Computational tools to detect signatures of mutational processes in DNA from tumours: a review and empirical comparison of performance

AbstractMutational signatures refer to patterns in the occurrence of somatic mutations that reflect underlying mutational processes. To date, after the analysis of tens of thousands of genomes and exomes from about 40 different cancers types, 30 mutational signatures characterized by a unique probability profile across the 96 mutation types have been identified, validated and listed on the COSMIC (Catalogue of Somatic Mutations in Cancer) website. Simultaneously with this development, the last few years saw the publication of several concurrent methods (mathematical algorithms implemented in publicly available software packages) for either the quantification of the contribution of prespecified signatures (e.g. COSMIC signatures) in a given cancer genome or the identification of new signatures from a sample of cancer genomes. A review about existing computational tools has been recently published to guide researchers and practitioners in conducting their mutational signatures analysis, however, other tools have been introduced since its publication and, to date, there has not been a systematic evaluation and comparison of the performance of such tools. In order to fill this gap, we carry on an empirical evaluation study of all available packages to date, using both real and simulated data.

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CaMuS: simultaneous fitting and de novo imputation of cancer mutational signature

Scientific Reports ◽

10.1038/s41598-020-75753-8 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Maria Cartolano ◽

Nima Abedpour ◽

Viktor Achter ◽

Tsun-Po Yang ◽

Sandra Ackermann ◽

...

Keyword(s):

De Novo ◽

Probability Distributions ◽

Simulated Data ◽

Whole Genome Sequencing Data ◽

Sequencing Data ◽

Mutational Signatures ◽

Computational Performance ◽

Reliable Parameter ◽

Similar Accuracy ◽

Mutational Processes

Abstract The identification of the mutational processes operating in tumour cells has implications for cancer diagnosis and therapy. These processes leave mutational patterns on the cancer genomes, which are referred to as mutational signatures. Recently, 81 mutational signatures have been inferred using computational algorithms on sequencing data of 23,879 samples. However, these published signatures may not always offer a comprehensive view on the biological processes underlying tumour types that are not included or underrepresented in the reference studies. To circumvent this problem, we designed CaMuS (Cancer Mutational Signatures) to construct de novo signatures while simultaneously fitting publicly available mutational signatures. Furthermore, we propose to estimate signature similarity by comparing probability distributions using the Hellinger distance. We applied CaMuS to infer signatures of mutational processes in poorly studied cancer types. We used whole genome sequencing data of 56 neuroblastoma, thus providing evidence for the versatility of CaMuS. Using simulated data, we compared the performance of CaMuS to sigfit, a recently developed algorithm with comparable inference functionalities. CaMuS and sigfit reconstructed the simulated datasets with similar accuracy; however two main features may argue for CaMuS over sigfit: (i) superior computational performance and (ii) a reliable parameter selection method to avoid spurious signatures.

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Identification of mutational signatures active in individual tumors

10.7287/peerj.preprints.3257 ◽

2017 ◽

Author(s):

Sandra Krüger ◽

Rosario M Piro

Keyword(s):

Somatic Mutations ◽

R Package ◽

Mutational Signatures ◽

The Individual ◽

Tumor Types ◽

User Friendly ◽

Mutational Processes ◽

Tumor Class

The mutational processes responsible for the somatic mutations observed in tumor samples can significantly vary not only between tumor types but also among the individual cancers within a tumor class. Mutational processes can be represented by so called “mutational signatures” which reflect the occurrences of base changes within their sequence contexts (i.e., in dependence on their flanking bases). We present a user-friendly R package, called decompTumor2Sig, that can be used to evaluate the contribution of Shiraishi signatures to the somatic mutations found in an individual tumor.

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The Repertoire of Mutational Signatures in Human Cancer

10.1101/322859 ◽

2018 ◽

Cited By ~ 74

Author(s):

Ludmil B Alexandrov ◽

Jaegil Kim ◽

Nicholas J Haradhvala ◽

Mi Ni Huang ◽

Alvin WT Ng ◽

...

Keyword(s):

Somatic Mutations ◽

Human Cancer ◽

Base Substitution ◽

Mutational Signatures ◽

Insertion And Deletion ◽

Damage Repair ◽

Cancer Genomes ◽

Dataset Size ◽

Dna Maintenance ◽

Mutational Processes

ABSTRACTSomatic mutations in cancer genomes are caused by multiple mutational processes each of which generates a characteristic mutational signature. Using 84,729,690 somatic mutations from 4,645 whole cancer genome and 19,184 exome sequences encompassing most cancer types we characterised 49 single base substitution, 11 doublet base substitution, four clustered base substitution, and 17 small insertion and deletion mutational signatures. The substantial dataset size compared to previous analyses enabled discovery of new signatures, separation of overlapping signatures and decomposition of signatures into components that may represent associated, but distinct, DNA damage, repair and/or replication mechanisms. Estimation of the contribution of each signature to the mutational catalogues of individual cancer genomes revealed associations with exogenous and endogenous exposures and defective DNA maintenance processes. However, many signatures are of unknown cause. This analysis provides a systematic perspective on the repertoire of mutational processes contributing to the development of human cancer including a comprehensive reference set of mutational signatures in human cancer.

