Signadot is a platform that enables ephemeral test environments in Kubernetes to enable rapid development & testing at scale of multi-service, cloud-native applications

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This paper is available on arxiv under CC 4.0 license. Authors: (1) Matu´s Medo, Department for BioMedical Research, Inselspital, Bern University Hospital, University of Bern, Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern and matus.medo@unifr.ch; (2)  Michaela Medova, Department for BioMedical Research, Inselspital, Bern University Hospital, University of Bern, and Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern. Table of Links Abstract & Introduction Results Discussion Materials and methods Acknowledgement & References Supporting Information Supporting Information This paper is available on under CC 4.0 license. Arxiv

Extra Information on the Comprehensive Comparison of Tools for Fitting Mutational Signatures

This paper is available on arxiv under CC 4.0 license. Authors: (1) Matu´s Medo, Department for BioMedical Research, Inselspital, Bern University Hospital, University of Bern, Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern and matus.medo@unifr.ch; (2)  Michaela Medova, Department for BioMedical Research, Inselspital, Bern University Hospital, University of Bern, and Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern. Table of Links Abstract & Introduction Results Discussion Materials and methods Acknowledgement & References Supporting Information Acknowledgement We thank Charlotte Ng and Peter Degen for their useful feedback. References Alexandrov, L.B., Kim, J., Haradhvala, N.J., Huang, M.N., Tian Ng, A.W., Wu, Y., Boot, A., Covington, K.R., Gordenin, D.A., Bergstrom, E.N., et al., 2020. The repertoire of mutational signatures in human cancer. Nature 578, 94–101. Alexandrov, L.B., Nik-Zainal, S., Wedge, D.C., Aparicio, S.A., Behjati, S., Biankin, A.V., Bignell, G.R., Bolli, N., Borg, A., Børresen-Dale, A.L., et al., 2013. Signatures of mutational processes in human cancer. Nature 500, 415–421. Berger, B., Yu, Y.W., 2022. Navigating bottlenecks and trade-offs in genomic data analysis. Nature Reviews Genetics , 1–16. Blokzijl, F., Janssen, R., van Boxtel, R., Cuppen, E., 2018. MutationalPatterns: Comprehensive genome-wide analysis of mutational processes. Genome Medicine 10, 1–11. Brady, S.W., Gout, A.M., Zhang, J., 2022. Therapeutic and prognostic insights from the analysis of cancer mutational signatures. Trends in Genetics 38, 194–208. Cannataro, V.L., Mandell, J.D., Townsend, J.P., 2022. Attribution of cancer origins to endogenous, exogenous, and preventable mutational processes. Molecular Biology and Evolution 39, msac084. Degasperi, A., Zou, X., Dias Amarante, T., Martinez-Martinez, A., Koh, G.C.C., Dias, J.M., Heskin, L., Chmelova, L., Rinaldi, G., Wang, V.Y.W., et al., 2022. Substitution mutational signatures in whole-genome–sequenced cancers in the UK population. Science 376, abl9283. Diaz-Gay, M., Vangara, R., Barnes, M., Wang, X., Islam, S.A., Vermes, I., Narasimman, N.B., Yang, T., Jiang, Z., Moody, S., et al., 2023. Assigning mutational signatures to individual samples and individual somatic mutations with SigProfilerAssignment. bioRxiv , 548264doi:10. 1101/2023.07.10.548264. bioRxiv 2023.07.10.548264. Gori, K., Baez-Ortega, A., 2020. sigfit: Flexible Bayesian inference of mutational signatures. bioRxiv , 372896doi:10.1101/372896. bioRxiv, 372896. Gulhan, D.C., Lee, J.J.K., Melloni, G.E., Cortes-Ciriano, I., Park, P.J., 2019. Detecting the mutational signature of homologous recombination deficiency in clinical samples. Nature Genetics 51, 912–919. Hubschmann, D., Jopp-Saile, L., Andresen, C., Kr ¨ amer, S., Gu, Z., Heilig, C.E., Kreutzfeldt, S., Teleanu, V., Fr ¨ ohling, S., Eils, R., et al., 2021. ¨ Analysis of mutational signatures with yet another package for signature analysis. Genes, Chromosomes and Cancer 60, 314–331. Islam, S.A., D´ıaz-Gay, M., Wu, Y., Barnes, M., Vangara, R., Bergstrom, E.N., He, Y., Vella, M., Wang, J., Teague, J.W., et al., 2022. Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor. Cell Genomics 2, 100179. Kim, Y.A., Leiserson, M.D., Moorjani, P., Sharan, R., Wojtowicz, D., Przytycka, T.M., 2021. Mutational signatures: From methods to mechanisms. Annual Review of Biomedical Data Science 4, 189–206. Koh, G., Degasperi, A., Zou, X., Momen, S., Nik-Zainal, S., 2021. Mutational signatures: Emerging concepts, caveats and clinical applications. Nature Reviews Cancer 21, 619–637. Li, S., 2020. sigLASSO: Optimizing cancer mutation signatures jointly with sampling likelihood. URL: https://github.com/gersteinlab/ siglasso. R package version 1.1. Li, S., Crawford, F.W., Gerstein, M.B., 2020. Using sigLASSO to optimize cancer mutation signatures jointly with sampling likelihood. Nature Communications 11, 3575. Maura, F., Degasperi, A., Nadeu, F., Leongamornlert, D., Davies, H., Moore, L., Royo, R., Ziccheddu, B., Puente, X.S., Avet-Loiseau, H., Campbell, P.J., Nik-Zainal, S., Campo, E., Munshi, N., Bolli, N., 2019. A practical guide for mutational signature analysis in hematological malignancies. Nature Communications 10, 1–12. Missa, O., Dytham, C., Morlon, H., 2016. Understanding how biodiversity unfolds through time under neutral theory. Philosophical Transactions of the Royal Society B: Biological Sciences 371, 20150226. Nik-Zainal, S., Alexandrov, L.B., Wedge, D.C., Van Loo, P., Greenman, C.D., Raine, K., Jones, D., Hinton, J., Marshall, J., Stebbings, L.A., et al., 2012. Mutational processes molding the genomes of 21 breast cancers. Cell 149, 979–993. Nik-Zainal, S., Davies, H., Staaf, J., Ramakrishna, M., Glodzik, D., Zou, X., Martincorena, I., Alexandrov, L.B., Martin, S., Wedge, D.C., et al., 2016. Landscape of somatic mutations in 560 breast cancer whole-genome sequences. Nature 534, 47–54. Omichessan, H., Severi, G., Perduca, V., 2019. Computational tools to detect signatures of mutational processes in DNA from tumours: A review and empirical comparison of performance. PLoS ONE 14, e0221235. Pandey, P., Arora, S., Rosen, G.L., 2022. MetaMutationalSigs: Comparison of mutational signature refitting results made easy. Bioinformatics 38, 2344–2347. Riva, L., Pandiri, A.R., Li, Y.R., Droop, A., Hewinson, J., Quail, M.A., Iyer, V., Shepherd, R., Herbert, R.A., Campbell, P.J., et al., 2020. The mutational signature profile of known and suspected human carcinogens in mice. Nature Genetics 52, 1189–1197. Rosenthal, R., McGranahan, N., Herrero, J., Taylor, B.S., Swanton, C., 2016. DeconstructSigs: Delineating mutational processes in single tumors distinguishes DNA repair deficiencies and patterns of carcinoma evolution. Genome Biology 17, 1–11. Rustad, E.H., Nadeu, F., Angelopoulos, N., Ziccheddu, B., Bolli, N., Puente, X.S., Campo, E., Landgren, O., Maura, F., 2021. mmsig: A fitting approach to accurately identify somatic mutational signatures in hematological malignancies. Communications Biology 4, 1–12. Schumann, F., Blanc, E., Messerschmidt, C., Blankenstein, T., Busse, A., Beule, D., 2019. SigsPack, A package for cancer mutational signatures. BMC Bioinformatics 20, 1–9. Van Hoeck, A., Tjoonk, N.H., van Boxtel, R., Cuppen, E., 2019. Portrait of a cancer: Mutational signature analyses for cancer diagnostics. BMC Cancer 19, 1–14. Wang, S., Li, H., Song, M., Tao, Z., Wu, T., He, Z., Zhao, X., Wu, K., Liu, X.S., 2021. Copy number signature analysis tool and its application in prostate cancer reveals distinct mutational processes and clinical outcomes. PLoS Genetics 17, e1009557. Wu, Y., Chua, E.H.Z., Ng, A.W.T., Boot, A., Rozen, S.G., 2022. Accuracy of mutational signature software on correlated signatures. Scientific Reports 12, 1–12. This paper is available on under CC 4.0 license. Arxiv

