Dataset Information

DIGGER-Bac: prediction of seed regions for high-fidelity construction of synthetic small RNAs in bacteria.

ABSTRACT: Synthetic small RNAs (sRNAs) are gaining increasing attention in the field of synthetic biology and bioengineering for efficient post-transcriptional regulation of gene expression. However, the optimal design of synthetic sRNAs is challenging because alterations may impair functions or off-target effects can arise. Here, we introduce DIGGER-Bac, a toolbox for Design and Identification of seed regions for Golden Gate assembly and Expression of synthetic sRNAs in Bacteria. The SEEDling tool predicts optimal sRNA seed regions in combination with user-defined sRNA scaffolds for efficient regulation of specified mRNA targets. Results are passed on to the G-GArden tool, which assists with primer design for high-fidelity Golden Gate assembly of the desired synthetic sRNA constructs.

SUBMITTER: Philipp N

PROVIDER: S-EPMC10172035 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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DIGGER-Bac: prediction of seed regions for high-fidelity construction of synthetic small RNAs in bacteria.

Philipp Niklas N Brinkmann Cedric K CK Georg Jens J Schindler Daniel D Berghoff Bork A BA

Bioinformatics (Oxford, England) 20230501 5

Synthetic small RNAs (sRNAs) are gaining increasing attention in the field of synthetic biology and bioengineering for efficient post-transcriptional regulation of gene expression. However, the optimal design of synthetic sRNAs is challenging because alterations may impair functions or off-target effects can arise. Here, we introduce DIGGER-Bac, a toolbox for Design and Identification of seed regions for Golden Gate assembly and Expression of synthetic sRNAs in Bacteria. The SEEDling tool predic ...[more]

PMID: 37086442

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Project description:BackgroundHigh-throughput sequencing technology and bioinformatics have identified chimeric RNAs (chRNAs), raising the possibility of chRNAs expressing particularly in diseases can be used as potential biomarkers in both diagnosis and prognosis.ResultsThe task of discriminating true chRNAs from the false ones poses an interesting Machine Learning (ML) challenge. First of all, the sequencing data may contain false reads due to technical artifacts and during the analysis process, bioinformatics tools may generate false positives due to methodological biases. Moreover, if we succeed to have a proper set of observations (enough sequencing data) about true chRNAs, chances are that the devised model can not be able to generalize beyond it. Like any other machine learning problem, the first big issue is finding the good data to build models. As far as we were concerned, there is no common benchmark data available for chRNAs detection. The definition of a classification baseline is lacking in the related literature too. In this work we are moving towards benchmark data and an evaluation of the fidelity of supervised classifiers in the prediction of chRNAs.ConclusionsWe proposed a modelization strategy that can be used to increase the tools performances in context of chRNA classification based on a simulated data generator, that permit to continuously integrate new complex chimeric events. The pipeline incorporated a genome mutation process and simulated RNA-seq data. The reads within distinct depth were aligned and analysed by CRAC that integrates genomic location and local coverage, allowing biological predictions at the read scale. Additionally, these reads were functionally annotated and aggregated to form chRNAs events, making it possible to evaluate ML methods (classifiers) performance in both levels of reads and events. Ensemble learning strategies demonstrated to be more robust to this classification problem, providing an average AUC performance of 95 % (ACC=94 %, Kappa=0.87 %). The resulting classification models were also tested on real RNA-seq data from a set of twenty-seven patients with acute myeloid leukemia (AML).

Project description:BackgroundThe genus Aquilegia, consisting of approximately 70 taxa, is a member of the basal eudicot lineage, Ranuculales, which is evolutionarily intermediate between monocots and core eudicots, and represents a relatively unstudied clade in the angiosperm phylogenetic tree that bridges the gap between these two major plant groups. Aquilegia species are closely related and their distribution covers highly diverse habitats. These provide rich resources to better understand the genetic basis of adaptation to different pollinators and habitats that in turn leads to rapid speciation. To gain insights into the genome structure and facilitate gene identification, comparative genomics and whole-genome shotgun sequencing assembly, BAC-based genomics resources are of crucial importance.ResultsBAC-based genomic resources, including two BAC libraries, a physical map with anchored markers and BAC end sequences, were established from A. formosa. The physical map was composed of a total of 50,155 BAC clones in 832 contigs and 3939 singletons, covering 21X genome equivalents. These contigs spanned a physical length of 689.8 Mb (~2.3X of the genome) suggesting the complex heterozygosity of the genome. A set of 197 markers was developed from ESTs induced by drought-stress, or involved in anthocyanin biosynthesis or floral development, and was integrated into the physical map. Among these were 87 genetically mapped markers that anchored 54 contigs, spanning 76.4 Mb (25.5%) across the genome. Analysis of a selection of 12,086 BAC end sequences (BESs) from the minimal tiling path (MTP) allowed a preview of the Aquilegia genome organization, including identification of transposable elements, simple sequence repeats and gene content. Common repetitive elements previously reported in both monocots and core eudicots were identified in Aquilegia suggesting the value of this genome in connecting the two major plant clades. Comparison with sequenced plant genomes indicated a higher similarity to grapevine (Vitis vinifera) than to rice and Arabidopsis in the transcriptomes.ConclusionsThe A. formosa BAC-based genomic resources provide valuable tools to study Aquilegia genome. Further integration of other existing genomics resources, such as ESTs, into the physical map should enable better understanding of the molecular mechanisms underlying adaptive radiation and elaboration of floral morphology.

Dataset Information

DIGGER-Bac: prediction of seed regions for high-fidelity construction of synthetic small RNAs in bacteria.

Publications

DIGGER-Bac: prediction of seed regions for high-fidelity construction of synthetic small RNAs in bacteria.

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