MOTIVATION: Cell type-specific activities of cis-regulatory elements (CRE) are central to understanding gene regulation and disease predisposition. Single-cell RNA 5'end sequencing (sc-end5-seq) captures the transcription start sites (TSS) which can be used as a proxy to measure the activity of transcribed CREs (tCREs). However, a substantial fraction of TSS identified from sc-end5-seq data may not be genuine due to various artifacts, hindering the use of sc-end5-seq for de novo discovery of tCREs. RESULTS: We developed SCAFE-Single-Cell Analysis of Five-prime Ends-a software suite that processes sc-end5-seq data to de novo identify TSS clusters based on multiple logistic regression. It annotates tCREs based on the identified TSS clusters and generates a tCRE-by-cell count matrix for downstream analyses. The software suite consists of a set of flexible tools that could either be run independently or as pre-configured workflows. AVAILABILITY AND IMPLEMENTATION: SCAFE is implemented in Perl and R. The source code and documentation are freely available for download under the MIT License from https://github.com/chung-lab/SCAFE. Docker images are available from https://hub.docker.com/r/cchon/scafe. The submitted software version and test data are archived at https://doi.org/10.5281/zenodo.7023163 and https://doi.org/10.5281/zenodo.7024060, respectively. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Gene-gene interactions (epistasis) are thought to be important in shaping complex traits, but they have been under-explored in genome-wide association studies (GWAS) due to the computational challenge of enumerating billions of single nucleotide polymorphism (SNP) combinations. Fast screening tools are needed to make epistasis analysis routinely available in GWAS. RESULTS: We present BiForce to support high-throughput analysis of epistasis in GWAS for either quantitative or binary disease (case-control) traits. BiForce achieves great computational efficiency by using memory efficient data structures, Boolean bitwise operations and multithreaded parallelization. It performs a full pair-wise genome scan to detect interactions involving SNPs with or without significant marginal effects using appropriate Bonferroni-corrected significance thresholds. We show that BiForce is more powerful and significantly faster than published tools for both binary and quantitative traits in a series of performance tests on simulated and real datasets. We demonstrate BiForce in analysing eight metabolic traits in a GWAS cohort (323 697 SNPs, >4500 individuals) and two disease traits in another (>340 000 SNPs, >1750 cases and 1500 controls) on a 32-node computing cluster. BiForce completed analyses of the eight metabolic traits within 1 day, identified nine epistatic pairs of SNPs in five metabolic traits and 18 SNP pairs in two disease traits. BiForce can make the analysis of epistasis a routine exercise in GWAS and thus improve our understanding of the role of epistasis in the genetic regulation of complex traits. AVAILABILITY AND IMPLEMENTATION: The software is free and can be downloaded from http://bioinfo.utu.fi/BiForce/. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The ability to identify the mineral composition of rocks and soils is an important tool for the exploration of geological sites. For instance, NASA intends to design robots that are sufficiently autonomous to perform this task on planetary missions. Spectrometer readings provide one important source of data for identifying sites with minerals of interest. Reflectance spectrometers measure intensities of light reflected from surfaces over a range of wavelengths. Spectral intensity patterns may in some cases be sufficiently distinctive for proper identification of minerals or classes of minerals. For some mineral classes, carbonates for example, specific short spectral intervals are known to carry a distinctive signature. Finding similar distinctive spectral ranges for other mineral classes is not an easy problem. We propose and evaluate data-driven techniques that automatically search for spectral ranges optimized for specific minerals. In one set of studies, we partition the whole interval of wavelengths available in our data into sub-intervals, or bins, and use a genetic algorithm to evaluate a candidate selection of subintervals. As alternatives to this computationally expensive search technique, we present an entropy-based heuristic that gives higher scores for wavelengths more likely to distinguish between classes, as well as other greedy search procedures. Results are presented for four different classes, showing reasonable improvements in identifying some, but not all, of the mineral classes tested.