The People We Have to Thank In Our Comparison of Tools for Fitting Mutational Signatures

This paper is available on arxiv under CC 4.0 license. Authors: (1) Matu´s Medo, Department for BioMedical Research, Inselspital, Bern University Hospital, University of Bern, Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern and matus.medo@unifr.ch; (2)  Michaela Medova, Department for BioMedical Research, Inselspital, Bern University Hospital, University of Bern, and Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern. Table of Links Abstract & Introduction Results Discussion Materials and methods Acknowledgement & References Supporting Information 4. Materials and methods 4.1. Reference signatures We used all signatures from version 3.0 of the COSMIC catalog (COSMICv3, https://cancer.sanger.ac. uk/signatures/sbs/), in particular the SBS signatures for human genome assembly GRCh38. COSMICv3 contains 67 mutational signatures (18 are artifact signatures) defined in 96 mutational contexts. While newer COSMIC versions (the latest version is 3.3.1 from June 2022) added more signatures, profiles of the COSMICv3 signatures have not been altered (all absolute weight differences are below 10−7 ). To measure the diversity of signature profiles, we compute their exponentiated Shannon index which is a common measure of diversity (Missa et al., 2016) that can be understood as the effective number of active mutational contexts. For a signature whose all weight is concentrated in one context, the exponentiated Shannon index is one. For a signature with the same weight 1/96 of all contexts, the exponentiated Shannon index is 96. For COSMICv3 signatures, the exponentiated Shannon index ranges from 2.7 (SBS48) to 80.3 (SBS3). 4.2. Synthetic mutational catalogs Absolute contributions of signatures to sequenced tissues from various cancer types were downloaded from the Catalogue Of Somatic Mutations In Cancer (COSMIC, https://cancer.sanger.ac.uk/signatures/sbs/, catalog version 3.2) on November 14, 2021 for all 46 non-artifact SBS signatures that have tissue contribution data available. These data included only samples with reconstruction accuracy above 0.90 and at least 10 mutations attributed to the specific SBS signature. Using the provided unique sample identifiers, we used these files to compute the relative signature contributions in individual WGS-sequenced samples that are stratified by the cancer type. We chose eight different cancer types for further analysis: Hepatocellular carcinoma (Liver-HCC, n = 422), stomach adenocarcinoma (Stomach-AdenoCA, n = 170), head and neck squamous cell carcinoma (Head-SCC, n = 57), colorectal carcinoma (ColoRect-AdenoCA, n = 72), lung adenocarcinoma (Lung-AdenoCA, n = 62), cutaneous melanoma (Skin-Melanoma, n = 280), non-Hodgkin Lymphoma (Lymph-BNHL, n = 117), and glioblastoma (CNS-GBM, n = 63). The approach described above allows us to reproduce empirical signature weights in previously analyzed samples without resorting to assumptions such as a log-normal distribution of the number of mutations due to a given signature (Alexandrov et al., 2020, Islam et al., 2022) or adding additional zeros to the Poisson distribution to reproduce signatures that are not active in a sample (Omichessan et al., 2019). The code for generating synthetic mutational catalogs and evaluating the signature fitting tools is available at https://github.com/8medom/SigFitTest. For Figure 6, we created synthetic mutational catalogs with a systematic difference in the activity of signature SBS40. After generating sample weights as described in the previous paragraph (assuming the CNS-GBM cancer type), the relative weights of SBS40 were multiplied by 1.3 in the first half of the samples and divided by 1.3 in the second half of the samples. The relative weights of all signatures in each sample were then normalized to one. Note that signature SBS40 is active in nearly all CNS-GBM samples (Fig. SF9 in SI) and its median relative weight is close to 50%. By the described process, the median weights in the two groups of samples become 42% and 55%, respectively. 4.3. Tools for signature fitting The task of fitting known mutational signatures to a mutational catalog consists of finding the combination of signatures from a reference set that “best” matches the given catalog. Denoting the matrix with reference signatures as S, where S cs is the relative weight of context c for signature s, and the given normalized mutational catalog as m, where mci is the fraction of mutations in context c in sample i, this amounts to solving where wsi is the relative weight of signature s in sample i. Mathematically, this is an over-determined system as the number of reference signatures is smaller than the number of mutational contexts (96 contexts for the common SBS signatures). Several fitting tools are therefore based on minimizing the difference between m and Sw through nonnegative least squares (as the signature weights cannot be negative) or quadratic programming. We evaluated several tools that belong to this class: MutationalPatterns v3.4.1 (Blokzijl et al., 2018), YAPSA v1.20.1 (Hubschmann et al. ¨ , 2021), SigsPack v1.8.0 (Schumann et al., 2019), and sigminer v2.2.0 (Wang et al., 2021) which has three separate methods based on quadratic programming, non-linear least squares, and simulated annealing. We find that all these tools produce similar results. Other tools use various iterative processes by which the provided set of reference signatures is gradually reduced by removing the signatures, for example, whose inclusion does not considerably improve the match between the observed and reconstructed mutational catalogs (or, opposite, signatures are gradually added as long as the reconstruction accuracy sufficiently improves). We evaluated