Sequence Matching Research Articles

CRISPR-Cas "Non-Target" Sites Inhibit On-Target Cutting Rates.

CRISPR-Cas systems have become ubiquitous for genome editing in eukaryotic as well as bacterial systems. Cas9 forms a complex with a guide RNA (gRNA) and searches DNA for a matching sequence (target site) next to a protospacer adjacent motif (PAM). Once found, Cas9 cuts the DNA. Cas9 is revolutionary for the ability to change the RNA sequence and target a new site easily. However, while algorithms have been developed to predict gRNA-specific Cas9 activity, a fundamental biological understanding of gRNA-specific activity is lacking. The number of PAM sites in the genome is effectively a large pool of inhibitory substrates, competing with the target site for the Cas9/gRNA complex. We demonstrate that increasing the number of non-target sites for a given gRNA reduces on-target activity in a dose-dependent manner. Furthermore, we show that the use of Cas9 mutants with increased PAM specificity toward a smaller subset of PAMs (or smaller pool of competitive substrates) improves cutting rates, while increased PAM promiscuity decreases cutting rates. Decreasing the potential search space by increasing PAM specificity provides a path toward improving on-target activity for slower high-fidelity Cas9 variants. Engineering improved PAM specificity to reduce the competitive search space offers an alternative strategy to engineer Cas9 variants with increased specificity and maintained on-target activity.

Fractal genomics of SOD1 evolution.

To understand the fundamental processes of gene evolution such as the impact of point mutations and segmental duplications on statistical topography, superoxide dismutase-1 (SOD1) orthologous sequences (n=50) are studied. These demonstrate scale invariant self-similarity patterns and long-range correlations (LRCs) indicating fractal organization. Phylogenetic hierarchies change when SOD1 orthologs are grouped according to fractal measures, indicating that statistical topographies can be used to study gene evolution. Sliding window k-mer analysis show that majority of k-mers across all SOD1 orthologs are unique, with very few duplications. Orthologs from simpler species contribute minimally (<1% of k-mers) to more complex species. Both simple and complex random processes fail to produce significant matching k-mer sequences for SOD1 orthologs. Point mutations causing amyotrophic lateral sclerosis do not impact the fractal organization of human SOD1. Hence, SOD1 did not evolve by a patchwork of repetitive sequences modified by point mutations. Moreover, fractal and other methods described here can be used to study the origin and evolution of genomes.

Assessment of the potential allergenicity and toxicity of Pichia proteins in a novel leghemoglobin preparation

The production of soy leghemoglobin C2 (LegH) by Pichia pastoris (syn. K. phaffii) was developed by Impossible Foods to serve as a sustainable source of flavor and aroma in plant-based meats. The potential allergenicity and toxicity of a LegH from a new production process was analyzed using bioinformatics, proteomics and a pepsin digestion assay on leghemoglobin, and residual host proteins. LegH in the new preparation had the same proteoform as in the previous preparations as well as in soy root nodule extracts. Results of seven Pichia proteins, each representing ≥1% of the total protein content, showed no significant sequence matches to any known allergens with the exception of one, which matched the highly conserved wheat GAPDH, whose protein homolog is found in fungi and humans. Based on the data, it is unlikely that there is any risk of cross reactivity between LegH Prep and GAPDH. Pichia protein sequences showed very good alignment to homologous proteins from many common yeasts including Saccharomyces sp. In addition, LegH and Pichia proteins were all rapidly digested in a pepsin digest assay. In conclusion, LegH Prep from this P. pastoris production process is unlikely to pose a risk of food allergenicity.

A putative chordate luciferase from a cosmopolitan tunicate indicates convergent bioluminescence evolution across phyla

Pyrosomes are tunicates in the phylum Chordata, which also contains vertebrates. Their gigantic blooms play important ecological and biogeochemical roles in oceans. Pyrosoma, meaning “fire-body”, derives from their brilliant bioluminescence. The biochemistry of this light production is unknown, but has been hypothesized to be bacterial in origin. We found that mixing coelenterazine—a eukaryote-specific luciferin—with Pyrosoma atlanticum homogenate produced light. To identify the bioluminescent machinery, we sequenced P. atlanticum transcriptomes and found a sequence match to a cnidarian luciferase (RLuc). We expressed this novel luciferase (PyroLuc) and, combined with coelenterazine, it produced light. A similar gene was recently predicted from a bioluminescent brittle star, indicating that RLuc-like luciferases may have evolved convergently from homologous dehalogenases across phyla (Cnidaria, Echinodermata, and Chordata). This report indicates that a widespread gene may be able to functionally converge, resulting in bioluminescence across animal phyla, and describes and characterizes the first putative chordate luciferase.

