Cristina Perez-Becerril, Andrew J Wallace, Helene Schlecht, Naomi L Bowers, Philip T Smith, Carolyn Gokhale, Helen Eaton, Chris Charlton, Rachel Robinson, Ruth S Charlton, D Gareth Evans, Miriam J Smith <<<

Schwannomatosis comprises a group of hereditary tumor predisposition syndromes characterized by, usually benign, multiple nerve sheath tumors, which frequently cause severe pain that does not typically respond to drug treatments. The most common schwannomatosis-associated gene is NF2, but SMARCB1 and LZTR1 are also associated. There are still many cases in which no pathogenic variants (PVs) have been identified, suggesting the existence of as yet unidentified genetic risk factors. In this study, we performed extended genetic screening of 75 unrelated schwannomatosis patients without identified germline PVs in NF2, LZTR1, or SMARCB1. Screening of the coding region of DGCR8, COQ6, CDKN2A, and CDKN2B was carried out, based on previous reports that point to these genes as potential candidate genes for schwannomatosis. Deletions or duplications in CDKN2A, CDKN2B, and adjacent chromosome 9 region were assessed by multiplex ligation-dependent probe amplification analysis. Sequencing analysis of a patient with multiple schwannomas and melanomas identified a novel duplication in the coding region of CDKN2A, disrupting both p14ARF and p16INK4a. Our results suggest that none of these genes are major contributors to schwannomatosis risk but the possibility remains that they may have a role in more complex mechanisms for tumor predisposition. <<<

Assaf Magen , Pauline Hamon , Nathalie Fiaschi , Leanna Troncoso , Etienne Humblin , Darwin D'souza , Travis Dawson , Matthew D. Park , Joel Kim , Steven Hamel , Mark Buckup , Christie Chang , Alexandra Tabachnikova , Hara Schwartz , Nausicaa Malissen , Yonit Lavin , Alessandra Soares-Schanoski , Bruno Giotti , Samarth Hegde , Raphael Mattiuz , Clotilde Hennequin , Jessica Le Berichel , Zhen Zhao , Stephen Ward , Isabel Fiel , Colles Price , Nicolas Fernandez , Jiang He , Baijun Kou , Michael Dobosz , Lianjie Li , Christina Adler , Min Ni , Yi Wei , Wei Wang , Namita T. Gupta , Kunal Kundu , Kamil Cygan , Raquel P. Deering , Alex Tsankov , Seunghee Kim-Schulze , Sacha Gnjatic , Ephraim Kenigsberg , Myron Schwartz , Thomas U. Marron , Gavin Thurston , Alice O. Kamphorst , Miriam Merad <<<

Here, we leveraged a large neoadjuvant PD-1 blockade trial in patients with hepatocellular carcinoma (HCC) to search for correlates of response to immune checkpoint blockade (ICB) within T cell-rich tumors. We show that ICB response correlated with the clonal expansion of intratumoral CXCL13+ CH25H+ IL-21+ PD-1+ CD4 T helper cells (CXCL13+ Th) and Granzyme K+ PD-1+ effector-like CD8 T cells, whereas terminally exhausted CD39hi TOXhi PD-1hi CD8 T cells dominated in non-responders. Strikingly, most T cell receptor (TCR) clones that expanded post-treatment were found in pre-treatment biopsies. Notably, PD-1+ TCF-1+ progenitor-like CD8 T cells were present in tumors of responders and non-responders and shared clones mainly with effector-like cells in responders or terminally differentiated cells in non-responders, suggesting that local CD8 T cell differentiation occurs upon ICB. We found that these progenitor CD8 T cells interact with CXCL13+ Th cells within cellular triads around dendritic cells enriched in maturation and regulatory molecules, or "mregDC". Receptor-ligand analysis revealed unique interactions within these triads that may promote the differentiation of progenitor CD8 T cells into effector-like cells upon ICB. These results suggest that discrete intratumoral niches that include mregDC and CXCL13+ Th cells control the differentiation of tumor-specific progenitor CD8 T cell clones in patients treated with ICB. <<<

