GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels.

Cancer initiation and progression are likely caused by the dysregulation of biological pathways. Gene set analysis (GSA) could improve the signal-to-noise ratio and identify potential biological insights on the gene set level. However, platforms exploring cancer multi-omics data using GSA methods are lacking. In this study, we upgraded our GSCALite to GSCA (gene set cancer analysis, http://bioinfo.life.hust.edu.cn/GSCA) for cancer GSA at genomic, pharmacogenomic and immunogenomic levels. In this improved GSCA, we integrated expression, mutation, drug sensitivity and clinical data from four public data sources for 33 cancer types. We introduced useful features to GSCA, including associations between immune infiltration with gene expression and genomic variations, and associations between gene set expression/mutation and clinical outcomes. GSCA has four main functional modules for cancer GSA to explore, analyze and visualize expression, genomic variations, tumor immune infiltration, drug sensitivity and their associations with clinical outcomes. We used case studies of three gene sets: (i) seven cell cycle genes, (ii) tumor suppressor genes of PI3K pathway and (iii) oncogenes of PI3K pathway to prove the advantage of GSCA over single gene analysis. We found novel associations of gene set expression and mutation with clinical outcomes in different cancer types on gene set level, while on single gene analysis level, they are not significant associations. In conclusion, GSCA is a user-friendly web server and a useful resource for conducting hypothesis tests by using GSA methods at genomic, pharmacogenomic and immunogenomic levels.

LOF variants identifying candidate genes of laterality defects patients with congenital heart disease.

Defects in laterality pattern can result in abnormal positioning of the internal organs during the early stages of embryogenesis, as manifested in heterotaxy syndrome and situs inversus, while laterality defects account for 3~7% of all congenital heart defects (CHDs). However, the pathogenic mechanism underlying most laterality defects remains unknown. In this study, we recruited 70 laterality defect patients with CHDs to identify candidate disease genes by exome sequencing. We then evaluated rare, loss-of-function (LOF) variants, identifying candidates by referring to previous literature. We chose TRIP11, DNHD1, CFAP74, and EGR4 as candidates from 776 LOF variants that met the initial screening criteria. After the variants-to-gene mapping, we performed function research on these candidate genes. The expression patterns and functions of these four candidate genes were studied by whole-mount in situ hybridization, gene knockdown, and gene rescue methods in zebrafish models. Among the four genes, trip11, dnhd1, and cfap74 morphant zebrafish displayed abnormalities in both cardiac looping and expression patterns of early signaling molecules, suggesting that these genes play important roles in the establishment of laterality patterns. Furthermore, we performed immunostaining and high-speed cilia video microscopy to investigate Kupffer's vesicle organogenesis and ciliogenesis of morphant zebrafish. Impairments of Kupffer's vesicle organogenesis or ciliogenesis were found in trip11, dnhd1, and cfap74 morphant zebrafish, which revealed the possible pathogenic mechanism of their LOF variants in laterality defects. These results highlight the importance of rare, LOF variants in identifying disease-related genes and identifying new roles for TRIP11, DNHD1, and CFAP74 in left-right patterning. Additionally, these findings are consistent with the complex genetics of laterality defects.

Population structure and selection signal analysis of indigenous sheep from the southern edge of the Taklamakan Desert.

Analyzing the genetic diversity and selection characteristics of sheep (Ovis aries) holds significant value in understanding their environmental adaptability, enhancing breeding efficiency, and achieving effective conservation and rational utilization of genetic resources. In this study, we utilized Illumina Ovine SNP 50 K BeadChip data from four indigenous sheep breeds from the southern margin of the Taklamakan Desert (Duolang sheep: n = 36, Hetian sheep: n = 74, Kunlun sheep: n = 27, Qira black sheep: n = 178) and three foreign meat sheep breeds (Poll Dorset sheep: n = 105, Suffolk sheep: n = 153, Texel sheep: n = 150) to investigate the population structure, genetic diversity, and genomic signals of positive selection within the indigenous sheep. According to the Principal component analysis (PCA), the Neighbor-Joining tree (NJ tree), and Admixture, we revealed distinct clustering patterns of these seven sheep breeds based on their geographical distribution. Then used Cross Population Extended Haplotype Homozygosity (XP-EHH), Fixation Index (FST), and Integrated Haplotype Score (iHS), we identified a collective set of 32 overlapping genes under positive selection across four indigenous sheep breeds. These genes are associated with wool follicle development and wool traits, desert environmental adaptability, disease resistance, reproduction, and high-altitude adaptability. This study reveals the population structure and genomic selection characteristics in the extreme desert environments of native sheep breeds from the southern edge of the Taklimakan Desert, providing new insights into the conservation and sustainable use of indigenous sheep genetic resources in extreme environments. Additionally, these findings offer valuable genetic resources for sheep and other mammals to adapt to global climate change.