deconstructSigs v1.8.0 (Rosenthal et al., 2016), SigProfilerSingleSample v0.0.0.27 (Alexandrov et al., 2020), SigProfilerAssignment v0.0.30 (Diaz-Gay et al., 2023), mmsig v0.0.0.9000 (Rustad et al., 2021), and signature.tools.lib v2.2.0 (Degasperi et al., 2022), that all belong to this category. The newest tool, SigProfilerAssignment, combines backward and forward iterative adjustment of the reference catalog and these steps are repeated until convergence. Finally, SigLASSO v1.1 combines the data likelihood in a generative model with L1 regularization and prior knowledge (Li, 2020, Li et al., 2020). To allow for a fair comparison with other tools, we did not use prior knowledge when evaluating sigLASSO. In our experience, this tool sometimes fails to halt but starting it again with the same input data resolves the issue. A similar Bayesian framework is used by sigfit v2.2 (Gori and Baez-Ortega, 2020). The authors of sigfit recommend setting all signatures whose lower bounds of the estimated 95% confidence intervals are below 0.01 to zero. The authors of deconstructSigs recommend setting all signatures whose relative weights are below 0.06 to zero. All results shown for sigfit and deconstructSigs follow these recommendations. 4.4. Trimming reference signatures It has been argued that the removal of irrelevant reference signatures can improve the fitting results (Kim et al., 2021). To assess this hypothesis, we use a two-step process where: (1) We fit signatures using all COSMICv3 signatures as a reference and (2) keep only the signatures whose relative weight exceeds threshold w0 for at least 5 samples in our cohorts with 100 samples. We use thresholds w0 = 0.05 (m = 100), w0 = 0.03 (m = 2, 000) and w0 = 0.01 (m = 50, 000) which correspond to absolute signature contributions 5, 60, and 500, respectively. This pruning of reference signatures is beneficial when the number of mutations per sample is small (see m = 100 in Fig. S17 in SI). However, the converse is true when the number of mutations per sample is high, in particular for tools that perform well with COSMICv3 as a reference (m = 50, 000 in Fig. S17 in SI). To overcome this problem, we have also tested a less strict pruning where we keep all signatures whose relative weight exceeds w0 for at least one sample (see Fig. S18 in SI for the results). We have also tested the approach based on de novo extraction of signatures as suggested in (Maura et al., 2019). For signature extraction, we used SignatureAnalyzed v0.0.7 (https://github.com/getzlab/SignatureAnalyzer) with L1 prior on both W and H and a Poisson objective function for optimization. As recommended in (Maura et al., 2019), each extracted signature was then compared with individual signatures from COSMICv3 as well as linear combinations of two signatures from COSMICv3. The signature (or a pair of signatures) that yielded the smallest cosine distance was then added to the list of trimmed reference signatures for the given input data. When the number of mutations is small (m = 100), less than five signatures are selected, on average. When the number of mutations is large (m = 50, 000), 89% of the active signatures are selected, on average. A comparison of the results obtained by the two reference-trimming schemes is shown in Fig. S19 in SI. 4.5. Evaluation metrics Recent literature often classifies fitting results as true positives, false positives, true negatives, and false negatives, which allows computing evaluation metrics such as precision and specificity Omichessan et al. (2019), Islam et al. (2022), Diaz-Gay et al. (2023). We use instead quantitative metrics that measure the differences between the true relative signature weights (which are used to generate the input mutational catalogs) and the estimated relative signature weights. For tools that estimate absolute signature weights, those are first normalized by the number of mutations in each sample. and average it over all samples in the cohort to obtain fitting error. The lowest fitting error, 0, is achieved when the estimated signature weights are exact for all signatures and all samples. The highest fitting error, 1, is achieved when the estimated signature weights sum to one for each sample but they are all false positives. For example, when a sample has 40% contribution of SBS1 and 60% of SBS4 but a tool estimates 20% contribution of SBS2 and 80% contribution of SBS3, the fitting error is 1. There are some tools whose signature weight estimates do not sum up to one for each sample, typically as a result of the uncertainty implied by a small number of mutations in samples. The fitting error of those tools can be inflated due to these tools’ conservativeness. However, we probed whether additionally normalizing the estimated weights to one improves the fitting error achieved by those tools and found that this is not the case. We can thus conclude that these tools are not disfavored by measuring their fitting error. Another way to quantify the agreement between the true and estimated signature weights is by computing their Pearson correlation. For each sample where at least three signatures had positive either true or estimated weights, we computed the Pearson correlation coefficient between these two weight vectors whilst excluding all signatures that are zero for both of them. The obtained values were then averaged over all samples in the cohort. We finally computed false positive weight which quantifies how much weight is assigned to the signatures that are not active in a given sample, and then this quantity is averaged over all samples. This quantity too ranges from 0 (when no weight is assigned to inactive signatures) to 1 (when all weight is assigned to inactive signatures). This paper is available on under CC 4.0 license. arxiv

