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SARS-CoV-2 has had an unprecedented impact on human health and highlights the need for genomic epidemiology studies to increase our understanding of the evolution and spread of pathogens and to inform policy decisions. Most efforts have focused on international or country-wide transmission, which are unable to highlight state-wide trends. We sequenced virus genomes from over 22,000 patients tested at Mayo Clinic Laboratories between 2020-2022 and leveraged detailed patient metadata to describe county-to-county spread in Minnesota. Our findings indicate that spread in the state was mostly dominated by viruses from Hennepin County, which contains the largest metropolis. For many counties, we found that state government restrictions eventually led to a decrease in the diversity of circulating viruses from other counties and that their complete removal in May of 2021 saw a drastic revert to levels at or greater than those observed during the months before. We also linked over 14,000 genomes with patient risk characteristics and infection-related phenotypes from the Mayo Clinic electronic health record. We found that the genetic relationship of Omicron viruses was structured by clinical outcomes when stratifying by patient risk factor and variant of concern. However, we were unable to identify nucleotide variants that drove this association.

SARS-CoV-2 has had an unprecedented impact on human health and highlights the need for genomic epidemiology studies to increase our understanding of virus evolution and spread, and to inform policy decisions. We sequenced viral genomes from over 22,000 patient samples tested at Mayo Clinic Laboratories between 2020-2022 and use Bayesian phylodynamics to describe county and regional spread in Minnesota.The earliest introduction into Minnesota was to Hennepin County from a domestic source around January 22, 2020; six weeks before the first confirmed case in the state. This led to the virus spreading to Northern Minnesota, and eventually, the rest of the state. International introductions were most abundant in Hennepin (home to the Minneapolis/St. Paul International (MSP) airport) totaling 45 (out of 107) over the two-year period. Southern Minnesota counties were most common for domestic introductions with 19 (out of 64), potentially driven by bordering states such as Iowa and Wisconsin as well as Illinois which is nearby. Hennepin also was, by far, the most dominant source of in-state transmissions to other Minnesota locations (n=772) over the two-year period.We also analyzed the diversity of the location source of SARS-CoV-2 viruses in each county and noted the timing of state-wide policies as well as trends in clinical cases. Neither the number of clinical cases or major policy decisions, such as the end of the lockdown period in 2020 or the end of all restrictions in 2021, appeared to have impact on virus diversity across each individual county.ImportanceWe analyzed over 22,000 SARS-CoV-2 genomes of patient samples tested at Mayo Clinic Laboratories during a two-year period in the COVID-19 pandemic that included Alpha, Delta, and Omicron VoCs to examine the roles and relationships of Minnesota virus transmission.We found that Hennepin County, the most populous county, drove the transmission of SARS-CoV-2 viruses in the state after including the formation of earlier clades including 20A, 20C, and 20G, as well as variants of concern Alpha and Delta. We also found that Hennepin County was the source for most of the county-to-county introductions after its initial introduction with the virus in early 2020 from an international source, while other counties acted as transmission “sinks”. In addition, major policies such as the end of the lockdown period in 2020 or the end of all restrictions in 2021, did not appear to have an impact on virus diversity across individual counties.

Bcl-2 and Mcl-1 proteins play a role in multiple myeloma (MM) cell survival, for which targeted inhibitors are being developed. AT-101 is an oral drug, which disrupts Bcl-2 and Mcl-1 function, impedes mitochondrial bioenergetic processes and induces apoptosis in MM cells. When combined with lenalidomide and dexamethasone (Rd), AT-101 significantly reduced tumor burden in an in vivo xenograft model of MM. These data provided rationale for a phase I/II study to establish the effective dose of AT-101 in combination with Rd (ARd regimen) in relapsed/refractory MM. A total of 10 patients were enrolled, most with high-risk cytogenetics (80%) and prior stem cell transplant (70%). Three patients were lenalidomide-refractory, 2 were bortezomib-refractory and 3 were daratumumab-refractory. The ARd combination was well tolerated with most common grade 3/4 adverse events being cytopenia’s. The overall response rate was 40% and clinical benefit rate was 90%. The median progression free survival was 14.9 months (95% CI 7.1-NE). Patients responsive to ARd showed a decrease in Bcl-2:Bim or Mcl-1:Noxa protein complexes, increased CD8+ T and NK cells and depletion of T and B-regulatory cells. The ARd regimen demonstrated an acceptable safety profile and promising efficacy in patients with relapsed/refractory MM prompting further investigation in additional patients.

