Tissue Classification Using RNA-Seq Transcriptomics with Distribution Analysis and SVM Models*

Simple SummaryWomen at high risk for breast cancer are regularly screened using contrast-enhanced magnetic resonance imaging (MRI) to identify potential malignancy. Diffusion-weighted MRI (DW-MRI) is a non-contrast technique that measures the differential movement of water molecules in tissues and is sensitive to cancer cells. In this study, we use a multi-exponential advanced DW-MRI model called restriction spectrum imaging (RSI) to characterize the diffusion characteristics of malignant lesions, benign lesions, and healthy breast tissue to help differentiate benign from malignant disease. In a cohort of patients, we show that cancer exhibits more restricted diffusion compared to benign breast lesions and healthy tissue, whereas benign and healthy tissue are not different from each other.Diffusion-weighted MRI (DW-MRI) offers a potential adjunct to dynamic contrast-enhanced MRI to discriminate benign from malignant breast lesions by yielding quantitative information about tissue microstructure. Multi-component modeling of the DW-MRI signal over an extended b-value range (up to 3000 s/mm2) theoretically isolates the slowly diffusing (restricted) water component in tissues. Previously, a three-component restriction spectrum imaging (RSI) model demonstrated the ability to distinguish malignant lesions from healthy breast tissue. We further evaluated the utility of this three-component model to differentiate malignant from benign lesions and healthy tissue in 12 patients with known malignancy and synchronous pathology-proven benign lesions. The signal contributions from three distinct diffusion compartments were measured to generate parametric maps corresponding to diffusivity on a voxel-wise basis. The three-component model discriminated malignant from benign and healthy tissue, particularly using the restricted diffusion C1 compartment and product of the restricted and intermediate diffusion compartments (C1 and C2). However, benign lesions and healthy tissue did not significantly differ in diffusion characteristics. Quantitative discrimination of these three tissue types (malignant, benign, and healthy) in non-pre-defined lesions may enhance the clinical utility of DW-MRI in reducing excessive biopsies and aiding in surveillance and surgical evaluation without repeated exposure to gadolinium contrast.

<strong class="journal-contentHeaderColor">Abstract.</strong> Distributional data such as detrital age populations or grain size distributions are common in the geological sciences. As analytical techniques become more sophisticated, increasingly large amounts of distributional data are being gathered. These advances require quantitative and objective methods, such as multidimensional scaling (MDS), to analyse large numbers of samples. Crucial to such methods is choosing a sensible measure of dissimilarity between samples. At present, the Kolmogorov-Smirnov (KS) statistic is the most widely used of these dissimilarity measures. However, the KS statistic has some limitations. It is very sensitive to differences between the modes of two distributions, and relatively insensitive to differences between their tails. Here we introduce the Wasserstein-2 distance (<em>W</em>₂) as an alternative to address this issue. Whereas the KS-distance is defined as the maximum vertical distance between two empirical cumulative distribution functions, the <em>W</em>₂-distance is a function of the horizontal distances (i.e., age differences) between individual observations. Using a combination of synthetic examples and a published zircon U-Pb dataset, we show that the <em>W</em>₂ distance produces similar MDS results to the KS-distance in most cases, but significantly different results in some cases. Where the results differ, the <em>W</em>₂ results are geologically more sensible. For the case study, we find that the MDS map that is produced using <em>W</em>₂ can be readily interpreted in terms of the shape and average age of the age spectra. The <em>W</em>₂-distance has been added to the R package IsoplotR, for immediate use in detrital geochronology and other applications. The <em>W</em>₂ distance can be generalised to multiple dimensions, which opens opportunities beyond distributional data.

While often confused, the Kolmogorov–Smirnov test and the Smirnov test are actually distinct. Specifically, the Kolmogorov–Smirnov test is used to test the goodness of fit of a given set of data to a theoretical distribution, making this a one‐sample test. In contrast, the Smirnov test is a two‐sample test, used to determine if two samples appear to follow the same distribution. The intuition behind the two tests is the same, however, in that both compare cumulative distribution functions, either two empirical cumulative distribution functions for the two‐sample Smirnov test, or one empirical cumulative distribution function and one known cumulative distribution function for the one‐sample Kolmogorov–Smirnov test.