The Materials and Methods That We Used to Compare Tools for Fitting Mutational Signatures

This paper is available on arxiv under CC 4.0 license. Authors: (1) Matu´s Medo, Department for BioMedical Research, Inselspital, Bern University Hospital, University of Bern, Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern and matus.medo@unifr.ch; (2)  Michaela Medova, Department for BioMedical Research, Inselspital, Bern University Hospital, University of Bern, and Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern. Table of Links Abstract & Introduction Results Discussion Materials and methods Acknowledgement & References Supporting Information 3. Discussion The broad range of tools for fitting mutational signatures makes it difficult to understand which tool to choose for a given project. In this work, we provide a comprehensive assessment of eleven different tools on synthetic mutational catalogs. Using mutational catalogs where only one signature is active allows us to quantify the differences in the fitting difficulty of individual signatures. We find that flat signatures whose average similarity with other signatures is high are the most difficult to fit. To assess fitting tools, we use synthetic mutational catalogs whose signature activities are modeled based on eight distinct cancer types. We find that when the number of mutations is small (100 mutations per sample), SigProfilerSingleSample is the best tool to use for all cancer types. As the number of mutations increases, SigProfilerAssignment becomes the best tool for all cancer types with mmsig best for some cancer types for an intermediate number of mutations (2,000 mutations per sample). The results change little when Pearson correlation is used instead of fitting error for the evaluation (Figs. S22 and S23 in SI). The risk of overfitting the data by including too many signatures in the reference catalog is often discussed in the literature (Maura et al., 2019, Koh et al., 2021, Degasperi et al., 2022). We show that three methods (SigProfilerSingleSample, SigProfilerExtractor, and mmsig) are robust to such overfitting when the number of mutations per sample is not too small (Figs. S12 and S13 in SI). Crucially, we find that the common practice of excluding signatures from the reference catalog because they do not seem to be active (or are little active) in the analyzed data is little effective or even harmful, unless the number of mutations per sample is small (Fig. S16 in SI). In most cases it is preferable to let the built-in statistical methods of these tools to decide using the complete COSMIC catalog as a reference. While our work gives clear recommendations on which tools to consider and which to avoid, many issues can be addressed in the future to further improve the fitting of mutational signatures. First, a similar assessment of fitting tools can be done for other types of signatures: doublet base substitutions, small insertions and deletions, and copy number variations. Then, some tools (e.g., sigfit and mmsig) can compute confidence intervals for their estimates. For sigfit, we used these estimates in the way recommended by the authors: When the lower bound of the confidence interval for the relative signature weight is below 0.01, the signature is marked as absent in the sample (its relative weight is set to zero). Our results show that this recommended practice indeed improves the results achieved by sigfit. By bootstrapping (resampling the original mutational catalogs with replacement), confidence intervals could be computed also for the tools that do not compute them by themselves. It would be worth investigating whether thus-determined confidence intervals could also improve the performance of other fitting tools. To fit mutational signatures, it is commonly required that each sample has at least 50 (Riva et al., 2020) or 200 (Blokzijl et al., 2018) single base substitutions. However, different signatures are differently difficult to fit (Figure 1), so it is unlikely that such a universal threshold is meaningful. Based on the simulation framework established here, it would be possible to study the minimum necessary number of mutations for different signatures of interest and different cancer types. To best match the needs of practitioners, it would be possible to extend the simulation framework so that it finds the best-performing fitting tool for a given cancer type, a list of signatures of interest, and a given distribution of sample mutation counts. When an active signature is missing from the reference catalog because it is new or is falsely deemed inactive in a given set of samples, most fitting tools cannot cope with this situation and distribute all (or nearly all) mutations among signatures from the reference set (Fig. S15 in SI). Three tools do not suffer from this drawback—deconstructSigs, signature.tools.lib, and sigfit—but their performance is generally weak. One could aim to enhance one of the bestperforming tools, such as SigProfilerAssignment, with an additional step that would quantify whether given signatures are indeed likely to produce given mutational catalogs. Another approach to this problem is offered in (Maura et al., 2019) where the authors suggest a multistep process to simultaneously avoid under- and over-fitting. Mutational signatures are first extracted de novo, then they are assigned to existing signatures from a reference catalog, and thusdetermined active signatures are finally used to fit the original input data. While there are well-defined statistical principles for the first and third steps of this process, the second assignment step is largely ad hoc. A more principled approach could improve the outcome. More generally, COSMIC signatures are the result of analyzing tumor tissues from many different organs, so they can be viewed as an average across them (Degasperi et al., 2022). It might be more appropriate to construct organ-specific reference catalogs that would better reflect mutational processes that occur in these biologically diverse systems. The fast-growing number of sequenced samples would make such an effort possible. This paper is available on under CC 4.0 license. Arxiv