The e4 allele of the apolipoprotein E gene (APOE4) is a strong genetic risk factor for Alzheimer's disease (AD) and multiple vascular conditions. ApoE is abundantly expressed in multiple brain cell types, including astrocytes, microglia, and vascular mural cells (VMCs). Here, we show that VMC-specific expression of apoE4 in mice impairs behavior and cerebrovascular function. Expression of either apoE3 or apoE4 in VMCs was sufficient to rescue the hypercholesterolemia and atherosclerosis phenotypes seen in Apoe knockout mice. Intriguingly, vascular expression of apoE4, but not apoE3, reduced arteriole blood flow, impaired spatial learning, and increased anxiety-like phenotypes. Single-cell RNA sequencing of vascular and glial cells revealed that apoE4 in VMCs was associated with astrocyte activation, while apoE3 was linked to angiogenic signature in pericytes. Together, our data support cell-autonomous effects of vascular apoE on brain homeostasis in an isoform-dependent manner, suggesting a critical contribution of vascular apoE to AD pathogenesis.

APOE4 is the strongest genetic risk factor associated with late-onset Alzheimer’s disease (AD). To address the underlying mechanism, we develop cerebral organoid models using induced pluripotent stem cells (iPSCs) with APOE ε3/ε3 or ε4/ε4 genotype from individuals with either normal cognition or AD dementia. Cerebral organoids from AD patients carrying APOE ε4/ε4 show greater apoptosis and decreased synaptic integrity. While AD patient-derived cerebral organoids have increased levels of Aβ and phosphorylated tau compared to healthy subject-derived cerebral organoids, APOE4 exacerbates tau pathology in both healthy subject-derived and AD patient-derived organoids. Transcriptomics analysis by RNA-sequencing reveals that cerebral organoids from AD patients are associated with an enhancement of stress granules and disrupted RNA metabolism. Importantly, isogenic conversion of APOE4 to APOE3 attenuates the APOE4-related phenotypes in cerebral organoids from AD patients. Together, our study using human iPSC-organoids recapitulates APOE4-related phenotypes and suggests APOE4-related degenerative pathways contributing to AD pathogenesis.

Tumors acquire numerous mutations during development and progression. When translated into proteins, these mutations give rise to neoantigens that can be recognized by T cells and generate antibodies, representing an exciting direction of cancer immunotherapy. While neoantigens have been reported in many cancer types, the profiling of neoantigens often focused on the class-I subtype that are presented to CD8 + T cells, and the relationship between neoantigen load and clinical outcomes was often inconsistent among cancer types. In this study, we described an informatics workflow, REAL-neo, for identification, quality control (QC), and prioritization of both class-I and class-II human leukocyte antigen (HLA) bound neoantigens that arise from somatic single nucleotide mutations (SNM), small insertions and deletions (INDEL), and gene fusions. We applied REAL-neo to 835 primary breast tumors in the Cancer Genome Atlas (TCGA) and performed comprehensive profiling and characterization of the detected neoantigens. We found recurrent HLA class-I and class-II restricted neoantigens across breast cancer cases, and uncovered associations between neoantigen load and clinical traits. Both class-I and class-II neoantigen loads from SNM and INDEL were found to predict overall survival independent of tumor mutational burden (TMB), breast cancer subtypes, tumor-infiltrating lymphocyte (TIL) levels, tumor stage, and age at diagnosis. Our study highlighted the importance of accurate and comprehensive neoantigen profiling and QC, and is the first to report the predictive value of neoantigen load for overall survival in breast cancer.

