MINRbase: a comprehensive database of nuclear- and mitochondrial-ribosomal-RNA-derived fragments (rRFs).

We describe the Mitochondrial and Nuclear rRNA fragment database (MINRbase), a knowledge repository aimed at facilitating the study of ribosomal RNA-derived fragments (rRFs). MINRbase provides interactive access to the profiles of 130 238 expressed rRFs arising from the four human nuclear rRNAs (18S, 5.8S, 28S, 5S), two mitochondrial rRNAs (12S, 16S) or four spacers of 45S pre-rRNA. We compiled these profiles by analyzing 11 632 datasets, including the GEUVADIS and The Cancer Genome Atlas (TCGA) repositories. MINRbase offers a user-friendly interface that lets researchers issue complex queries based on one or more criteria, such as parental rRNA identity, nucleotide sequence, rRF minimum abundance and metadata keywords (e.g. tissue type, disease). A 'summary' page for each rRF provides a granular breakdown of its expression by tissue type, disease, sex, ancestry and other variables; it also allows users to create publication-ready plots at the click of a button. MINRbase has already allowed us to generate support for three novel observations: the internal spacers of 45S are prolific producers of abundant rRFs; many abundant rRFs straddle the known boundaries of rRNAs; rRF production is regimented and depends on 'personal attributes' (sex, ancestry) and 'context' (tissue type, tissue state, disease). MINRbase is available at https://cm.jefferson.edu/MINRbase/.

The Typical tRNA Co-Expresses Multiple 5' tRNA Halves Whose Sequences and Abundances Depend on Isodecoder and Isoacceptor and Change with Tissue Type, Cell Type, and Disease.

Transfer RNA-derived fragments (tRFs) are noncoding RNAs that arise from either mature transfer RNAs (tRNAs) or their precursors. One important category of tRFs comprises the tRNA halves, which are generated through cleavage at the anticodon. A given tRNA typically gives rise to several co-expressed 5'-tRNA halves (5'-tRHs) that differ in the location of their 3' ends. These 5'-tRHs, even though distinct, have traditionally been treated as indistinguishable from one another due to their near-identical sequences and lengths. We focused on co-expressed 5'-tRHs that arise from the same tRNA and systematically examined their exact sequences and abundances across 10 different human tissues. To this end, we manually curated and analyzed several hundred human RNA-seq datasets from NCBI's Sequence Run Archive (SRA). We grouped datasets from the same tissue into their own collection and examined each group separately. We found that a given tRNA produces different groups of co-expressed 5'-tRHs in different tissues, different cell lines, and different diseases. Importantly, the co-expressed 5'-tRHs differ in their sequences, absolute abundances, and relative abundances, even among tRNAs with near-identical sequences from the same isodecoder or isoacceptor group. The findings suggest that co-expressed 5'-tRHs that are produced from the same tRNA or closely related tRNAs have distinct, context-dependent roles. Moreover, our analyses show that cell lines modeling the same tissue type and disease may not be interchangeable when it comes to experimenting with tRFs.

Characterization of all small RNAs in and comparisons across cultured megakaryocytes and platelets of healthy individuals and COVID-19 patients.

BACKGROUND: The small noncoding RNAs (sncRNAs) in megakaryocytes (MKs) and platelets are not well characterized. Neither is the impact of SARS-CoV-2 infection on the sncRNAs of platelets. OBJECTIVES: To investigate the sorting of MK sncRNAs into platelets, and the differences in the platelet sncRNAomes of healthy donors (HDs) and COVID-19 patients. METHODS: We comprehensively profiled sncRNAs from MKs cultured from cord blood-derived CD34+ cells, platelets from HDs, and platelets from patients with moderate and severe SARS-CoV-2 infection. We also comprehensively profiled Argonaute (AGO)-bound sncRNAs from the cultured MKs. RESULTS: We characterized the sncRNAs in MKs and platelets and can account for ∼95% of all sequenced reads. We found that MKs primarily comprise microRNA isoforms (isomiRs), tRNA-derived fragments (tRFs), rRNA-derived fragments (rRFs), and Y RNA-derived fragments (yRFs) in comparable abundances. The platelets of HDs showed a skewed distribution by comparison: 56.7% of all sncRNAs are yRFs, 34.4% are isomiRs, and <2.0% are tRFs and rRFs. Most isomiRs in MKs and platelets are either noncanonical, nontemplated, or both. When comparing MKs and platelets from HDs, we found numerous isomiRs, tRFs, rRFs, and yRFs showing opposite enrichments or depletions, including molecules from the same parental miRNA arm, tRNA, rRNA, or Y RNA. The sncRNAome of platelets from patients with COVID-19 is skewed compared to that of HDs with only 19.8% of all sncRNAs now being yRFs, isomiRs increasing to 63.6%, and tRFs and rRFs more than tripling their presence to 6.1%. CONCLUSION: The sncRNAomes of MKs and platelets are very rich and more complex than it has been believed. The evidence suggests complex mechanisms that sort MK sncRNAs into platelets. SARS-CoV-2 infection acutely alters the contents of platelets by changing the relative proportions of their sncRNAs.

