Streptomyces rare codon UUA: from features associated with 2 adpA related locations to candidate phage regulatory translational bypassing.

In Streptomyces species, the cell cycle involves a switch from an early and vegetative state to a later phase where secondary products including antibiotics are synthesized, aerial hyphae form and sporulation occurs. AdpA, which has two domains, activates the expression of numerous genes involved in the switch from the vegetative growth phase. The adpA mRNA of many Streptomyces species has a UUA codon in a linker region between 5' sequence encoding one domain and 3' sequence encoding its other and C-terminal domain. UUA codons are exceptionally rare in Streptomyces, and its functional cognate tRNA is not present in a fully modified and acylated form, in the early and vegetative phase of the cell cycle though it is aminoacylated later. Here, we report candidate recoding signals that may influence decoding of the linker region UUA. Additionally, a short ORF 5' of the main ORF has been identified with a GUG at, or near, its 5' end and an in-frame UUA near its 3' end. The latter is commonly 5 nucleotides 5' of the main ORF start. Ribosome profiling data show translation of that 5' region. Ten years ago, UUA-mediated translational bypassing was proposed as a sensor by a Streptomyces phage of its host's cell cycle stage and an effector of its lytic/lysogeny switch. We provide the first experimental evidence supportive of this proposal.

The Association of Previous Vaccination with Live-Attenuated Varicella Zoster Vaccine and COVID-19 Positivity: An Israeli Population-Based Study.

The Bacillus Calmette-Guérin (BCG) vaccine affords indirect protection against COVID-19, which is presumably due to priming of the innate immune system. It was hypothesized that the live attenuated Varicella Zoster (LAVZ) vaccine, recommended for the elderly population, would also protect against COVID-19 infection. A retrospective population-based cross-sectional study was conducted using the Leumit Health Services (LHS) database. LAVZ-vaccinated patients were matched with controls based on a propensity score model using 1:9 nearest-neighbor matching. Matching was based on age, gender, and the presence of some chronic disorders, which were selected according to their association with COVID-19 infection. Multivariate logistic regression analyses, adjusted for sex, age, smoking status, comorbidities, and chronic medications associated with COVID-19 risk, were used to estimate the association between LAVZ vaccination and COVID-19 RT-PCR results. Subjects (625) vaccinated with LAVZ and RT-PCR-tested for COVID-19 were identified. After 1:9 matching of subjects who received the LAVZ vaccine, 6250 subjects were included in the study. Multivariate logistic regression analysis demonstrated a significant and independent negative association between having received the LAVZ vaccine and the likelihood of COVID-19 infection (adjusted OR = 0.47 (95% CI 0.33-0.69, p < 0.001)). This association was further strengthened after separate analysis based on the time of LAVZ vaccination before COVID-19 RT-PCR testing. Individuals aged ≥50 years vaccinated with LAVZ had a decreased likelihood of being tested positive for COVID-19.

Seasonal UV exposure and vitamin D: association with the dynamics of COVID-19 transmission in Europe.

Several recent studies have demonstrated that low plasma 25(OH) vitamin D levels are associated with the risk of COVID-19 infection. The primary source of vitamin D production in humans is environmental UV radiation. In many viral respiratory diseases, peak infection rates are observed during winter due to reduced UV exposure and low temperatures. In Europe, the second wave of COVID-19 began early in the winter of 2020. Investigating the impact of seasonal temperature and UV exposure on COVID-19 transmission could thus aid in prevention and intervention. As such, we first performed a comprehensive meta-analysis of all related published literature based on the association between vitamin D and COVID-19, which supported the hypothesis that the low vitamin D level is a critical risk factor for COVID-19 infection. Next, to understand the potential impact of seasonal UV and temperature levels on COVID-19 cases, we analyzed meteorological data and daily COVID-19 cases per million in the populations of 26 European countries. We observed that low temperature, UV index, and cloud-free vitamin D UV dose (UVDVF) levels are negatively correlated with COVID-19 prevalence in Europe. Furthermore, a distributed lag nonlinear model was used to assess the nonlinear delayed effects of individual seasonal factors on COVID-19 cases. Such analysis highlighted the significantly delayed impact of UVDVF on the cumulative relative risk of COVID-19 infection. The findings of this study suggest that low UV exposure can affect the required production of vitamin D in the body, which substantially influences the dynamics of COVID-19 transmission and severity.