Discussing Tools for Fitting Mutational Signatures: A Comprehensive Comparison

Comparing Tools for Fitting Mutational Signatures: The Results of Our Work

This paper is available on arxiv under CC 4.0 license. Authors: (1) Matu´s Medo, Department for BioMedical Research, Inselspital, Bern University Hospital, University of Bern, Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern and matus.medo@unifr.ch; (2)  Michaela Medova, Department for BioMedical Research, Inselspital, Bern University Hospital, University of Bern, and Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern. Table of Links Abstract & Introduction Results Discussion Materials and methods Acknowledgement & References Supporting Information Abstract Mutational signatures connect characteristic mutational patterns in the genome with biological processes that take place in the tumor tissues. Analysis of mutational signatures can help elucidate tumor evolution, prognosis, and therapeutic strategies. Although tools for extracting mutational signatures de novo have been extensively benchmarked, a similar effort is lacking for tools that fit known mutational signatures to a given catalog of mutations. We fill this gap by comprehensively evaluating eleven signature fitting tools (well-established as well as recent) on synthetic input data. To create realistic input data, we use empirical signature weights in tumor tissue samples from the COSMIC database. The study design allows us to assess the effects of the number of mutations, type of cancer, and the catalog of reference signatures on the results obtained with various fitting tools. We find substantial performance differences between the evaluated tools. Averaged over 120,000 simulated mutational catalogs corresponding to eight different cancer types, SigProfilerSingleSample and SigProfilerAssignment perform best for small and large numbers of mutations per sample, respectively. We further show that ad hoc constraining the list of reference signatures is likely to produce inferior results and that noisy estimates of signature weights in samples with as few as 100 mutations can still be useful in downstream analysis. Cancer genomics, statistical methods, mutational signatures, computational models Keywords: 1. Introduction Since their introduction a decade ago by Nik-Zainal et al. (2012), Alexandrov et al. (2013), mutational signatures have become a widely used tool in genomics (Maura et al., 2019, Koh et al., 2021). They allow researchers to move from individual mutations in the genome to biological processes that take place in living tissues (Kim et al., 2021). In (Cannataro et al., 2022), for example, signature activities have been used to attribute mutations to endogenous, exogenous, and preventable mutational processes. The activity of various mutational signatures can also serve as prognostic or therapeutic biomarkers (Van Hoeck et al., 2019, Brady et al., 2022). Homologous recombination defficiency leads to the accumulation of DNA damage and manifests itself in a specific mutational signature (signature SBS3 from the COSMIC catalog) (Nik-Zainal et al., 2016, Gulhan et al., 2019). Mutational signatures can be introduced for single base substitutions (SBS), doublet base substitutions (DBS), small insertions and deletions (ID), as well as for copy number variations (CN). We focus here on SBS-based mutational signatures which are most commonly used in the literature. Current SBS signatures are defined using 6 possible classes of substitutions (C>A, C>G, C>T, T>A, T>C, and T>G) together with their two immediate neighboring bases, thus giving rise to 6 × 4 × 4 = 96 different nucleotide contexts into which all SBS mutations in a given sample are classified. De novo extraction of signatures from somatic mutations in sequenced samples has been used to gradually map the landscape of mutational signatures in cancer tissues. Over time, the initial catalogue of 22 SBS-based mutational signatures in the first version of the Catalogue Of Somatic Mutations In Cancer (COSMIC) released in August 2013 has expanded to 80 signatures in the COSMICv3.3 version released in June 2022. This expansion was possible owing to the increased availability of WES and WGS sequencing data as well as improved tools for signature extraction. Extensive benchmarking of extraction tools on synthetic data has recently shown that SigProfilerExtractor outperforms other methods in terms of sensitivity, precision, and false discovery rate, particularly in cohorts with more than 20 active signatures (Islam et al., 2022). A two-step process consisting of first extracting common signatures and then rare signatures has been recently recommended in (Degasperi et al., 2022). Nevertheless, with the existing signatures extracted from cohorts including thousands of sequenced samples (Alexandrov et al., 2020, Islam et al., 2022, Degasperi et al., 2022), the authors of smaller studies are unlikely to discover new signatures. For example, the analysis of more than 23,000 WGS and WES cancers in (Islam et al., 2022) has only discovered four new signatures. The much more relevant task is thus that of fitting existing signatures to given sequenced samples. In this process, the catalogs of somatic mutations are used to determine the signature contributions to each individual sample. Many different tools have been developed for this task (Koh et al., 2021) (see Methods for their description and classification). However, while tools for extracting mutational signatures have been recently extensively benchmarked on synthetic data by various studies (Omichessan et al., 2019, Alexandrov et al., 2020, Islam et al., 2022, Wu et al., 2022), a similar quantitative comparison is lacking for tools for fitting mutational signatures. This need is further exacerbated by substantial variations between results obtained by different methods (Pandey et al., 2022). Furthermore, newly introduced tools for fitting mutational signatures are commonly compared with only a few existing tools, rarely the most recent ones, and a standardized comparison methodology is lacking. In this study, our aim is to fill this gap and provide a comprehensive evaluation of a broad range of fitting tools on synthetic data motivated by various types of cancer. We constrain on fitting SBS signatures as they are the most widely used signature type. Many of the included tools can be nevertheless used for other types of signatures as their fitting is mathematically not different from the fitting of SBS signatures. We generate two classes of synthetic mutational catalogs. In the first one, only one mutational signature is responsible for all mutations (single-signature cohorts). In the second one, signature activities in each sample are modeled after the activities found in real tumor samples from various cancers (heterogeneous cohorts). We have collected eleven tools for fitting mutational signatures, from earlier tools such as deconstructSigs to very recent ones such as SigProfilerAssignment. We assess their performance by comparing the known true signature activities in the created catalogs with results obtained by various fitting tools. We find that there is no single fitting tool that performs best regardless of how many mutations are in the samples and which cancer type is chosen to model signature activities. Averaged across all cancer types, two related tools (SigProfilerSingleSample and SigProfilerAssignment) perform best when the number of mutations per sample is small (below 1000, roughly) and large (above 1000), respectively. We also compare the tools by how prone they are to overfitting caused by increasing the size of the reference signature catalog and evaluate whether it is beneficial to constrain the reference catalog based on which signatures seem to be absent or little active in the analyzed samples. We close with a discussion of open problems in fitting mutational signatures. Figure 1: (a, b) The bars show the 95th percentile range of the observed fraction of mutations in synthetic data for SBS mutational contexts (horizontal axis) and different total mutation counts (100, 1,000 and 10,000 represented with red, blue and gray bars, respectively). The effect of the number of mutations in single-signature cohorts. Panels a and b show signature SBS1 with four distinct C>T peaks and signature SBS5 with a flat mutational spectrum, respectively. (c) The fitting errors of the evaluated fitting tools for synthetic data corresponding to 100 samples with 100% contributions of a single signature; the catalog of all 67 COSMICv3 signatures was used for fitting. We show results for ten common signatures (horizontal axis) and various mutation counts (vertical axis). The tools are ordered by the error averaged over the displayed signatures achieved for 51,200 mutations. This paper is available on under CC 4.0 license. arxiv

