MOTIVATION: Correctly identifying epitope-binding T-cell receptors (TCRs) is important to both understand their underlying biological mechanism in association to some phenotype and accordingly develop T-cell mediated immunotherapy treatments. Although the importance of the CDR3 region in TCRs for epitope recognition is well recognized, methods for profiling their interactions in association to a certain disease or phenotype remains less studied. We developed EpicPred to identify phenotype-specific TCR-epitope interactions. EpicPred first predicts and removes unlikely TCR-epitope interactions to reduce false positives using the Open-set Recognition (OSR). Subsequently, multiple instance learning was used to identify TCR-epitope interactions specific to a cancer type or severity levels of COVID-19 infected patients. RESULTS: From six public TCR databases, 244 552 TCR sequences and 105 unique epitopes were used to predict epitope-binding TCRs and to filter out non-epitope-binding TCRs using the OSR method. The predicted interactions were used to further predict the phenotype groups in two cancer and four COVID-19 TCR-seq datasets of both bulk and single-cell resolution. EpicPred outperformed the competing methods in predicting the phenotypes, achieving an average AUROC of 0.80 ± 0.07. AVAILABILITY AND IMPLEMENTATION: The EpicPred Software is available at https://github.com/jaeminjj/EpicPred.

BACKGROUND: Selecting informative genes or eliminating uninformative ones before any downstream gene expression analysis is a standard task with great impact on the results. A carefully curated gene set significantly enhances the likelihood of identifying meaningful biomarkers. METHOD: In contrast to the conventional forward gene search methods that focus on selecting highly informative genes, we propose a backward search method, DenoiseIt, that aims to remove potential outlier genes yielding a robust gene set with reduced noise. The gene set constructed by DenoiseIt is expected to capture biologically significant genes while pruning irrelevant ones to the greatest extent possible. Therefore, it also enhances the quality of downstream comparative gene expression analysis. DenoiseIt utilizes non-negative matrix factorization in conjunction with isolation forests to identify outlier rank features and remove their associated genes. RESULTS: DenoiseIt was applied to both bulk and single-cell RNA-seq data collected from TCGA and a COVID-19 cohort to show that it proficiently identified and removed genes exhibiting expression anomalies confined to specific samples rather than a known group. DenoiseIt also showed to reduce the level of technical noise while preserving a higher proportion of biologically relevant genes compared to existing methods. The DenoiseIt Software is publicly available on GitHub at https://github.com/cobi-git/DenoiseIt.

Recent advances in single-cell RNA sequencing (scRNA-seq) technology have enabled the acquisition of RNA at the single-cell level, which showed that the expression level of genes is highly variable across and within the cell types. Even well-known housekeeping genes showed high expression variance in a single condition and within the same cell types. Previous studies made efforts to identify stably expressed genes and use them as a yardstick for robust gene expression normalization. On the other hand, drugs were shown to be less effective on genes with high expression variance. Thus, identifying both stably and variably expressed genes is an important task, especially at the single-cell level. In this study, using the Kullback-Leibler divergence method, we proposed a metric to measure the expression stability of each gene. Using private scRNA-seq data composed of 25 severe COVID-19 patients and 40 healthy individuals, we identified variably expressed genes specific to COVID-19-infected patients and healthy cohorts.

Whole-exome sequencing (WES) has become a standard method for detecting genetic variants in human diseases. Although the primary use of WES data has been the identification of single nucleotide variations and indels, these data also offer a possibility of detecting copy number variations (CNVs) at high resolution. However, WES data have uneven read coverage along the genome owing to the target capture step, and the development of a robust WES-based CNV tool is challenging. Here, we evaluate six WES somatic CNV detection tools: ADTEx, CONTRA, Control-FREEC, EXCAVATOR, ExomeCNV and Varscan2. Using WES data from 50 kidney chromophobe, 50 bladder urothelial carcinoma, and 50 stomach adenocarcinoma patients from The Cancer Genome Atlas, we compared the CNV calls from the six tools with a reference CNV set that was identified by both single nucleotide polymorphism array 6.0 and whole-genome sequencing data. We found that these algorithms gave highly variable results: visual inspection reveals significant differences between the WES-based segmentation profiles and the reference profile, as well as among the WES-based profiles. Using a 50% overlap criterion, 13-77% of WES CNV calls were covered by CNVs from the reference set, up to 21% of the copy gains were called as losses or vice versa, and dramatic differences in CNV sizes and CNV numbers were observed. Overall, ADTEx and EXCAVATOR had the best performance with relatively high precision and sensitivity. We suggest that the current algorithms for somatic CNV detection from WES data are limited in their performance and that more robust algorithms are needed.

UNLABELLED: Gene Set Analyzer (GAzer) is a web-based integrated gene set analysis tool covering previously reported parametric and non-parametric models. Based on a simulation test for the reported algorithms, we classified and implemented three main statistical methods consisting of the z-statistic, gene permutation and sample permutation for ten gene set categories including Gene Ontology (GO) for human, mouse, rat and yeast. This tool identifies significantly altered gene sets scored by z-statistics and P-values from the z-test or permutation test and provides q-values and Bonferroni P-values to correct multiple hypothesis testing. GAzer allows users to observe changes in expression of each gene in a gene set or to see the significance of the gene sets containing a gene(s) of interest, thus allowing interactive data analysis both at the gene and gene set level. Moreover, GAzer offers extensive annotation for each gene. AVAILABILITY: The GAzer gene set analyzer is freely available at http://integromics.kobic.re.kr/GAzer/. SUPPLEMENTARY INFORMATION: This can be found on the web page (http://integromics.kobic.re.kr/GAzer/supplement.jsp).

Consider directed acyclic graph (DAG) scheduling for a large heterogeneous system, which consists of processors with varying processing capabilities and network links with varying bandwidths. The search space of possible task schedules for this problem is immense. One possible approach for this optimization problem, which is NP-hard, is to start with the best task schedule found by a fast deterministic task scheduling algorithm and then iteratively attempt to improve the task schedule by employing a general random guided search method. However, such an approach can lead to extremely long search times, and the solutions found are sometimes not significantly better than those found by the original deterministic task scheduling algorithm. In this paper, we propose an alternative strategy, termed Push-Pull, which starts with the best task schedule found by a fast deterministic task scheduling algorithm and then iteratively attempts to improve the current best solution using a deterministic guided search method. Our simulation results show that given similar runtimes, the Push-Pull algorithm performs well, achieving results similar to or better than all of the other algorithms being compared.

Consider a heterogeneous cluster system, consisting of processors with varying processing capabilities and network links with varying bandwidths. Given a DAG application to be scheduled on such a system, the search space of possible task schedules is immense. One possible approach for this type of NP-complete problem, which has been proposed by previous researchers, starts with the best task schedule found by a fast deterministic task scheduling algorithm, and then iteratively improves the task schedule using a random search method such as genetic algorithm search. However, such an approach can lead to extremely long search times, and the solutions found are sometimes not significantly better than those found by the original deterministic task scheduling algorithm. In this paper, we propose an alternative strategy, termed push-pull, which starts with the best task schedule found by a fast deterministic task scheduling algorithm, and then iteratively attempts to improve the current best solution using a deterministic guided search method. Our simulation results show that this new method performs quite well, executing faster and finding better solutions than a genetic algorithm-based method, which in turn has been shown to perform better than previous algorithms.