Forecasting from multivariate time series data is a difficult task, made more so in the situation where the number of series (p) is much larger than the length of each series (T), which makes dimension reduction desirable prior to obtaining a model. The LASSO has become a widely-used method to choose relevant covariates out of many candidates, and it has many variations and extensions, such as grouped LASSO, adaptive LASSO, weighted lag adaptive LASSO, and fused LASSO. Of these, only the weighted lag adaptive LASSO and the fused LASSO take into account natural ordering among series. To examine the ability of variations on the LASSO to choose relevant covariates for short time series we use simulations for series with fewer than 50 observations. We then apply the methods to a data set on significant changes in self-reported psycho-social symptoms in the 30 years after the Chornobyl nuclear catastrophe.

MOTIVATION: As next generation sequencing is rapidly adding new genomes, their correct placement in the taxonomy needs verification. However, the current methods for confirming classification of a taxon or suggesting revision for a potential misplacement relies on computationally intense multi-sequence alignment followed by an iterative adjustment of the distance matrix. Due to intra-heterogeneity issues with the 16S rRNA marker, no classifier is available for sub-genus level, which could readily suggest a classification for a novel 16S rRNA sequence. Metagenomics further complicates the issue by generating fragmented 16S rRNA sequences. This article proposes a novel alignment-free method for representing the microbial profiles using extensible Markov models (EMMs) with an extended Karlin-Altschul statistical framework similar to the classic alignment paradigm. We propose a log odds (LODs) score classifier based on Gumbel difference distribution that confirms correct classifications with statistical significance qualifications and suggests revisions where necessary. RESULTS: We tested our method by generating a sub-genus level classifier with which we re-evaluated classifications of 676 microbial organisms using the NCBI FTP database for the 16S rRNA. The results confirm current classification for all genera while ascertaining significance at 95%. Furthermore, this novel classifier isolates heterogeneity issues to a mere 12 strains while confirming classifications with significance qualification for the remaining 98%. The models require less memory than that needed by multi-sequence alignments and have better time complexity than the current methods. The classifier operates at sub-genus level, and thus outperforms the naive Bayes classifier of the RNA Database Project where much of the taxonomic analysis is available online. Finally, using information redundancy in model building, we show that the method applies to metagenomic fragment classification of 19 Escherichia coli strains. AVAILABILITY AND IMPLEMENTATION: Source code and binaries freely available for download at http://lyle.smu.edu/IDA/EMMSA/, implemented in JAVA and supported on MS Windows.

MOTIVATION: Affymetrix GeneChip arrays require summarization in order to combine the probe-level intensities into one value representing the expression level of a gene. However, probe intensity measurements are expected to be affected by different levels of non-specific- and cross-hybridization to non-specific transcripts. Here, we present a new summarization technique, the Distribution Free Weighted method (DFW), which uses information about the variability in probe behavior to estimate the extent of non-specific and cross-hybridization for each probe. The contribution of the probe is weighted accordingly during summarization, without making any distributional assumptions for the probe-level data. RESULTS: We compare DFW with several popular summarization methods on spike-in datasets, via both our own calculations and the 'Affycomp II' competition. The results show that DFW outperforms other methods when sensitivity and specificity are considered simultaneously. With the Affycomp spike-in datasets, the area under the receiver operating characteristic curve for DFW is nearly 1.0 (a perfect value), indicating that DFW can identify all differentially expressed genes with a few false positives. The approach used is also computationally faster than most other methods in current use. AVAILABILITY: The R code for DFW is available upon request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

We present a new DNA/RNA sequence visualization technique, Temporal Chaos Game Representation (TCGR), which is an extension of Frequency Chaos Game Representation (FCGR). Unlike earlier CGR variants, TCGR illustrates the distribution of nucleotide sub-patterns at different positions within a sequence, and it can be applied to sets of sequences as well as individual sequences. TCGRs are suitable for short or long sequences. Preliminary experiments examining patterns of nucleotide occurrences in micro-RNA data illustrate not only the informative nature of the images, but clearly indicate the variability of distributions based on position, and the similarity and differences of distributions across various species