Whole-genome sequencing of acral melanoma reveals genomic complexity and diversity

To increase understanding of the genomic landscape of acral melanoma, a rare form of melanoma occurring on palms, soles or nail beds, whole genome sequencing of 87 tumors with matching transcriptome sequencing for 63 tumors was performed. Here we report that mutational signature analysis reveals a subset of tumors, mostly subungual, with an ultraviolet radiation signature. Significantly mutated genes are BRAF, NRAS, NF1, NOTCH2, PTEN and TYRP1. Mutations and amplification of KIT are also common. Structural rearrangement and copy number signatures show that whole genome duplication, aneuploidy and complex rearrangements are common. Complex rearrangements occur recurrently and are associated with amplification of TERT, CDK4, MDM2, CCND1, PAK1 and GAB2, indicating potential therapeutic options.

A hydrophobic proclivity index for protein alignments

Sequence alignment algorithms are fundamental to modern bioinformatics. Sequence alignments are widely used in diverse applications such as phylogenetic analysis, database searches for related sequences to aid identification of unknown protein domain structures and classification of proteins and protein domains. Additionally, alignment algorithms are integral to the location of related proteins to secure understanding of unknown protein functions, to suggest the folded structure of proteins of unknown structure from location of homologous proteins and/or by locating homologous domains of known 3D structure. For proteins, alignment algorithms depend on information about amino acid substitutions that allows for matching sequences that are similar, but not exact. When primary sequence percent identity falls below about 25%, algorithms often fail to identify proteins that may have similar 3D structure. We have created a hydrophobicity scale and a matching dynamic programming algorithm called TMATCH (preprint report) that is able to match proteins with remote homologs with similar secondary/tertiary structure, even with very low primary sequence matches. In this paper, we describe how we arrived at the hydrophobic scale, how it provides much more information than percent identity matches and some of the implications for better alignments and understanding protein structure.

Ventral dermatitis in rowi (Apteryx rowi) caused by cutaneous capillariasis.

In 2013 there was an outbreak of crusting ventral dermatitis among a group of juvenile rowi (Apteryx rowi), a species of the endangered New Zealand kiwi, that were being raised on an off-shore island sanctuary. Biopsies taken at the time found nematodes migrating within the epidermis of affected skin but the specific identity and origin of the organisms was not established, and sporadic cases of similar skin disease continue to occur on the island. On examination of additional sections from the original skin biopsies, adult nematodes and eggs were identified, the histomorphology of which was consistent with Capillaria sensu lato. PCR was performed on DNA extracted from archived formalin-fixed, paraffin-embedded tissue blocks of skin from eight affected rowi, using primers targeting the 18S region of nuclear ribosomal DNA and the COI gene of mitochondrial DNA of capillarid nematodes. The 18S sequences from all rowi samples were identical and matched sequences from members of the genus Eucoleus. In contrast, two distinct capillarid COI sequences were obtained, in one case both from the same rowi skin biopsy. While there were no close matches, both COI sequences also aligned nearest to sequences identified as Eucoleus spp. It is considered unlikely that two different nematode species are involved in the rowi skin lesions and the possible amplification of a COI pseudogene or “numt” is discussed. A species-level identification of the capillarid nematodes causing skin disease in rowi was not obtained, however based on histological evaluation the infections include reproductively-active adult nematodes. This finding indicates the possibility of perpetuation of the skin disease in the absence of the original source, as well as raising potential for the transfer of infection from the island when the juvenile rowi are translocated to their new habitats.

Salmonella Typhimurium peptidyl-prolyl cis-trans isomerase C (PPIase C) plays a substantial role in protein folding to maintain the protein structure.

