MOTIVATION: Cancer is the second leading cause of death worldwide, and although there have been advances in treatments, including immunotherapies, these often require biopsies which can be costly and invasive to obtain. Due to lack of pre-emptive cancer detection methods, many cases of cancer are detected at a late stage when the definitive symptoms appear. Plasma samples are relatively easy to obtain, and they can be used to monitor the molecular signatures of ongoing processes in the body. Profiling cell-free DNA is a popular method for monitoring cancer, but only a few studies have explored the use of cell-free RNA (cfRNA), which shows the recent footprint of systemic transcription. RESULTS: Here, we developed FastNeo, a computational method for detecting known neoepitopes in human cfRNA. We show that neoepitopes and other biomarkers detected in cfRNA can discern Hepatocellular carcinoma patients from the healthy patients with a sensitivity of 0.84 and a specificity of 0.79. For colorectal cancer we achieve a sensitivity of 0.87 and a specificity of 0.8. An important advantage of our cfRNA based approach is that it also reports putative neoepitopes which are important for therapeutic purposes. AVAILABILITY AND IMPLEMENTATION: The FastNeo package is available at https://github.com/yashumayank/FastNeo and https://zenodo.org/records/11521368. The benchmark pipelines to detect Immune Epitope database and Tumor-Specific Neoantigen database neoepitopes using HaplotypeCaller, bcftools, and Lofreq, and to run FastNeo with STAR instead of Bowtie2 are also available in the above github repository.

BACKGROUND: Today it is possible to profile the transcriptome of individual cells, and a key step in the analysis of these datasets is unsupervised clustering. For very large datasets, efficient algorithms are required to ensure that analyses can be conducted with reasonable time and memory requirements. RESULTS: Here, we present a highly efficient k-means based approach, and we demonstrate that it scales favorably with the number of cells with regards to time and memory. CONCLUSIONS: We have demonstrated that our streaming k-means clustering algorithm gives state-of-the-art performance while resource requirements scale favorably for up to 2 million cells.

MOTIVATION: Most genomes contain thousands of genes, but for most functional responses, only a subset of those genes are relevant. To facilitate many single-cell RNASeq (scRNASeq) analyses the set of genes is often reduced through feature selection, i.e. by removing genes only subject to technical noise. RESULTS: We present M3Drop, an R package that implements popular existing feature selection methods and two novel methods which take advantage of the prevalence of zeros (dropouts) in scRNASeq data to identify features. We show these new methods outperform existing methods on simulated and real datasets. AVAILABILITY AND IMPLEMENTATION: M3Drop is freely available on github as an R package and is compatible with other popular scRNASeq tools: https://github.com/tallulandrews/M3Drop. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: With the rapid advances in DNA synthesis and sequencing technologies and the continuing decline in the associated costs, high-throughput experiments can be performed to investigate the regulatory role of thousands of oligonucleotide sequences simultaneously. Nevertheless, designing high-throughput reporter assay experiments such as massively parallel reporter assays (MPRAs) and similar methods remains challenging. RESULTS: We introduce MPRAnator, a set of tools that facilitate rapid design of MPRA experiments. With MPRA Motif design, a set of variables provides fine control of how motifs are placed into sequences, thereby allowing the investigation of the rules that govern transcription factor (TF) occupancy. MPRA single-nucleotide polymorphism design can be used to systematically examine the functional effects of single or combinations of single-nucleotide polymorphisms at regulatory sequences. Finally, the Transmutation tool allows for the design of negative controls by permitting scrambling, reversing, complementing or introducing multiple random mutations in the input sequences or motifs. AVAILABILITY AND IMPLEMENTATION: MPRAnator tool set is implemented in Python, Perl and Javascript and is freely available at www.genomegeek.com and www.sanger.ac.uk/science/tools/mpranator The source code is available on www.github.com/hemberg-lab/MPRAnator/ under the MIT license. The REST API allows programmatic access to MPRAnator using simple URLs. CONTACT: [email protected] or [email protected] information: Supplementary data are available at Bioinformatics online.

BACKGROUND: The advent of high throughput RNA-seq at the single-cell level has opened up new opportunities to elucidate the heterogeneity of gene expression. One of the most widespread applications of RNA-seq is to identify genes which are differentially expressed between two experimental conditions. RESULTS: We present a discrete, distributional method for differential gene expression (D(3)E), a novel algorithm specifically designed for single-cell RNA-seq data. We use synthetic data to evaluate D(3)E, demonstrating that it can detect changes in expression, even when the mean level remains unchanged. Since D(3)E is based on an analytically tractable stochastic model, it provides additional biological insights by quantifying biologically meaningful properties, such as the average burst size and frequency. We use D(3)E to investigate experimental data, and with the help of the underlying model, we directly test hypotheses about the driving mechanism behind changes in gene expression. CONCLUSION: Evaluation using synthetic data shows that D(3)E performs better than other methods for identifying differentially expressed genes since it is designed to take full advantage of the information available from single-cell RNA-seq experiments. Moreover, the analytical model underlying D(3)E makes it possible to gain additional biological insights.

We describe the first steps in the adoption of Shape Grammars with Grammatical Evolution for application in Evolutionary Design. Combining the concepts of Shape Grammars and Genetic Programming opens up the exciting possibility of truly generative design assist tools. In this initial study we provide some background on the adoption of grammar-based Genetic Programming for Evolutionary Design, describe Shape Grammars, and give a brief overview of Grammatical Evolution before detailing how Grammatical Evolution used Shape Grammars to successfully rediscover some benchmark target structures.