MOTIVATION: Single-strand DNA-specific Hi-C (ssHi-C) is a recently developed technique enabling the capture of chromatin interactions involving single-stranded DNA (ssDNA), an intermediate of various DNA metabolic processes. ssHi-C entails the restoration of restriction sites in ssDNA regions of interest upon introduction of designer, internally barcoded "annealing oligonucleotides" prior to the restriction digestion step of Hi-C. The design of these "annealing oligonucleotides," as well as the analysis of the resulting ssHi-C data presents specific challenges, such as (i) differentiating ssDNA from dsDNA-derived contacts, (ii) tracking probe-specific interactions, and (iii) calibrating the amount of ssDNA contacts across biological samples. Dedicated computational tools are therefore needed to facilitate the design of, and extract biological information from, ssHi-C experiments. RESULTS: We present ssHiCstuff, a Rust- and Python-based package for the design of key reagents for ssHi-C experiments and for the analysis of ssHi-C data. ssHiCstuff provides (i) an automated annealing oligonucleotides design module, (ii) an end-to-end analyses pipeline, and (iii) a graphical user interface. ssHiCstuff simplifies the high-resolution analysis of ssDNA interactions at genome-wide scale. A graphical user interface (GUI) implemented in Python is also available for biologists without coding skills. AVAILABILITY: ssHiCstuff is freely available at https://github.com/Piazzalab/ssHiCstuff and https://zenodo.org/records/19677479 (https://doi.org/10.5281/zenodo.19677479) under the GPL 3.0 license. The annealing oligonucleotides design and the visualization modules are additionally freely available on a web browser at https://bioshiny.ens-lyon.fr/public/app/sshicstuff. A test dataset is available at https://zenodo.org/records/20035366 (https://doi.org/10.5281/zenodo.20035366).

MOTIVATION: The development of high-throughput single-cell sequencing technologies now allows the investigation of the population diversity of cellular transcriptomes. The expression dynamics (gene-to-gene variability) can be quantified more accurately, thanks to the measurement of lowly expressed genes. In addition, the cell-to-cell variability is high, with a low proportion of cells expressing the same genes at the same time/level. Those emerging patterns appear to be very challenging from the statistical point of view, especially to represent a summarized view of single-cell expression data. Principal component analysis (PCA) is a most powerful tool for high dimensional data representation, by searching for latent directions catching the most variability in the data. Unfortunately, classical PCA is based on Euclidean distance and projections that poorly work in presence of over-dispersed count data with dropout events like single-cell expression data. RESULTS: We propose a probabilistic Count Matrix Factorization (pCMF) approach for single-cell expression data analysis that relies on a sparse Gamma-Poisson factor model. This hierarchical model is inferred using a variational EM algorithm. It is able to jointly build a low dimensional representation of cells and genes. We show how this probabilistic framework induces a geometry that is suitable for single-cell data visualization, and produces a compression of the data that is very powerful for clustering purposes. Our method is competed against other standard representation methods like t-SNE, and we illustrate its performance for the representation of single-cell expression data. AVAILABILITY AND IMPLEMENTATION: Our work is implemented in the pCMF R-package (https://github.com/gdurif/pCMF). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: The high dimensionality of genomic data calls for the development of specific classification methodologies, especially to prevent over-optimistic predictions. This challenge can be tackled by compression and variable selection, which combined constitute a powerful framework for classification, as well as data visualization and interpretation. However, current proposed combinations lead to unstable and non convergent methods due to inappropriate computational frameworks. We hereby propose a computationally stable and convergent approach for classification in high dimensional based on sparse Partial Least Squares (sparse PLS). Results: We start by proposing a new solution for the sparse PLS problem that is based on proximal operators for the case of univariate responses. Then we develop an adaptive version of the sparse PLS for classification, called logit-SPLS, which combines iterative optimization of logistic regression and sparse PLS to ensure computational convergence and stability. Our results are confirmed on synthetic and experimental data. In particular, we show how crucial convergence and stability can be when cross-validation is involved for calibration purposes. Using gene expression data, we explore the prediction of breast cancer relapse. We also propose a multicategorial version of our method, used to predict cell-types based on single-cell expression data. Availability and implementation: Our approach is implemented in the plsgenomics R-package. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: Quality control is a necessary step of any Next Generation Sequencing analysis. Although customary, this step still requires manual interventions to empirically choose tuning parameters according to various quality statistics. Moreover, current quality control procedures that provide a "good quality" data set, are not optimal and discard many informative nucleotides. To address these drawbacks, we present a new quality control method, implemented in UrQt software, for Unsupervised Quality trimming of Next Generation Sequencing reads. RESULTS: Our trimming procedure relies on a well-defined probabilistic framework to detect the best segmentation between two segments of unreliable nucleotides, framing a segment of informative nucleotides. Our software only requires one user-friendly parameter to define the minimal quality threshold (phred score) to consider a nucleotide to be informative, which is independent of both the experiment and the quality of the data. This procedure is implemented in C++ in an efficient and parallelized software with a low memory footprint. We tested the performances of UrQt compared to the best-known trimming programs, on seven RNA and DNA sequencing experiments and demonstrated its optimality in the resulting tradeoff between the number of trimmed nucleotides and the quality objective. CONCLUSIONS: By finding the best segmentation to delimit a segment of good quality nucleotides, UrQt greatly increases the number of reads and of nucleotides that can be retained for a given quality objective. UrQt source files, binary executables for different operating systems and documentation are freely available (under the GPLv3) at the following address: https://lbbe.univ-lyon1.fr/-UrQt-.html .