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Jens Allmer is the corresponding author. © 2019, Jens Allmer, published by Walter de Gruyter GmbH, Berlin/Boston. This work is licensed under the Creative Commons Attribution 4.0 Public License. ... For future development of the internet of science, a public repository has been initialized: https://bitbucket.org/allmer/ios/src/master/. ...doi:10.1515/jib-2019-0024 pmid:31145694 pmcid:PMC6798852 fatcat:ju6lyzsan5ekfga65fc245mwxy
MicroRNAs (miRNAs) are small non-coding RNA sequences that have been implicated in many physiological processes. Furthermore, miRNAs have been shown to be important biomarkers for diseases and their mimics are tested as drug candidates. The experimental discovery of miRNAs is complicated because both miRNAs and their targets need to be expressed for the confirmation of functional interaction. This is difficult since miRNA expression is under spatiotemporal control. This has motivated thedoi:10.1101/840579 fatcat:cudwnhdiqjh45bnihw64zxccky
more »... ment of computational methods for miRNA detection. Such computational methods typically involve the characterization of candidate sequences with features designed by domain experts and the application of statistical or machine learning algorithms. While such features can successfully encode domain knowledge, feature engineering is a difficult and time consuming task. Additionally, some engineered features pose excessive computational complexity that can hinder the large scale detection of miRNA. In contrast, advances of representation learning methods such as deep learning provide for automatic development of effective features directly from data. In this work, we propose a method that uses domain knowledge to create an efficient image representation of miRNA molecules encoding sequence, structure, and implicitly some thermodynamic information. We then use this low-level feature representation of the molecules to develop a hierarchical deep representation using a convolutional neural network model, which directly detects precursor miRNAs. With this method we achieve state-of-the-art performance on all previously used datasets. Additionally, detection is achieved in real time thereby overcoming the high computational cost for current pre-miRNA feature calculations such as p-value based ones. Finally, the encoding and modeling process opens possibilities for interpretability of the models' behavior, which may lead to novel biological interpretations of miRNA genesis and targeting.
We recently developed 2DB, a database to hold, study, and publish proteomics data based on MS experiments (Allmer et al. 2008) . ... We recently developed 2DB, a database to hold, study, and publish proteomics data, as generated from MS experiments (Allmer et al. 2008 ). ...doi:10.1007/s00726-009-0317-9 pmid:19575279 fatcat:73xwfit4y5gtnnndenrga6732u
Allmer is the corresponding author. ... integration • Big data and data mining • Precision Medicine and Biomedical Informatics • Semantic web, standards, and ontologies • Tool integration and workflow systems • Network simulation and analysis Jens ... Allmer is the corresponding author. ©2017, Jens Allmer et al., Published by De Gruyter. ...doi:10.1515/jib-2017-0012 pmid:28609294 fatcat:oh3qzsh5nvgvfl4boxcrwzalfq
., 2009; Cakir and Allmer, 2010; Ding et al., 2010; Grundhoff, 2011; Ritchie et al., 2012) . ... is to determine the data distribution of selected parameters and then define a linear combination to describe a true hairpin (Bentwich, 2008) , require thresholds that need to be passed (Cakir and Allmer ...doi:10.3389/fgene.2012.00209 pmid:23087705 pmcid:PMC3467617 fatcat:p2exae4kybd5tpkis675bkcskq
Allmer: SARS-CoV-2 genome browser | V. Gltekin and J. Allmer: SARS-CoV-2 genome browser ...doi:10.1515/jib-2021-0001 pmid:33721918 fatcat:xt7lku5ypvafrp6gsrshys6nsm
At least MSMAG (Allmer 2010) in conjunction with 2DB (Allmer et al. 2008 ) is able to relatively quantify one or more arbitrary labels or PTMs without further modifications. ... In addition the count can be weighted by the total ion current of the counted spectra which can improve its dynamic range (Asara et al. 2008; Allmer 2010) . ...doi:10.1007/s00726-010-0614-3 pmid:20473535 fatcat:7yyuxdrbxvactltf4gwugrpela
MicroRNAs are crucial for post-transcriptional gene regulation, and their dysregulation has been associated with diseases like cancer and, therefore, their analysis has become popular. The experimental discovery of miRNAs is cumbersome and, thus, many computational tools have been proposed. Here we assess 13 ab initio pre-miRNA detection approaches using all relevant, published, and novel data sets while judging algorithm performance based on ten intrinsic performance measures. We present andoi:10.1038/s41467-017-00403-z pmid:28839141 pmcid:PMC5571158 fatcat:ntux7m5iq5eypkfyrkwn522jki
more »... ensible framework, izMiR, which allows for the unbiased comparison of existing algorithms, adding new ones, and combining multiple approaches into ensemble methods. In an exhaustive attempt, we condense the results of millions of computations and show that no method is clearly superior; however, we provide a guideline for biomedical researchers to select a tool. Finally, we demonstrate that combining all of the methods into one ensemble approach, for the first time, allows reliable purely computational pre-miRNA detection in large eukaryotic genomes.