Using in vitro amplification of cDNA by the polymerase chain reaction, we analyzed alternatively spliced events of cystic fibrosis transmembrane conductance regulator gene in lymphoblastoid cells. Ten alternatively spliced transcripts were identified using analysis of 6 overlapping segments of amplified cDNA, 4 of which have not been described previously. These include transcripts lacking exon 16, 17b, 22 and a transcript resulting from the use of a cryptic acceptor and donor splice sites. Moreover, in 2 cystic fibrosis (CF) patients bearing nonsense mutations E60X or W1282X, we observed that nonsense mutations are associated with an alteration of splice site selection in vivo resulting in exon skipping of constitutive exons or in the use of cryptic splice sites. In addition, even though lymphoblastoid cells are not the relevant tissue to address the question of the relationship between clinical respiratory phenotype and genotype, our results concerning adult CF patients (delta F508/ delta F508) suggest that individual-specific RNA splicing patterns could influence the severity of the CF pulmonary disease. If this phenomenon of alternative splicing events proves to be significant in CF and to be a common feature of disease genes, the study of RNA splicing could become an important tool for the analysis of the genotype-phenotype relationship in many inherited disorders.

While often confused, the Kolmogorov–Smirnov test and the Smirnov test are actually distinct. Specifically, the Kolmogorov–Smirnov test is used to test the goodness of fit of a given set of data to a theoretical distribution, making this a one‐sample test. In contrast, the Smirnov test is a two‐sample test, used to determine if two samples appear to follow the same distribution. The intuition behind the two tests is the same, however, in that both compare cumulative distribution functions, either two empirical cumulative distribution functions for the two‐sample Smirnov test, or one empirical cumulative distribution function and one known cumulative distribution function for the one‐sample Kolmogorov–Smirnov test.

New methods are presented that utilize the optimization of goodness-of-fit statistics in order to estimate Weibull parameters from failure data. It is assumed that the underlying population is characterized by a three-parameter Weibull distribution. Goodness-of-fit tests are based on the empirical distribution function (EDF). The EDF is a step function, calculated using failure data, and represents an approximation of the cumulative distribution function for the underlying population. Statistics (such as the Kolmogorov-Smirnov statistic and the Anderson-Darling statistic) measure the discrepancy between the EDF and the cumulative distribution function (CDF). These statistics are minimized with respect to the three Weibull parameters. Due to nonlinearities encountered in the minimization process, Powell’s numerical optimization procedure is applied to obtain the optimum value of the EDF. Numerical examples show the applicability of these new estimation methods. The results are compared to the estimates obtained with Cooper’s nonlinear regression algorithm.

The Calculation of the Spare Parts in the Auto-service Enterprise on the Base of Real Demand