Salmonella is a well-known food-borne pathogen causing disease in humans and animals worldwide. Peptidyl-prolyl isomerases (PPIases) catalyse the cis-trans isomerisation of prolyl bound, which is a slow and rate-limiting step of protein folding. Here, we present the biochemical and molecular characterisation of a novel multi-domain parvulin-type, PPIases-C from the pathogenic bacteria Salmonella Typhimurium, annotated as rPpiC. The recombinant plasmid PpiC_pET28c was used for protein induction using1.5mM concentration of isopropyl-β-D-thiogalactopyranoside at 30°C. Subsequently, the protein was identified by using the LC-MS technique showing high match score and sequence coverage with available PPIases-C proteins database. Using the succinyl-ala-phe-pro-phe-p nitroanilide as a substrate, Vmax of the enzyme was found to be 0.8187 ± 0.1352 µmoles/min and Km = 1.6014 ± 0.8449µM, respectively. With this, we conclude that rPpiC protein is an active form of protein from Salmonella Typhimurium and plays an important role in protein folding.

Two‐stage spoken term detection system for under‐resourced languages

Spoken Term Detection (STD) is the process of locating the occurrences of spoken queries in a given speech database. Generally, two methods are adopted for STD: an ASR based sequence matching and ASR-free, feature-based template matching. If a well-performing ASR is available, the former STD method is accurate. However, to build an ASR with consistent performance, several hours of labelled corpora is required. Template matching methods work well for small or chopped utterances. However, in practice, the volume of the search database can be huge, containing sentences of varying lengths. Hence time complexity of template matching techniques will be high, which makes them impractical for realistic search applications. In this work, a two-stage STD system is proposed, which combines the ASR-based phoneme sequence matching in the first stage and feature sequence template matching of selected locations in the second stage. The time complexity of the second stage is reduced by performing DTW-based template matching only at probable query locations identified by the first stage. ‘Split and match’ approach helps to reduce the false-positives in case of longer query words. Effectiveness of the proposed method is demonstrated using English and Malayalam datasets.

Motto: Representing Motifs in Consensus Sequences with Minimum Information Loss.

Sequence analysis frequently requires intuitive understanding and convenient representation of motifs. Typically, motifs are represented as position weight matrices (PWMs) and visualized using sequence logos. However, in many scenarios, in order to interpret the motif information or search for motif matches, it is compact and sufficient to represent motifs by wildcard-style consensus sequences (such as [GC][AT]GATAAG[GAC]). Based on mutual information theory and Jensen-Shannon divergence, we propose a mathematical framework to minimize the information loss in converting PWMs to consensus sequences. We name this representation as sequence Motto and have implemented an efficient algorithm with flexible options for converting motif PWMs into Motto from nucleotides, amino acids, and customized characters. We show that this representation provides a simple and efficient way to identify the binding sites of 1156 common transcription factors (TFs) in the human genome. The effectiveness of the method was benchmarked by comparing sequence matches found by Motto with PWM scanning results found by FIMO. On average, our method achieves a 0.81 area under the precision-recall curve, significantly (P-value < 0.01) outperforming all existing methods, including maximal positional weight, Cavener’s method, and minimal mean square error. We believe this representation provides a distilled summary of a motif, as well as the statistical justification.

Generalized Pattern Matching of Industrial Alarm Flood Sequences via Word Processing and Sequence Alignment

Analyzing alarm floods in a large-scale industrial facility is a sophisticated task, because of too many fault types and their associated consequential alarms. However, there exist similar alarm floods in different processes, where the alarms are associated with the same fault types, but configured with different tag names. If these alarm floods are discovered and grouped, the obtained results could facilitate root cause analysis and lead to generalized solutions. Motivated by this practical problem, a systematic pattern-matching method to compare alarm floods across different processes is proposed in this article. The contributions are twofold: 1) A word processing approach is proposed to generalize alarm representations; 2) a pattern-matching approach is developed to compare alarm floods across different processes. The effectiveness of the proposed method is demonstrated by a case study using alarm data from an industrial facility.

Distortion-free 3D diffusion imaging of the prostate using a multishot diffusion-prepared phase-cycled acquisition and dictionary matching.

