Single-cell RNA sequencing (scRNA-seq) has enabled the dissection of complex tumor ecosystems. Recognition of malignant cells as an essential step has a profound impact on downstream interpretation. However, most existing computational strategies are based on prior knowledge of canonical cell-type markers. We have developed a marker-free approach, the Seed-Cluster based Approach for NEoplastic cells Recognition (SCANER), to identify malignant cells based on significant gene expression variations caused by genomic instability. Upon analyzing different cancer types, SCANER achieved superior accuracy and robustness in identifying malignant cells, effectively addressing dropout events and tumor purity variations. Besides, SCANER can significantly detect copy number variations (CNVs) in malignant cells compared to nonmalignant cells, which is further confirmed through the paired whole exome sequencing data. In conclusion, SCANER has the potential to facilitate the biological exploration of the tumor ecosystem by accurately identifying malignant cells and it is applicable across various solid cancer types regardless of prior knowledge. SCANER is available at https://github.com/woolingxiang/SCANER.

Novel coronavirus disease 2019 (COVID-19) is an emerging, rapidly evolving crisis, and the ability to predict prognosis for individual COVID-19 patient is important for guiding treatment. Laboratory examinations were repeatedly measured during hospitalization for COVID-19 patients, which provide the possibility for the individualized early prediction of prognosis. However, previous studies mainly focused on risk prediction based on laboratory measurements at one time point, ignoring disease progression and changes of biomarkers over time. By using historical regression trees (HTREEs), a novel machine learning method, and joint modeling technique, we modeled the longitudinal trajectories of laboratory biomarkers and made dynamically predictions on individual prognosis for 1997 COVID-19 patients. In the discovery phase, based on 358 COVID-19 patients admitted between 10 January and 18 February 2020 from Tongji Hospital, HTREE model identified a set of important variables including 14 prognostic biomarkers. With the trajectories of those biomarkers through 5-day, 10-day and 15-day, the joint model had a good performance in discriminating the survived and deceased COVID-19 patients (mean AUCs of 88.81, 84.81 and 85.62% for the discovery set). The predictive model was successfully validated in two independent datasets (mean AUCs of 87.61, 87.55 and 87.03% for validation the first dataset including 112 patients, 94.97, 95.78 and 94.63% for the second validation dataset including 1527 patients, respectively). In conclusion, our study identified important biomarkers associated with the prognosis of COVID-19 patients, characterized the time-to-event process and obtained dynamic predictions at the individual level.

SUMMARY: Technological advances in high-throughput sequencing necessitate improved computational tools for processing and analyzing large-scale datasets in a systematic automated manner. For that purpose, we have developed PRADA (Pipeline for RNA-Sequencing Data Analysis), a flexible, modular and highly scalable software platform that provides many different types of information available by multifaceted analysis starting from raw paired-end RNA-seq data: gene expression levels, quality metrics, detection of unsupervised and supervised fusion transcripts, detection of intragenic fusion variants, homology scores and fusion frame classification. PRADA uses a dual-mapping strategy that increases sensitivity and refines the analytical endpoints. PRADA has been used extensively and successfully in the glioblastoma and renal clear cell projects of The Cancer Genome Atlas program. AVAILABILITY AND IMPLEMENTATION: http://sourceforge.net/projects/prada/ CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: In the functional genomic era, a large number of gene sets have been identified via high-throughput genomic and proteomic technologies. These gene sets of interest are often related to the same or similar disorders or phenotypes, and are commonly presented as differentially expressed gene lists, co-expressed gene modules, protein complexes or signaling pathways. However, biologists are still faced by the challenge of comparing gene sets and interpreting the functional relationships between gene sets into an understanding of the underlying biological mechanisms. RESULTS: We introduce a novel network-based method, designated corrected cumulative rank score (CCRS), which analyzes the functional communication and physical interaction between genes, and presents an easy-to-use web-based toolkit called GsNetCom to quantify the functional relationship between two gene sets. To evaluate the performance of our method in assessing the functional similarity between two gene sets, we analyzed the functional coherence of complexes in functional catalog and identified protein complexes in the same functional catalog. The results suggested that CCRS can offer a significant advance in addressing the functional relationship between different gene sets compared with several other available tools or algorithms with similar functionality. We also conducted the case study based on our method, and succeeded in prioritizing candidate leukemia-associated protein complexes and expanding the prioritization and analysis of cancer-related complexes to other cancer types. In addition, GsNetCom provides a new insight into the communication between gene modules, such as exploring gene sets from the perspective of well-annotated protein complexes. AVAILABILITY AND IMPLEMENTATION: GsNetCom is a freely available web accessible toolkit at http://bioinfo.hrbmu.edu.cn/GsNetCom.