Using medicare claims to estimate risk-adjusted performance of Pennsylvania trauma centers.

Trauma centers use registry data to benchmark performance using a standardized risk adjustment model. Our objective was to utilize national claims to develop a risk adjustment model applicable across all hospitals, regardless of designation or registry participation. Patients from 2013-14 Pennsylvania Trauma Outcomes Study (PTOS) registry data were probabilistically matched to Medicare claims using demographic and injury characteristics. Pairwise comparisons established facility linkages and matching was then repeated within facilities to link records. Registry models were estimated using GLM and compared with five claims-based LASSO models: demographics, clinical characteristics, diagnosis codes, procedures codes, and combined demographics/clinical characteristics. Area under the curve and correlation with registry model probability of death were calculated for each linked and out-of-sample cohort. From 29 facilities, a cohort comprising 16,418 patients were linked between datasets. Patients were similarly distributed: median age 82 (PTOS IQR: 74-87 vs. Medicare IQR: 75-88); non-white 6.2% (PTOS) vs. 5.8% (Medicare). The registry model AUC was 0.86 (0.84-0.87). Diagnosis and procedure codes models performed poorest. The demographics/clinical characteristics model achieved an AUC = 0.84 (0.83-0.86) and Spearman = 0.62 with registry data. Claims data can be leveraged to create models that accurately measure the performance of hospitals that treat trauma patients.

A comprehensive map of alternative polyadenylation in African American and European American lung cancer patients.

Deciphering the post-transcriptional mechanisms (PTM) regulating gene expression is critical to understand the dynamics underlying transcriptomic regulation in cancer. Alternative polyadenylation (APA)-regulation of mRNA 3'UTR length by alternating poly(A) site usage-is a key PTM mechanism whose comprehensive analysis in cancer remains an important open challenge. Here we use a method and analysis pipeline that sequences 3'end-enriched RNA directly to overcome the saturation limitation of traditional 5'-3' based sequencing. We comprehensively map the APA landscape in lung cancer in a cohort of 98 tumor/non-involved tissues derived from European American and African American patients. We identify a global shortening of 3'UTR transcripts in lung cancer, with notable functional implications on the expression of both coding and noncoding genes. We find that APA of non-coding RNA transcripts (long non-coding RNAs and microRNAs) is a recurrent event in lung cancer and discover that the selection of alternative polyA sites is a form of non-coding RNA expression control. Our results indicate that mRNA transcripts from EAs are two times more likely than AAs to undergo APA in lung cancer. Taken together, our findings comprehensively map and identify the important functional role of alternative polyadenylation in determining transcriptomic heterogeneity in lung cancer.

The Short and the Long of it: Timing of Mortality for Older Adults in a State Trauma System.