To achieve three-dimensional (3D) distortion-free apparent diffusion coefficient (ADC) maps for prostate imaging using a multishot diffusion prepared-gradient echo (msDP-GRE) sequence and ADC dictionary matching. The msDP-GRE sequence is combined with a 3D Cartesian, centric k-space trajectory with center oversampling. Oversampled k-space center averaging and phase cycling are used to address motion- and eddy current-induced magnitude corruption. Extended-phase-graph (EPG) simulations and ADC dictionary matching are used to compensate for T1 effects. To shorten the acquisition time, each volume is undersampled by a factor of two and reconstructed using iterative sensitivity encoding. The proposed approach is characterized using simulations and validated in a kiwifruit phantom, comparing the msDP-GRE ADC maps obtained using both standard monoexponential fitting and dictionary matching with the clinical standard single-shot diffusion weighted-echo planar imaging (ssDW-EPI) ADC. Initial in vivo feasibility is tested in three healthy subjects, and geometric distortion is compared with anatomical T2 -weighted-turbo spin echo. In the kiwifruit phantom experiment, the signal magnitude could be recovered using k-space center averaging and phase cycling. No statistically significant difference was observed in the ADC values estimated using msDP-GRE with dictionary matching and clinical standard DW-EPI (P < .05). The in vivo prostate msDP-GRE scans were free of geometric distortion caused by off-resonance susceptibility, and the ADC values in the prostate were in agreement with values found in the published literature. Nondistorted 3D ADC maps of the prostate can be achieved using a msDP sequence and dictionary matching.

Reconstructing double-stranded DNA fragments on a single-molecule level reveals patterns of degradation in ancient samples.

Extensive manipulations involved in the preparation of DNA samples for sequencing have hitherto made it impossible to determine the precise structure of double-stranded DNA fragments being sequenced, such as the presence of blunt ends, single-stranded overhangs, or single-strand breaks. We here describe MatchSeq, a method that combines single-stranded DNA library preparation from diluted DNA samples with computational sequence matching, allowing the reconstruction of double-stranded DNA fragments on a single-molecule level. The application of MatchSeq to Neanderthal DNA, a particularly complex source of degraded DNA, reveals that 1- or 2-nt overhangs and blunt ends dominate the ends of ancient DNA molecules and that short gaps exist, which are predominantly caused by the loss of individual purines. We further show that deamination of cytosine to uracil occurs in both single- and double-stranded contexts close to the ends of molecules, and that single-stranded parts of DNA fragments are enriched in pyrimidines. MatchSeq provides unprecedented resolution for interrogating the structures of fragmented double-stranded DNA and can be applied to fragmented double-stranded DNA isolated from any biological source. The method relies on well-established laboratory techniques and can easily be integrated into routine data generation. This possibility is shown by the successful reconstruction of double-stranded DNA fragments from previously published single-stranded sequence data, allowing a more comprehensive characterization of the biochemical properties not only of ancient DNA but also of cell-free DNA from human blood plasma, a clinically relevant marker for the diagnosis and monitoring of disease.

Peptide-Spectrum Match Validation with Internal Standards (P-VIS): Internally-Controlled Validation of Mass Spectrometry-Based Peptide Identifications.

Liquid chromatography-tandem mass spectrometry is an increasingly powerful tool for studying proteins in the context of disease. As technological advances in instrumentation and data analysis have enabled deeper profiling of proteomes and peptidomes, the need for a rigorous, standardized approach to validate individual peptide-spectrum matches (PSMs) has emerged. To address this need, we developed a novel and broadly applicable workflow: PSM validation with internal standards (P-VIS). In this approach, the fragmentation spectrum and chromatographic retention time of a peptide within a biological sample are compared with those of a synthetic version of the putative peptide sequence match. Similarity measurements obtained for a panel of internal standard peptides are then used to calculate a prediction interval for valid matches. If the observed degree of similarity between the biological and the synthetic peptide falls within this prediction interval, then the match is considered valid. P-VIS enables systematic and objective assessment of the validity of individual PSMs, providing a measurable degree of confidence when identifying peptides by mass spectrometry.

Towards real-time DNA biometrics using GPU-accelerated processing

Abstract Decoding the human genome in the past decades has brought into focus a computationally intensive operation through DNA profiling. The typical search space for these kinds of problems is extremely large and requires specialized hardware and algorithms to perform the necessary sequence analysis. In this paper, we propose an innovative and scalable approach to exact multi-pattern matching of nucleotide sequences by harnessing the massively parallel computing power found in commodity graphical processing units. Our approach places careful consideration on preprocessing of DNA datasets and runtime performance, while exploiting the full capabilities of the heterogeneous platform it runs on. Finally, we evaluate our models against real-world DNA sequences.

