Computational Prediction of MicroRNAs Encoded in Viral and Other Genomes

We present an overview of selected computational methods for microRNA prediction. It is especially aimed at viral miRNA detection. As the number of microRNAs increases and the range of genomes encoding miRNAs expands, it seems that these small regulators have a more important role than has been previously thought. Most microRNAs have been detected by cloning and Northern blotting, but experimental methods are biased towards abundant microRNAs as well as being time-consuming. Computational detection methods must therefore be refined to serve as a faster, better, and more affordable method for microRNA detection. We also present data from a small study investigating the problems of computational miRNA prediction. Our findings suggest that the prediction of microRNA precursor candidates is fairly easy, while excluding false positives as well as exact prediction of the mature microRNA is hard. Finally, we discuss possible improvements to computational microRNA detection

Detection of non-coding RNA genes by searching for transcription signals in intergenic regions. : Summary

Detection of non-coding RNA genes by searching for transcription signals in intergenic regions. Background Non-coding RNA (ncRNA) genes produce transcripts that exert their function without ever being a recipe for proteins. ncRNA gene sequences, unlike protein coding genes, do not have strong transcription signals. This study was conducted to investigate a special version of a previously tested and suggested method of detecting RNAs. This study is a part of a larger project where many such methods are to be combined to create a general purpose ncRNA finding program. There are many possible ways to locate ncRNA. ncRNA genes have to be transcribed to produce ncRNA, and must therefore be surrounded by sequence regions that regulate transcription. Good candidates for new ncRNA genes would therefore be parts of intergenic sequences where transcription signals are present. Searching for transcription signals has previously been applied with success to find ncRNA genes in the bacteria Escherichia coli (E.coli) and yeast. This strategy has later been applied once more to the E.coli genome with some success by Chen et al. Methods The method chosen in this study is a version of the above mentioned search for transcription signals. During this study 8 promoter consensus sequences have been suggested using data from earlier studies, the consensus sequences cover the promoter sequence of five of the seven known socalled sigma factors in E.coli. A novel promoter sequence score function has been created resulting in the implementation of a new promoter search algorithm. This promoter search has been combined with an implementation of a previously developed terminator search and scoring algorithm. The output data has been analyzed by comparing the candidates to 52 verified and 1056 suggested ncRNAs. The number of located promoters has been compared with the estimated number of promoter hits that would occur in a random sequence which maintains the basic features of the riginal inputstring. Some output data have also been multiple aligned with intergenic regions of genomes from bacteria closely related to E.coli. Results During this study at least three novel promoter consensus sequences for the E.coli polymerase have been suggested. A novel promoter sequence scoring algorithm has been implemented together with a previously used method to locate rho-independent terminators in E.coli. The implemented program has eight different promoter sequences it may search for by using user-defined thresholds. A comparison has been made on the program's candidates against the suggested and verified ncRNAs. This comparison shows a very low hit ratio. Analysis has also been made to check the program's hit ratio towards the random case to verify the significance of the search criteria. Using about 850 ncRNA candidates from the program, multiple alignments have been made to intergenic regions in related bacteria. This has resulted in a suggestion of 20 novel ncRNAs having a high level of conservation and high scores on promoter and terminator regions. Of the 20 suggested ncRNA candidates two were inside already known ncRNA genes, this leaves 18 novel ncRNA candidates. At http://folk.uio.no/gardt/Hovedfag/index.html the search program developed in this study can be downloaded along with the BioJava packages needed. At this site one can also download the Java code, JavaDoc for the program and also the file containing the intergenic regions of E.coli that were used in this study. Conclusion This study concludes with a suggestion of 18 novel ncRNA candidates. The search algorithm and criteria used in this study represent a slightly new approach to the problem of detecting ncRNAs, specially by including searches for promoters recognized by other sigma factor mthan the widely used sigma 70. Analyses have shown that the program has a low hit ratio on already known or suggested ncRNAs, however other analyses have shown that the promoter consensus sequences used in this search are significant in promoter sequences to protein coding genes. The problems of detecting ncRNAs are rather connected to their weak transcription signals. Of the 18 candidates, none have structural similarities with known ncRNA families. This is not very remarkable since if they had shown such similarities they would have been known already, consequently the 18 candidates represent novel families of ncRNAs or they are false. The answer to whether they are real ncRNA genes will be given when the 18 novel ncRNA candidates are tested in the laboratory. As an independent program for ncRNA detection this program is not very suited as of today, but, as indicated above, when combined with other analyses it might represent a useful tool

Custom Design and Analysis of High-Density Oligonucleotide Bacterial Tiling Microarrays