BACKGROUND: The impact of injury extends beyond the hospital stay, but trauma center performance metrics typically focus on in-hospital mortality. We compared risk adjusted rates of in-hospital and long-term mortality among Pennsylvania trauma centers. We hypothesized that centers with low rates of in-hospital mortality would also have low rates of long-term mortality. METHODS: We identified injured patients (age ≥ 65) admitted to Pennsylvania trauma centers in 2013 and 2014 using the Pennsylvania Trauma Outcomes Study, a robust, state-wide trauma registry. We matched trauma registry records to Medicare claims from the y 2013 to 2015. Matching variables included admission date and patient demographics including date of birth, zip, sex, and race and/or ethnicity. Outcomes examined were inpatient, 30-day, and 1-y mortality. Multivariable logistic regression models including presenting physiology, comorbidities, injury characteristics, and demographics were developed to calculate expected mortality rates for each trauma center at each time point. Trauma center performance was assessed using observed-to-expected ratios and ranking for in-hospital, 30-day, and 1-y mortality. RESULTS: Of the 15,451 patients treated at 28 centers, 8.1% died before discharge or were discharged to hospice. Another 3.4% died within 30 d, and another 14.7% died within 1 y of injury. Of patients who survived hospitalization but died within 30 d, 92.5% were injured due to fall, and 75.0% sustained head injuries. Survival at 1 y was higher in patients discharged home (88.4%), compared to those discharged to a skilled nursing facility or long-term acute care hospital (72.7% and 52.6%, respectively). Three centers were identified as outliers (two low and one high) for in-hospital mortality, none of which were outliers when the horizon was stretched to 30 d from injury. At 30 d, two different low and two different high outliers were found. CONCLUSION: Nearly one-in-three injured older adults who die within 30 d of injury dies after hospital discharge. Hospital rankings for in-hospital mortality correlate poorly with long-term outcomes. These findings underscore the importance of looking beyond survival to discharge for quality improvement and benchmarking.

Unraveling the role of microRNA/isomiR network in multiple primary melanoma pathogenesis.

Malignant cutaneous melanoma (CM) is a potentially lethal form of skin cancer whose worldwide incidence has been constantly increasing over the past decades. During their lifetime, about 8% of CM patients will develop multiple primary melanomas (MPMs), usually at a young age and within 3 years from the first tumor/diagnosis. With the aim of improving our knowledge on MPM biology and pathogenesis, we explored the miRNome of 24 single and multiple primary melanomas, including multiple tumors from the same patient, using a small RNA-sequencing approach. From a supervised analysis, 22 miRNAs were differentially expressed in MPM compared to single CM, including key miRNAs involved in epithelial-mesenchymal transition. The first and second melanoma from the same patient presented a different miRNA profile. Ten miRNAs, including miR-25-3p, 149-5p, 92b-3p, 211-5p, 125a-5p, 125b-5p, 205-5p, 200b-3p, 21-5p, and 146a-5p, were further validated in 47 single and multiple melanoma samples. Pathway enrichment analysis of miRNA target genes revealed a more differentiated and less invasive status of MPMs compared to CMs. Bioinformatic analyses at the miRNA isoform (isomiR) level detected a panel of highly expressed isomiRs belonging to miRNA families implicated in human tumorigenesis, including miR-200, miR-30, and miR-10 family. Moreover, we identified hsa-miR-125a-5p|0|-2 isoform as tenfold over-represented in melanoma than the canonical form and differentially expressed in MPMs arising in the same patient. Target prediction analysis revealed that the miRNA shortening could change the pattern of target gene regulation, specifically in genes implicated in cell adhesion and neuronal differentiation. Overall, we provided a putative and comprehensive characterization of the miRNA/isomiR regulatory network of MPMs, highlighting mechanisms of tumor development and molecular features differentiating this subtype from single melanomas.

IsoMiRmap: fast, deterministic and exhaustive mining of isomiRs from short RNA-seq datasets.

MOTIVATION: MicroRNA (miRNA) precursor arms give rise to multiple isoforms simultaneously called 'isomiRs.' IsomiRs from the same arm typically differ by a few nucleotides at either their 5' or 3' termini or both. In humans, the identities and abundances of isomiRs depend on a person's sex and genetic ancestry as well as on tissue type, tissue state and disease type/subtype. Moreover, nearly half of the time the most abundant isomiR differs from the miRNA sequence found in public databases. Accurate mining of isomiRs from deep sequencing data is thus important. RESULTS: We developed isoMiRmap, a fast, standalone, user-friendly mining tool that identifies and quantifies all isomiRs by directly processing short RNA-seq datasets. IsoMiRmap is a portable 'plug-and-play' tool, requires minimal setup, has modest computing and storage requirements, and can process an RNA-seq dataset with 50 million reads in just a few minutes on an average laptop. IsoMiRmap deterministically and exhaustively reports all isomiRs in a given deep sequencing dataset and quantifies them accurately (no double-counting). IsoMiRmap comprehensively reports all miRNA precursor locations from which an isomiR may be transcribed, tags as 'ambiguous' isomiRs whose sequences exist both inside and outside of the space of known miRNA sequences and reports the public identifiers of common single-nucleotide polymorphisms and documented somatic mutations that may be present in an isomiR. IsoMiRmap also identifies isomiRs with 3' non-templated post-transcriptional additions. Compared to similar tools, isoMiRmap is the fastest, reports more bona fide isomiRs, and provides the most comprehensive information related to an isomiR's transcriptional origin. AVAILABILITY AND IMPLEMENTATION: The codes for isoMiRmap are freely available at https://cm.jefferson.edu/isoMiRmap/ and https://github.com/TJU-CMC-Org/isoMiRmap/. IsomiR profiles for the datasets of the 1000 Genomes Project, spanning five population groups, and The Cancer Genome Atlas (TCGA), spanning 33 cancer studies, are also available at https://cm.jefferson.edu/isoMiRmap/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Gene selection for optimal prediction of cell position in tissues from single-cell transcriptomics data.