TMB is used to guide PD-1-directed immunotherapy in solid tumor Oncology. However, it has not been systematically studied in AML, where the focus has been on cytogenetic risk and individual driver gene mutations (GM's). TMB contribution to AML epidemiology is also uncertain. We therefore studied its association with epidemiologic risk factors; driver GM's and somatic mutations (SM's) in AML risk genes which we recently demonstrated (ABCB1; CYP1A1; CYP2B6; EPHX1; ERCC1,2,& 5; MEFV; MTRR; and TERT); clinical and cytogenetic features; and outcome after therapy in the MCAEC, a highly annotated clinical epidemiology cohort of consecutive AML pts [Finn, Cancer Epidemiol 39:1084, 2015]. Methods: We obtained somatic leukemia DNA from remnant diagnostic cytogenetic pellets in 98 MCAEC patients (pts), as previously described [Foran, Blood (2017) 130:570a]. Whole exome sequencing (WES) was performed at depth of ~100 million paired end 100bp reads using Agilent SureSelectXT Human All Exon V5 + UTRs target enrichment kit. Reads were mapped to human genome build hg19 using BWA-MEM. Single nucleotide variants (SNVs) and small INDELs were identified using Mayo Clinic (MC) analytic pipeline GenomeGPS 4.0.1 following Broad GATK variant discovery best practices of alignment, realignment and recalibration, and multi-sample joint genotyping; and filtered using both germline whole exome and genome sequencing of ~1200 MC Biobank samples and public germline variant databases of 1000 genome project, 6500 individuals in exome sequencing project, and HapMap phase 3. Remaining variants were annotated using ANNOVAR, and functional variants of non-synonymous, truncating, frame-shift, and splice-sites were used in the statistical association analyses. TMB was defined as the number of functional mutations per Mb of coding region, heterozygous or homozygous. TMB associations with epidemiologic risk, clinical characteristics, and SM's in AML risk genes (listed above) or driver GM's (occurring in 5 or more pts: ASXL1, BCOR, CEBPA, DNMT3A, FLT3, IDH2, KRAS, MLL2-5, NF1, NPM1, NRAS, PHF6, RUNX1, SF3B1, STAG2, TET2, TP53, U2AF1) were evaluated using linear regression models; a rank transformation of TMB was utilized due to its skewed distribution. Multivariable analysis (MVA) models were adjusted for variables with p-value Results Median age at AML diagnosis was 70 yrs (Range: 19-94 yrs), and 67 pts were male. Cytogenetic risk group was favorable (7%), intermediate-normal (46%) or abnormal (20%), and poor risk (27%). 40/61 pts (66%) achieved complete remission (CR). With a median follow-up of 8.0 months (Range: 0.1 - 186), 80 pts (82%) died and 20 (20%) underwent allogeneic transplantation (AlloBMT). Median TMB was 18.2 (Range: 15.0-70.1). In MVA, significant associations with increased TMB were observed in pts with history of prior immunosuppressive therapy or solid organ transplantation (β=19.48, P=0.015), and with FLT3 (β=21.12, P=0.015), MLL2 (β=20.91, P=0.001), and MLL3 (β=11.31, P=0.031) GM's. A borderline association was observed for U2AF1 (β=16.14, P=0.057). TMB was also associated with SM's in TERT (β=25.13, P=0.028); a borderline association with SM's in ABCB1 was not confirmed in MVA (β=-17.98, P=0.069). Additionally, cytogenetic risk group was associated with TMB in MVA (P=0.005), being highest in intermediate-normal and lowest in poor risk groups. Body Mass Index was inversely associated with TMB (unadjusted β=-16.99, P=0.005), but unconfirmed in MVA (β=-8.29, P=0.12). There was no association with CR (OR=0.93, P=0.46), use of (HR=0.96, P=0.64) or relapse risk (HR=1.00, P=0.98) following AlloBMT, or survival (HR=0.97, P=0.56) (Figure). Conclusions Measurement of TMB is feasible in this AML epidemiologic cohort, and we observed important associations with AML risk factors, risk gene SM's, cytogenetic risk group, and driver GM's. We acknowledge the relatively small sample size and possibility of type II error, and therefore these observations require validation in a large prospective cohort which is planned. Figure 1 Figure 1. Disclosures Foran: OncLive: Honoraria; certara: Honoraria; actinium: Research Funding; boehringer ingelheim: Research Funding; novartis: Honoraria; abbvie: Research Funding; servier: Honoraria; taiho: Honoraria; pfizer: Honoraria; revolution medicine: Honoraria; gamida: Honoraria; takeda: Research Funding; sanofi aventis: Honoraria; trillium: Research Funding; syros: Honoraria; aptose: Research Funding; bms: Honoraria; kura: Research Funding; h3bioscience: Research Funding; aprea: Research Funding; sellas: Research Funding; stemline: Research Funding. Murthy: CRISPR Therapeutics: Research Funding. Finn: Jazz: Consultancy, Speakers Bureau; BMS: Consultancy, Speakers Bureau; BeiGene: Consultancy, Speakers Bureau; ADC Therapeutics: Consultancy, Speakers Bureau. Badar: Pfizer Hematology-Oncology: Membership on an entity's Board of Directors or advisory committees. Cerhan: Celgene/BMS: Other: Connect Lymphoma Scientific Steering Committee, Research Funding; Genentech: Research Funding; NanoString: Research Funding; Regeneron Genetics Center: Other: Research Collaboration.

