ALBERT

Description: Motivation: Most data analysis tools for high-throughput screening (HTS) seek to uncover interesting hits for further analysis. They typically assume a low hit rate per plate. Hit rates can be dramatically higher in secondary screening, RNAi screening and in drug sensitivity testing using biologically active drugs. In particular, drug sensitivity testing on primary cells is often based on dose–response experiments, which pose a more stringent requirement for data quality and for intra- and inter-plate variation. Here, we compared common plate normalization and noise-reduction methods, including the B -score and the Loess a local polynomial fit method under high hit-rate scenarios of drug sensitivity testing. We generated simulated 384-well plate HTS datasets, each with 71 plates having a range of 20 (5%) to 160 (42%) hits per plate, with controls placed either at the edge of the plates or in a scattered configuration. Results: We identified 20% (77/384) as the critical hit-rate after which the normalizations started to perform poorly. Results from real drug testing experiments supported this estimation. In particular, the B -score resulted in incorrect normalization of high hit-rate plates, leading to poor data quality, which could be attributed to its dependency on the median polish algorithm. We conclude that a combination of a scattered layout of controls per plate and normalization using a polynomial least squares fit method, such as Loess helps to reduce column, row and edge effects in HTS experiments with high hit-rates and is optimal for generating accurate dose–response curves. Contact: john.mpindi@helsinki.fi Availability and implementation, Supplementary information: R code and Supplementary data are available at Bioinformatics online.

Description: Motivation: Matrix Assisted Laser Desorption Ionization-Imaging Mass Spectrometry (MALDI-IMS) in ‘omics’ data acquisition generates detailed information about the spatial distribution of molecules in a given biological sample. Various data processing methods have been developed for exploring the resultant high volume data. However, most of these methods process data in the spectral domain and do not make the most of the important spatial information available through this technology. Therefore, we propose a novel streamlined data analysis pipeline specifically developed for MALDI-IMS data utilizing significant spatial information for identifying hidden significant molecular distribution patterns in these complex datasets. Methods: The proposed unsupervised algorithm uses Sliding Window Normalization (SWN) and a new spatial distribution based peak picking method developed based on Gray level Co-Occurrence (GCO) matrices followed by clustering of biomolecules. We also use gist descriptors and an improved version of GCO matrices to extract features from molecular images and minimum medoid distance to automatically estimate the number of possible groups. Results: We evaluated our algorithm using a new MALDI-IMS metabolomics dataset of a plant (Eucalypt) leaf. The algorithm revealed hidden significant molecular distribution patterns in the dataset, which the current Component Analysis and Segmentation Map based approaches failed to extract. We further demonstrate the performance of our peak picking method over other traditional approaches by using a publicly available MALDI-IMS proteomics dataset of a rat brain. Although SWN did not show any significant improvement as compared with using no normalization, the visual assessment showed an improvement as compared to using the median normalization. Availability and implementation: The source code and sample data are freely available at http://exims.sourceforge.net/ . Contact: awgcdw@student.unimelb.edu.au or chalini_w@live.com Supplementary information : Supplementary data are available at Bioinformatics online.

Description: An approach to infer the unknown microbial population structure within a metagenome is to cluster nucleotide sequences based on common patterns in base composition, otherwise referred to as binning. When functional roles are assigned to the identified populations, a deeper understanding of microbial communities can be attained, more so than gene-centric approaches that explore overall functionality. In this study, we propose an unsupervised, model-based binning method with two clustering tiers, which uses a novel transformation of the oligonucleotide frequency-derived error gradient and GC content to generate coarse groups at the first tier of clustering; and tetranucleotide frequency to refine these groups at the secondary clustering tier. The proposed method has a demonstrated improvement over PhyloPythia, S-GSOM, TACOA and TaxSOM on all three benchmarks that were used for evaluation in this study. The proposed method is then applied to a pyrosequenced metagenomic library of mud volcano sediment sampled in southwestern Taiwan, with the inferred population structure validated against complementary sequencing of 16S ribosomal RNA marker genes. Finally, the proposed method was further validated against four publicly available metagenomes, including a highly complex Antarctic whale-fall bone sample, which was previously assumed to be too complex for binning prior to functional analysis.