Single-cell RNA-sequencing (scRNAseq) technologies are rapidly evolving. Although very informative, in standard scRNAseq experiments, the spatial organization of the cells in the tissue of origin is lost. Conversely, spatial RNA-seq technologies designed to maintain cell localization have limited throughput and gene coverage. Mapping scRNAseq to genes with spatial information increases coverage while providing spatial location. However, methods to perform such mapping have not yet been benchmarked. To fill this gap, we organized the DREAM Single-Cell Transcriptomics challenge focused on the spatial reconstruction of cells from the Drosophila embryo from scRNAseq data, leveraging as silver standard, genes with in situ hybridization data from the Berkeley Drosophila Transcription Network Project reference atlas. The 34 participating teams used diverse algorithms for gene selection and location prediction, while being able to correctly localize clusters of cells. Selection of predictor genes was essential for this task. Predictor genes showed a relatively high expression entropy, high spatial clustering and included prominent developmental genes such as gap and pair-rule genes and tissue markers. Application of the top 10 methods to a zebra fish embryo dataset yielded similar performance and statistical properties of the selected genes than in the Drosophila data. This suggests that methods developed in this challenge are able to extract generalizable properties of genes that are useful to accurately reconstruct the spatial arrangement of cells in tissues.

Ribosomal RNA fragmentation into short RNAs (rRFs) is modulated in a sex- and population of origin-specific manner.

BACKGROUND: The advent of next generation sequencing (NGS) has allowed the discovery of short and long non-coding RNAs (ncRNAs) in an unbiased manner using reverse genetics approaches, enabling the discovery of multiple categories of ncRNAs and characterization of the way their expression is regulated. We previously showed that the identities and abundances of microRNA isoforms (isomiRs) and transfer RNA-derived fragments (tRFs) are tightly regulated, and that they depend on a person's sex and population origin, as well as on tissue type, tissue state, and disease type. Here, we characterize the regulation and distribution of fragments derived from ribosomal RNAs (rRNAs). rRNAs form a group that includes four (5S, 5.8S, 18S, 28S) rRNAs encoded by the human nuclear genome and two (12S, 16S) by the mitochondrial genome. rRNAs constitute the most abundant RNA type in eukaryotic cells. RESULTS: We analyzed rRNA-derived fragments (rRFs) across 434 transcriptomic datasets obtained from lymphoblastoid cell lines (LCLs) derived from healthy participants of the 1000 Genomes Project. The 434 datasets represent five human populations and both sexes. We examined each of the six rRNAs and their respective rRFs, and did so separately for each population and sex. Our analysis shows that all six rRNAs produce rRFs with unique identities, normalized abundances, and lengths. The rRFs arise from the 5'-end (5'-rRFs), the interior (i-rRFs), and the 3'-end (3'-rRFs) or straddle the 5' or 3' terminus of the parental rRNA (x-rRFs). Notably, a large number of rRFs are produced in a population-specific or sex-specific manner. Preliminary evidence suggests that rRF production is also tissue-dependent. Of note, we find that rRF production is not affected by the identity of the processing laboratory or the library preparation kit. CONCLUSIONS: Our findings suggest that rRFs are produced in a regimented manner by currently unknown processes that are influenced by both ubiquitous as well as population-specific and sex-specific factors. The properties of rRFs mirror the previously reported properties of isomiRs and tRFs and have implications for the study of homeostasis and disease.

Machine Learning Approaches Identify Genes Containing Spatial Information From Single-Cell Transcriptomics Data.