CNN-based image processing has been actively applied to histopathological analysis to detect and classify cancerous tumors automatically. However, CNN-based classifiers generally predict a label with overconfidence, which becomes a serious problem in the medical domain. The objective of this study is to propose a new training method, called MixPatch, designed to improve a CNN-based classifier by specifically addressing the prediction uncertainty problem and examine its effectiveness in improving diagnosis performance in the context of histopathological image analysis. MixPatch generates and uses a new sub-training dataset, which consists of mixed-patches and their predefined ground-truth labels, for every single mini-batch. Mixed-patches are generated using a small size of clean patches confirmed by pathologists while their ground-truth labels are defined using a proportion-based soft labeling method. Our results obtained using a large histopathological image dataset shows that the proposed method performs better and alleviates overconfidence more effectively than any other method examined in the study. More specifically, our model showed 97.06% accuracy, an increase of 1.6% to 12.18%, while achieving 0.76% of expected calibration error, a decrease of 0.6% to 6.3%, over the other models. By specifically considering the mixed-region variation characteristics of histopathology images, MixPatch augments the extant mixed image methods for medical image analysis in which prediction uncertainty is a crucial issue. The proposed method provides a new way to systematically alleviate the overconfidence problem of CNN-based classifiers and improve their prediction accuracy, contributing toward more calibrated and reliable histopathology image analysis. <<<

Short-read RNA sequencing and long-read RNA sequencing each have their strengths and weaknesses for transcriptome assembly. While short reads are highly accurate, they are rarely able to span multiple exons. Long-read technology can capture full-length transcripts, but its relatively high error rate often leads to mis-identified splice sites. Here we present a new release of StringTie that performs hybrid-read assembly. By taking advantage of the strengths of both long and short reads, hybrid-read assembly with StringTie is more accurate than long-read only or short-read only assembly, and on some datasets it can more than double the number of correctly assembled transcripts, while obtaining substantially higher precision than the long-read data assembly alone. Here we demonstrate the improved accuracy on simulated data and real data from Arabidopsis thaliana, Mus musculus, and human. We also show that hybrid-read assembly is more accurate than correcting long reads prior to assembly while also being substantially faster. StringTie is freely available as open source software at https://github.com/gpertea/stringtie. <<<

The volume of data is growing exponentially and becoming more valuable to organizations that collect it, from e-commerce data, shipping, audio and video logs, text messages, internet search queries, stock market activity, financial transactions, the Internet of Things, and various other sources. The major challenges are related with the way to extract insights from such a rich data environment and whether Deep Learning can be successful with Big Data. To get some insight on these topics, social network data are employed as a case study on how sentiments can affect decisions in stock market environments. In this paper, we propose a generalized Deep Learning-based classification framework for Stock Market Sentiment Analysis. This work comprises the study, the development, and implementation of an automatic classification system based on Deep Learning and the validation of its adequacy and efficiency in any scenario, particularly Stock Market Sentiment Analysis. Distinct datasets and several Deep Learning approaches with different layers and embedded techniques are used, and their performances are evaluated. These developments show how Deep Learning reacts to distinct contexts. The results also give context on how different techniques with different parameter combinations react to certain types of data. Convolution obtained the best results when dealing with complex data inputs, and long short-term layers kept a memory of data, allowing inputs which are not as common to still be considered for decisions. The models that resulted from Stock Market Sentiment Analysis datasets were applied with some success to real-life problems. The best models reached accuracies of 73% in training and 69% in certain test datasets. In a simulation, a model was able to provide a Return on Investment of 4.4%. The results contribute to understanding how to process Big Data efficiently using Deep Learning and specialized hardware techniques. <<<