Introduction: Genome sequencing (GS) often identifies rare variants with in silico predictions suggestive of aberrant splicing, which are often classified as Variants of Uncertain Significance (VUS) due to lack of functional evidence.RNA Sequencing (RNA-seq) has the ability to evaluate mRNA splicing, but typical splicing patterns for fetal specimens are not well established.We performed RNA-seq on cultured amniocytes from 60 fetal specimens which were negative for reportable Pathogenic/Likely Pathogenic variants by GS for a severe early-onset disorder.We established quantitative splicing ranges in prenatal transcriptomes, creating a resource to resolve VUS with damaging splicing predictions, and enabling results to be returned based on observed splicing patterns.Methods: Total RNA was extracted from 60 cultured amniotic fluid samples collected between 16 and 28 weeks gestation, for which GS had been previously performed.Library preps underwent poly-A selection and were sequenced with 150bp paired end reads.Reads were aligned to GRCh38, and splice junctions (SJ) were extracted.Variants were manually inspected in IGV, and aberrant splicing events were confirmed as real if total read depth exceeded 5 and aberrant events were supported by >2 reads.Percent spliced in (PSI) scores were calculated for 3' and 5' ends of all SJs,and PSI ranges were established for all samples.Results: RNA-seq data was interrogated at SJs predicted by spliceAI to be aberrant and compared with samples lacking the DNA variant of interest.The splicing impact of 895 rare DNA variants from 60 fetal samples was investigated.These variants were (1) in OMIM disease genes (2) had SpliceAI scores >0.2.Predicted splicing impact included donor gain (DG, 437 variants), acceptor gain (AG, 345), donor loss (DL, 67), and acceptor loss (AL, 46 variants).Average spliceAI scores for variants with aberrant splicing were 0.492 (DG), 0.726 (DL), 0.485 (AG), and 0.565 (AL).Scores for variants with aberrant splicing were significantly higher for DG, AG, and DL predictions (p < 0.05), but not for AL.Among variants with confirmed splicing effects in RNA-seq data, 97/192 (51%) generated SJs present in three or fewer samples, typically only in those with the DNA variant.Other variants were associated with splicing patterns observed at some level in individuals without the variant and confirmed as outliers (p < 0.05). Conclusion:We have generated the first dataset establishing quantitative splicing patterns during fetal development.This dataset enabled objective assessment of effects of DNA variants on mRNA splicing, aiding in the classification of splicing-related variants.Our findings reveal that many aberrant splicing events occur at low levels in the absence of obvious DNA splicing variants but are pronounced in the presence of deleterious DNA variants.This approach offers an objective method to identify splicing-variants.While in silico tools like SpliceAI are valuable for identifying candidate variants, our data reveal that it is difficult to distinguish functional splicing variants based on predictions alone.This discrepancy likely reflects the broad scope of SpliceAI predictions, which encompasses splicing probabilities across diverse tissue types and developmental states.These findings underscore the importance of tissue-specific and developmental-stage-specific datasets for interpreting splicing effects.Importantly, RNA-seq provides a robust, universal platform for functionally evaluating splicing variants genome-wide, enabling timely and improved variant classification in the prenatal setting.

Studies have shown that elemental/metal homeostasis is very essential to the development of the cancer phenotype in many cancers including lung and liver carcinomas. The balance in the composition of trace elements/metals was recognized to be essential to normal homeostasis. Low Zn levels were implicated in the development of prostate as well as other cancers. The aim of this study was to investigate the differential relationship of metal concentrations and profiles in cancer and normal tissues of humans. It is hypothesized that essential elements/metal concentrations and profiles will show significant differences between cancer and normal tissues as well as between different tissue types in human tissue material. The study will establish critical element/metal profiles that provide correlations with the development of three major carcinomas. Normal human and tumor tissues of lung, breast and liver tissues used in this study were obtained from US Biomax Company. Tissue samples were prepared using standardized digestion procedure and the ICP-AES (Inductively Coupled Plasma-Atomic Emission Spectrometry) was used to determine the concentrations and profiles of 21 elements including Ag, Al, As, Ba, Ca, Cd, Co, Cr, Cu, Fe, Mg, Mn, Na, Ni, Pb, Sb, Se, Sr, Tl, V, and Zn. Results showed that nine major elements of Al, Ba, Ca, Cr, Cu, Fe, Mg, Na, and Zn were found to be significantly different in term of their concentration profiles in normal and tumor tissues of human lung, breast and liver. It is concluded that metal/elemental homeostasis is essential for normal tissue function and that shifts in elemental distribution and content are tissue specific as well as carcinoma specific. These results are promising and warrant further studies to exploit the possibility of manipulating elemental content and distribution as a cancer therapeutic modality. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 1327. doi:10.1158/1538-7445.AM2011-1327

