Custom workflows to improve joint variant calling from multiple related tumour samples: FreeBayesSomatic and Strelka2Pass

Summary This work describes two novel workflows for variant calling that extend the widely used algorithms of Strelka2 and FreeBayes to call somatic mutations from multiple related tumour samples and one matched normal sample. We show that these workflows offer higher precision and recall than their single tumour-normal pair equivalents in both simulated and clinical sequencing data. Availability and Implementation Source code freely available at the following link: https://atlassian.petermac.org.au/bitbucket/projects/DAW/repos/multisamplevariantcalling and executable through Janis (https://github.com/PMCC-BioinformaticsCore/janis) under the GPLv3 licence. Supplementary information Supplementary data are available at Bioinformatics online.

Determining mutational burden and signature using RNA-seq from tumor-only samples

Background Traditionally, mutational burden and mutational signatures have been assessed by tumor-normal pair DNA sequencing. The requirement of having both normal and tumor samples is not always feasible from a clinical perspective, and led us to investigate the efficacy of using RNA sequencing of only the tumor sample to determine the mutational burden and signatures, and subsequently molecular cause of the cancer. The potential advantages include reducing the cost of testing, and simultaneously providing information on the gene expression profile and gene fusions present in the tumor. Results In this study, we devised supervised and unsupervised learning methods to determine mutational signatures from tumor RNA-seq data. As applications, we applied the methods to a training set of 587 TCGA uterine cancer RNA-seq samples, and examined in an independent testing set of 521 TCGA colorectal cancer RNA-seq samples. Both diseases are known associated with microsatellite instable high (MSI-H) and driver defects in DNA polymerase ɛ (POLɛ). From RNA-seq called variants, we found majority (> 95%) are likely germline variants, leading to C > T enriched germline variants (dbSNP) widely applicable in tumor and normal RNA-seq samples. We found significant associations between RNA-derived mutational burdens and MSI/POLɛ status, and insignificant relationship between RNA-seq total coverage and derived mutational burdens. Additionally we found that over 80% of variants could be explained by using the COSMIC mutational signature-5, -6 and -10, which are implicated in natural aging, MSI-H, and POLɛ, respectively. For classifying tumor type, within UCEC we achieved a recall of 0.56 and 0.78, and specificity of 0.66 and 0.99 for MSI and POLɛ respectively. By applying learnt RNA signatures from UCEC to COAD, we were able to improve our classification of both MSI and POLɛ. Conclusions Taken together, our work provides a novel method to detect RNA-seq derived mutational signatures with effective procedures to remove likely germline variants. It can leads to accurate classification of underlying driving mechanisms of DNA damage deficiency.

Determining mutational burden and signature using RNA-seq from tumor-only samples

Background: Traditionally, mutational burden and mutational signatures have been assessed by tumor-normal pair DNA sequencing. The requirement of having both normal and tumor samples is not always feasible from a clinical perspective, and led us to investigate the efficacy of using RNA sequencing of only the tumor sample to determine the mutational burden and signatures, and subsequently molecular cause of the cancer. The potential advantages include reducing the cost of testing, and simultaneously providing information on the gene expression profile and gene fusions present in the tumor. Results: In this study, we devised supervised and unsupervised learning methods to determine mutational signatures from tumor RNA-seq data. As applications, we applied the methods to a training set of 587 TCGA uterine cancer RNA-seq samples, and examined in an independent testing set of 521 TCGA colorectal cancer RNA-seq samples. Both diseases are known associated with microsatellite instable high (MSI-H) and driver defects in DNA polymerase ɛ (POLɛ). From RNA-seq called variants, we found majority (>95%) are likely germline variants, leading to C>T enriched germline variants (dbSNP) widely applicable in tumor and normal RNA-seq samples. We found significant associations between RNA-derived mutational burdens and MSI/POLɛ status, and insignificant relationship between RNA-seq total coverage and derived mutational burdens. Additionally we found that over 80% of variants could be explained by using the COSMIC mutational signature-5, -6 and -10, which are implicated in natural aging, MSI-H, and POLɛ, respectively. For classifying tumor type, within UCEC we achieved a recall of 0.56 and 0.78, and specificity of 0.66 and 0.99 for MSI and POLɛ respectively. By applying learnt RNA signatures from UCEC to COAD, we were able to improve our classification of both MSI and POLɛ. Conclusions: Taken together, our work provides a novel method to detect RNA-seq derived mutational signatures with effective procedures to remove likely germline variants. It can leads to accurate classification of underlying driving mechanisms of DNA damage deficiency.