The emergency use authorizations (EUAs) of two mRNA-based severe acute respiratory syndrome coronavirus (SARS-CoV)-2 vaccines approximately 11 months after publication of the viral sequence highlights the transformative potential of this nucleic acid technology. Most clinical applications of mRNA to date have focused on vaccines for infectious disease and cancer for which low doses, low protein expression and local delivery can be effective because of the inherent immunostimulatory properties of some mRNA species and formulations. In addition, work on mRNA-encoded protein or cellular immunotherapies has also begun, for which minimal immune stimulation, high protein expression in target cells and tissues, and the need for repeated administration have led to additional manufacturing and formulation challenges for clinical translation. Building on this momentum, the past year has seen clinical progress with second-generation coronavirus disease 2019 (COVID-19) vaccines, Omicron-specific boosters and vaccines against seasonal influenza, Epstein-Barr virus, human immunodeficiency virus (HIV) and cancer. Here we review the clinical progress of mRNA therapy as well as provide an overview and future outlook of the transformative technology behind these mRNA-based drugs. <<<

BACKGROUND: Drug-target interactions (DTIs) are critical for drug repurposing and elucidation of drug mechanisms, and are manually curated by large databases, such as ChEMBL, BindingDB, DrugBank and DrugTargetCommons. However, the number of curated articles likely constitutes only a fraction of all the articles that contain experimentally determined DTIs. Finding such articles and extracting the experimental information is a challenging task, and there is a pressing need for systematic approaches to assist the curation of DTIs. To this end, we applied Bidirectional Encoder Representations from Transformers (BERT) to identify such articles. Because DTI data intimately depends on the type of assays used to generate it, we also aimed to incorporate functions to predict the assay format.RESULTS: Our novel method identified 0.6 million articles (along with drug and protein information) which are not previously included in public DTI databases. Using 10-fold cross-validation, we obtained ~ 99% accuracy for identifying articles containing quantitative drug-target profiles. The F1 micro for the prediction of assay format is 88%, which leaves room for improvement in future studies.CONCLUSION: The BERT model in this study is robust and the proposed pipeline can be used to identify previously overlooked articles containing quantitative DTIs. Overall, our method provides a significant advancement in machine-assisted DTI extraction and curation. We expect it to be a useful addition to drug mechanism discovery and repurposing. <<<

Oliver Lester Saldanha, Philip Quirke, Nicholas P West, Jacqueline A James, Maurice B Loughrey, Heike I Grabsch, Manuel Salto-Tellez, Elizabeth Alwers, Didem Cifci, Narmin Ghaffari Laleh, Tobias Seibel, Richard Gray, Gordon G A Hutchins, Hermann Brenner, Marko van Treeck, Tanwei Yuan, Titus J Brinker, Jenny Chang-Claude, Firas Khader, Andreas Schuppert, Tom Luedde, Christian Trautwein, Hannah Sophie Muti, Sebastian Foersch, Michael Hoffmeister, Daniel Truhn, Jakob Nikolas Kather <<<

Artificial intelligence (AI) can predict the presence of molecular alterations directly from routine histopathology slides. However, training robust AI systems requires large datasets for which data collection faces practical, ethical and legal obstacles. These obstacles could be overcome with swarm learning (SL), in which partners jointly train AI models while avoiding data transfer and monopolistic data governance. Here, we demonstrate the successful use of SL in large, multicentric datasets of gigapixel histopathology images from over 5,000 patients. We show that AI models trained using SL can predict BRAF mutational status and microsatellite instability directly from hematoxylin and eosin (H&E)-stained pathology slides of colorectal cancer. We trained AI models on three patient cohorts from Northern Ireland, Germany and the United States, and validated the prediction performance in two independent datasets from the United Kingdom. Our data show that SL-trained AI models outperform most locally trained models, and perform on par with models that are trained on the merged datasets. In addition, we show that SL-based AI models are data efficient. In the future, SL can be used to train distributed AI models for any histopathology image analysis task, eliminating the need for data transfer. <<<