EVAtool: an optimized reads assignment tool for small ncRNA quantification and its application in extracellular vesicle datasets.

Extracellular vesicles (EVs) carrying various small non-coding RNAs (sncRNAs) play a vital roles in cell communication and diseases. Correct quantification of multiple sncRNA biotypes simultaneously in EVs is a challenge due to the short reads (<30 bp) could be mapped to multiple sncRNA types. To address this question, we developed an optimized reads assignment algorithm (ORAA) to dynamically map multi-mapping reads to the sncRNA type with a higher proportion. We integrated ORAA with reads processing steps into EVAtool Python-package (http://bioinfo.life.hust.edu.cn/EVAtool) to quantify sncRNAs, especially for sncRNA-seq from EV samples. EVAtool allows users to specify interested sncRNA types in advanced mode or use default seven sncRNAs (microRNA, small nucleolar RNA, PIWI-interacting RNAs, small nuclear RNA, ribosomal RNA, transfer RNA and Y RNA). To prove the utilities of EVAtool, we quantified the sncRNA expression profiles for 200 samples from cognitive decline and multiple sclerosis. We found that more than 20% of short reads on average were mapped to multiple sncRNA biotypes in multiple sclerosis. In cognitive decline, the proportion of Y RNA is significantly higher than other sncRNA types. EVAtool is a flexible and extensible tool that would benefit to mine potential biomarkers and functional molecules in EVs.

EVAtlas: a comprehensive database for ncRNA expression in human extracellular vesicles.

Extracellular vesicles (EVs) packing various molecules play vital roles in intercellular communication. Non-coding RNAs (ncRNAs) are important functional molecules and biomarkers in EVs. A comprehensive investigation of ncRNAs expression in EVs under different conditions is a fundamental step for functional discovery and application of EVs. Here, we curated 2030 small RNA-seq datasets for human EVs (1506 sEV and 524 lEV) in 24 conditions and over 40 diseases. We performed a unified reads dynamic assignment algorithm (RDAA) considering mismatch and multi-mapping reads to quantify the expression profiles of seven ncRNA types (miRNA, snoRNA, piRNA, snRNA, rRNA, tRNA and Y RNA). We constructed EVAtlas (http://bioinfo.life.hust.edu.cn/EVAtlas), a comprehensive database for ncRNA expression in EVs with four functional modules: (i) browse and compare the distribution of ncRNAs in EVs from 24 conditions and eight sources (plasma, serum, saliva, urine, sperm, breast milk, primary cell and cell line); (ii) prioritize candidate ncRNAs in condition related tissues based on their expression; (iii) explore the specifically expressed ncRNAs in EVs from 24 conditions; (iv) investigate ncRNA functions, related drugs, target genes and EVs isolation methods. EVAtlas contains the most comprehensive ncRNA expression in EVs and will be a key resource in this field.

A comprehensive survey for human transcription factors on expression, regulation, interaction, phenotype and cancer survival.

Transcription factors (TFs) act as key regulators in biological processes through controlling gene expression. Here, we conducted a systematic study for all human TFs on the expression, regulation, interaction, mutation, phenotype and cancer survival. We revealed that the average expression levels of TFs in normal tissues were lower than 50% expression of non-TFs, whereas TF expression was increased in cancers. TFs that are specifically expressed in an individual tissue or cancer may be potential marker genes. For instance, TGIF2LX/Y were preferentially expressed in testis and NEUROG1, PRDM14, SRY, ZNF705A and ZNF716 were specifically highly expressed in germ cell tumors. We found different distributions of target genes and TF co-regulations in different TF families. Some small TF families have huge protein interaction pairs, suggesting their central roles in transcriptional regulation. The bZIP family is a small family involving many signaling pathways. Survival analysis indicated that most TFs significantly affect survival of one or more cancers. Some survival-related TFs were also specifically highly expressed in the corresponding cancer types, which may be potential targets for cancer therapy. Finally, we identified 43 TFs whose mutations were closely correlated to survival, suggesting their cancer-driven roles. The systematic analysis of TFs provides useful clues for further investigation of TF regulatory mechanisms and the role of TFs in diseases.