Determining mutational burden and signature using RNA-seq from tumor-only samples

Background: Traditionally, mutational burden and mutational signatures have been assessed by tumor-normal pair DNA sequencing. The requirement of having both normal and tumor samples is not always feasible from a clinical perspective, and led us to investigate the efficacy of using RNA sequencing of only the tumor sample to determine the mutational burden and signatures, and subsequently molecular cause of the cancer. The potential advantages include reducing the cost of testing, and simultaneously providing information on the gene expression profile and gene fusions present in the tumor. Results: In this study, we devised supervised and unsupervised learning methods to determine mutational signatures from tumor RNA-seq data. As applications, we applied the methods to a training set of 587 TCGA uterine cancer RNA-seq samples, and examined in an independent testing set of 521 TCGA colorectal cancer RNA-seq samples. Both diseases are known associated with microsatellite instable high (MSI-H) and driver defects in DNA polymerase ɛ (POLɛ).From RNA-seq called variants, we found majority (>95%) are likely germline variants, leading to C>T enriched germline variants (dbSNP) widely applicable in tumor and normal RNA-seq samples. We found significant associations between RNA-derived mutational burdens and MSI/POLɛ status, and insignificant relationship between RNA-seq total coverage and derived mutational burdens. Additionally we found that over 80% of variants could be explained by using the COSMIC mutational signature-5, -6 and -10, which are implicated in natural aging, MSI-H, and POLɛ, respectively. For classifying tumor type, within UCEC we achieved a recall of 0.56 and 0.78, and specificity of 0.66 and 0.99 for MSI and POLɛ respectively. By applying learnt RNA signatures from UCEC to COAD, we were able to improve our classification of both MSI and POLɛ. Conclusions: Taken together, our work provides a novel method to detect RNA-seq derived mutational signatures with effective procedures to remove likely germline variants. It can leads to accurate classification of underlying driving mechanisms of DNA damage deficiency.

Determining mutational burden and signature using RNA-seq from tumor-only samples

Background: Traditionally, mutational burden and mutational signatures have been assessed by tumor-normal pair DNA sequencing. The requirement of having both normal and tumor samples is not always feasible from a clinical perspective, and led us to investigate the efficacy of using RNA sequencing of only the tumor sample to determine the mutational burden and signatures, and subsequently molecular cause of the cancer. The potential advantages include reducing the cost of testing, and simultaneously providing information on the gene expression profile and gene fusions present in the tumor.Results: In this study, we devised supervised and unsupervised learning methods to determine mutational signatures from tumor RNA-seq data. As applications, we applied the methods to a training set of 587 TCGA uterine cancer RNAseq samples, and examined in an independent testing set of xxx TCGA colorectal cancer RNAseq samples. Both diseases are known associated with microsatellite instable high (MSI-H) and driver defects in DNA polymerase ɛ (POLɛ). From RNAseq called variants, we found majority (>95%) are likely germline variants, leading to C>T enriched germline variants (dbSNP) widely applicable in tumor and normal RNAseq samples. We found significant associations between RNA-derived mutational burdens and MSI/POLɛ status, and insignificant relationship between RNAseq total coverage and derived mutational burdens. Additionally we found that over 80% of variants could be explained by using the COSMIC mutational signature-5, -6 and -10, which are implicated in natural aging, MSI-H, and POLɛ, respectively. For classifying tumor type, within UCEC we achieved a recall of 0.56 and 0.78, and specificity of 0.66 and 0.99 for MSI and POLɛ respectively. By applying learnt RNA signatures from UCEC to COAD, we were able to improve our classification of both MSI and POLɛ. Conclusions: Taken together, our work provides a novel method to detect RNAseq derived mutational signatures with effective procedures to remove likely germline variants. It can leads to accurate classification of underlying driving mechanisms of DNA damage deficiency.