Ulrich Wagner, Christine Wong, Ulrike Camenisch, Kathrin Zimmermann, Markus Rechsteiner, Nadejda Valtcheva, Alexandre Theocharides, Corinne C Widmer, Markus G Manz, Holger Moch, Peter J Wild, Stefan Balabanov <<<

Next-generation sequencing has greatly advanced the molecular diagnostics of malignant hematological diseases and provides useful information for clinical decision making. Studies have shown that certain mutations are associated with prognosis and have a direct impact on treatment of affected patients. Therefore, reliable detection of pathogenic variants is critically important. Here, we compared four sequencing panels with different characteristics, from number of genes covered to technical aspects of library preparation and data analysis workflows, to find the panel with the best clinical utility for myeloid neoplasms with a special focus on acute myeloid leukemia. Using the Acrometrix Oncology Hotspot Control DNA and DNA from acute myeloid leukemia patients, panel performance was evaluated in terms of coverage, precision, recall, and reproducibility and different bioinformatics tools that can be used for the evaluation of any next-generation sequencing panel were tested. Taken together, our results support the reliability of the Acrometrix Oncology Hotspot Control to validate and compare sequencing panels for hematological diseases and show which panel-software combination (platform) has the best performance. <<<

Gaby Schobers, Jolanda H Schieving, Helger G Yntema, Maartje Pennings, Rolph Pfundt, Ronny Derks, Tom Hofste, Ilse de Wijs, Nienke Wieskamp, Simone van den Heuvel, Jordi Corominas Galbany, Christian Gilissen, Marcel Nelen, Han G Brunner, Tjitske Kleefstra, Erik-Jan Kamsteeg, Michèl A A P Willemsen, Lisenka E L M Vissers <<<

BACKGROUND: Approximately two third of patients with a rare genetic disease remain undiagnosed after exome sequencing (ES). As part of our post-test counseling procedures, patients without a conclusive diagnosis are advised to recontact their referring clinician to discuss new diagnostic opportunities in due time. We performed a systematic study of genetically undiagnosed patients 5 years after their initial negative ES report to determine the efficiency of diverse reanalysis strategies.METHODS: We revisited a cohort of 150 pediatric neurology patients originally enrolled at Radboud University Medical Center, of whom 103 initially remained genetically undiagnosed. We monitored uptake of physician-initiated routine clinical and/or genetic re-evaluation (ad hoc re-evaluation) and performed systematic reanalysis, including ES-based resequencing, of all genetically undiagnosed patients (systematic re-evaluation).RESULTS: Ad hoc re-evaluation was initiated for 45 of 103 patients and yielded 18 diagnoses (including 1 non-genetic). Subsequent systematic re-evaluation identified another 14 diagnoses, increasing the diagnostic yield in our cohort from 31% (47/150) to 53% (79/150). New genetic diagnoses were established by reclassification of previously identified variants (10%, 3/31), reanalysis with enhanced bioinformatic pipelines (19%, 6/31), improved coverage after resequencing (29%, 9/31), and new disease-gene associations (42%, 13/31). Crucially, our systematic study also showed that 11 of the 14 further conclusive genetic diagnoses were made in patients without a genetic diagnosis that did not recontact their referring clinician.CONCLUSIONS: We find that upon re-evaluation of undiagnosed patients, both reanalysis of existing ES data as well as resequencing strategies are needed to identify additional genetic diagnoses. Importantly, not all patients are routinely re-evaluated in clinical care, prolonging their diagnostic trajectory, unless systematic reanalysis is facilitated. We have translated our observations into considerations for systematic and ad hoc reanalysis in routine genetic care. <<<