CCLA: an accurate method and web server for cancer cell line authentication using gene expression profiles.

Cancer cell lines (CCLs) as important model systems play critical roles in cancer research. The misidentification and contamination of CCLs are serious problems, leading to unreliable results and waste of resources. Current methods for CCL authentication are mainly based on the CCL-specific genetic polymorphism, whereas no method is available for CCL authentication using gene expression profiles. Here, we developed a novel method and homonymic web server (CCLA, Cancer Cell Line Authentication, http://bioinfo.life.hust.edu.cn/web/CCLA/) to authenticate 1291 human CCLs of 28 tissues using gene expression profiles. CCLA showed an excellent speed advantage and high accuracy for CCL authentication, a top 1 accuracy of 96.58 or 92.15% (top 3 accuracy of 100 or 95.11%) for microarray or RNA-Seq validation data (719 samples, 461 CCLs), respectively. To the best of our knowledge, CCLA is the first approach to authenticate CCLs using gene expression data. Users can freely and conveniently authenticate CCLs using gene expression profiles or NCBI GEO accession on CCLA website.

Expression profile of immune checkpoint genes and their roles in predicting immunotherapy response.

Immune checkpoint genes (ICGs) play critical roles in circumventing self-reactivity and represent a novel target to develop treatments for cancers. However, a comprehensive analysis for the expression profile of ICGs at a pan-cancer level and their correlation with patient response to immune checkpoint blockade (ICB) based therapy is still lacking. In this study, we defined three expression patterns of ICGs using a comprehensive survey of RNA-seq data of tumor and immune cells from the functional annotation of the mammalian genome (FANTOM5) project. The correlation between the expression patterns of ICGs and patients survival and response to ICB therapy was investigated. The expression patterns of ICGs were robust across cancers, and upregulation of ICGs was positively correlated with high lymphocyte infiltration and good prognosis. Furthermore, we built a model (ICGe) to predict the response of patients to ICB therapy using five features of ICG expression. A validation scenario of six independent datasets containing data of 261 patients with CTLA-4 and PD-1 blockade immunotherapies demonstrated that ICGe achieved area under the curves of 0.64-0.82 and showed a robust performance and outperformed other mRNA-based predictors. In conclusion, this work revealed expression patterns of ICGs and underlying correlations between ICGs and response to ICB, which helps to understand the mechanisms of ICGs in ICB signal pathways and other anticancer treatments.

miRNASNP-v3: a comprehensive database for SNPs and disease-related variations in miRNAs and miRNA targets.

MicroRNAs (miRNAs) related single-nucleotide variations (SNVs), including single-nucleotide polymorphisms (SNPs) and disease-related variations (DRVs) in miRNAs and miRNA-target binding sites, can affect miRNA functions and/or biogenesis, thus to impact on phenotypes. miRNASNP is a widely used database for miRNA-related SNPs and their effects. Here, we updated it to miRNASNP-v3 (http://bioinfo.life.hust.edu.cn/miRNASNP/) with tremendous number of SNVs and new features, especially the DRVs data. We analyzed the effects of 7 161 741 SNPs and 505 417 DRVs on 1897 pre-miRNAs (2630 mature miRNAs) and 3'UTRs of 18 152 genes. miRNASNP-v3 provides a one-stop resource for miRNA-related SNVs research with the following functions: (i) explore associations between miRNA-related SNPs/DRVs and diseases; (ii) browse the effects of SNPs/DRVs on miRNA-target binding; (iii) functional enrichment analysis of miRNA target gain/loss caused by SNPs/DRVs; (iv) investigate correlations between drug sensitivity and miRNA expression; (v) inquire expression profiles of miRNAs and their targets in cancers; (vi) browse the effects of SNPs/DRVs on pre-miRNA secondary structure changes; and (vii) predict the effects of user-defined variations on miRNA-target binding or pre-miRNA secondary structure. miRNASNP-v3 is a valuable and long-term supported resource in functional variation screening and miRNA function studies.

Comparison of the efficiency and precision of Base editor and CRISPR/Cas9 for inducing defined point mutation (S395F) in ovine embryos.

