The performance of homopolymer detection using dichromatic and tetrachromatic fluorogenic next-generation sequencing platforms.

OBJECTIVES: Homopolymer (HP) sequencing is error-prone in next-generation sequencing (NGS) assays, and may induce false insertion/deletions and substitutions. This study aimed to evaluate the performance of dichromatic and tetrachromatic fluorogenic NGS platforms when sequencing homopolymeric regions. RESULTS: A HP-containing plasmid was constructed and diluted to serial frequencies (3%, 10%, 30%, 60%) to determine the performance of an MGISEQ-2000, MGISEQ-200, and NextSeq 2000 in HP sequencing. An evident negative correlation was observed between the detected frequencies of four nucleotide HPs and the HP length. Significantly decreased rates (P < 0.01) were found in all 8-mer HPs in all three NGS systems at all four expected frequencies, except in the NextSeq 2000 at 3%. With the application of a unique molecular identifier (UMI) pipeline, there were no differences between the detected frequencies of any HPs and the expected frequencies, except for poly-G 8-mers using the MGI 200 platform. UMIs improved the performance of all three NGS platforms in HP sequencing. CONCLUSIONS: We first constructed an HP-containing plasmid based on an EGFR gene backbone to evaluate the performance of NGS platforms when sequencing homopolymeric regions. A highly comparable performance was observed between the MGISEQ-2000 and NextSeq 2000, and introducing UMIs is a promising approach to improve the performance of NGS platforms in sequencing homopolymeric regions.

CRISPR-Cas9 screening identifies INTS3 as an anti-apoptotic RNA-binding protein and therapeutic target for colorectal cancer.

Growing evidences indicate that RNA-binding proteins (RBPs) play critical roles in regulating the RNA splicing, polyadenylation, stability, localization, translation, and turnover. Abnormal expression of RBPs can promote tumorigenesis. Here, we performed a CRISPR screen using an RBP pooled CRISPR knockout library and identified 27 potential RBPs with role in supporting colorectal cancer (CRC) survival. We found that the deletion/depletion of INTS3 triggered apoptosis in CRC. The in vitro experiments and RNA sequencing revealed that INTS3 destabilized pro-apoptotic gene transcripts and contributed to the survival of CRC cells. INTS3 loss delayed CRC cells growth in vivo. Furthermore, delivery of DOTAP/cholesterol-mshINTS3 nanoparticles inhibited CRC tumor growth. Collectively, our work highlights the role of INTS3 in supporting CRC survival and provides several novel therapeutic targets for treatment.

OncoCTMiner: streamlining precision oncology trial matching via molecular profile analysis.

By establishing omics sequencing of patient tumors as a crucial element in cancer treatment, the extensive implementation of precision oncology necessitates effective and prompt execution of clinical studies for approving molecular-targeted therapies. However, the substantial volume of patient sequencing data, combined with strict clinical trial criteria, increasingly complicates the process of matching patients to precision oncology studies. To streamline enrollment in these studies, we developed OncoCTMiner, an automated pre-screening platform for molecular cancer clinical trials. Through manual tagging of eligibility criteria for 2227 oncology trials, we identified key bio-concepts such as cancer types, genes, alterations, drugs, biomarkers and therapies. Utilizing this manually annotated corpus along with open-source biomedical natural language processing tools, we trained multiple named entity recognition models specifically designed for precision oncology trials. These models analyzed 460 952 clinical trials, revealing 8.15 million precision medicine concepts, 9.32 million entity-criteria-trial triplets and a comprehensive precision oncology eligibility criteria database. Most significantly, we developed a patient-trial matching system based on cancer patients' clinical and genetic profiles, which can seamlessly integrate with the omics data analysis platform. This system expedites the pre-screening process for potentially suitable precision oncology trials, offering patients swifter access to promising treatment options. Database URL  https://oncoctminer.chosenmedinfo.com.

OncoPubMiner: a platform for mining oncology publications.

Updated and expert-quality knowledge bases are fundamental to biomedical research. A knowledge base established with human participation and subject to multiple inspections is needed to support clinical decision making, especially in the growing field of precision oncology. The number of original publications in this field has risen dramatically with the advances in technology and the evolution of in-depth research. Consequently, the issue of how to gather and mine these articles accurately and efficiently now requires close consideration. In this study, we present OncoPubMiner (https://oncopubminer.chosenmedinfo.com), a free and powerful system that combines text mining, data structure customisation, publication search with online reading and project-centred and team-based data collection to form a one-stop 'keyword in-knowledge out' oncology publication mining platform. The platform was constructed by integrating all open-access abstracts from PubMed and full-text articles from PubMed Central, and it is updated daily. OncoPubMiner makes obtaining precision oncology knowledge from scientific articles straightforward and will assist researchers in efficiently developing structured knowledge base systems and bring us closer to achieving precision oncology goals.