DNA molecules have been used as novel computing tools, by which Synthetic DNA was designed to execute computing processes with a programmable sequence. Here, we proposed a parallel computing method using DNA origamis as agents to solve the three-color problem, an example of the graph problem. Each agent was fabricated with a DNA origami of ∼50 nm diameter and contained DNA probes with programmable sticky ends that execute preset computing processes. With the interaction of different nanoagents, DNA molecules self-assemble into spatial nanostructures, which embody the computation results of the three-color problem with polynomial numbers of computing nanoagents in a one-pot annealing step. The computing results were confirmed by atomic force microscopy. Our method is completely different from existing DNA computing methods in its computing algorithm, and it has an advantage in terms of computational complexity and results detection for solving graph problems. <<<

Motivation: Amplicon sequencing is widely applied to explore heterogeneity and rare variants in genetic populations. Resolving true biological variants and quantifying their abundance is crucial for downstream analyses, but measured abundances are distorted by stochasticity and bias in amplification, plus errors during Polymerase Chain Reaction (PCR) and sequencing. One solution attaches Unique Molecular Identifiers (UMIs) to sample sequences before amplification eliminating amplification bias by clustering reads on UMI and counting clusters to quantify abundance. While modern methods improve over naive clustering by UMI identity, most do not account for UMI reuse, or collision, and they do not adequately model PCR and sequencing errors in the UMIs and sample sequences. Results: We introduce Deduplication and accurate Abundance estimation with UMIs (DAUMI), a probabilistic framework to detect true biological sequences and accurately estimate their deduplicated abundance from amplicon sequence data. DAUMI recognizes UMI collision, even on highly similar sequences, and detects and corrects most PCR and sequencing errors in the UMI and sampled sequences. We demonstrate DAUMI performs better on simulated and real data compared to other UMI-aware clustering methods. Availability: Source code is available at https://github.com/xiyupeng/AmpliCI-UMI. <<<

While many germline cancer risk variants have been identified through genome-wide association studies (GWAS), the mechanisms by which these variants operate remain largely unknown. Here we used 406 cancer ATAC-Seq samples across 23 cancer types to identify 7,262 germline allele-specific accessibility QTLs (as-aQTLs). Cancer as-aQTLs had stronger enrichment for cancer risk heritability (up to 145 fold) than any other functional annotation across seven cancer GWAS. Most cancer as-aQTLs directly altered transcription factor (TF) motifs and exhibited differential TF binding and gene expression in functional screens. To connect as-aQTLs to putative risk mechanisms, we introduced the regulome-wide associations study (RWAS). RWAS identified genetically associated accessible peaks at >70% of known breast and prostate loci and discovered new risk loci in all examined cancer types. Integrating as-aQTL discovery, motif analysis and RWAS identified candidate causal regulatory elements and their probable upstream regulators. Our work establishes cancer as-aQTLs and RWAS analysis as powerful tools to study the genetic architecture of cancer risk. <<<

BACKGROUND: Despite remarkable advances in cancer research, cancer remains one of the leading causes of death worldwide. Early detection of cancer and localization of the tissue of its origin are key to effective treatment. Here, we leverage technological advances in machine learning or artificial intelligence to design a novel framework for cancer diagnostics. Our proposed framework detects cancers and their tissues of origin using a unified model of cancers encompassing 33 cancers represented in The Cancer Genome Atlas (TCGA). Our model exploits the learned features of different cancers reflected in the respective dysregulated epigenomes, which arise early in carcinogenesis and differ remarkably between different cancer types or subtypes, thus holding a great promise in early cancer detection.RESULTS: Our comprehensive assessment of the proposed model on the 33 different tissues of origin demonstrates its ability to detect and classify cancers to a high accuracy (> 99% overall F-measure). Furthermore, our model distinguishes cancers from pre-cancerous lesions to metastatic tumors and discriminates between hypomethylation changes due to age related epigenetic drift and true cancer.CONCLUSIONS: Beyond detection of primary cancers, our proposed computational model also robustly detects tissues of origin of secondary cancers, including metastatic cancers, second primary cancers, and cancers of unknown primaries. Our assessment revealed the ability of this model to characterize pre-cancer samples, a significant step forward in early cancer detection. Deployed broadly this model can deliver accurate diagnosis for a greatly expanded target patient population. <<<

