting one or multiple lncRNAs without affecting neighboring protein-coding genes. These focal aberrations are highly suggestive for a tumor suppressive or oncogenic role of the targeted lncRNA gene. Although functional validation remains an essential step in the further characterization of the involved candidate cancer lncRNAs, our results provide a direct way of prioritizing candidate lncRNAs involved in cancer pathogenesis.

genetics↗