Not until recently have custom made high-density oligonucleotide microarrays been available at an affordable price. The aim of this thesis was to design microarrays and analysis algorithms for DNA repair and DNA damage detection, and to apply the methods in real experiments. Thomassen et al. have used their custom designed whole genome-tiling microarrays for detection of transcriptional changes in Escherichia coli after exposure to DNA damageing reagents. The transcriptional changes in E. coli treated with UV light or the methylating reagent MNNG were shown to be larger and to include far more genes than previously reported. To optimize the data analysis for the custom made arrays, Thomassen and coworkers designed their own normalization and analysis algorithms, and showed these more suitable than established methods that are currently applied on custom tiling arrays. Among other findings several novel stress-induced transcripts were detected, of which one is predicted to be a UV-induced short transmembrane protein. Additionally, no upregulation of the previously described UV-inducible aidB is shown. In the MNNG study several genes are shown as downregulated in response to DNA damage although having upstream regulatory sequences similar to the established LexA box A and B. This indicates that the LexA regulon also might control gene repression and that the box A and B sequence can not alone answer for the LexA controlled gene regulation. Thomassen et al. have also custom designed a microarray for oncogenic fusion gene detection. Cancer specific fusion genes are often used to subgroup cancers and to define the optimal treatment, but currently the laboratory detection procedure is both laborious and tedious. In a blinded study on six cancer cell lines proof of principle was shown by detection of six out of six positive controls. The design and analysis methods for this microarray are now being refined to make a diagnostic fusion gene detection tool

A universal assay for detection of oncogenic fusion transcripts by oligo microarray analysis

<p>Abstract</p> <p>Background</p> <p>The ability to detect neoplasia-specific fusion genes is important not only in cancer research, but also increasingly in clinical settings to ensure that correct diagnosis is made and the optimal treatment is chosen. However, the available methodologies to detect such fusions all have their distinct short-comings.</p> <p>Results</p> <p>We describe a novel oligonucleotide microarray strategy whereby one can screen for all known oncogenic fusion transcripts in a single experiment. To accomplish this, we combine measurements of chimeric transcript junctions with exon-wise measurements of individual fusion partners. To demonstrate the usefulness of the approach, we designed a DNA microarray containing 68,861 oligonucleotide probes that includes oligos covering all combinations of chimeric exon-exon junctions from 275 pairs of fusion genes, as well as sets of oligos internal to all the exons of the fusion partners. Using this array, proof of principle was demonstrated by detection of known fusion genes (such as <it>TCF3:PBX1</it>, <it>ETV6:RUNX1</it>, and <it>TMPRSS2:ERG</it>) from all six positive controls consisting of leukemia cell lines and prostate cancer biopsies.</p> <p>Conclusion</p> <p>This new method bears promise of an important complement to currently used diagnostic and research tools for the detection of fusion genes in neoplastic diseases.</p

Towards a European Health Research and Innovation Cloud (HRIC)

The European Union (EU) initiative on the Digital Transformation of Health and Care (Digicare) aims to provide the conditions necessary for building a secure, flexible, and decentralized digital health infrastructure. Creating a European Health Research and Innovation Cloud (HRIC) within this environment should enable data sharing and analysis for health research across the EU, in compliance with data protection legislation while preserving the full trust of the participants. Such a HRIC should learn from and build on existing data infrastructures, integrate best practices, and focus on the concrete needs of the community in terms of technologies, governance, management, regulation, and ethics requirements. Here, we describe the vision and expected benefits of digital data sharing in health research activities and present a roadmap that fosters the opportunities while answering the challenges of implementing a HRIC. For this, we put forward five specific recommendations and action points to ensure that a European HRIC: i) is built on established standards and guidelines, providing cloud technologies through an open and decentralized infrastructure; ii) is developed and certified to the highest standards of interoperability and data security that can be trusted by all stakeholders; iii) is supported by a robust ethical and legal framework that is compliant with the EU General Data Protection Regulation (GDPR); iv) establishes a proper environment for the training of new generations of data and medical scientists; and v) stimulates research and innovation in transnational collaborations through public and private initiatives and partnerships funded by the EU through Horizon 2020 and Horizon Europe

TSD: A Research Platform for Sensitive Data

Digitalisation has led to a strong increase of research data, but most of these data are managed in unsatisfactory ways, and research data management has been characterized as a “wicked problem”. Several research data platforms have been launched, but security and privacy issues remain. Our research question is how can a research platform for sensitive data be built and used? Based on platform research, we propose a framework to analyze requirements. Our empirical evidence is a research platform called TSD, i.e. a platform for sensitive data. We analyze the development of TSD and offer two contributions; first, we discuss a framework to understand the architectural requirements for a research data platform, and second, we show how a research platform can be developed through a process of platformization

Tiling array study of MNNG treated Escherichia coli reveals a widespread transcriptional response