BackgroundThere is growing interest in the possible effect of perioperative anesthetic management on the growth and spread of cancer. The impact of perioperative use of opioids on cancer recurrence remains controversial and an assessment cannot yet be established based on current publications. This study aimed to assess the differential expression of opioid receptors between healthy and tumor tissues in patients with stage II and III colorectal cancer undergoing elective surgery by immunohistochemistry (IHC).MethodsPropensity–score matched case–control study nested in a retrospective cohort of patients with stage II or III colorectal. The primary endpoint was the difference in µ–opioid receptor (MOR) expression measured by IHC between tumor and healthy tissue in subject with or without recurrence. Secondary endpoints were to evaluate the differences in Opioid Growth Factor Receptor (OGFR), cyclic adenosine monophosphate (cAMP) production and protein kinase A (PKA) in the matched sample and from a from samples of colorectal cancer stored in the Cancer Genome Atlas (TCGA) and Genotype Tissue Expression Project (GTEx).ResultsThere was a significant difference in MOR receptor (median 3 [intequartile range IQR: 1–3] and 0 [IQR: 0–2], P<0.001) and OGFR receptor (median 6 [IQR: 5–6] and 2 [IQR: 1–2], P<0.001) in tumor and control tissue respectively. However, there were no significant differences in cAMP nor PKA expression between both types of tissues and in expression in any of the analyzed variables by recurrence status. The MOR and OGFR expression data from TCGA database were similar to our sample size data with lower expression of MOR and higher expression of OGFR in tumoural samples with a skewed distribution for MOR expression in tumor tissue both in patients with and without recurrence.ConclusionIn patients with stage II and III colorectal cancer, overall expression of MOR and OGFR was significantly increased but was not different between previously matched patients with or without recurrence. No differences were found in the analyzed metabolic pathway of cAMP–PKA: These results were confirmed by an in silico analysis of samples from the TCGA–GTEx database.

HintergrundDie Entschlüsselung des Transkriptoms hat in den letzten Jahren unser Verständnis zahlreicher Erkrankungen verbessert. Öffentlich zugängliche Datenbanken, wie z. B. die Gene Expression Omnibus-Datenbank des National Center for Biotechnology Information, sammeln Transkriptomrohdaten aus einer Vielfalt von Proben, ohne jedoch dem bioinformatischen Laien einen intuitiven Zugang zu den Daten zu gewähren. Daher wurden in den vergangenen Jahren spezielle Transkriptomdatenbanken programmiert, die eine benutzerfreundliche Web-basierte Datenanalyse ermöglichen und damit niederschwellig molekulare Einblicke in okuläre Gewebe ermöglichen.FragestellungZiel dieser Arbeit ist es, einen Überblick über die aktuell verfügbaren okulären Transkriptomdatenbanken zu geben und diese mit dem in Freiburg neu etablierten Human Eye Transcriptome Atlas zu vergleichen.MethodenLiteratursuche in PubMed.ErgebnisseNeun okuläre Transkriptomdatenbanken mit unterschiedlichem Anwendungsschwerpunkt wurden identifiziert. Die Plattformen iSyTE und Express spezialisieren sich auf die Genexpression während der Linsen- und Netzhautentwicklung der Maus, wohingegen retina.tigem.it, Eye in a Disk und Spectacle ihren Fokus auf einzelne okuläre Gewebe wie die Netzhaut legen. Spectacle, UCSC Cell Browser und Single Cell Portal erlauben die intuitive Exploration von Einzelzell-RNA-Sequenzierungsdaten von Netzhaut‑, Aderhaut‑, Kornea‑, Iris‑, Trabekelmaschenwerk- und Skleragewebe. Die Microarray-Profile verschiedener gesunder okulärer Gewebe werden in der Ocular Tissue Database bereitgestellt. Der Human Eye Transcriptome Atlas erfasst derzeit die größte Vielfalt an Augengeweben und Erkrankungen des Auges. Er zeichnet sich durch einen hohen Qualitätsstandard aus, der durch methodische Homogenität erreicht wird.SchlussfolgerungenOkuläre Transkriptomdatenbanken bieten einen umfassenden und intuitiven Einblick in die Transkriptionsprofile verschiedener gesunder und erkrankter Augengewebe. So verbessern sie unser Verständnis der zugrunde liegenden molekularen Krankheitsprozesse, unterstützen die Hypothesengenerierung und helfen bei der Suche nach neuen diagnostischen und therapeutischen Zielen für verschiedene Augenerkrankungen.