A Comprehensive Comparison of Tools for Fitting Mutational Signatures

In today’s digital landscape, where communication happens at the speed of light, email remains a steadfast channel for professional interactions. Whether you’re a seasoned executive, a freelancer, or a startup founder, your email signature is more than just a sign-off—it’s a powerful tool that can leave a lasting impression. In this article, we’ll explore the significance of email signatures, discuss the latest trends, and provide actionable tips for creating an effective one. What Is an Email Signature? is a personalized block of text or graphics that appears at the end of your email messages. It serves as a digital business card, providing essential information about you, your role, and your organization. Email signatures are more than just a formality—they play a crucial role in professional communication. An email signature Why Email Signatures Matter Professionalism and Credibility When recipients receive your emails, they subconsciously evaluate your professionalism. —such as your full name, job title, company, and contact information—adds credibility to your correspondence. It’s like handing out a digital business card. A well-crafted email signature with essential details Brand Consistency Consistent branding across all touchpoints reinforces your brand identity. An email signature is prime real estate to showcase your logo, brand colors, and even a succinct tagline. It’s a subtle yet powerful way to reinforce your brand image with every email you send. Contact Information at a Glance Imagine a potential client or collaborator wanting to reach out to you. An easily accessible email signature provides them with your phone number, website, and social media links—all conveniently located in one place. No more digging through old emails to find your contact details! Designing an Impactful Email Signature Creating a . Here’s how to design one that stands out: n effective email signature involves striking a balance between simplicity and impact : Limit your signature to essential information. Include your full name, job title, company, and a professional headshot (if applicable). Avoid clutter—less is more. Keep It Concise : Use a legible font (such as Arial or Calibri) and maintain consistent formatting. Avoid excessive colors or fonts that distract from the content. Remember, readability matters. Font and Formatting : Hyperlink your website, LinkedIn profile, or portfolio. Make it easy for recipients to explore further. A clickable signature is a user-friendly signature. Hyperlinked Elements Animated Call-to-Action Banners: Elevating Your Signature In 2024, static signatures are passé. Consider adding . These eye-catching elements can: animated banners to your email signature : Use banners to highlight webinars, product launches, or conferences. Imagine a subtle animation inviting recipients to your upcoming webinar—it’s attention-grabbing! Promote Events : Link banners to your latest blog post or landing page. A well-placed banner can direct recipients to valuable content, increasing engagement. Drive Traffic : Animated buttons within your signature can lead recipients to your social media profiles or a specific call-to-action. Think of it as a mini marketing campaign within your email. Encourage Interaction Boosting Brand Awareness Through Email Signatures Your signature is more than just a sign-off; Here’s how to maximize its impact: it’s a brand ambassador. : Position your logo strategically. It should be visible but not overpowering. A well-sized logo reinforces brand recognition. Logo Placement : Craft a concise tagline that encapsulates your brand’s essence. Whether it’s witty, inspiring, or informative, make it memorable. Tagline Reinforcement : Include icons linking to your social media profiles. Encourage engagement and connect with your audience beyond email. Social Media Icons How to Manage Email Signatures With BulkSignature Managing email signatures across an organization can be challenging. That’s where comes in. Here’s how to streamline the process: BulkSignature : BulkSignature allows administrators to create and manage signatures for all users from a central dashboard. No more manual updates for each employee. Centralized Control : Ensure brand consistency by enforcing standardized signatures. BulkSignature lets you set guidelines for fonts, logos, and contact details. Consistent Branding : Customize signatures dynamically. Insert user-specific information (like names and titles) automatically. Dynamic Fields : Use banners within signatures to promote events, webinars, or product launches. BulkSignature makes it easy to update banners across the organization. Campaign Banners : Monitor signature performance. Track clicks, conversions, and engagement. Analytics and Tracking Remember, your email signature is a reflection of your professionalism and brand identity. Invest time in creating and managing it effectively—it’s a small detail that can leave a big impact. Conclusion Your email signature isn’t just a formality—it’s a strategic tool for leaving a lasting impression. Craft it thoughtfully, and watch it enhance your professional interactions. Remember, in the digital age, every email you send is an opportunity to reinforce your brand and build meaningful connections.