Molecular Depiction of Thirteen Indian Toxic Plants with ITS Markers

Plant identification is an overwhelming task due to different biological attributes and great diversity in plant species. In the absence of physical markers, molecular techniques have become useful for the identification of species of origin of medicinal plant seeds, pastes, and formulations of suspected plants. The ITS region of nuclear rRNA was amplified from thirteen different toxic plant species by using universal primer ITS 1 &amp; 4. Nucleotide sequences of all selected plants were submitted in NCBI and accession numbers were acquired. The results of this study give accurate identification of thirteen plant species and proved the ITS region of 18s-26s nuclear ribosome to be an important tool for phylogenetic analysis and species identification of plants. The sequence was aligned with top matched reference sequence and presented in Clustal Omega software for making a phylogenetic neighbour tree. The significance of these findings is paramount in forensic toxicology scenarios especially when fragmentary plant material is found in the stomach/intestine and its morphological identification becomes impossible. In these circumstances, the PCR based molecular technique surely plays a significant role in solving complicated forensic cases.

Cohort analytics: efficiency and applicability

The abundant availability of health-care data calls for effective analysis methods to help medical experts gain a better understanding of their patients and their health. The focus of existing work has been largely on prediction. In this paper, we introduce Core, a framework for cohort “representation” and “exploration.” Our contributions are twofold: First, we formalize cohort representation as the problem of aggregating the trajectories of its patients. This problem is challenging because cohorts often consist of hundreds of patients who underwent medical actions of various types at different points in time. We prove that producing a representative cohort trajectory is NP-complete with a reduction in the multiple sequence alignment problem. We propose a heuristic that extends the Needleman–Wunsch algorithm for sequence matching to handle temporal sequences. To further improve cohort representation efficiency, we introduce “trajectory families” and “stratified sampling.” Our second contribution is formalizing the problem of cohort exploration as finding a set of cohorts that are similar to a cohort of interest and that maximize entropy. This problem is challenging because the potential number of similar cohorts is huge. We prove NP-completeness with a reduction in the maximum edge subgraph problem. To address complexity, we develop a multi-staged approach based on limiting the search space to “contrast cohorts.” To speed up the computation of cohort similarity, we use “event sets” that are inspired from the double dictionary encoding proposed for keyword search. Moreover, we explore the usefulness and efficiency of Core using an extensive set of qualitative and quantitative experiments on two real health-care datasets. In a user study with medical experts, we show that Core reduces time-to-insight from hours to seconds and helps them find better insights than baseline approaches. Moreover, we show that the obtained cohort representations offer the right trade-off between quality and performance. We study the benefits of trajectory families and stratified sampling for cohort representation and show their applicability on large and heterogeneous cohorts. We also show the benefit of event sets for cohort exploration in providing interactive performance.

Sequence-Activity Relationships for the Snf7 Insecticidal dsRNA in Chrysomelidae.

The responsiveness of insects to oral delivery of insecticidal dsRNA has been shown to be dependent on dsRNA length and sequence match. Previous work with the western corn rootworm (WCR, Diabrotica virgifera virgifera; Coleoptera: Chrysomelidae) demonstrated that at least one ≥21 nt match must be present in the DvSnf7 dsRNA of approximately ≥60 base-pairs (bp) for activity. Further data is needed on the activity of <21 nt matches along with characterization of relationship between activity and the number of ≥21 nt matches. To characterize the sequence–activity relationship for insecticidal dsRNA further, the activity of orthologous Snf7 dsRNAs with 19, 20, and 21 nt contiguous matches against WCR was compared. Neither 19 nor 20 nt sequence matches were active, supporting that a ≥21 nt sequence match is required for activity. The relationship between the number of 21 nt matches with activity of Snf7 dsRNA orthologs from several Chrysomelid species was characterized using WCR and Colorado potato beetle (CPB, Leptinotarsa decemlineata; Coleoptera Chrysomelidae). For WCR, there was a strong relationship between an increasing number of 21 nt matches and increased activity (i.e., lower LC50 values). A similar relationship was observed for CPB with an exception for a single ortholog, which may be related to the exceptionally high rate of polymorphisms in CPB. Overall, these results demonstrate a general relationship between the number of 21 nt matches and activity, and this relationship could be used to inform a testing and assessment plan for an ecological risk assessment for an insecticidal dsRNA.