Germline and somatic SNVS calling in NGS panel tumor samples: Approaches to optimize tumor only genomic analysis for cancer precision medicine.

e13011 Background: Clinical NGS is often limited by tumor only profiling. Discrimination between somatic and likely germline mutations when calling from tumor patient samples is a critical step for clinical genotyping. Many algorithms have been developed for somatic single nucleotide variant (SNV) detection in matched tumor-normal whole genome and whole exome sequencing. Here, we demonstrate approaches of how a cost-effective large gene panel sequencing can be used to call somatic and germline SNVs for tumor only samples. Methods: Tumor, adjacent normal, and matched normal samples are collected from five patients. The somatic mutations were called with GATK Mutect2 in tumor only and adjacent normal. The germline mutations were called individually for all 15 samples with GATK Haplotype caller. To remove germline mutations from tumor only somatic calls, the filters ExAc pop freq, 1000G pop freq, COSMIC were applied on the tumor only somatic calls. PPV (Positive predictive value) for each filter was calculated by dividing the number of somatic mutations in the post-filtering mutation data by the total number of unfiltered mutations. TPR (True Positive Rate, representing sensitivity) was calculated by dividing the number of true somatic mutations in the tumor-only post-filter. Results: Compared with germline mutations called from matched normal, 70% germline mutations were called in RAWE somatic calls. A PPV of 0.71 and a TPR of 0.95 were optimally provided when the filter ExAc pop freq > 0.01 and COSMIC ( > 5 occurrence) applied. For germline mutations called in tumor samples, when compared with those in blood samples and in adjacent normal samples, PPV is 0.99 and TPR is 0.97. For somatic mutations called with tumor-adjacent normal pair mode in Mutect2, PPV is 0.5 and TPR is 0.99. Conclusions: Optimization of tools and parameters in NGS large panels could detect somatic and germline variants with high specificity, sensitivity and accuracy, without matched or adjacent normal. For the germline variants, when adjacent normal is available, it could replace matched normal with high accuracy.

A visit to maximal non-ACCP subrings

In this paper, we characterize maximal non-ACCP subrings R of a domain S in case (R, S) is a residually algebraic pair and R is semilocal. In this paper, we also consider a K-algebra S, a nonzero proper ideal I of S and a subring D of the field K and we determine necessary and sufficient conditions in order that D + I is a maximal non-ACCP subring of S. This gives an example of a maximal non-ACCP subring R of a domain S such that (R, S) is a normal pair and R is not semilocal.

Abnormal reproductive morphology in two species of spiny lobster: Jasus edwardsii and Jasus paulensis

Jasus edwardsiiandJasus paulensisare two species of spiny lobster that are distributed across south-eastern Australia and isolated islands of the southern Indian Ocean, respectively. We present rare examples of abnormal reproductive morphology in both species. ForJasus edwardsii, this included a female lobster captured in South Australia, which, in addition to the normal pair of gonopores at the base of the third pair of pereiopods, also exhibited a second a pair of gonopores on the coxopodites of the fourth pair of pereiopods. In Tasmania, a male individual exhibited the specialized pincer on the right propopodite, normally only observed in females. The frequency of abnormal morphological reproductive characteristics appeared higher inJasus paulensis. This included 14 individuals that exhibited a range of additional and abnormal gonopore locations in both males and females. We discuss the findings in relation to rare cases of gynandromorphism in crustaceans.

Finite maximal chains of commutative rings

Let R ⊆ S be a unital extension of commutative rings, with [Formula: see text] the integral closure of R in S, such that there exists a finite maximal chain of rings from R to S. Then S is a P-extension of R, [Formula: see text] is a normal pair, each intermediate ring of R ⊆ S has only finitely many prime ideals that lie over any given prime ideal of R, and there are only finitely many [Formula: see text]-subalgebras of S. Each chain of rings from R to S is finite if dim (R) = 0; or if R is a Noetherian (integral) domain and S is contained in the quotient field of R; or if R is a one-dimensional domain and S is contained in the quotient field of R; but not necessarily if dim (R) = 2 and S is contained in the quotient field of R. Additional domain-theoretic applications are given.

A GENERALIZATION OF PRÜFER'S ASCENT RESULT TO NORMAL PAIRS OF COMPLEMENTED RINGS

Let R ⊆ T be a (unital) extension of (commutative) rings, such that the total quotient ring of R is a von Neumann regular ring and T is torsion-free as an R-module. Let T ⊆ B be a ring extension such that B is a reduced ring that is torsion-free as a T-module. Let R* (respectively, A) be the integral closure of R in T (respectively, in B). Then (R*, T) is a normal pair (i.e. S is integrally closed in T for each ring S such that R* ⊆ S ⊆ T) if and only if (A, AT) is a normal pair. This generalizes results of Prüfer and Heinzer on Prüfer domains to normal pairs of complemented rings.