What an Email Signature Is and Why You Need It in 2024

একটি ইমেল স্বাক্ষর কি এবং কেন আপনার 2024 সালে এটি প্রয়োজন

Was eine E-Mail-Signatur ist und warum Sie sie im Jahr 2024 brauchen

Qué es una firma de correo electrónico y por qué la necesitará en 2024

Qu'est-ce qu'une signature électronique et pourquoi vous en avez besoin en 2024

ईमेल हस्ताक्षर क्या है और 2024 में आपको इसकी आवश्यकता क्यों है

電子メール署名とは何か、そして 2024 年にそれが必要となる理由

2024년에 이메일 서명이 무엇이며 왜 필요한가요?

O que é uma assinatura de e-mail e por que você precisa dela em 2024

Что такое подпись электронной почты и зачем она вам нужна в 2024 году

E-posta İmzası Nedir ve 2024'te Buna Neden İhtiyacınız Var?

Chữ ký email là gì và tại sao bạn cần nó vào năm 2024

什么是电子邮件签名以及 2024 年您为何需要它

Threshold Signature Explained — Bringing Exciting Applications with TSS A Deep Dive Into Threshold Signature Without Mathematics Threshold signature is a distributed multi-party signature protocol that includes distributed key generation, signature, and verification algorithms. In recent years, with the rapid development of technology, signature algorithms have gained widespread attention in both academic research and real-world applications. Its properties, including those related to security, practicability, scalability, and decentralization of signature, have been pored over. blockchain Because blockchain and signature are closely connected, the development of signature algorithms and the introduction of new signature paradigms will directly affect the characteristics and efficiency of blockchain networks. In addition, institutional and personal account key management requirements stimulated by distributed ledgers have also spawned many wallet applications, and this change has also affected traditional enterprises. Whether in the blockchain or traditional financial institutions, the threshold signature scheme can bring security and privacy improvement in various scenarios. As an emerging technology, threshold signatures are still subject to academic research and discussion concerning possible security risks and practical problems. This article will start with the technical rationale and discuss cryptography and blockchain. Then, we will compare multi-party computation and threshold signature before discussing the pros and cons of different signature paradigms. In the end, there will be a list of use cases of threshold signature. Finally, there will be a list of use cases of threshold signature. I. Cryptography in Daily Life Before introducing threshold signatures, let’s get a general understanding of cryptography. How does cryptography protect digital information? How do we create an identity in the digital world? From the very beginning, people wanted secure storage and transmission. After creating a key, one can use symmetric encryption to store secrets. If two people have the same key, they can achieve secure transmission between themselves. To use a military example, the king encrypts a command, and the general decrypts it with the corresponding key. But when two people do not have a safe channel to use, how can they create a shared key? Solving this issue is why the key exchange protocol came into being. Analogously, if the king issues an order to all the people in the digital world, how can everyone prove that the sentence originated from the king? As such, the was invented. Both protocols are based on public key cryptography or . digital signature protocol asymmetric cryptographic algorithms Symmetric and asymmetric encryption constitute the main components of modern cryptography. They both have three fixed parts: key generation, encryption, and decryption. Here, we focus on digital signature protocols. The key generation process generates a pair of associated keys: the public key and the private key. The public key is open to everyone, and the private key represents the identity and is only revealed to the owner. Whoever owns the private key has the identity represented by the key. The encryption algorithm, or signature algorithm, takes the private key as input and generates a signature on a piece of information. The decryption algorithm, or signature verification algorithm, uses public keys to verify the validity of the signature and the correctness of the information. II. Signature in the Blockchain uses a consensus algorithm to construct distributed books, and signature provides identity information for blockchain. The signature of the transaction initiator identifies all the transaction information on the blockchain. The blockchain can verify the signature according to specific rules to check the transaction validity, all thanks to the immutability and verifiability of the signature. Blockchain technology From a cryptographic perspective, the blockchain is more than using a signature protocol, or the consensus algorithm based on Proof-of-Work uses a hash function. Blockchain builds an infrastructure layer of consensus and transaction. On top of that, novel cryptographic protocols such as secure multi-party computation, zero-knowledge proof, and homomorphic encryption thrive. For example, secure multi-party computation, which is naturally adapted to distributed networks, can build secure data transfer and machine learning platforms on the blockchain. The special nature of zero-knowledge proof provides feasibility for verifiable anonymous transactions. The combination of these cutting-edge cryptographic protocols and blockchain technology will drive the development of the digital world in the next decade, leading to secure data sharing, privacy protection, and more applications now unimaginable. III. Secure Multi-party Computation and Threshold Signature After introducing how the digital signature protocol affects our lives and how it helps the blockchain build identities and record transactions, we will mention secure multi-party computation (MPC), from where we can see how threshold signatures achieve decentralization. For more about MPC, please refer to our previous posts, which detailed the technical background and application scenarios. MPC, by definition, is a secure computation that several participants jointly execute. Security here means that, in one computation, all participants provide their own private input and can obtain results from the calculation. It is not possible to get any private information entered by other parties. In 1982, when Prof. Yao proposed the concept of MPC, he gave an example called the “Millionaires Problem” — two millionaires who want to know who is richer than the other without telling the true amount of assets. Specifically, the secure multiparty computation would care about the following properties: : Any participant cannot obtain any private input from other participants except for information that can be inferred from the computation results. Privacy : The computation should ensure correct execution, and the legitimacy and correctness of this process should be verifiable by participants or third parties. Correctness and verifiability : All parties involved in the calculation, if not agreed in advance, should be able to obtain the computation results at the same time or cannot obtain the results. Fairness or robustness Supposing we use secure multi-party computation to make a digital signature in a general sense, we will proceed as follows: : all future participants will be involved together to do two things: 1) each involved party generates a secret private key; 2) The public key is calculated according to the sequence of private keys. Key generation phase : Participants joining in a certain signature use their own private keys as private inputs and the information to be signed as public input to perform a joint signature