Detection of gene mutations and gene-gene fusions in circulating cell-free DNA of glioblastoma patients: an avenue for clinically relevant diagnostic analysis.

Glioblastoma (GBM) is the most common type of glioma and is uniformly fatal. Currently, tumour heterogeneity and mutation acquisition are major impedances for tailoring personalized therapy. We collected blood and tumour tissue samples from 25 GBM patients and 25 blood samples from healthy controls. Cell-free DNA (cfDNA) was extracted from the plasma of GBM patients and from healthy controls. Tumour DNA was extracted from fresh tumour samples. Extracted DNA was sequenced using a whole-genome sequencing procedure. We also collected 180 tumour DNA datasets from GBM patients publicly available at the TCGA/PANCANCER project. These data were analysed for mutations and gene-gene fusions that could be potential druggable targets. We found that plasma cfDNA concentrations in GBM patients were significantly elevated (22.6 ± 5 ng·mL-1 ), as compared to healthy controls (1.4 ± 0.4 ng·mL-1 ) of the same average age. We identified unique mutations in the cfDNA and tumour DNA of each GBM patient, including some of the most frequently mutated genes in GBM according to the COSMIC database (TP53, 18.75%; EGFR, 37.5%; NF1, 12.5%; LRP1B, 25%; IRS4, 25%). Using our gene-gene fusion database, ChiTaRS 5.0, we identified gene-gene fusions in cfDNA and tumour DNA, such as KDR-PDGFRA and NCDN-PDGFRA, which correspond to previously reported alterations of PDGFRA in GBM (44% of all samples). Interestingly, the PDGFRA protein fusions can be targeted by tyrosine kinase inhibitors such as imatinib, sunitinib, and sorafenib. Moreover, we identified BCR-ABL1 (in 8% of patients), COL1A1-PDGFB (8%), NIN-PDGFRB (8%), and FGFR1-BCR (4%) in cfDNA of patients, which can be targeted by analogues of imatinib. ROS1 fusions (CEP85L-ROS1 and GOPC-ROS1), identified in 8% of patient cfDNA, might be targeted by crizotinib, entrectinib, or larotrectinib. Thus, our study suggests that integrated analysis of cfDNA plasma concentration, gene mutations, and gene-gene fusions can serve as a diagnostic modality for distinguishing GBM patients who may benefit from targeted therapy. These results open new avenues for precision medicine in GBM, using noninvasive liquid biopsy diagnostics to assess personalized patient profiles. Moreover, repeated detection of druggable targets over the course of the disease may provide real-time information on the evolving molecular landscape of the tumour.

ChiTaH: a fast and accurate tool for identifying known human chimeric sequences from high-throughput sequencing data.

Fusion genes or chimeras typically comprise sequences from two different genes. The chimeric RNAs of such joined sequences often serve as cancer drivers. Identifying such driver fusions in a given cancer or complex disease is important for diagnosis and treatment. The advent of next-generation sequencing technologies, such as DNA-Seq or RNA-Seq, together with the development of suitable computational tools, has made the global identification of chimeras in tumors possible. However, the testing of over 20 computational methods showed these to be limited in terms of chimera prediction sensitivity, specificity, and accurate quantification of junction reads. These shortcomings motivated us to develop the first 'reference-based' approach termed ChiTaH (Chimeric Transcripts from High-throughput sequencing data). ChiTaH uses 43,466 non-redundant known human chimeras as a reference database to map sequencing reads and to accurately identify chimeric reads. We benchmarked ChiTaH and four other methods to identify human chimeras, leveraging both simulated and real sequencing datasets. ChiTaH was found to be the most accurate and fastest method for identifying known human chimeras from simulated and sequencing datasets. Moreover, especially ChiTaH uncovered heterogeneity of the BCR-ABL1 chimera in both bulk and single-cells of the K-562 cell line, which was confirmed experimentally.

Computational analysis of sense-antisense chimeric transcripts reveals their potential regulatory features and the landscape of expression in human cells.