In parallel with the development of nucleotide sequencing an equally important interest in further describing the sequence in terms of function arose and the latter represents the current bottleneck in the overall research question. Sequencing the transcriptome allows determination of expressed nucleotide sequences and using mass spectrometry allows sequencing on the protein level. Both approaches can only sequence a subset of the existing transcripts. Moreover, for example post translationaldoi:10.1016/j.ins.2016.08.005 fatcat:oulcoeryojavrfy3uhw4tkosui
more »... dification events can only be determined on the proteomics level. Therefore, it is essential to combine proteomics and genomics. For that purpose, proteogenomics data analysis pipelines have been described. Here, we describe a novel proteogenomics workflow which encompasses everything from the acquisition of data to result visualization in the Konstanz Information Miner (KNIME), a state of the art workflow management and data analytics platform. We amended KN-IME with a number of processes like peptide consensus prediction, peptide mapping, and database equalizing, as well as result visualization. This enabled construction of our new workflow, entitled PGMiner, which not only includes all data analysis steps, but is highly customizable which is rather cumbersome for most existing pipelines. Furthermore, no burdensome installation processes have to be performed making PGMiner the most user friendly tool available.
Post-transcriptional gene dysregulation can be a hallmark of diseases like cancer and microRNAs (miRNAs) play a key role in the modulation of translation efficiency. Known pre-miRNAs are listed in miRBase, and they have been discovered in a variety of organisms ranging from viruses and microbes to eukaryotic organisms. The computational detection of pre-miRNAs is of great interest, and such approaches usually employ machine learning to discriminate between miRNAs and other sequences. Manydoi:10.1186/s12859-017-1584-1 pmid:28292266 pmcid:PMC5351198 fatcat:itjjzalzu5d4lmbsx4rxhsrbji
more »... es have been proposed describing pre-miRNAs, and we have previously introduced the use of sequence motifs and k-mers as useful ones. There have been reports of xeno-miRNAs detected via next generation sequencing. However, they may be contaminations and to aid that important decisionmaking process, we aimed to establish a means to differentiate pre-miRNAs from different species. Results: To achieve distinction into species, we used one species' pre-miRNAs as the positive and another species' pre-miRNAs as the negative training and test data for the establishment of machine learned models based on sequence motifs and k-mers as features. This approach resulted in higher accuracy values between distantly related species while species with closer relation produced lower accuracy values. Conclusions: We were able to differentiate among species with increasing success when the evolutionary distance increases. This conclusion is supported by previous reports of fast evolutionary changes in miRNAs since even in relatively closely related species a fairly good discrimination was possible.