MIAOME: Human microbiome affect the host epigenome.

Besides the genetic factors having tremendous influences on the regulations of the epigenome, the microenvironmental factors have recently gained extensive attention for their roles in affecting the host epigenome. There are three major types of microenvironmental factors: microbiota-derived metabolites (MDM), microbiota-derived components (MDC) and microbiota-secreted proteins (MSP). These factors can regulate host physiology by modifying host gene expression through the three highly interconnected epigenetic mechanisms (e.g. histone modifications, DNA modifications, and non-coding RNAs). However, no database was available to provide the comprehensive factors of these types. Herein, a database entitled 'Human Microbiome Affect The Host Epigenome (MIAOME)' was constructed. Based on the types of epigenetic modifications confirmed in the literature review, the MIAOME database captures 1068 (63 genus, 281 species, 707 strains, etc.) human microbes, 91 unique microbiota-derived metabolites & components (16 fatty acids, 10 bile acids, 10 phenolic compounds, 10 vitamins, 9 tryptophan metabolites, etc.) derived from 967 microbes; 50 microbes that secreted 40 proteins; 98 microbes that directly influence the host epigenetic modification, and provides 3 classifications of the epigenome, including (1) 4 types of DNA modifications, (2) 20 histone modifications and (3) 490 ncRNAs regulations, involved in 160 human diseases. All in all, MIAOME has compiled the information on the microenvironmental factors influence host epigenome through the scientific literature and biochemical databases, and allows the collective considerations among the different types of factors. It can be freely assessed without login requirement by all users at: http://miaome.idrblab.net/ttd/.

LncRNA functional annotation with improved false discovery rate achieved by disease associations.

The long non-coding RNAs (lncRNAs) play critical roles in various biological processes and are associated with many diseases. Functional annotation of lncRNAs in diseases attracts great attention in understanding their etiology. However, the traditional co-expression-based analysis usually produces a significant number of false positive function assignments. It is thus crucial to develop a new approach to obtain lower false discovery rate for functional annotation of lncRNAs. Here, a novel strategy termed DAnet which combining disease associations with cis-regulatory network between lncRNAs and neighboring protein-coding genes was developed, and the performance of DAnet was systematically compared with that of the traditional differential expression-based approach. Based on a gold standard analysis of the experimentally validated lncRNAs, the proposed strategy was found to perform better in identifying the experimentally validated lncRNAs compared with the other method. Moreover, the majority of biological pathways (40%∼100%) identified by DAnet were reported to be associated with the studied diseases. In sum, the DAnet is expected to be used to identify the function of specific lncRNAs in a particular disease or multiple diseases.

OncoPDSS: an evidence-based clinical decision support system for oncology pharmacotherapy at the individual level.

BACKGROUND: Precision oncology pharmacotherapy relies on precise patient-specific alterations that impact drug responses. Due to rapid advances in clinical tumor sequencing, an urgent need exists for a clinical support tool that automatically interprets sequencing results based on a structured knowledge base of alteration events associated with clinical implications. RESULTS: Here, we introduced the Oncology Pharmacotherapy Decision Support System (OncoPDSS), a web server that systematically annotates the effects of alterations on drug responses. The platform integrates actionable evidence from several well-known resources, distills drug indications from anti-cancer drug labels, and extracts cancer clinical trial data from the ClinicalTrials.gov database. A therapy-centric classification strategy was used to identify potentially effective and non-effective pharmacotherapies from user-uploaded alterations of multi-omics based on integrative evidence. For each potentially effective therapy, clinical trials with faculty information were listed to help patients and their health care providers find the most suitable one. CONCLUSIONS: OncoPDSS can serve as both an integrative knowledge base on cancer precision medicine, as well as a clinical decision support system for cancer researchers and clinical oncologists. It receives multi-omics alterations as input and interprets them into pharmacotherapy-centered information, thus helping clinicians to make clinical pharmacotherapy decisions. The OncoPDSS web server is freely accessible at https://oncopdss.capitalbiobigdata.com .