operation to obtain a signature. In this process, the privacy of secure multi-party computing ensures the security of private keys. The correctness and robustness guarantee the unforgeability of the signature, and everyone can all get signatures. Signature phase : Use the public key corresponding to the transaction to verify the signature as a traditional algorithm. There is no “secret input” during the verification, this means that the verification can be performed without multi-party computation, which will become an advantage of multi-party computation type distributed signature. Verification phase It should be noted that we have omitted some details because secure multiparty computing is actually a collective name for a type of cryptographic protocol. For different security assumptions and threshold settings, there are different construction methods. Therefore, the threshold signatures of different settings will also have distinctive properties. This article will not explain each setting, but the comparative result with other signature schemes will be introduced in the next section. The signature protocol constructed on the idea of ​​secure multiparty computing is the threshold signature. IV. Single Signature, Multi-Signature, and Threshold Signature Besides the threshold signature, what other methods can we choose? , at the beginning, used a single signature, which allocated each account with one private key. The message signed by this key is considered legitimate. Later, in order to avoid a single point of failure or introduce account management by multiple people, Bitcoin provides a multi-signature function. Multi-signature can be simply understood as each account owner signing successively and posting all signatures to the chain. Then, signatures are verified in order on the chain. When certain conditions are met, the transaction is legitimate. This method achieves multiple private key control purposes. Bitcoin So, what’s the difference between multi-signature and threshold signature? Several constraints of multi-signature are: If an account’s access structure is given, that is, which private keys can complete a legal signature, this structure cannot be adjusted at a later stage. For example, a participant withdraws, or a new involved party needs to change the access structure. If you must change, you need to complete the initial setup process again, which will change the public key and account address as well. The access structure is not flexible. The first is that the verification on the chain consumes the power of all nodes and, therefore, requires a processing fee. The verification of multiple signatures is equivalent to multiple single signatures. The verification takes more time, which also impacts performance. Low efficiency. Because multi-sig is not naturally supported due to the possible vulnerabilities in smart contracts, this support is considered risky. Requirements of smart contract support and algorithm adaptation that varies from chain to chain. This cannot able to be trivially called a disadvantage or advantage because anonymity is required for specific conditions. Anonymity here means that multi-signature directly exposes all participating signers of the transaction. No anonymity. Correspondingly, the threshold signature has the following features: Through an additional multi-party computation, the existing private key sequence can be expanded to assign private keys to new participants. This process will not expose the old and newly generated private key, nor will it change the public key and account address. The access structure is flexible. For the chain, the signature generated by the threshold signature is not different from a single signature, which means the following improvements: a) The verification is the same as the single signature and needs no additional fee; b ) the information of the signer is invisible because, for other nodes, the information is decrypted with the same public key; c) No smart contract on the chain is needed to provide additional support. In addition to the above discussion, there is a distributed signature scheme supported by Shamir secret sharing. The secret sharing algorithm has a long history which is used to slice information storage and perform error correction information. From the underlying algorithm of secure computation to the error correction of the disc. This technology has always played an important role, but the main problem is that when used in a signature protocol, Shamir's secret sharing needs to recover the master private key. It provides more efficiency. As for multiple signatures or threshold signatures, the master private key has never been reconstructed, even if it is in memory or cache. This short-term reconstruction is not tolerable for vital accounts. V. Limitations Just like other secure multi-party computation protocols, the introduction of other participants makes the security model different from traditional point-to-point encrypted transmission. The problem of conspiracy and malicious participants was not taken into account in algorithms before. The behavior of physical entities cannot be restricted, and perpetrators are introduced into participating groups. Therefore, multi-party cryptographic protocols cannot obtain the security strength as before. Effort is needed to develop threshold signature applications, integrate existing infrastructure, and test the true strength of the threshold signature scheme. VI. Usage Scenarios 1. Key Management The use of threshold signatures in key management systems, such as ARPA’s enterprise key management API, can achieve a more flexible administration. One can use the access structure to design authorization patterns for users with different priorities. In addition, for the entry of new entities, the threshold signature can quickly refresh the key. This operation can also be performed periodically to level up the difficulty of hacking multiple private keys at the same time. Finally, for the verifier, the threshold signature is not different from the traditional signature, so it is compatible with old equipment and reduces the update cost. ARPA enterprise key management modules already support Elliptic Curve Digital Signature Scheme secp256k1 and ed25519 parameters. In the future, it will be compatible with more parameters. 2. Crypto Wallet Wallets based on threshold signatures are more secure because the private key doesn’t need to be rebuilt. Also, without all signatures posted publicly, anonymity can be achieved. Compared to the multi-signature, threshold signature needs less transaction fees. Similar to key management applications, the administration of digital asset accounts can also be more flexible. Furthermore, threshold signature wallet can support various blockchains that do not natively support multi-signature, which reduces the risk of . smart contract bugs Conclusion This article describes why people need the threshold signature and what inspiring properties it may bring. One can see that threshold signature has higher security, more flexible control, and a more efficient verification process. In fact, different signature technologies have different application scenarios, such as aggregate signatures not mentioned in the article and BLS-based multi-signature. At the same time, readers are also welcome to read more about secure multi-party computation. Secure computation is the holy grail of cryptographic protocols. It can accomplish much more than the application of threshold signatures. In the near future, secure computation will solve more specific application questions in the digital world. Also published . here

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