Many human genes are transcribed from both strands and produce sense-antisense gene pairs. Sense-antisense (SAS) chimeric transcripts are produced upon the coalescing of exons/introns from both sense and antisense transcripts of the same gene. SAS chimera was first reported in prostate cancer cells. Subsequently, numerous SAS chimeras have been reported in the ChiTaRS-2.1 database. However, the landscape of their expression in human cells and functional aspects are still unknown. We found that longer palindromic sequences are a unique feature of SAS chimeras. Structural analysis indicates that a long hairpin-like structure formed by many consecutive Watson-Crick base pairs appears because of these long palindromic sequences, which possibly play a similar role as double-stranded RNA (dsRNA), interfering with gene expression. RNA-RNA interaction analysis suggested that SAS chimeras could significantly interact with their parental mRNAs, indicating their potential regulatory features. Here, 267 SAS chimeras were mapped in RNA-seq data from 16 healthy human tissues, revealing their expression in normal cells. Evolutionary analysis suggested the positive selection favoring sense-antisense fusions that significantly impacted the evolution of their function and structure. Overall, our study provides detailed insight into the expression landscape of SAS chimeras in human cells and identifies potential regulatory features.

EvoProDom: evolutionary modeling of protein families by assessing translocations of protein domains.

Here, we introduce a novel 'evolution of protein domains' (EvoProDom) model for describing the evolution of proteins based on the 'mix and merge' of protein domains. We assembled and integrated genomic and proteomic data comprising protein domain content and orthologous proteins from 109 organisms. In EvoProDom, we characterized evolutionary events, particularly, translocations, as reciprocal exchanges of protein domains between orthologous proteins in different organisms. We showed that protein domains that translocate with highly frequency are generated by transcripts enriched in trans-splicing events, that is, the generation of novel transcripts from the fusion of two distinct genes. In EvoProDom, we describe a general method to collate orthologous protein annotation from KEGG, and protein domain content from protein sequences using tools such as KoFamKOAL and Pfam. To summarize, EvoProDom presents a novel model for protein evolution based on the 'mix and merge' of protein domains rather than DNA-based evolution models. This confers the advantage of considering chromosomal alterations as drivers of protein evolutionary events.

Non-optimal codon usage preferences of coronaviruses determine their promiscuity for infecting multiple hosts.

Circulating animal coronaviruses occasionally infect humans. The SARS-CoV-2 is responsible for the current worldwide outbreak of COVID-19 that has resulted in 2 112 844 deaths as of late January 2021. We compared genetic code preferences in 496 viruses, including 34 coronaviruses and 242 corresponding hosts, to uncover patterns that distinguish single- and 'promiscuous' multiple-host-infecting viruses. Based on a codon usage preference score, promiscuous viruses were shown to significantly employ nonoptimal codons, namely codons that involve 'wobble' binding to anticodons, as compared to single-host viruses. The codon adaptation index (CAI) and the effective number of codons (ENC) were calculated for all viruses and hosts. Promiscuous viruses were less adapted hosts vs single-host viruses (P-value = 4.392e-11). All coronaviruses exploit nonoptimal codons to infect multiple hosts. We found that nonoptimal codon preferences at the beginning of viral coding sequences enhance the translational efficiency of viral proteins within the host. Finally, coronaviruses lack endogenous RNA degradation motifs to a significant degree, thereby increasing viral mRNA burden and infection load. To conclude, we found that promiscuously infecting coronaviruses prefer nonoptimal codon usage to remove degradation motifs from their RNAs and to dramatically increase their viral RNA production rates.

Specific Susceptibility to COVID-19 in Adults with Down Syndrome.

The current SARS-CoV-2 outbreak, which causes COVID-19, is particularly devastating for individuals with chronic medical conditions, in particular those with Down Syndrome (DS) who often exhibit a higher prevalence of respiratory tract infections, immune dysregulation and potential complications. The incidence of Alzheimer's disease (AD) is much higher in DS than in the general population, possibly increasing further the risk of COVID-19 infection and its complications. Here we provide a biological overview with regard to specific susceptibility of individuals with DS to SARS-CoV-2 infection as well as data from a recent survey on the prevalence of COVID-19 among them. We see an urgent need to protect people with DS, especially those with AD, from COVID-19 and future pandemics and focus on developing protective measures, which also include interventions by health systems worldwide for reducing the negative social effects of long-term isolation and increased periods of hospitalization.

The use of aspirin for primary prevention of cardiovascular disease is associated with a lower likelihood of COVID-19 infection.