The development of single-cell sequencing technologies has allowed researchers to gain important new knowledge about the expression profile of genes in thousands of individual cells of a model organism or tissue. A common disadvantage of this technology is the loss of the three-dimensional (3-D) structure of the cells. Consequently, the Dialogue on Reverse Engineering Assessment and Methods (DREAM) organized the Single-Cell Transcriptomics Challenge, in which we participated, with the aim to address the following two problems: (a) to identify the top 60, 40, and 20 genes of the Drosophila melanogaster embryo that contain the most spatial information and (b) to reconstruct the 3-D arrangement of the embryo using information from those genes. We developed two independent techniques, leveraging machine learning models from least absolute shrinkage and selection operator (Lasso) and deep neural networks (NNs), which are applied to high-dimensional single-cell sequencing data in order to accurately identify genes that contain spatial information. Our first technique, Lasso.TopX, utilizes the Lasso and ranking statistics and allows a user to define a specific number of features they are interested in. The NN approach utilizes weak supervision for linear regression to accommodate for uncertain or probabilistic training labels. We show, individually for both techniques, that we are able to identify important, stable, and a user-defined number of genes containing the most spatial information. The results from both techniques achieve high performance when reconstructing spatial information in D. melanogaster and also generalize to zebrafish (Danio rerio). Furthermore, we identified novel D. melanogaster genes that carry important positional information and were not previously suspected. We also show how the indirect use of the full datasets' information can lead to data leakage and generate bias in overestimating the model's performance. Lastly, we discuss the applicability of our approaches to other feature selection problems outside the realm of single-cell sequencing and the importance of being able to handle probabilistic training labels. Our source code and detailed documentation are available at https://github.com/TJU-CMC-Org/SingleCell-DREAM/.

Unification of miRNA and isomiR research: the mirGFF3 format and the mirtop API.

MOTIVATION: MicroRNAs (miRNAs) are small RNA molecules (∼22 nucleotide long) involved in post-transcriptional gene regulation. Advances in high-throughput sequencing technologies led to the discovery of isomiRs, which are miRNA sequence variants. While many miRNA-seq analysis tools exist, the diversity of output formats hinders accurate comparisons between tools and precludes data sharing and the development of common downstream analysis methods. RESULTS: To overcome this situation, we present here a community-based project, miRNA Transcriptomic Open Project (miRTOP) working towards the optimization of miRNA analyses. The aim of miRTOP is to promote the development of downstream isomiR analysis tools that are compatible with existing detection and quantification tools. Based on the existing GFF3 format, we first created a new standard format, mirGFF3, for the output of miRNA/isomiR detection and quantification results from small RNA-seq data. Additionally, we developed a command line Python tool, mirtop, to create and manage the mirGFF3 format. Currently, mirtop can convert into mirGFF3 the outputs of commonly used pipelines, such as seqbuster, isomiR-SEA, sRNAbench, Prost! as well as BAM files. Some tools have also incorporated the mirGFF3 format directly into their code, such as, miRge2.0, IsoMIRmap and OptimiR. Its open architecture enables any tool or pipeline to output or convert results into mirGFF3. Collectively, this isomiR categorization system, along with the accompanying mirGFF3 and mirtop API, provide a comprehensive solution for the standardization of miRNA and isomiR annotation, enabling data sharing, reporting, comparative analyses and benchmarking, while promoting the development of common miRNA methods focusing on downstream steps of miRNA detection, annotation and quantification. AVAILABILITY AND IMPLEMENTATION: https://github.com/miRTop/mirGFF3/ and https://github.com/miRTop/mirtop. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Exploration of CCA-added RNAs revealed the expression of mitochondrial non-coding RNAs regulated by CCA-adding enzyme.

