Johannes Kepler University of Linz

About: Johannes Kepler University of Linz is a education organization based out in Linz, Oberösterreich, Austria. It is known for research contribution in the topics: Thin film & Quantum dot. The organization has 6605 authors who have published 19243 publications receiving 385667 citations.

Fast model-based protein homology detection without alignment

Abstract: Motivation: As more genomes are sequenced, the demand for fast gene classification techniques is increasing. To analyze a newly sequenced genome, first the genes are identified and translated into amino acid sequences which are then classified into structural or functional classes. The best-performing protein classification methods are based on protein homology detection using sequence alignment methods. Alignment methods have recently been enhanced by discriminative methods like support vector machines (SVMs) as well as by position-specific scoring matrices (PSSM) as obtained from PSI-BLAST. However, alignment methods are time consuming if a new sequence must be compared to many known sequences—the same holds for SVMs. Even more time consuming is to construct a PSSM for the new sequence. The best-performing methods would take about 25 days on present-day computers to classify the sequences of a new genome (20 000 genes) as belonging to just one specific class—however, there are hundreds of classes. Another shortcoming of alignment algorithms is that they do not build a model of the positive class but measure the mutual distance between sequences or profiles. Only multiple alignments and hidden Markov models are popular classification methods which build a model of the positive class but they show low classification performance. The advantage of a model is that it can be analyzed for chemical properties common to the class members to obtain new insights into protein function and structure. We propose a fast model-based recurrent neural network for protein homology detection, the ‘Long Short-Term Memory’ (LSTM). LSTM automatically extracts indicative patterns for the positive class, but in contrast to profile methods it also extracts negative patterns and uses correlations between all detected patterns for classification. LSTM is capable to automatically extract useful local and global sequence statistics like hydrophobicity, polarity, volume, polarizability and combine them with a pattern. These properties make LSTM complementary to alignment-based approaches as it does not use predefined similarity measures like BLOSUM or PAM matrices. Results: We have applied LSTM to a well known benchmark for remote protein homology detection, where a protein must be classified as belonging to a SCOP superfamily. LSTM reaches state-of-the-art classification performance but is considerably faster for classification than other approaches with comparable classification performance. LSTM is five orders of magnitude faster than methods which perform slightly better in classification and two orders of magnitude faster than the fastest SVM-based approaches (which, however, have lower classification performance than LSTM). Only PSI-BLAST and HMM-based methods show comparable time complexity as LSTM, but they cannot compete with LSTM in classification performance. To test the modeling capabilities of LSTM, we applied LSTM to PROSITE classes and interpreted the extracted patterns. In 8 out of 15 classes, LSTM automatically extracted the PROSITE motif. In the remaining 7 cases alternative motifs are generated which give better classification results on average than the PROSITE motifs. Availability: The LSTM algorithm is available from http://www.bioinf.jku.at/software/LSTM_protein/ Contact: hochreit@bioinf.jku.at

Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures

Abstract: There is a critical need for standard approaches to assess, report and compare the technical performance of genome-scale differential gene expression experiments. Here we assess technical performance with a proposed standard 'dashboard' of metrics derived from analysis of external spike-in RNA control ratio mixtures. These control ratio mixtures with defined abundance ratios enable assessment of diagnostic performance of differentially expressed transcript lists, limit of detection of ratio (LODR) estimates and expression ratio variability and measurement bias. The performance metrics suite is applicable to analysis of a typical experiment, and here we also apply these metrics to evaluate technical performance among laboratories. An interlaboratory study using identical samples shared among 12 laboratories with three different measurement processes demonstrates generally consistent diagnostic power across 11 laboratories. Ratio measurement variability and bias are also comparable among laboratories for the same measurement process. We observe different biases for measurement processes using different mRNA-enrichment protocols.

Analysis of melamine resins by capillary zone electrophoresis with electrospray ionization‐mass spectrometric detection

Abstract: A method for the determination of the major components of (methoxymethyl)melamine resins, with quantitative analysis of unreacted melamine by capillary zone electrophoresis (CZE) using electrospray ionization-mass spectrometry (ESI-MS) is presented. Using a low background electrolyte (BGE) pH, components are separated according to their charge/ionic radius ratio with a distinctly different separation selectivity compared to the HPLC methods commonly employed in melamine-resin analysis. The use of a time-of-flight mass spectrometer (TOF-MS) was concluded to be necessary, as the complex samples studied required maximum sensitivity and resolution, which is clearly superior for TOF-MS detectors over their quadrupole counterparts. A standard curve of free melamine was determined with an R(2) = 0.999 over a concentration range of an order of magnitude. This method offers the unique separation selectivity of CZE as well as a quicker analysis time, especially for dimers compared to the HPLC methods used to date.

Software Security

Abstract: The importance of IT security is out of doubt. Data, computer and network security are essential for any business or organization. Software security often remains out of focus, from an organization's, a developer's and from an end-user's point of view. We will consider security terminology, security bugs, security flaws, and mitigation issues.