The alkylating agent N-methyl-N'-nitro-N-nitrosoguanidine (MNNG) is known to trigger the adaptive response by inducing the ada-regulon – consisting of three DNA repair enzymes Ada, AlkB, AlkA and the enigmatic AidB. We have applied custom designed tiling arrays to study transcriptional changes in Escherichia coli following a MNNG challenge. Along with the expected upregulation of the adaptive response genes (ada, alkA and alkB), we identified a number of differentially expressed transcripts, both novel and annotated. This indicates a wider regulatory response than previously documented. There were 250 differentially-expressed and 2275 similarly-expressed unannotated transcripts. We found novel upregulation of several stress-induced transcripts, including the SOS inducible genes recN and tisAB, indicating a novel role for these genes in alkylation repair. Furthermore, the ada-regulon A and B boxes were found to be insufficient to explain the regulation of the adaptive response genes after MNNG exposure, suggesting that additional regulatory elements must be involved

Assessment of fusion gene status in sarcomas using a custom made fusion gene microarray.

Sarcomas are relatively rare malignancies and include a large number of histological subgroups. Based on morphology alone, the differential diagnoses of sarcoma subtypes can be challenging, but the identification of specific fusion genes aids correct diagnostication. The presence of individual fusion products are routinely investigated in Pathology labs. However, the methods used are time-consuming and based on prior knowledge about the expected fusion gene and often the most likely break-point. In this study, 16 sarcoma samples, representing seven different sarcoma subtypes with known fusion gene status from a diagnostic setting, were investigated using a fusion gene microarray. The microarray was designed to detect all possible exon-exon breakpoints between all known fusion genes in a single analysis. An automated scoring of the microarray data from the 38 known sarcoma-related fusion genes identified the correct fusion gene among the top-three hits in 11 of the samples. The analytical sensitivity may be further optimised, but we conclude that a sarcoma-fusion gene microarray is suitable as a time-saving screening tool to identify the majority of the correct fusion genes

Tiling Array Analysis of UV Treated Escherichia coli Predicts Novel Differentially Expressed Small Peptides

Background Despite comprehensive investigation, the Escherichia coli SOS response system is not yet fully understood. We have applied custom designed whole genome tiling arrays to measure UV invoked transcriptional changes in E. coli. This study provides a more complete insight into the transcriptome and the UV irradiation response of this microorganism. Results We detected a number of novel differentially expressed transcripts in addition to the expected SOS response genes (such as sulA, recN, uvrA, lexA, umuC and umuD) in the UV treated cells. Several of the differentially expressed transcripts might play important roles in regulation of the cellular response to UV damage. We have predicted 23 novel small peptides from our set of detected non-gene transcripts. Further, three of the predicted peptides were cloned into protein expression vectors to test the biological activity. All three constructs expressed the predicted peptides, in which two of them were highly toxic to the cell. Additionally, a remarkably high overlap with previously in-silico predicted non-coding RNAs (ncRNAs) was detected. Generally we detected a far higher transcriptional activity than the annotation suggests, and these findings correspond with previous transcription mappings from E. coli and other organisms. Conclusions Here we demonstrate that the E. coli transcriptome consists of far more transcripts than the present annotation suggests, of which many transcripts seem important to the bacterial stress response. Sequence alignment of promoter regions suggest novel regulatory consensus sequences for some of the upregulated genes. Finally, several of the novel transcripts identified in this study encode putative small peptides, which are biologically active. © 2010 Thomassen et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Fusion Gene Microarray Reveals Cancer Type-Specificity Among Fusion Genes

Detection of fusion genes for diagnostic purposes and as a guide to treatment is well-established in hematological malignancies, and the prevalence of fusion genes in epithelial cancers is also increasingly appreciated. To study whether established fusion genes are present within additional cancer types, we have used an updated version of our fusion gene microarray in a systematic survey of reported fusion genes in multiple cancer types. We assembled a comprehensive database of published fusion genes, including those reported only in individual studies and samples, and fusion genes resulting from deep sequencing of cancer genomes and transcriptomes. From the total set of 548 fusion genes, we designed 599,839 oligonucleotides, targeting both chimeric transcript junctions as well as sequences internal to each of the fusion gene partners. We investigated the presence of fusion genes in a series of 67 cell lines representing 15 different cancer types. Data from ten leukemia cell lines with known fusion gene status were used to develop an automated scoring algorithm, and in five cell lines the correct fusion gene was the top scoring hit, and one came second. Two additional fusion genes, BCAS4-BCAS3 in the MCF-7 breast cancer cell line and CCDC6-RET in the TPC-1 thyroid cancer cell line were validated as true positive fusion transcripts. However, these fusion genes were not new to these cancer types, and none of 548 fusion genes were identified from a novel cancer type. We therefore find it unlikely that the assayed fusion genes are commonly present across multiple cancer types. (C) 2011 Wiley-Liss, Inc