BackgroundSome mutations in the internal regions of exons occur within splicing enhancers and silencers, influencing the pattern of alternative splicing in the corresponding genes. To understand how these sequence changes affect splicing, we created a database of these mutations.FindingsThe Alternative Splicing Mutation Database (ASMD) serves as a repository for all exonic mutations not associated with splicing junctions that measurably change the pattern of alternative splicing. In this initial published release (version 1.2), only human sequences are present, but the ASMD will grow to include other organisms, (see Availability and requirements section for the ASMD web address).This relational database allows users to investigate connections between mutations and features of the surrounding sequences, including flanking sequences, RNA secondary structures and strengths of splice junctions. Splicing effects of the mutations are quantified by the relative presence of alternative mRNA isoforms with and without a given mutation. This measure is further categorized by the accuracy of the experimental methods employed. The database currently contains 170 mutations in 66 exons, yet these numbers increase regularly.We developed an algorithm to derive a table of oligonucleotide Splicing Potential (SP) values from the ASMD dataset. We present the SP concept and tools in detail in our corresponding article.ConclusionThe current data set demonstrates that mutations affecting splicing are located throughout exons and might be enriched within local RNA secondary structures. Exons from the ASMD have below average splicing junction strength scores, but the difference is small and is judged not to be significant.

In this study, we quantify vegetation fire activity in India using the MODerate resolution Imaging Spectroradiometer (MODIS) active fire datasets. We assessed different fire regime attributes, i.e., fire frequency, seasonality, intensity and the type of vegetation burnt in diverse geographical regions. MODIS data from 2002–2010 revealed an average of 63696 fire counts per year with the highest during 2009. Fire season in India extends from October to June with the peak during March. The K-means algorithm identified hotspot regions of fire clusters in diverse regions of India. We examined fire radiative power (FRP) data in the hotspot regions to address which fires burn intensively than others based on the vegetation type. We first assessed the best statistical fit distributions for the FRP data using the probability density functions (PDFs) and ranked them based on Kolmogorov-Smirnov statistic. We then described the fire intensities using empirical cumulative distribution functions (CDFs). Results suggest diverse pdfs for the FRP data that included Burr, Dagum, Johnson as well as Pearson distribution and they varied based on the vegetation type burnt. Analysis from empirical CDFs suggested relatively high fire intensity for closed broadleaved evergreen/ semi-deciduous forests than the other vegetation types. Although, annual sum of FRP for agricultural fires was less than the closed broadleaved evergreen forests, the values were higher than the mosaic vegetation category and broadleaved deciduous forests. These results on fire hotspots and FRP will be useful to address the impact of vegetation fires on air pollution and climate in India.

Probability distribution for the shear strength of seafloor sediment in the KR5 area for the development of manganese nodule miner

Bladder cancer is among the most common cancers in the UK and conventional detection techniques suffer from low sensitivity, low specificity, or both. Recent attempts to address the disparity have led to progress in the field of autofluorescence as a means to diagnose the disease with high efficiency, however there is still a lot not known about autofluorescence profiles in the disease. The multi-functional diagnostic system "LAKK-M" was used to assess autofluorescence profiles of healthy and cancerous bladder tissue to identify novel biomarkers of the disease. Statistically significant differences were observed in the optical redox ratio (a measure of tissue metabolic activity), the amplitude of endogenous porphyrins and the NADH/porphyrin ratio between tissue types. These findings could advance understanding of bladder cancer and aid in the development of new techniques for detection and surveillance.