| 1. |
- Colbourne, JK, et al.
(författare)
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The Precision Toxicology initiative
- 2023
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Ingår i: Toxicology letters. - 1879-3169. ; 383, s. 33-42
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 2. |
- Shard, A. G., et al.
(författare)
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Measuring Compositions in Organic Depth Profiling: Results from a VAMAS Interlaboratory Study
- 2015
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Ingår i: Journal of Physical Chemistry B. - : American Chemical Society (ACS). - 1520-6106 .- 1520-5207. ; 119:33, s. 10784-10797
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Tidskriftsartikel (refereegranskat)abstract
- We report the results of a VAMAS (Versailles Project on Advanced Materials and Standards) interlaboratory study on the measurement of composition in organic depth profiling. Layered samples with known binary compositions of Irganox 1010 and either Irganox 1098 or Fmoc-pentafluoro-L-phenylalanine in each layer were manufactured in a single batch and distributed to more than 20 participating laboratories. The samples were analyzed using argon cluster ion sputtering and either X-ray photoelectron spectroscopy (XPS) or time-of-flight secondary ion mass spectrometry (ToF-SIMS) to generate depth profiles. Participants were asked to estimate the volume fractions in two of the layers and were provided with the compositions of all other layers. Participants using XPS provided volume fractions within 0.03 of the nominal values. Participants using ToF-SIMS either made no attempt, or used various methods that gave results ranging in error from 0.02 to over 0.10 in volume fraction, the latter representing a 50% relative error for a nominal volume fraction of 0.2. Error was predominantly caused by inadequacy in the ability to compensate for primary ion intensity variations and the matrix effect in SIMS. Matrix effects in these materials appear to be more pronounced as the number of atoms in both the primary analytical ion and the secondary ion increase. Using the participants' data we show that organic SIMS matrix effects can be measured and are remarkably consistent between instruments. We provide recommendations for identifying and compensating for matrix effects. Finally, we demonstrate, using a simple normalization method, that virtually all ToF-SIMS participants could have obtained estimates of volume fraction that were at least as accurate and consistent as XPS.
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| 3. |
- Wucher, Valentin, et al.
(författare)
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FEELnc : a tool for long non-coding RNA annotation and its application to the dog transcriptome
- 2017
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Ingår i: Nucleic Acids Research. - : Oxford University Press (OUP). - 0305-1048 .- 1362-4962. ; 45:8
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Tidskriftsartikel (refereegranskat)abstract
- Whole transcriptome sequencing (RNA-seq) has become a standard for cataloguing and monitoring RNA populations. One of the main bottlenecks, however, is to correctly identify the different classes of RNAs among the plethora of reconstructed transcripts, particularly those that will be translated (mRNAs) from the class of long non-coding RNAs (lncRNAs). Here, we present FEELnc (FlExible Extraction of LncRNAs), an alignment-free program that accurately annotates lncRNAs based on a Random Forest model trained with general features such as multi k-mer frequencies and relaxed open reading frames. Benchmarking versus five state-of-the-art tools shows that FEELnc achieves similar or better classification performance on GENCODE and NONCODE data sets. The program also provides specific modules that enable the user to fine-tune classification accuracy, to formalize the annotation of lncRNA classes and to identify lncRNAs even in the absence of a training set of non-coding RNAs. We used FEELnc on a real data set comprising 20 canine RNA-seq samples produced by the European LUPA consortium to substantially expand the canine genome annotation to include 10 374 novel lncRNAs and 58 640 mRNA transcripts. FEELnc moves beyond conventional coding potential classifiers by providing a standardized and complete solution for annotating lncRNAs and is freely available at https://github.com/tderrien/FEELnc.
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