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Light Wellness in NICU

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Axel Gray
Axel Gray

Translated Base Data.zip


When basic data is entered into IFS Applications, the texts might need to be shown in different languages. For system defined texts (texts added in the core product) that are visible to users, this is always the case.. For text entered in a specific installation the customer can decide themselves whether the text needs to be translated into different languages or not. This means that it is end-users who must carry out any translation themselves, otherwise only one translation will exist. In order to gain an understanding of the basic concepts of Basic Data Translation please read the following section.




Translated Base Data.zip



This entity definition in this example will generate code to insert basic data translation entries for new records and return the translated value from basic data translation when getting records. In this example the attribute Name has the property BasicDataTranslation set to "true". The generated get_Name method will fetch the description for Name from the Basic Data Translation table for the current language being used. When inserting a new record to this entity a new Basic Data Translation entry is created for the new Name attribute with the key InternalId which is the key in the Entity. The description attribute will have the BasicDataTranslation property set to "custom" and also the basic data translation key is set to another value than the key. This will generate methods that need to be implemented; plus how to extract and insert a basic data translation for description.


A fundamental goal of genomics is to identify the complete set of expressed proteins. Automated annotation strategies rely on assumptions about protein-coding sequences (CDSs), e.g., they are conserved, do not overlap, and exceed a minimum length. However, an increasing number of newly discovered proteins violate these rules. Here we present an experimental and analytical framework, based on ribosome profiling and linear regression, for systematic identification and quantification of translation. Application of this approach to lipopolysaccharide-stimulated mouse dendritic cells and HCMV-infected human fibroblasts identifies thousands of novel CDSs, including micropeptides and variants of known proteins, that bear the hallmarks of canonical translation and exhibit translation levels and dynamics comparable to that of annotated CDSs. Remarkably, many translation events are identified in both mouse and human cells even when the peptide sequence is not conserved. Our work thus reveals an unexpected complexity to mammalian translation suited to provide both conserved regulatory or protein-based functions.


Here, we present an analytical tool that models the overall trinucleotide periodicity of ribosomal occupancy using a classifier based on spectral coherence. Our software, SPECtre, examines the relationship of normalized ribosome profiling read coverage over a rolling series of windows along a transcript relative to an idealized reference signal without the matched requirement of mRNA-Seq.


A comparison of SPECtre against previously published methods on existing data shows a marked improvement in accuracy for detecting active translation and exhibits overall high accuracy at a low false discovery rate. In addition, SPECtre performs comparably to a recently published method similarly based on spectral coherence, however with reduced runtime and memory requirements. SPECtre is available as an open source software package at -lab/spectre.


Various algorithms have been developed to differentiate protein-coding and non-coding transcripts in ribosome profiling sequence data using fragment length distribution differences [6] and read frame enrichment of aligned reads [2]. However, classification based on extreme outlier analysis of fragment length organization similarity score (FLOSS) differences is agnostic to the ribosome-protected fragment abundance over a transcript. Furthermore, classification based on read frame alignment enrichment (ORFscore) is optimized for canonical open reading frame (ORF) usage only. In addition, neither of the algorithms described above are available as standalone packages and must be implemented by the user. Published more recently, RiboTaper [4] utilizes a coherence-based approach to detect actively translated transcripts from the alignment of ribosome-protected fragments; however, the RiboTaper algorithm requires matched ribosome profiling and mRNA sequence libraries and can take multiple days to analyze a single sample.


Here we introduce SPECtre, a classification algorithm based on spectral coherence to identify regions of active translation with high sensitivity and specificity using aligned ribosome profiling sequence reads without the requirement of a matched mRNA sequence library (Fig. 1a). SPECtre leverages a key feature of ribosome profiling where sequence reads aligned to a reference transcriptome will track the tri-nucleotide periodicity characteristic of transcripts as they are translated by ribosomes, and reports both significant signals of translation as well as windowed periodicity scores for visualizing results within a genomic context. Options to change the size of windows analyzed, the step size between adjacent windows, false discovery rate, abundance cutoffs to define actively translated versus nontranslated score distributions, and parameters to optimize runtime performance are provided to the user to customize. Implementations of FLOSS and ORFscore are included with SPECtre for comparative purposes.