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Identification of mutational signatures active in individual tumors

10.7287/peerj.preprints.3257v1 ◽

2017 ◽

Cited By ~ 1

Author(s):

Sandra Krüger ◽

Rosario M Piro

Keyword(s):

Somatic Mutations ◽

R Package ◽

Mutational Signatures ◽

The Individual ◽

Tumor Types ◽

User Friendly ◽

Mutational Processes ◽

Tumor Class

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Somatic Mutations Render Human Exome and Pathogen DNA more Similar

10.1101/322016 ◽

2018 ◽

Author(s):

Ehsan Ebrahimzadeh ◽

Maggie Engler ◽

David Tse ◽

Razvan Cristescu ◽

Aslan Tchamkerten

Keyword(s):

Somatic Mutations ◽

Immune Recognition ◽

Mutational Signatures ◽

Natural Processes ◽

Equal Rate ◽

Functional Tolerance ◽

Mutagenic Process ◽

Functional Relevance ◽

Pathogen Dna ◽

Mutational Processes

AbstractImmunotherapy has recently shown important clinical successes in a substantial number of oncology indications. Additionally, the tumor somatic mutation load has been shown to associate with response to these therapeutic agents, and specific mutational signatures are hypothesized to improve this association, including signatures related to pathogen insults. We sought to study in silico the validity of these observations and addressed three questions. First, we investigated whether somatic mutations typically involved in cancer may increase, in a statistically meaningful manner, the similarity between common pathogens and the human exome. Our study shows that specific common mutagenic processes like those resulting from exposure to ultraviolet light (in melanoma) or smoking (in lung cancer) induce, in the upper range of biologically plausible frequencies, peptides in the cancer exome that are statistically more similar to pathogen peptides than the normal exome. Second, we investigated whether this increased similarity is due to the specificities of the mutagenic process or uniformly random mutations at equal rate would trigger the same effect. For certain pathogens the increased similarity is more pronounced for specific mutagenic processes than for uniformly random mutations and for other pathogens the effects cannot be distinguished. Finally, we investigated whether specific mutational processes result in amino-acid changes with functional relevance that are more likely to be immunogenic. We showed that functional tolerance to mutagenic processes across species generally suggests more resilience to natural processes than to denovo mutagenesis. These results support the idea that recognition of pathogen sequences as well as differential functional tolerance to mutagenic processes may play an important role in the immune recognition process involved in tumor infiltration by lymphocytes.

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Fabrication of Gum Arabic-Graphene (GGA) Modified Polyphenylsulfone (PPSU) Mixed Matrix Membranes: A Systematic Evaluation Study for Ultrafiltration (UF) Applications

Membranes ◽

10.3390/membranes11070542 ◽

2021 ◽

Vol 11 (7) ◽

pp. 542

Author(s):

Alaa Mashjel Ali ◽

Khalid T. Rashid ◽

Ali Amer Yahya ◽

Hasan Sh. Majdi ◽

Issam K. Salih ◽

...

Keyword(s):

Fourier Transforms ◽

Thermal Characteristics ◽

Gum Arabic ◽

Evaluation Study ◽

Membrane Properties ◽

Systematic Evaluation ◽

Mixed Matrix Membranes ◽

Solute Flux ◽

Xrd Patterns ◽

Modified Graphene

In the current work, a Gum, Arabic-modified Graphene (GGA), has been synthesized via a facile green method and employed for the first time as an additive for enhancement of the PPSU ultrafiltration membrane properties. A series of PPSU membranes containing very low (0–0.25) wt.% GGA were prepared, and their chemical structure and morphology were comprehensively investigated through atomic force microscopy (AFM), Fourier transforms infrared spectroscopy (FTIR), X-ray diffraction (XRD), and field emission scanning electron microscopy (FESEM). Besides, thermogravimetric analysis (TGA) was harnessed to measure thermal characteristics, while surface hydrophilicity was determined by the contact angle. The PPSU-GGA membrane performance was assessed through volumetric flux, solute flux, and retention of sodium alginate solution as an organic polysaccharide model. Results demonstrated that GGA structure had been successfully synthesized as confirmed XRD patterns. Besides, all membranes prepared using low GGA content could impart enhanced hydrophilic nature and permeation characteristics compared to pristine PPSU membranes. Moreover, greater thermal stability, surface roughness, and a noticeable decline in the mean pore size of the membrane were obtained.

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De Novo Mutational Signature Discovery in Tumor Genomes using SparseSignatures

10.1101/384834 ◽

2018 ◽

Cited By ~ 5

Author(s):

Avantika Lal ◽

Keli Liu ◽

Robert Tibshirani ◽

Arend Sidow ◽

Daniele Ramazzotti

Keyword(s):

Cross Validation ◽

De Novo ◽

State Of The Art ◽

Point Mutations ◽

Simulated Data ◽

Large Datasets ◽

Genome Sequences ◽

Mutational Signatures ◽

Mutational Signature ◽

Current State

AbstractCancer is the result of mutagenic processes that can be inferred from tumor genomes by analyzing rate spectra of point mutations, or “mutational signatures”. Here we present SparseSignatures, a novel framework to extract signatures from somatic point mutation data. Our approach incorporates DNA replication error as a background, employs regularization to reduce noise in non-background signatures, uses cross-validation to identify the number of signatures, and is scalable to large datasets. We show that SparseSignatures outperforms current state-of-the-art methods on simulated data using standard metrics. We then apply SparseSignatures to whole genome sequences of 147 tumors from pancreatic cancer, discovering 8 signatures in addition to the background.

