MOTIVATION A critical task in high-throughput sequencing is aligning millions of short reads to a reference genome. Alignment is especially complicated for RNA sequencing (RNA-Seq) because of RNA splicing. A number of RNA-Seq algorithms are available, and claim to align reads with high accuracy and efficiency while detecting splice junctions. RNA-Seq data are discrete in nature; therefore, with...

Next-generation RNA sequencing (RNA-seq) technology has been widely used to assess full-length RNA isoform abundance in a highthroughput manner. RNA-seq data offer insight into gene expression levels and transcriptome structures, enabling us to better understand the regulation of gene expression and fundamental biological processes. Accurate isoform quantification from RNA-seq data is challengi...

Single cell RNA-seq (scRNA-seq) experiments suffer from a range of characteristic technical biases, such as dropouts (zero or near zero counts) and high variance. Current analysis methods rely on imputing missing values by various means of local averaging or regression, often amplifying biases inherent in the data. We present netSmooth, a network-diffusion based method that uses priors for the ...

Summary We present 'Threshold-seq,' a new approach for determining thresholds in deep-sequencing datasets of short RNA transcripts. Threshold-seq addresses the critical question of how many reads need to support a short RNA molecule in a given dataset before it can be considered different from 'background.' The proposed scheme is easy to implement and incorporate into existing pipelines. Avai...

The throughput and single-base resolution of RNA-Sequencing (RNA-Seq) have contributed to a dramatic change in transcriptomic-based inquiries and resulted in many new insights into the complexities of bacterial transcriptomes. RNA-Seq could contribute to similar advances in our understanding of plant pathogenic bacteria but it is still a technology under development with limitations and unknown...

The rapid growth of single-cell RNA-seq studies (scRNA-seq) demands efficient data storage, processing, and analysis. Big-data technology provides a framework that facilitates the comprehensive discovery of biological signals from inter-institutional scRNA-seq datasets. The strategies to solve the stochastic and heterogeneous single-cell transcriptome signal are discussed in this article. After...

Chromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq) experiments are routinely utilized for studying epigenomics of transcriptional regulation. We review some of the important statistical issues in the analysis of these experiments and extend our previous model for the analysis of ChIP-seq data of transcription factors, named MOSAiCS, with a hidden Markov model archit...

• P = {1, 2, 3, . . .} • Seq = {< n1, . . . , nk > | k > 0, n1, . . . , nk ∈ P}. p, q, r and s, possibly decorated, are metavariables ranging over Seq. • If p =< m1, . . . ,mk > and q =< n1, . . . , nl > then p ∗ q = 〈m1, . . . ,mk, n1, . . . , nl〉. • p ∗ 〈 〉 = 〈 〉 ∗ p = p for every p ∈ Seq. • p 6 q iff there is s such that p ∗ s = q. Definition 1.1 (Trees). T ⊆ Seq is a tree iff: 1. q ∈ T and ...