Acetylsalicylic acid (aspirin) is commonly used for primary and secondary prevention of cardiovascular diseases. Aspirin use is associated with better outcomes among COVID-19 positive patients. We hypothesized that the aspirin use for primary cardiovascular disease prevention might have a protective effect on COVID-19 susceptibility and disease duration. We conducted a retrospective population-based cross-sectional study, utilizing data from the Leumit Health Services database. The proportion of patients treated with aspirin was significantly lower among the COVID-19-positive group, as compared to the COVID-19-negative group [73 (11.03%) vs. 1548 (15.77%); P = 0.001]. Aspirin use was associated with lower likelihood of COVID-19 infection, as compared to nonusers (adjusted OR 0.71 (95% CI, 0.52 to 0.99; P = 0.041). Aspirin users were older (68.06 ± 12.79 vs. 56.63 ± 12.28 years of age; P < 0.001), presented a lower BMI (28.77 ± 5.4 vs. 30.37 ± 4.55; P < 0.0189), and showed higher prevalence of hypertension (56, 76.71%), diabetes (47, 64.38%), and COPD (11, 15.07%) than the aspirin nonusers (151, 25.64%, P < 0.001; 130, 22.07%, P < 0.001; and 43, 7.3%, P = 0.023, respectively). Moreover, COVID-19 disease duration (considered as the time between the first positive and second negative COVID-19 RT-PCR test results) among aspirin users was significantly shorter, as compared to aspirin nonusers (19.8 ± 7.8 vs. 21.9 ± 7.9 P = 0.045). Among hospitalized COVID-positive patients, a higher proportion of surviving subjects were treated with aspirin (20, 19.05%), as opposed to 1 dead subject (14.29%), although this difference was not significant (P = 0.449). In conclusion, we observed an inverse association between the likelihood of COVID-19 infection, disease duration and mortality, and aspirin use for primary prevention.

COVID19 Drug Repository: text-mining the literature in search of putative COVID19 therapeutics.

The recent outbreak of COVID-19 has generated an enormous amount of Big Data. To date, the COVID-19 Open Research Dataset (CORD-19), lists ∼130,000 articles from the WHO COVID-19 database, PubMed Central, medRxiv, and bioRxiv, as collected by Semantic Scholar. According to LitCovid (11 August 2020), ∼40,300 COVID19-related articles are currently listed in PubMed. It has been shown in clinical settings that the analysis of past research results and the mining of available data can provide novel opportunities for the successful application of currently approved therapeutics and their combinations for the treatment of conditions caused by a novel SARS-CoV-2 infection. As such, effective responses to the pandemic require the development of efficient applications, methods and algorithms for data navigation, text-mining, clustering, classification, analysis, and reasoning. Thus, our COVID19 Drug Repository represents a modular platform for drug data navigation and analysis, with an emphasis on COVID-19-related information currently being reported. The COVID19 Drug Repository enables users to focus on different levels of complexity, starting from general information about (FDA-) approved drugs, PubMed references, clinical trials, recipes as well as the descriptions of molecular mechanisms of drugs' action. Our COVID19 drug repository provide a most updated world-wide collection of drugs that has been repurposed for COVID19 treatments around the world.

Design principles of gene evolution for niche adaptation through changes in protein-protein interaction networks.

In contrast to fossorial and above-ground organisms, subterranean species have adapted to the extreme stresses of living underground. We analyzed the predicted protein-protein interactions (PPIs) of all gene products, including those of stress-response genes, among nine subterranean, ten fossorial, and 13 aboveground species. We considered 10,314 unique orthologous protein families and constructed 5,879,879 PPIs in all organisms using ChiPPI. We found strong association between PPI network modulation and adaptation to specific habitats, noting that mutations in genes and changes in protein sequences were not linked directly with niche adaptation in the organisms sampled. Thus, orthologous hypoxia, heat-shock, and circadian clock proteins were found to cluster according to habitat, based on PPIs rather than on sequence similarities. Curiously, "ordered" domains were preserved in aboveground species, while "disordered" domains were conserved in subterranean organisms, and confirmed for proteins in DistProt database. Furthermore, proteins with disordered regions were found to adopt significantly less optimal codon usage in subterranean species than in fossorial and above-ground species. These findings reveal design principles of protein networks by means of alterations in protein domains, thus providing insight into deep mechanisms of evolutionary adaptation, generally, and particularly of species to underground living and other confined habitats.

Low plasma 25(OH) vitamin D level is associated with increased risk of COVID-19 infection: an Israeli population-based study.