Htoo A Wai, Matthew Constable, Cosima Drewes, Ian C Davies, Eliska Svobodova, Esther Dempsey, Anand Saggar, Tessa Homfray, Sahar Mansour, Sofia Douzgou, Kate Barr, Catherine Mercer, David Hunt, Andrew G L Douglas, Diana Baralle <<<

Use of blood RNA sequencing (RNA-seq) as a splicing analysis tool for clinical interpretation of variants of uncertain significance (VUSs) found via whole-genome and exome sequencing can be difficult for genes that have low expression in the blood due to insufficient read count coverage aligned to specific genes of interest. Here, we present a short amplicon reverse transcription-polymerase chain reaction(RT-PCR) for the detection of genes with low blood expression. Short amplicon RT-PCR, is designed to span three exons where an exon harboring a variant is flanked by one upstream and one downstream exon. We tested short amplicon RT-PCRs for genes that have median transcripts per million (TPM) values less than one according to the genotype-tissue expression database. Median TPM values of genes analyzed in this study are SYN1 = 0.8549, COL1A1 = 0.6275, TCF4 = 0.4009, DSP = .2894, TTN = 0.2851, COL5A2 = 0.1036, TERT = 0.04452, NTRK2 = 0.0344, ABCA4 = 0.00744, PRPH = 0, and WT1 = 0. All these genes show insufficient exon-spanning read coverage in our RNA-seq data to allow splicing analysis. We successfully detected all genes tested except PRPH and WT1. Aberrant splicing was detected in SYN1, TCF4, NTRK2, TTN, and TERT VUSs. Therefore, our results show short amplicon RT-PCR is a useful alternative for the analysis of splicing events in genes with low TPM in blood RNA for clinical diagnostics. <<<

Esther H Lips, Tapsi Kumar, Anargyros Megalios, Lindy L Visser, Michael Sheinman, Angelo Fortunato, Vandna Shah, Marlous Hoogstraat, Emi Sei, Diego Mallo, Maria Roman-Escorza, Ahmed A Ahmed, Mingchu Xu, Alexandra W van den Belt-Dusebout, Wim Brugman, Anna K Casasent, Karen Clements, Helen R Davies, Liping Fu, Anita Grigoriadis, Timothy M Hardman, Lorraine M King, Marielle Krete, Petra Kristel, Michiel de Maaker, Carlo C Maley, Jeffrey R Marks, Brian A Menegaz, Lennart Mulder, Frank Nieboer, Salpie Nowinski, Sarah Pinder, Jelmar Quist, Carolina Salinas-Souza, Michael Schaapveld, Marjanka K Schmidt, Abeer M Shaaban, Rana Shami, Mathini Sridharan, John Zhang, Hilary Stobart, Deborah Collyar, Serena Nik-Zainal, Lodewyk F A Wessels, E Shelley Hwang, Nicholas E Navin, P Andrew Futreal, Grand Challenge PRECISION consortium, Alastair M Thompson, Jelle Wesseling, Elinor J Sawyer <<<

Ductal carcinoma in situ (DCIS) is the most common form of preinvasive breast cancer and, despite treatment, a small fraction (5-10%) of DCIS patients develop subsequent invasive disease. A fundamental biologic question is whether the invasive disease arises from tumor cells in the initial DCIS or represents new unrelated disease. To address this question, we performed genomic analyses on the initial DCIS lesion and paired invasive recurrent tumors in 95 patients together with single-cell DNA sequencing in a subset of cases. Our data show that in 75% of cases the invasive recurrence was clonally related to the initial DCIS, suggesting that tumor cells were not eliminated during the initial treatment. Surprisingly, however, 18% were clonally unrelated to the DCIS, representing new independent lineages and 7% of cases were ambiguous. This knowledge is essential for accurate risk evaluation of DCIS, treatment de-escalation strategies and the identification of predictive biomarkers. <<<

