SPECS: a non-parametric method to identify tissue-specific molecular features for unbalanced sample groups

Background 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 can’t handle replicates at all. Results 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.com/celineeveraert/SPECS. The precalculated SPECS results on the GTEx data are available through a user-friendly browser at specs.cmgg.be. Conclusions SPECS is a non-parametric method that identifies known and novel specific-expressed genes. In addition, SPECS could be adopted for other features and applications

Candidate RNA biomarkers in biofluids for early diagnosis of ovarian cancer : a systematic review

Ovarian cancer is often diagnosed in an advanced stage and is associated with a high mortality rate. It is assumed that early detection of ovarian cancer could improve patient outcomes. Unfortunately, effective screening methods for early diagnosis of ovarian cancer are still lacking. Extracellular RNAs circulating in human biofluids can reliably be measured and are emerging as potential biomarkers in cancer. In this systematic review, we present 75 RNA biomarkers detectable in human biofluids that have been studied for early diagnosis of ovarian cancer. The majority of these markers are microRNAs identified using RT-qPCR or microarrays in blood-based fluids. A handful of studies used RNA-sequencing and explored alternative fluids, such as urine and ascites. Candidate RNA biomarkers that were more abundant in biofluids of ovarian cancer patients compared to controls in at least two independent studies include miR-21, the miR-200 family, miR-205, miR-10a and miR-346. Amongst the markers confirmed to be lower in at least two studies are miR-122, miR-193a, miR-223, miR-126 and miR-106b. While these biomarkers show promising diagnostic potential, further validation is required before implementation in routine clinical care. Challenges related to biomarker validation and reflections on future perspectives to accelerate progress in this field are discussed

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

2020 The Author(s). Background: 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 can\u27t handle replicates at all. Results: 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.com/celineeveraert/SPECS. The precalculated SPECS results on the GTEx data are available through a user-friendly browser at specs.cmgg.be. Conclusions: SPECS is a non-parametric method that identifies known and novel specific-expressed genes. In addition, SPECS could be adopted for other features and applications

Genome-wide study of the effect of blood collection tubes on the cell-free DNA methylome

The methylation pattern of cfDNA, isolated from liquid biopsies, is gaining substantial interest for diagnosis and monitoring of diseases. We have evaluated the impact of type of blood collection tube and time delay between blood draw and plasma preparation on bisulphite-based cfDNA methylation profiling. Fifteen tubes of blood were drawn from three healthy volunteer subjects (BD Vacutainer K2E EDTA spray tubes, Streck Cell-Free DNA BCT tubes, PAXgene Blood ccfDNA tubes, Roche Cell-Free DNA Collection tubes and Biomatrica LBgard blood tubes in triplicate). Samples were either immediately processed or stored at room temperature for 24 or 72 hours before plasma preparation. DNA fragment size was evaluated by capillary electrophoresis. Reduced representation bisulphite sequencing was performed on the cell-free DNA isolated from these plasma samples. We evaluated the impact of blood tube and time delay on several quality control metrics. All preservation tubes performed similar on the quality metrics that were evaluated. Furthermore, a considerable increase in cfDNA concentration and the fraction of it derived from NK cells was observed after a 72-hour time delay in EDTA tubes. The methylation pattern of cfDNA is robust and reproducible in between the different preservation tubes. EDTA tubes processed as soon as possible, preferably within 24 hours, are the most cost effective. If immediate processing is not possible, preservation tubes are valid alternatives

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

2020 The Author(s). Background: 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 can\u27t handle replicates at all. Results: 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.com/celineeveraert/SPECS. The precalculated SPECS results on the GTEx data are available through a user-friendly browser at specs.cmgg.be. Conclusions: SPECS is a non-parametric method that identifies known and novel specific-expressed genes. In addition, SPECS could be adopted for other features and applications

Custom long non-coding RNA capture enhances detection sensitivity in different human sample types

Long non-coding RNAs (lncRNAs) are a heterogeneous group of transcripts that lack protein coding potential and display regulatory functions in various cellular processes. As a result of their cell- and cancer-specific expression patterns, lncRNAs have emerged as potential diagnostic and therapeutic targets. The accurate characterization of lncRNAs in bulk transcriptome data remains challenging due to their low abundance compared to protein coding genes. To tackle this issue, we describe a unique short-read custom lncRNA capture sequencing approach that relies on a comprehensive set of 565,878 capture probes for 49,372 human lncRNA genes. This custom lncRNA capture approach was evaluated on various sample types ranging from artificial high-quality RNA mixtures to more challenging formalin-fixed paraffin-embedded tissue and biofluid material. The custom enrichment approach allows the detection of a more diverse repertoire of lncRNAs, with better reproducibility and higher coverage compared to classic total RNA-sequencing

CiLiQuant : quantification of RNA junction reads based on their circular or linear transcript origin

Distinguishing circular RNA reads from reads derived from the linear host transcript is a challenging task because of sequence overlap. We developed a computational approach, CiLiQuant, that determines the relative circular and linear abundance of transcripts and gene loci using back-splice and unambiguous forward-splice junction reads generated by existing mapping and circular RNA discovery tools