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Implementing Visual Analytics Pipelines with Simulation Data

10.5772/intechopen.96152 ◽

2021 ◽

Author(s):

Taimur Khan ◽

Syed Samad Shakeel ◽

Afzal Gul ◽

Hamza Masud ◽

Achim Ebert

Keyword(s):

Visual Analytics ◽

Visual Analysis ◽

Simulated Data ◽

Evaluation Study ◽

Ease Of Use ◽

Preliminary Evaluation ◽

Simulation Data ◽

Visual Data Analytics ◽

High Level ◽

Simulation Parameters

Visual analytics has been widely studied in the past decade both in academia and industry to improve data exploration, minimize the overall cost, and improve data analysis. In this chapter, we explore the idea of visual analytics in the context of simulation data. This would then provide us with the capability to not only explore our data visually but also to apply machine learning models in order to answer high-level questions with respect to scheduling, choosing optimal simulation parameters, finding correlations, etc. More specifically, we examine state-of-the-art tools to be able to perform these above-mentioned tasks. Further, to test and validate our methodology we followed the human-centered design process to build a prototype tool called ViDAS (Visual Data Analytics of Simulated Data). Our preliminary evaluation study illustrates the intuitiveness and ease-of-use of our approach with regards to visual analysis of simulated data.

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Detection and benchmarking of somatic mutations in cancer genomes using RNA-seq data

10.1101/249219 ◽

2018 ◽

Cited By ~ 1

Author(s):

Alexandre Coudray ◽

Anna M. Battenhouse ◽

Philipp Bucher ◽

Vishwanath R. Iyer

Keyword(s):

Exome Sequencing ◽

Somatic Mutations ◽

Low Cost ◽

Systematic Evaluation ◽

Rna Seq ◽

Whole Exome ◽

Cancer Genomes ◽

Additional Support ◽

Coding Variants ◽

Measure Gene Expression

ABSTRACTTo detect functional somatic mutations in tumor samples, whole-exome sequencing (WES) is often used for its reliability and relative low cost. RNA-seq, while generally used to measure gene expression, can potentially also be used for identification of somatic mutations. However there has been little systematic evaluation of the utility of RNA-seq for identifying somatic mutations. Here, we develop and evaluate a pipeline for processing RNA-seq data from glioblastoma multiforme (GBM) tumors in order to identify somatic mutations. The pipeline entails the use of the STAR aligner 2-pass procedure jointly with MuTect2 from GATK to detect somatic variants. Variants identified from RNA-seq data were evaluated by comparison against the COSMIC and dbSNP databases, and also compared to somatic variants identified by exome sequencing. We also estimated the putative functional impact of coding variants in the most frequently mutated genes in GBM. Interestingly, variants identified by RNA-seq alone showed better representation of GBM-related mutations cataloged by COSMIC. RNA-seq-only data substantially outperformed the ability of WES to reveal potentially new somatic mutations in known GBM-related pathways, and allowed us to build a high-quality set of somatic mutations common to exome and RNA-seq calls. Using RNA-seq data in parallel with WES data to detect somatic mutations in cancer genomes can thus broaden the scope of discoveries and lend additional support to somatic variants identified by exome sequencing alone.

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Design and Evaluation of Accessible Collaborative Writing Techniques for People with Vision Impairments

ACM Transactions on Computer-Human Interaction ◽

10.1145/3480169 ◽

2022 ◽

Vol 29 (2) ◽

pp. 1-42

Author(s):

Maitraye Das ◽

Anne Marie Piper ◽

Darren Gergle

Keyword(s):

Present Article ◽

Collaborative Writing ◽

Evaluation Study ◽

Systematic Evaluation ◽

Collaborative Systems ◽

Text To Speech ◽

Screen Reader ◽

Academic Organizations ◽

Collaboration Awareness ◽

Writing Tools

Collaborative writing tools have been used widely in professional and academic organizations for many years. Yet, there has not been much work to improve screen reader access in mainstream collaborative writing tools. This severely affects the way people with vision impairments collaborate in ability-diverse teams. As a step toward addressing this issue, the present article aims at improving screen reader representation of collaborative features such as comments and track changes (i.e., suggested edits). Building on our formative interviews with 20 academics and professionals with vision impairments, we developed auditory representations that indicate comments and edits using non-speech audio (e.g., earcons, tone overlay), multiple text-to-speech voices, and contextual presentation techniques. We then performed a systematic evaluation study with 48 screen reader users that indicated that non-speech audio, changing voices, and contextual presentation can potentially improve writers’ collaboration awareness. We discuss implications of these results for the design of accessible collaborative systems.

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