A gentle transition from Java programming to Web Services using XML-RPC

Exposing students to leading edge vocational areas of relevance such as Web Services can be difficult. We show a lightweight approach by embedding a key component of Web Services within a Level 3 BSc module in Distributed Computing. We present a ready to use collection of lecture slides and student activities based on XML-RPC. In addition we show that this material addresses the central topics in the context of web services as identified by Draganova (2003)

Dictionary learning allows model-free pseudotime estimation of transcriptomic data

Background Pseudotime estimation from dynamic single-cell transcriptomic data enables characterisation and understanding of the underlying processes, for example developmental processes. Various pseudotime estimation methods have been proposed during the last years. Typically, these methods start with a dimension reduction step because the low-dimensional representation is usually easier to analyse. Approaches such as PCA, ICA or t-SNE belong to the most widely used methods for dimension reduction in pseudotime estimation methods. However, these methods usually make assumptions on the derived dimensions, which can result in important dataset properties being missed. In this paper, we suggest a new dictionary learning based approach, dynDLT, for dimension reduction and pseudotime estimation of dynamic transcriptomic data. Dictionary learning is a matrix factorisation approach that does not restrict the dependence of the derived dimensions. To evaluate the performance, we conduct a large simulation study and analyse 8 real-world datasets. Results The simulation studies reveal that firstly, dynDLT preserves the simulated patterns in low-dimension and the pseudotimes can be derived from the low-dimensional representation. Secondly, the results show that dynDLT is suitable for the detection of genes exhibiting the simulated dynamic patterns, thereby facilitating the interpretation of the compressed representation and thus the dynamic processes. For the real-world data analysis, we select datasets with samples that are taken at different time points throughout an experiment. The pseudotimes found by dynDLT have high correlations with the experimental times. We compare the results to other approaches used in pseudotime estimation, or those that are method-wise closely connected to dictionary learning: ICA, NMF, PCA, t-SNE, and UMAP. DynDLT has the best overall performance for the simulated and real-world datasets. Conclusions We introduce dynDLT, a method that is suitable for pseudotime estimation. Its main advantages are: (1) It presents a model-free approach, meaning that it does not restrict the dependence of the derived dimensions; (2) Genes that are relevant in the detected dynamic processes can be identified from the dictionary matrix; (3) By a restriction of the dictionary entries to positive values, the dictionary atoms are highly interpretable

Are NoSQL Data Stores Useful for Bioinformatics Researchers?

The big data challenge in bioinformatics is approaching. Data storage and processing, instead of experimental technologies, are becoming the slower and more costly part of research. Biological data typically have large size and a variety of structures. The ability to efficiently store and retrieve the data is important in bioinformatics research. Traditionally, large datasets are either stored as disk-based flat-files or in relational databases. These systems become more complicated to plan, maintain and adjust to big data applications as they follow rigid table schema and often lack scalability, e.g. for data aggregation. Meanwhile, non-relational databases (NoSQL) emerge to provide alternative, flexible and more scalable data stores. In this study, we aim to quantitatively compare the latencies of different data stores on storing and querying proteomics datasets. We show benchmarks for typical relational and non-relational systems for both, in-memory and disk-based configurations and compare them to a simple flat-file based approach. We will focus on the latencies of storing and querying proteomics mass spectrometry datasets and the actual space consumption inside the data stores. Experiments are carried out on a local desktop with medium-sized data, which is the typical experimental settings of individual bioinformatics researchers. Results show that there are significant latency differences among the considered data stores (up to 30 folds). In certain use cases, flat file system can achieve comparable performance with the data stores. DOI: 10.17762/ijritcc2321-8169.150317

Acfs: accurate circRNA identification and quantification from RNA-Seq data

Circular RNAs (circRNAs) are a group of single-stranded RNAs in closed circular form. They are splicing-generated, widely expressed in various tissues and have functional implications in development and diseases. To facilitate genome-wide characterization of circRNAs using RNA-Seq data, we present a freely available software package named acfs. Acfs allows de novo, accurate and fast identification and abundance quantification of circRNAs from single- and paired-ended RNA-Seq data. On simulated datasets, acfs achieved the highest F1 accuracy and lowest false discovery rate among current state- of-the-art tools. On real-world datasets, acfs efficiently identified more bona fide circRNAs. Furthermore, we demonstrated the power of circRNA analysis on two leukemia datasets. We identified a set of circRNAs that are differentially expressed between AML and APL samples, which might shed light on the potential molecular classification of complex diseases using circRNA profiles. Moreover, chromosomal translocation, as manifested in numerous diseases, could produce not only fusion transcripts but also fusion circRNAs of clinical relevance. Featured with high accuracy, low FDR and the ability to identify fusion circRNAs, we believe that acfs is well suited for a wide spectrum of applications in characterizing the landscape of circRNAs from non- model organisms to cancer biology

Fractionation of Flash Pyrolysis Condensates by Staged Condensation

The utilization of condensates from flash pyrolysis is challenging due to several unwanted properties. The condensates consist of a mixture of many high value compounds, but each of them is only contained in a low concentration. As additional challenge instantaneous phase separation into an aqueous and a sludgy heavy organic phase takes place, if agricultural residues like barley straw are used as raw material for pyrolysis. A separation by means of distillation is not possible as the compounds undergo polymerization reactions when exposed to higher temperature. A different approach for separation based on boiling temperature is staged condensation of original vapors. Ablative flash pyrolysis is performed in a laboratory. The pyrolysis vapors are condensed in either two or three stages, each composed of a double-effect cooler followed by an electrostatic precipitator. The higher boiling fractions are low in water and acid and show a high heating value

An Interpretable Deep Learning Approach for Biomarker Detection in LC-MS Proteomics Data

Analyzing mass spectrometry-based proteomics data with deep learning (DL) approaches poses several challenges due to the high dimensionality, low sample size, and high level of noise. Additionally, DL-based workflows are often hindered to be integrated into medical settings due to the lack of interpretable explanation. We present DLearnMS, a DL biomarker detection framework, to address these challenges on proteomics instances of liquid chromatography-mass spectrometry (LC-MS) - a well-established tool for quantifying complex protein mixtures. Our DLearnMS framework learns the clinical state of LC-MS data instances using convolutional neural networks. Based on the trained neural networks, we show how biomarkers can be identified using layer-wise relevance propagation. This enables detecting discriminating regions of the data and the design of more robust networks. One of the main advantages over other established methods is that no explicit preprocessing step is needed in our DLearnMS framework. Our evaluation shows that DLearnMS outperforms conventional LC-MS biomarker detection approaches in identifying fewer false positive peaks while maintaining a comparable amount of true positives peaks. Code availability: The code is available from the following GIT repository: https://github.com/SaharIravani/DlearnM