Cytosine base editors (CBEs) and CRISPR/Cas9-mediated HDR method both have the ability to introduce nucleotide substitution into genomes, which exhibit great potential for improving economically important traits in livestock species. The FecGH mutation (g. C1184T, p. S395F) of growth differentiation factor 9 (GDF9) gene increases prolificacy in Cambridge sheep and Belclare sheep. In the present study, we aimed to compare the efficiency and precision of BE4-Gam and CRISPR/Cas9 systems on generating FecGH mutation in ovine genome. First, the microinjection of BE4-Gam mRNA had no adverse effects on development rate after cleavage, and the efficiencies of total mutants and targeted mutants were 8.9% and 7.1%, respectively. Then, the total mutation and targeted mutation rates were improved from 8.5% to 22.5% (p < .01), and 6.4% to 16.3%, respectively, by adjusting the injection time of BE4-Gam mRNA from 14 to 12 hr post-insemination (hpi). Furthermore, CRISPR/Cas9-mediated HDR method introduced the FecGH mutation at the efficiency of 16.1%, which was comparable to BE4-Gam system (16.3%). There was no bystander editing event happened in edited embryos caused by CRISPR/Cas9, but the bystander editing efficiency was as high as 15.0% in BE4-Gam-edited embryos. In summary, our findings demonstrated that CRISPR/Cas9-mediated HDR method was more accurate than BE4-Gam system in introducing FecGH into ovine genome, and highlight the potential of the former strategy to modify economically important trait-associated SNPs.

Peroxisome Proliferator FpPEX11 Is Involved in the Development and Pathogenicity in Fusarium pseudograminearum.

Fusarium crown rot (FCR) of wheat, an important soil-borne disease, presents a worsening trend year by year, posing a significant threat to wheat production. Fusarium pseudograminearum cv. b was reported to be the dominant pathogen of FCR in China. Peroxisomes are single-membrane organelles in eukaryotes that are involved in many important biochemical metabolic processes, including fatty acid ß-oxidation. PEX11 is important proteins in peroxisome proliferation, while less is known in the fungus F. pseudograminearum. The functions of FpPEX11a, FpPEX11b, and FpPEX11c in F. pseudograminearum were studied using reverse genetics, and the results showed that FpPEX11a and FpPEX11b are involved in the regulation of vegetative growth and asexual reproduction. After deleting FpPEX11a and FpPEX11b, cell wall integrity was impaired, cellular metabolism processes including active oxygen metabolism and fatty acid ß-oxidation were significantly blocked, and the production ability of deoxynivalenol (DON) decreased. In addition, the deletion of genes of FpPEX11a and FpPEX11b revealed a strongly decreased expression level of peroxisome-proliferation-associated genes and DON-synthesis-related genes. However, deletion of FpPEX11c did not significantly affect these metabolic processes. Deletion of the three protein-coding genes resulted in reduced pathogenicity of F. pseudograminearum. In summary, FpPEX11a and FpPEX11b play crucial roles in the growth and development, asexual reproduction, pathogenicity, active oxygen accumulation, and fatty acid utilization in F. pseudograminearum.

An ultra-sensitive T-cell receptor detection method for TCR-Seq and RNA-Seq data.

MOTIVATION: T-cell receptors (TCRs) function to recognize antigens and play vital roles in T-cell immunology. Surveying TCR repertoires by characterizing complementarity-determining region 3 (CDR3) is a key issue. Due to the high diversity of CDR3 and technological limitation, accurate characterization of CDR3 repertoires remains a great challenge. RESULTS: We propose a computational method named CATT for ultra-sensitive and precise TCR CDR3 sequences detection. CATT can be applied on TCR sequencing, RNA-Seq and single-cell TCR(RNA)-Seq data to characterize CDR3 repertoires. CATT integrated de Bruijn graph-based micro-assembly algorithm, data-driven error correction model and Bayesian inference algorithm, to self-adaptively and ultra-sensitively characterize CDR3 repertoires with high performance. Benchmark results of datasets from in silico and experimental data demonstrated that CATT showed superior recall and precision compared with existing tools, especially for data with short read length and small size and single-cell sequencing data. Thus, CATT will be a useful tool for TCR analysis in researches of cancer and immunology. AVAILABILITY AND IMPLEMENTATION: http://bioinfo.life.hust.edu.cn/CATT or https://github.com/GuoBioinfoLab/CATT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Pancan-meQTL: a database to systematically evaluate the effects of genetic variants on methylation in human cancer.