Abstract 840: Pathway enrichment analysis of gene expression data from formalin-fixed paraffin embedded (FFPE) samples using the GeoMx™ DSP Platform

Abstract The GeoMx Digital Spatial Profiling (DSP) Platform enables robust detection of high-plex protein and RNA expression from user-defined sections within FFPE samples. As the number of targets detected within such tissues increases, it becomes important to apply systems biology strategies in order to better interpret the complex biology of the tumor microenvironment. In this study, we investigated the expression profiles of more than 1600 genes by utilizing more than 10,000 DSP-specific in situ hybridized (ISH) probes on FFPE samples. The bioinformatics tools we have development are enabling us to move beyond single-gene profiling to a better understanding of pathway-based expression. Our study includes colorectal cancer patient samples from which we have matching bulk RNA sequencing and NanoString analysis to compare. The panel of genes profiled have a strong focus around capturing biological signaling along canonical signaling pathways and cell-intrinsic signaling from immune cells and other cell types. We demonstrate the ability to leverage foundational pathway interrogation tools, including Reactome, with the data to capture spatially-resolved pathway interactions and signaling within FFPE tissues. As we look towards the future of the GeoMx platform and high-plex RNA profiling of tissue samples, these experiments highlight not only the need but the capacity for this platform to derive deep understanding of the biology within and across a single slide of tissue. These experiments are being used to drive development of the software features within the GeoMx ecosystem, which will provide further support for pathway-level exploration of expression when working with highly multiplexed reagents in future platform offerings. GeoMx™ DSP technology is for Research Use Only and not for use in diagnostic procedures. Citation Format: Tressa R. Hood, Jason Reeves, Zach Norgaard, Margaret Hoang, Sarah Warren, Erin Piazza, Rich Boykin, Joseph Beechem. Pathway enrichment analysis of gene expression data from formalin-fixed paraffin embedded (FFPE) samples using the GeoMx™ DSP Platform [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 840.

Abstract 829: Global tumor transcriptional activity reveals aggressiveness across multiple cancers

Abstract Background: At present, conventional clinical and histopathological evaluations are not sufficient to distinguish biologically indolent cancers from those that will exhibit aggressive behavior. We hypothesize that global transcriptomic activity of tumor cells reflects the end cumulative result of somatic, germline, and epigenetic alterations, as well as additional transcriptional regulatory events. Therefore, it may be more directly associated with clinical outcomes. However, the total number of mRNA molecules is not directly measurable, either in bulk or single-cell RNA sequencing data. To this end, we develop a novel metric: the transcriptional activity score (TAS), to measure the relative global tumor-cell specific transcriptional activity in heterogeneous tumor samples. Materials and Methods: We propose TAS as the ratio of average total transcript proportion over the count proportion of tumor cells versus surrounding non-tumor cells. The transcript proportions are estimated using RNAseq deconvolution method DeMixT and the count proportions are estimated using DNAseq deconvolution methods such as ASCAT and ABSOLUTE. Using matching bulk RNA and DNA sequencing data from the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), we calculated TAS for a total of 5,031 patient samples across 15 cancer types. For validation, we obtained TAS for two genomic studies: 1) from patients with early-onset prostate cancer (n=99) as part of the ICGC, and 2) from patients with localized prostate cancer as part of the Canadian Prostate Cancer Genome Network (CPC-GENE, n=144). Results: We found that higher TAS corresponds to a more aggressive state of cancer, as characterized by MYC dysregulation, genome instability, known marker genes, and molecular subtypes. By examining the association between TAS and survival outcomes across cancer types, we also found that TAS refines the prognostic ability of pathologic stage, identifying aggressive early-stage tumors associated with poor survival as well as late-stage tumors with favorable outcomes. In prostate cancer, TAS is linearly associated with progression-free probabilities, useful to rank patients within the median risk group (Gleason score = 7). This added prediction power is consistent in TCGA and two independent validation data (ICGC and CPC-GENE). Conclusion: We have developed a new summary metric using matched DNA and RNA sequencing data from tumor samples, to compute, in vivo and using deconvolution, the relative global gene expression level of tumor cells. The TAS metric evaluates global transcriptional activity, an intrinsic behavior of cells that is well-known, but now for the first time is shown through TAS to be associated with prognosis. TAS may serve as a tractable phenotype to help elucidate the biology that underlies metastasis, prognosis and response to treatment in cancer patients. Citation Format: Shaolong Cao, Jennifer R. Wang, Jonas Demeulemeester, Jingxiao Chen, Kaixian Yu, Peng Yang, Bora Lim, Alfonso Urbanucci, Peter Campbell, Hongtu Zhu, Peter Van loo, Wenyi Wang. Global tumor transcriptional activity reveals aggressiveness across multiple cancers [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 829.