Alternatively, the number of sliding windows (W n ) over the coordinate set C, may be modified based on the step size between each window. Therefore, given a coordinate set C, and step size of L:


Distributions of these scores are generated using a user-defined fragments per kilobase per million reads, or FPKM [9], cutoff to differentiate transcripts under active translation from those that are not; these distributions are then used to derive a minimum SPECtre score threshold for active translation given a pre-determined false discovery rate, as well as the posterior probability that a given transcript or region is actively translated.


We assessed the sensitivity and specificity of each classification algorithm using recently published ribosome profiling and mRNA-Seq data derived from HEK293 cells [4]. For the comparative analysis of each classification algorithm in the HEK293 ribosome profiling library, RiboTaper (version 1.3) was run against published read alignments using the included GENCODE (v19) transcript annotation database [5]. The highest scoring RiboTaper ORFs were extracted from the orfs_found results file using the transcript identifiers and scoring method from the ORFs_max output. These ORFs were then scored by SPECtre (using default parameters), FLOSS and ORFscore, and then relative performance of each algorithm was assessed by receiver operating characteristic (ROC) analysis. Previous work has benchmarked classifier performance using a series of transcript FPKM cutoffs [4] or other coverage-based metrics [2, 7]. Therefore ROC analyses were performed using a series of ORF abundance cutoffs based on FPKM to differentiate those under active translation from those that are not. In this manner, we are able to assess the ability of each approach to identify ORFs with signatures of active translation in the interrogated cell type. We performed ROC analyses and calculated the area under the curve (AUC) over pre-defined RPF abundance cutoffs (0.5, 1.0, 3.0, 5.0 and 10.0 FPKM) to assess the relative performance of each classification algorithm to accurately define regions of active translation. In HEK293 cells, SPECtre conforms with high fidelity to RiboTaper classification and outperforms both FLOSS and ORFscore to identify actively translated ORFs (Fig. 2a and b).


Comparative analysis of SPECtre against previously published translational classification algorithms. a Performance of SPECtre, RiboTaper, FLOSS and ORFscore classification of ORF translation at various RPF abundance cutoffs as measured by area under the curve (AUC) in ribosome profiling of HEK293 cells [4]. b Receiver operating characteristic (ROC) curves of SPECtre, RiboTaper, FLOSS, and ORFscore at a cutoff of 1.0 FPKM. c Performance of SPECtre, FLOSS, and ORFscore classification of ORF translation in ribosome profiling of mESC [7] at various RPF abundance cutoffs as measured by AUC. d Performance of SPECtre, FLOSS, and ORFscore classification of ORF translation in a meta-analysis of ribosome profiling in zebrafish [2] over various RPF abundance cutoffs as measured by AUC. All SPECtre analyses were based on 30 nt sliding windows, using a step size of three between each window


We also used previously published ribosome profiling data derived from mouse [7] embryonic stem cells (mESC) and zebrafish embryos [2] to assess the performance of SPECtre, FLOSS and ORFscore in the absence of mRNA-Seq data (Additional file 1: Table S1); RiboTaper was excluded from these analyses due to its requirement of matched mRNA-Seq data. Ribosome profiling sequence reads from each set were aligned to the mouse or zebrafish reference genome and transcriptome, respectively. Antisense, overlapping and neighboring protein-coding and non-coding transcripts were removed from the analysis using methods described previously [7]. The FLOSS, ORFscore and SPECtre metrics were calculated for each remaining transcript and ROC analyses were carried out as described above. SPECtre remains robust in its classification of actively translated transcripts in the standalone mESC ribosome profiling library (Fig. 2c and Additional file 1: Table S2), and exhibits a marked improvement in accuracy in a meta-analysis of ribosome profiling libraries derived from zebrafish embryos (Fig. 2d).


SPECtre is a flexible, lightweight, command-line driven analytical package that identifies regions of active translation through modeling of the tri-nucleotide periodicity characteristic of translation by ribosomes, and does so with high fidelity to a recently published method that relies on a similar coherence-based approach. SPECtre classification also out-performs prevailing algorithms based on fragment length distribution profiling and reading frame occupancy enrichment. SPECtre is robust across ribosome profiling libraries derived from multiple organisms and cell types, even in the absence of matching mRNA-Seq data, and is capable of identifying active translation in regions previously thought to be non-coding. Further, SPECtre is under continuous development to optimize compute runtime and memory overhead in order to facilitate the efficient and accurate investigation of translational dynamics through ribosome profiling sequence analysis. 041b061a72


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