Post-transcriptional non-template additions of nucleotides to 3'-ends of RNAs play important roles in the stability and function of RNA molecules. Although tRNA nucleotidyltransferase (CCA-adding enzyme) is known to add CCA trinucleotides to 3'-ends of tRNAs, whether other RNA species can be endogenous substrates of CCA-adding enzyme has not been widely explored yet. Herein, we used YAMAT-seq to identify non-tRNA substrates of CCA-adding enzyme. YAMAT-seq captures RNA species that form secondary structures with 4-nt protruding 3'-ends of the sequence 5'-NCCA-3', which is the hallmark structure of RNAs that are generated by CCA-adding enzyme. By executing YAMAT-seq for human breast cancer cells and mining the sequence data, we identified novel candidate substrates of CCA-adding enzyme. These included fourteen 'CCA-RNAs' that only contain CCA as non-genomic sequences, and eleven 'NCCA-RNAs' that contain CCA and other nucleotides as non-genomic sequences. All newly-identified (N)CCA-RNAs were derived from the mitochondrial genome and were localized in mitochondria. Knockdown of CCA-adding enzyme severely reduced the expression levels of (N)CCA-RNAs, suggesting that the CCA-adding enzyme-catalyzed CCA additions stabilize the expression of (N)CCA-RNAs. Furthermore, expression levels of (N)CCA-RNAs were severely reduced by various cellular treatments, including UV irradiation, amino acid starvation, inhibition of mitochondrial respiratory complexes, and inhibition of the cell cycle. These results revealed a novel CCA-mediated regulatory pathway for the expression of mitochondrial non-coding RNAs.

tRNA Fragments Show Intertwining with mRNAs of Specific Repeat Content and Have Links to Disparities.

tRNA-derived fragments (tRF) are a class of potent regulatory RNAs. We mined the datasets from The Cancer Genome Atlas (TCGA) representing 32 cancer types with a deterministic and exhaustive pipeline for tRNA fragments. We found that mitochondrial tRNAs contribute disproportionally more tRFs than nuclear tRNAs. Through integrative analyses, we uncovered a multitude of statistically significant and context-dependent associations between the identified tRFs and mRNAs. In many of the 32 cancer types, these associations involve mRNAs from developmental processes, receptor tyrosine kinase signaling, the proteasome, and metabolic pathways that include glycolysis, oxidative phosphorylation, and ATP synthesis. Even though the pathways are common to multiple cancers, the association of specific mRNAs with tRFs depends on and differs from cancer to cancer. The associations between tRFs and mRNAs extend to genomic properties as well; specifically, tRFs are positively correlated with shorter genes that have a higher density in repeats, such as ALUs, MIRs, and ERVLs. Conversely, tRFs are negatively correlated with longer genes that have a lower repeat density, suggesting a possible dichotomy between cell proliferation and differentiation. Analyses of bladder, lung, and kidney cancer data indicate that the tRF-mRNA wiring can also depend on a patient's sex. Sex-dependent associations involve cyclin-dependent kinases in bladder cancer, the MAPK signaling pathway in lung cancer, and purine metabolism in kidney cancer. Taken together, these findings suggest diverse and wide-ranging roles for tRFs and highlight the extensive interconnections of tRFs with key cellular processes and human genomic architecture. SIGNIFICANCE: Across 32 TCGA cancer contexts, nuclear and mitochondrial tRNA fragments exhibit associations with mRNAs that belong to concrete pathways, encode proteins with particular destinations, have a biased repeat content, and are sex dependent.

Profiles of miRNA Isoforms and tRNA Fragments in Prostate Cancer.

MicroRNA (miRNA) isoforms ("isomiRs") and tRNA-derived fragments ("tRFs") are powerful regulatory non-coding RNAs (ncRNAs). In human tissues, both types of molecules are abundant, with expression patterns that depend on a person's race, sex and population origin. Here, we present our analyses of the Prostate Cancer (PRAD) datasets of The Cancer Genome Atlas (TCGA) from the standpoint of isomiRs and tRFs. This study represents the first simultaneous examination of isomiRs and tRFs in a large cohort of PRAD patients. We find that isomiRs and tRFs have extensive correlations with messenger RNAs (mRNAs). These correlations are disrupted in PRAD, which suggests disruptions of the regulatory network in the disease state. Notably, we find that the profiles of isomiRs and tRFs differ in patients belonging to different races. We hope that the presented findings can lay the groundwork for future research efforts aimed at elucidating the functional roles of the numerous and distinct members of these two categories of ncRNAs that are present in PRAD.

MINTbase v2.0: a comprehensive database for tRNA-derived fragments that includes nuclear and mitochondrial fragments from all The Cancer Genome Atlas projects.