Vitamin D deficiency is a worldwide pandemic. The aim of this study was to evaluate associations of plasma 25(OH)D levels with the likelihood of coronavirus disease 2019 (COVID-19) infection and hospitalization. The study population included the 14 000 members of Leumit Health Services, who were tested for COVID-19 infection from February 1st to April 30th , 2020, and who had at least one previous blood test for the plasma 25(OH)D level. 'Suboptimal' or 'low' plasma 25(OH)D level was defined as plasma 25-hydroxyvitamin D, or 25(OH)D, concentration below the level of 30 ng/mL. Of 7807 individuals, 782 (10.02%) were COVID-19-positive, and 7025 (89.98%) COVID-19-negative. The mean plasma vitamin D level was significantly lower among those who tested positive than negative for COVID-19 [19.00 ng/mL (95% confidence interval (CI) 18.41-19.59) vs. 20.55 (95% CI: 20.32-20.78)]. Univariate analysis demonstrated an association between the low plasma 25(OH)D level and increased likelihood of COVID-19 infection [crude odds ratio (OR) of 1.58 (95% CI: 1.24-2.01, P < 0.001)], and of hospitalization due to the SARS-CoV-2 virus [crude OR of 2.09 (95% CI: 1.01-4.30, P < 0.05)]. In multivariate analyses that controlled for demographic variables, and psychiatric and somatic disorders, the adjusted OR of COVID-19 infection [1.45 (95% CI: 1.08-1.95, P < 0.001)] and of hospitalization due to the SARS-CoV-2 virus [1.95 (95% CI: 0.98-4.845, P = 0.061)] were preserved. In the multivariate analyses, age over 50 years, male gender and low-medium socioeconomic status were also positively associated with the risk of COVID-19 infection; age over 50 years was positively associated with the likelihood of hospitalization due to COVID-19. We concluded that low plasma 25(OH)D levels appear to be an independent risk factor for COVID-19 infection and hospitalization.

ChiTaRS 5.0: the comprehensive database of chimeric transcripts matched with druggable fusions and 3D chromatin maps.

Chimeric RNA transcripts are formed when exons from two genes fuse together, often due to chromosomal translocations, transcriptional errors or trans-splicing effect. While these chimeric RNAs produce functional proteins only in certain cases, they play a significant role in disease phenotyping and progression. ChiTaRS 5.0 (http://chitars.md.biu.ac.il/) is the latest and most comprehensive chimeric transcript repository, with 111 582 annotated entries from eight species, including 23 167 known human cancer breakpoints. The database includes unique information correlating chimeric breakpoints with 3D chromatin contact maps, generated from public datasets of chromosome conformation capture techniques (Hi-C). In this update, we have added curated information on druggable fusion targets matched with chimeric breakpoints, which are applicable to precision medicine in cancers. The introduction of a new section that lists chimeric RNAs in various cell-lines is another salient feature. Finally, using text-mining techniques, novel chimeras in Alzheimer's disease, schizophrenia, dyslexia and other diseases were collected in ChiTaRS. Thus, this improved version is an extensive catalogue of chimeras from multiple species. It extends our understanding of the evolution of chimeric transcripts in eukaryotes and contributes to the analysis of 3D genome conformational changes and the functional role of chimeras in the etiopathogenesis of cancers and other complex diseases.

ProtFus: A Comprehensive Method Characterizing Protein-Protein Interactions of Fusion Proteins.

Tailored therapy aims to cure cancer patients effectively and safely, based on the complex interactions between patients' genomic features, disease pathology and drug metabolism. Thus, the continual increase in scientific literature drives the need for efficient methods of data mining to improve the extraction of useful information from texts based on patients' genomic features. An important application of text mining to tailored therapy in cancer encompasses the use of mutations and cancer fusion genes as moieties that change patients' cellular networks to develop cancer, and also affect drug metabolism. Fusion proteins, which are derived from the slippage of two parental genes, are produced in cancer by chromosomal aberrations and trans-splicing. Given that the two parental proteins for predicted fusion proteins are known, we used our previously developed method for identifying chimeric protein-protein interactions (ChiPPIs) associated with the fusion proteins. Here, we present a validation approach that receives fusion proteins of interest, predicts their cellular network alterations by ChiPPI and validates them by our new method, ProtFus, using an online literature search. This process resulted in a set of 358 fusion proteins and their corresponding protein interactions, as a training set for a Naïve Bayes classifier, to identify predicted fusion proteins that have reliable evidence in the literature and that were confirmed experimentally. Next, for a test group of 1817 fusion proteins, we were able to identify from the literature 2908 PPIs in total, across 18 cancer types. The described method, ProtFus, can be used for screening the literature to identify unique cases of fusion proteins and their PPIs, as means of studying alterations of protein networks in cancers. Availability: http://protfus.md.biu.ac.il/.