A Introduction Proteomics is a quickly developing eld. New and better mass spectrometers, the platform of choice in proteomics, are being introduced frequently. New algorithms for the analysis of mass spectrometric data and assignment of amino acid sequence to tandem mass spectra are also presented on a frequent basis. Unfortunately, the best application area for these algorithms cannot be established at the moment. Furthermore, even the accuracy of the algorithms and their relative performancedoi:10.5584/jiomics.v2i2.113 fatcat:rl3rv65kivf4vmy43sad7tr3sq
more »... cannot be established. is is due to the lack of proper benchmark data. is letter rst introduces the eld of mass spectrometry-based proteomics and then de nes the expectations of a well-designed benchmark dataset. erea er, the current situation is compared to this ideal. A call for the creation of a proper benchmark dataset is then placed and it is explained how measurement should be performed. Finally, the bene ts for the research community are highlighted.
ADDITIONAL INFORMATION AND DECLARATIONS Funding The work was supported by the Scientific and Technological Research Council of Turkey (Grant no. 113E326) to Jens Allmer. ... .3131 Saçar Demirci and Allmer (2017), PeerJ, DOI 10.7717/peerj.3131 7/16 Saçar Demirci and Allmer (2017), PeerJ, DOI 10.7717/peerj.3131 9/16 Saçar Demirci and Allmer (2017), PeerJ, DOI ... Competing Interests Jens Allmer is a founder of Bionia Incorporated. The authors declare there are no competing interests. ...doi:10.7717/peerj.3131 pmid:28367373 pmcid:PMC5374968 fatcat:dhvth22zzferljshimtyfmc3he
MicroRNAs (miRNAs) are short RNA sequences that are actively involved in gene regulation. These regulators on the post-transcriptional level have been discovered in virtually all eukaryotic organisms. Additionally, miRNAs seem to exist in viruses and might also be produced in microbial pathogens. Initially, transcribed RNA is cleaved by Drosha, producing precursor miRNAs. We have previously shown that it is possible to distinguish between microRNA precursors of different clades by representingdoi:10.3390/a14050132 doaj:ef01af41922545cc878e3a182b36b050 fatcat:74ejw4ia7faxfgcduorginrige
more »... he sequences in a k-mer feature space. The k-mer representation considers the frequency of a k-mer in the given sequence. We further hypothesized that the relationship between k-mers (e.g., distance between k-mers) could be useful for classification. Three different distance-based features were created, tested, and compared. The three feature sets were entitled inter k-mer distance, k-mer location distance, and k-mer first–last distance. Here, we show that classification performance above 80% (depending on the evolutionary distance) is possible with a combination of distance-based and regular k-mer features. With these novel features, classification at closer evolutionary distances is better than using k-mers alone. Combining the features leads to accurate classification for larger evolutionary distances. For example, categorizing Homo sapiens versus Brassicaceae leads to an accuracy of 93%. When considering average accuracy, the novel distance-based features lead to an overall increase in effectiveness. On the contrary, secondary-structure-based features did not lead to any effective separation among clades in this study. With this line of research, we support the differentiation between true and false miRNAs detected from next-generation sequencing data, provide an additional viewpoint for confirming miRNAs when the species of origin is known, and open up a new strategy for analyzing miRNA evolution.
Features of hairpins were calculated on AWS (Amazon Web Services) using an in-house java package; but could also be calculated using online services (Yones et al., 2015; Bagci and Allmer, 2016) . ... We and others previously analyzed the possible miRNAs of T. gondii (Cakir and Allmer, 2010; Wang et al., 2012) and how they could be useful to modulate host protein abundance (Saçar Demirci et al., ... Copyright © 2018 Acar, Saçar Demirci, Groß and Allmer. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). ...doi:10.3389/fmicb.2017.02630 pmid:29354114 pmcid:PMC5759179 fatcat:qbzvkbt3cvej7cyp2c3lexykdq
doi:10.1515/jib-2021-0006 pmid:33743557 fatcat:7bupba6mufgvrc5lu6htd76dni
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