DNA methylation is an important epigenetic mechanism for regulating gene expression. Aberrant DNA methylation has been observed in various human diseases, including cancer. Single-nucleotide polymorphisms can contribute to tumor initiation, progression and prognosis by influencing DNA methylation, and DNA methylation quantitative trait loci (meQTL) have been identified in physiological and pathological contexts. However, no database has been developed to systematically analyze meQTLs across multiple cancer types. Here, we present Pancan-meQTL, a database to comprehensively provide meQTLs across 23 cancer types from The Cancer Genome Atlas by integrating genome-wide genotype and DNA methylation data. In total, we identified 8 028 964 cis-meQTLs and 965 050 trans-meQTLs. Among these, 23 432 meQTLs are associated with patient overall survival times. Furthermore, we identified 2 214 458 meQTLs that overlap with known loci identified through genome-wide association studies. Pancan-meQTL provides a user-friendly web interface (http://bioinfo.life.hust.edu.cn/Pancan-meQTL/) that is convenient for browsing, searching and downloading data of interest. This database is a valuable resource for investigating the roles of genetics and epigenetics in cancer.

SAGD: a comprehensive sex-associated gene database from transcriptomes.

Many animal species present sex differences. Sex-associated genes (SAGs), which have female-biased or male-biased expression, have major influences on the remarkable sex differences in important traits such as growth, reproduction, disease resistance and behaviors. However, the SAGs resulting in the vast majority of phenotypic sex differences are still unknown. To provide a useful resource for the functional study of SAGs, we manually curated public RNA-seq datasets with paired female and male biological replicates from the same condition and systematically re-analyzed the datasets using standardized methods. We identified 27,793 female-biased SAGs and 64,043 male-biased SAGs from 2,828 samples of 21 species, including human, chimpanzee, macaque, mouse, rat, cow, horse, chicken, zebrafish, seven fly species and five worm species. All these data were cataloged into SAGD, a user-friendly database of SAGs (http://bioinfo.life.hust.edu.cn/SAGD) where users can browse SAGs by gene, species, drug and dataset. In SAGD, the expression, annotation, targeting drugs, homologs, ontology and related RNA-seq datasets of SAGs are provided to help researchers to explore their functions and potential applications in agriculture and human health.

Rapid and Visual Detection of Meloidogyne hapla Using Recombinase Polymerase Amplification Combined with a Lateral Flow Dipstick Assay.

The northern root-knot nematode, Meloidogyne hapla, is a biotrophic parasite that infects many crops and causes severe economic losses worldwide. Rapid and accurate detection of M. hapla is crucial for disease forecasting and control. We developed a recombinase polymerase amplification combined with a lateral flow dipstick (RPA-LFD) assay for rapid detection of M. hapla. The primers and probe were designed based on the effector gene 16D10 sequence and were highly specific to M. hapla. The RPA reaction was performed at a wide range of temperatures from 25 to 45°C within 5 to 25 min, and the amplicon was visualized directly on the LFD within 5 min. The detection limits of the RPA-LFD assay were 10-3 females and 10-2 second-stage juveniles/0.5 g of soil, which was 10 times more sensitive than the conventional PCR assay. In addition, the RPA-LFD assay can detect M. hapla from infested plant roots and soil samples, and the entire detection process can be completed within 1.5 h. These results indicate that the RPA-LFD assay is a simple, rapid, specific, sensitive, and visual method that can be used for rapid detection of M. hapla in the field and in resource-limited conditions.

Bacterial strain for bast fiber crops degumming and its bio-degumming technique.

The research and development of bio-degumming technology is under a slow progress due to the shortage of proper efficient bacterial strains and processes. A degumming bacterial strain-Pectobacterium wasabiae (PW)-with broad-spectrum degumming abilities was screened out in this study. After the fermentation for 12 h, the residual gum contents of kenaf bast, ramie bast, hemp bast, flax bast, and Apocynum venetum bast were all lower than 15%. This bacterial strain could realize the simultaneous extracellular secretion of pectinase, mannase, and xylanase with the maximum enzyme activity levels of 130.25, 157.58, and 115.24 U/mL, respectively. The optimal degumming conditions of this bacterial strain were as follows: degumming time of 12 h, bath ratio of 1:10, temperature of 33 °C, and inoculum size of 2%. After the bio-degumming through this bacterial strain, the COD in wastewater was below 4000 mg/L, which was over 60% lower than that in boiling-off wastewater generated by chemical degumming. This technology achieves higher efficiency, higher quality, and lower pollution.

A hemizygous mutation in the androgen receptor gene causes different phenotypes of androgen insensitivity syndrome in two siblings by disrupting the nuclear translocation.