ChiPPI: a novel method for mapping chimeric protein-protein interactions uncovers selection principles of protein fusion events in cancer.

Fusion proteins, comprising peptides deriving from the translation of two parental genes, are produced in cancer by chromosomal aberrations. The expressed fusion protein incorporates domains of both parental proteins. Using a methodology that treats discrete protein domains as binding sites for specific domains of interacting proteins, we have cataloged the protein interaction networks for 11 528 cancer fusions (ChiTaRS-3.1). Here, we present our novel method, chimeric protein-protein interactions (ChiPPI) that uses the domain-domain co-occurrence scores in order to identify preserved interactors of chimeric proteins. Mapping the influence of fusion proteins on cell metabolism and pathways reveals that ChiPPI networks often lose tumor suppressor proteins and gain oncoproteins. Furthermore, fusions often induce novel connections between non-interactors skewing interaction networks and signaling pathways. We compared fusion protein PPI networks in leukemia/lymphoma, sarcoma and solid tumors finding distinct enrichment patterns for each disease type. While certain pathways are enriched in all three diseases (Wnt, Notch and TGF ß), there are distinct patterns for leukemia (EGFR signaling, DNA replication and CCKR signaling), for sarcoma (p53 pathway and CCKR signaling) and solid tumors (FGFR and EGFR signaling). Thus, the ChiPPI method represents a comprehensive tool for studying the anomaly of skewed cellular networks produced by fusion proteins in cancer.

ChiTaRS-3.1-the enhanced chimeric transcripts and RNA-seq database matched with protein-protein interactions.

Discovery of chimeric RNAs, which are produced by chromosomal translocations as well as the joining of exons from different genes by trans-splicing, has added a new level of complexity to our study and understanding of the transcriptome. The enhanced ChiTaRS-3.1 database (http://chitars.md.biu.ac.il) is designed to make widely accessible a wealth of mined data on chimeric RNAs, with easy-to-use analytical tools built-in. The database comprises 34 922: chimeric transcripts along with 11 714: cancer breakpoints. In this latest version, we have included multiple cross-references to GeneCards, iHop, PubMed, NCBI, Ensembl, OMIM, RefSeq and the Mitelman collection for every entry in the 'Full Collection'. In addition, for every chimera, we have added a predicted Chimeric Protein-Protein Interaction (ChiPPI) network, which allows for easy visualization of protein partners of both parental and fusion proteins for all human chimeras. The database contains a comprehensive annotation for 34 922: chimeric transcripts from eight organisms, and includes the manual annotation of 200 sense-antiSense (SaS) chimeras. The current improvements in the content and functionality to the ChiTaRS database make it a central resource for the study of chimeric transcripts and fusion proteins.

ChiTaRS 2.1--an improved database of the chimeric transcripts and RNA-seq data with novel sense-antisense chimeric RNA transcripts.

Chimeric RNAs that comprise two or more different transcripts have been identified in many cancers and among the Expressed Sequence Tags (ESTs) isolated from different organisms; they might represent functional proteins and produce different disease phenotypes. The ChiTaRS 2.1 database of chimeric transcripts and RNA-Seq data (http://chitars.bioinfo.cnio.es/) is the second version of the ChiTaRS database and includes improvements in content and functionality. Chimeras from eight organisms have been collated including novel sense-antisense (SAS) chimeras resulting from the slippage of the sense and anti-sense intragenic regions. The new database version collects more than 29,000 chimeric transcripts and indicates the expression and tissue specificity for 333 entries confirmed by RNA-seq reads mapping the chimeric junction sites. User interface allows for rapid and easy analysis of evolutionary conservation of fusions, literature references and experimental data supporting fusions in different organisms. More than 1428 cancer breakpoints have been automatically collected from public databases and manually verified to identify their correct cross-references, genomic sequences and junction sites. As a result, the ChiTaRS 2.1 collection of chimeras from eight organisms and human cancer breakpoints extends our understanding of the evolution of chimeric transcripts in eukaryotes as well as their functional role in carcinogenic processes.