Lupus nephritis (LN) is a crucial complication of systemic lupus erythematosus (SLE). IKZF2 was identified as a lupus susceptibility locus, while its exact molecular function in LN is unknown. We aimed to explore the relationship between IKZF2 and LN based on multi-omics data. In our study, we carried out a meta-analysis of publicly available data, including not only tubulointerstitium, but also glomerulus tissue samples from LN patients and controls. Based on the common differentially expressed genes (co-DEGs) and previous researches, we selected IKZF2 for further analysis. Then, we analyzed potential molecular mechanisms of co-DEGs and IKZF2 in LN. To explore the possible targets of IKZF2, protein-protein interaction network (PPI) network and ceRNA network of IKZF2 were also constructed. Moreover, we performed immune infiltration analysis and evaluated clinical value of IKZF2. A total of 26 co-DEGs were observed in the integration of the above DEGs coming from the four sets of data, of which IKZF2 was selected for further analysis. Functional enrichment analysis from IKZF2 and related PPI network confirmed the tight relationship between IKZF2 and the immune reaction. Moreover, immune filtration analysis revealed the significant correlation between IKZF2 and naïve B cell, NK cell activation, NK cell rest and other immune cells. Receiver operating characteristic (ROC) analysis showed that the areas under the ROC curves were 0.721, 0.80, 0.682, and 0.859 for IKZF2 in four datasets, which demonstrated the clinical value of IKZF2. Our study revealed that IKZF2 may play an essential role in the molecular function and development of LN, and might be a potential biomarker for distinguishing LN patients and healthy ones. <<<

RNA quality control relies on co-factors and adaptors to identify and prepare substrates for degradation by ribonucleases such as the 3' to 5' ribonucleolytic RNA exosome. Here, we determined cryogenic electron microscopy structures of human nuclear exosome targeting (NEXT) complexes bound to RNA that reveal mechanistic insights to substrate recognition and early steps that precede RNA handover to the exosome. The structures illuminate ZCCHC8 as a scaffold, mediating homodimerization while embracing the MTR4 helicase and flexibly anchoring RBM7 to the helicase core. All three subunits collaborate to bind the RNA, with RBM7 and ZCCHC8 surveying sequences upstream of the 3' end to facilitate RNA capture by MTR4. ZCCHC8 obscures MTR4 surfaces important for RNA binding and extrusion as well as MPP6-dependent recruitment and docking onto the RNA exosome core, interactions that contribute to RNA surveillance by coordinating RNA capture, translocation, and extrusion from the helicase to the exosome for decay. <<<

BACKGROUND: The integrity of RNA molecules is of paramount importance for experiments that try to reflect the snapshot of gene expression at the moment of RNA extraction. Until recently, there has been no reliable standard for estimating the integrity of RNA samples and the ratio of 28S:18S ribosomal RNA, the common measure for this purpose, has been shown to be inconsistent. The advent of microcapillary electrophoretic RNA separation provides the basis for an automated high-throughput approach, in order to estimate the integrity of RNA samples in an unambiguous way.METHODS: A method is introduced that automatically selects features from signal measurements and constructs regression models based on a Bayesian learning technique. Feature spaces of different dimensionality are compared in the Bayesian framework, which allows selecting a final feature combination corresponding to models with high posterior probability.RESULTS: This approach is applied to a large collection of electrophoretic RNA measurements recorded with an Agilent 2100 bioanalyzer to extract an algorithm that describes RNA integrity. The resulting algorithm is a user-independent, automated and reliable procedure for standardization of RNA quality control that allows the calculation of an RNA integrity number (RIN).CONCLUSION: Our results show the importance of taking characteristics of several regions of the recorded electropherogram into account in order to get a robust and reliable prediction of RNA integrity, especially if compared to traditional methods. <<<