Description: Motivation : Modern proteomics studies utilize high-throughput mass spectrometers which can produce data at an astonishing rate. These big mass spectrometry (MS) datasets can easily reach peta-scale level creating storage and analytic problems for large-scale systems biology studies. Each spectrum consists of thousands of peaks which have to be processed to deduce the peptide. However, only a small percentage of peaks in a spectrum are useful for peptide deduction as most of the peaks are either noise or not useful for a given spectrum. This redundant processing of non-useful peaks is a bottleneck for streaming high-throughput processing of big MS data. One way to reduce the amount of computation required in a high-throughput environment is to eliminate non-useful peaks. Existing noise removing algorithms are limited in their data-reduction capability and are compute intensive making them unsuitable for big data and high-throughput environments. In this paper we introduce a novel low-complexity technique based on classification, quantization and sampling of MS peaks. Results : We present a novel data-reductive strategy for analysis of Big MS data. Our algorithm, called MS-REDUCE, is capable of eliminating noisy peaks as well as peaks that do not contribute to peptide deduction before any peptide deduction is attempted. Our experiments have shown up to 100 x speed up over existing state of the art noise elimination algorithms while maintaining comparable high quality matches. Using our approach we were able to process a million spectra in just under an hour on a moderate server. Availability and implementation: The developed tool and strategy has been made available to wider proteomics and parallel computing community and the code can be found at https://github.com/pcdslab/MSREDUCE Contact : fahad.saeed@wmich.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: The heat flux instability is an electromagnetic mode excited by a relative drift between the protons and two-component core–halo electrons. The most prominent application may be in association with the solar wind where drifting electron velocity distributions are observed. The heat flux instability is somewhat analogous to the electrostatic Buneman or ion-acoustic instability driven by the net drift between the protons and bulk electrons, except that the heat flux instability operates in magnetized plasmas and possesses transverse electromagnetic polarization. The heat flux instability is also distinct from the electrostatic counterpart in that it requires two electron species with relative drifts with each other. In the literature, the heat flux instability is often called the ‘whistler’ heat flux instability, but it is actually polarized in the opposite sense to the whistler wave. This paper elucidates all of these fundamental plasma physical properties associated with the heat flux instability starting from a simple model, and gradually building up more complexity towards a solar wind-like distribution functions. It is found that the essential properties of the instability are already present in the cold counter-streaming electron model, and that the instability is absent if the protons are ignored. These instability characteristics are highly reminiscent of the electron firehose instability driven by excessive parallel temperature anisotropy, propagating in parallel direction with respect to the ambient magnetic field, except that the free energy source for the heat flux instability resides in the effective parallel pressure provided by the counter-streaming electrons.

Description: Motivation: The combined effect of a high replication rate and the low fidelity of the viral polymerase in most RNA viruses and some DNA viruses results in the formation of a viral quasispecies. Uncovering information about quasispecies populations significantly benefits the study of disease progression, antiviral drug design, vaccine design and viral pathogenesis. We present a new analysis pipeline called ViQuaS for viral quasispecies spectrum reconstruction using short next-generation sequencing reads. ViQuaS is based on a novel reference-assisted de novo assembly algorithm for constructing local haplotypes. A significantly extended version of an existing global strain reconstruction algorithm is also used. Results: Benchmarking results showed that ViQuaS outperformed three other previously published methods named ShoRAH, QuRe and PredictHaplo, with improvements of at least 3.1–53.9% in recall, 0–12.1% in precision and 0–38.2% in F-score in terms of strain sequence assembly and improvements of at least 0.006–0.143 in KL-divergence and 0.001–0.035 in root mean-squared error in terms of strain frequency estimation, over the next-best algorithm under various simulation settings. We also applied ViQuaS on a real read set derived from an in vitro human immunodeficiency virus (HIV)-1 population, two independent datasets of foot-and-mouth-disease virus derived from the same biological sample and a real HIV-1 dataset and demonstrated better results than other methods available. Availability and implementation: http://sourceforge.net/projects/viquas/ Contact: d.jayasundara@student.unimelb.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.

Description: In situ observations reveal the existence of electron velocity distribution function in the solar wind, where the net distribution can be modelled by a combination of core, halo and strahl. These components often possess a relative drift and with respective temperature anisotropies. The relative drift between the core and halo components leads to heat flux (HF) instability, while temperature anisotropies drive electromagnetic electron-cyclotron (EMEC) instability. These instabilities have been separately studied in the literature, but for the first time, the present study combines both unstable modes in the presence of two free energy sources, namely, excessive parallel pressure and excessive perpendicular temperature. HF instability (which is a left-hand circularly polarized mode) is effectively similar to electron firehose instability, except that the free energy is provided by net relative drift among two component electrons in the background of protons. The HF instability is discussed here along with (the right-hand polarized) EMEC instability driven by temperature anisotropy. The unstable HF mode is conventionally termed the ‘whistler’ HF instability, but it is actually polarized in the opposite sense to the whistler wave. EMEC mode, on the other hand, reduces to the proper whistler wave in the absence of free energy source. The present combined analysis clarifies the polarization characteristics of these two modes in an unambiguous manner.