The androgen insensitivity syndrome (AIS) is a congenital disease characterized by androgen resistance due to androgen receptor (AR) gene mutations, resulting in disorders of sex differentiation in 46,XY individuals. However, the underlying mechanisms in the majority of AR variants and the phenotype-genotype correlations are unclear. Here, we identified a p.Y764H variant of the AR gene that results in different phenotypes in a family. Structural analyses revealed that amino acid substitution affected protein properties and spatial conformation, and in vitro, functional studies showed impaired nuclear translocation ability of the mutated protein. Moreover, the extent to which this variant reduced nuclear translocation depends on the dihydrotestosterone (DHT) concentrations. Our results, for the first time, demonstrated a pathogenesis of the p.Y764H mutations in AR resulting in AIS phenotype, and indicated that AIS patients with p.Y764H mutation and preserved gonad might have residual AR activity at high androgen levels, putting patients at risk for pubertal virilization in the future. We provide an in-depth insight into the pathogenesis in AIS based on the amino acid substitution, which may help aid its precise diagnosis, personalized treatment, and organized follow-up to avoid gender dysphoria.

The roles of FgPEX2 and FgPEX12 in virulence and lipid metabolism in Fusarium graminearum.

Fusarium head blight (FHB) is a wheat disease with a worldwide prevalence, caused by Fusarium graminearum. Peroxisomes are ubiquitous in eukaryotic cells and are involved in various biochemical phenomena. FgPEX2 and FgPEX12 encode RING-finger peroxins PEX2 and PEX12 in F. graminearum. This study aimed to functionally characterize FgPEX2 and FgPEX12 in F. graminearum. We constructed deletion mutants of FgPEX2 and FgPEX12 via homologous recombination. The ΔPEX2 and ΔPEX12 mutants displayed defects in sexual and asexual development, virulence, cell wall integrity (CWI), and lipid metabolism. Deletion of FgPEX2 and FgPEX12 significantly decreased deoxynivalenol production. Furthermore, fluorescence microscopic analysis of the subcellular localization of GFP-PMP70 and GFP-HEX1 revealed that FgPEX2 and FgPEX12 maintain Woronin bodies. These results show that FgPEX2 and FgPEX12 are required for growth, conidiation, virulence, cell wall integrity, and lipid metabolism in F. graminearum and do not influence their peroxisomes.

SEGtool: a specifically expressed gene detection tool and applications in human tissue and single-cell sequencing data.

Different tissues and diseases have distinct transcriptional profilings with specifically expressed genes (SEGs). So, the identification of SEGs is an important issue in the studies of gene function, biological development, disease mechanism and biomarker discovery. However, few accurate and easy-to-use tools are available for RNA sequencing (RNA-seq) data to detect SEGs. Here, we presented SEGtool, a tool based on fuzzy c-means, Jaccard index and greedy annealing method for SEG detection automatically and self-adaptively ignoring data distribution. Testing result showed that our SEGtool outperforms the existing tools, which was mainly developed for microarray data. By applying SEGtool to Genotype-Tissue Expression (GTEx) human tissue data set, we detected 3181 SEGs with tissue-related functions. Regulatory networks reveal tissue-specific transcription factors regulating many SEGs, such as ETV2 in testis, HNF4A in liver and NEUROD1 in brain. Applied to a case study of single-cell sequencing (SCS) data from embryo cells, we identified many SEGs in specific stages of human embryogenesis. Notably, SEGtool is suitable for RNA-seq data and even SCS data with high specificity and accuracy. An implementation of SEGtool R package is freely available at http://bioinfo.life.hust.edu.cn/SEGtool/.

tRic: a user-friendly data portal to explore the expression landscape of tRNAs in human cancers.

Transfer RNAs (tRNAs) play critical roles in human cancer. Currently, no database provides the expression landscape and clinical relevance of tRNAs across a variety of human cancers. Utilizing miRNA-seq data from The Cancer Genome Atlas, we quantified the relative expression of tRNA genes and merged them into the codon level and amino level across 31 cancer types. The expression of tRNAs is associated with clinical features of patient smoking history and overall survival, and disease stage, subtype, and grade. We further analysed codon frequency and amino acid frequency for each protein coding gene and linked alterations of tRNA expression with protein translational efficiency. We include these data resources in a user-friendly data portal, tRic (tRNA in cancer, https://hanlab.uth.edu/tRic/ or http://bioinfo.life.hust.edu.cn/tRic/), which can be of significant interest to the research community.