MINTbase is a repository that comprises nuclear and mitochondrial tRNA-derived fragments ('tRFs') found in multiple human tissues. The original version of MINTbase comprised tRFs obtained from 768 transcriptomic datasets. We used our deterministic and exhaustive tRF mining pipeline to process all of The Cancer Genome Atlas datasets (TCGA). We identified 23 413 tRFs with abundance of ≥ 1.0 reads-per-million (RPM). To facilitate further studies of tRFs by the community, we just released version 2.0 of MINTbase that contains information about 26 531 distinct human tRFs from 11 719 human datasets as of October 2017. Key new elements include: the ability to filter tRFs on-the-fly by minimum abundance thresholding; the ability to filter tRFs by tissue keywords; easy access to information about a tRF's maximum abundance and the datasets that contain it; the ability to generate relative abundance plots for tRFs across cancer types and convert them into embeddable figures; MODOMICS information about modifications of the parental tRNA, etc. Version 2.0 of MINTbase contains 15x more datasets and nearly 4x more distinct tRFs than the original version, yet continues to offer fast, interactive access to its contents. Version 2.0 is available freely at http://cm.jefferson.edu/MINTbase/.

Accurate Profiling and Quantification of tRNA Fragments from RNA-Seq Data: A Vade Mecum for MINTmap.

There is an increasing interest within the scientific community in identifying tRNA-derived fragments (tRFs) and elucidating the roles they play in the cell. Such endeavors can be greatly facilitated by mining the numerous datasets from many cellular contexts that exist publicly. However, the standard mapping tools cannot be used for the purpose. Several factors complicate this endeavor including: the presence of multiple identical or nearly identical isodecoders at various genomic locations; the presence of identical sequence segments that are shared by isodecoders of the same or even different anticodons; the existence of numerous partial tRNA sequences across the genome; the existence of hundreds of "lookalike" sequences that resemble true tRNAs; and others. This is generating a need for specialized tools that can mine deep sequencing data to identify and quantify tRFs. We discuss the various complicating factors and their ramifications, and how to use and run MINTmap, a tool that addresses these considerations.

Increasing cell density globally enhances the biogenesis of Piwi-interacting RNAs in Bombyx mori germ cells.

Piwi proteins and their bound Piwi-interacting RNAs (piRNAs) are predominantly expressed in the germline and play crucial roles in germline development by silencing transposons and other targets. Bombyx mori BmN4 cells are culturable germ cells that equip the piRNA pathway. Because of the scarcity of piRNA-expressing culturable cells, BmN4 cells are being utilized for the analyses of piRNA biogenesis. We here report that the piRNA biogenesis in BmN4 cells is regulated by cell density. As cell density increased, the abundance of Piwi proteins and piRNA biogenesis factors was commonly upregulated, resulting in an increased number of perinuclear nuage-like granules where Piwi proteins localize. Along with these phenomena, the abundance of mature piRNAs also globally increased, whereas levels of long piRNA precursor and transposons decreased, suggesting that increasing cell density promotes piRNA biogenesis pathway and that the resultant accumulation of mature piRNAs is functionally significant for transposon silencing. Our study reveals a previously uncharacterized link between cell density and piRNA biogenesis, designates cell density as a critical variable in piRNA studies using BmN4 cell system, and suggests the alteration of cell density as a useful tool to monitor piRNA biogenesis and function.

The biogenesis pathway of tRNA-derived piRNAs in Bombyx germ cells.

Transfer RNAs (tRNAs) function in translational machinery and further serves as a source of short non-coding RNAs (ncRNAs). tRNA-derived ncRNAs show differential expression profiles and play roles in many biological processes beyond translation. Molecular mechanisms that shape and regulate their expression profiles are largely unknown. Here, we report the mechanism of biogenesis for tRNA-derived Piwi-interacting RNAs (td-piRNAs) expressed in Bombyx BmN4 cells. In the cells, two cytoplasmic tRNA species, tRNAAspGUC and tRNAHisGUG, served as major sources for td-piRNAs, which were derived from the 5'-part of the respective tRNAs. cP-RNA-seq identified the two tRNAs as major substrates for the 5'-tRNA halves as well, suggesting a previously uncharacterized link between 5'-tRNA halves and td-piRNAs. An increase in levels of the 5'-tRNA halves, induced by BmNSun2 knockdown, enhanced the td-piRNA expression levels without quantitative change in mature tRNAs, indicating that 5'-tRNA halves, not mature tRNAs, are the direct precursors for td-piRNAs. For the generation of tRNAHisGUG-derived piRNAs, BmThg1l-mediated nucleotide addition to -1 position of tRNAHisGUG was required, revealing an important function of BmThg1l in piRNA biogenesis. Our study advances the understanding of biogenesis mechanisms and the genesis of specific expression profiles for tRNA-derived ncRNAs.