Introduction: Population studies have identified genes with germline polymorphisms associated with acute myeloid leukemia (AML) risk and outcome. However, somatic mutations in these genes have not been reported in an AML clinical population and whether they are associated with epidemiologic exposures, clinical AML phenotypes and outcome after therapy. Methods: We systemically interrogated PubMed database (1998-2018), to identify genes with germline polymorphisms associated with AML risk, response to chemotherapy or outcome. To determine the prevalence and relevance of somatic mutations in these genes in an unselected AML population, we performed an analysis using Whole-Exome Sequencing (WES) on remnant diagnostic cytogenetic pellets from 98 patients from the Mayo Clinic AML Epidemiology Cohort, a detailed and highly-annotated cohort of 295 consecutive AML patients treated at Mayo Clinic Florida & Arizona between October, 2000 and December, 2011. Patient characteristics are shown in Table 1. Samples were sequenced at a depth of ~100 million paired-end 100bp reads using Agilent SureSelectXT Human All Exon V5 + UTRs target enrichment kit. Sequencing reads were aligned to human reference genome, and somatic mutations including non-synonymous and truncating single nucleotide variants and small INDELs were identified and filtered using Exome Sequencing Project, 1000 genome, HapMap, & Mayo Clinic internal biobank genetic variants database. Copy number aberrations were identified & filtered using public copy number polymorphism databases. The association analyses were performed at the gene level, with a primary endpoint of whether a given patient harbored a somatic mutation in any genes linked to AML risk or outcome in literature, and to determine the associations of these mutations with epidemiologic exposures, AML phenotype and clinical outcomes. Results: From the literature search, we identified 77 unique genes with known germline polymorphisms associated with AML risk, response to chemotherapy or outcome. Fifty-eight of these were found to be somatically mutated in our WES dataset, with subsequent analysis focusing on the 11 genes (ABCB1, CYP1A1, CYP2B6, EPHX1, ERCC1, ERCC2, ERCC5, JAK2, MEFV, MTRR, and TERT) that had greater than 5 patients with nonsynonymous somatic mutations in the given gene. Significant associations with epidemiologic exposures and outcomes were noted in patients with somatic mutations in ERCC2, CYP1A1 and ERCC5 genes. Table 2 shows a comparison of patient characteristics and associations according to the presence of somatic mutations in these genes. Patients with mutations in CYP1A1 had a significantly younger age at AML diagnosis (Median: 51.7 vs. 71.0 years, P=.02) and significantly shorter OS in age-adjusted analysis (HR=4.45, P=.003). The former is a novel finding, whereas the latter is consistent with previous reports. Patients with mutations in ERCC2 more commonly used statins (66.7% vs. 21.7%, P=.03). Patients with ERCC5 mutations had a lower rate of tobacco use (20.0% vs. 54.5%, P=.049). In unadjusted analysis, there was a significant association between presence of somatic mutations in JAK2 and poorer survival after AML diagnosis (HR=2.83, P=.017), but this attenuated and did not retain significance when adjusting for age at AML diagnosis (HR=2.22, P=.067). Conclusion: Our exploratory study describes a novel association of CYP1A1 somatic nonsynonymous mutations with age of AML onset, as well as novel associations of ERCC2 and ERCC5 mutations with epidemiologic exposures in an unselect cohort of patients with AML. We confirm the association of CYP1A1 with inferior overall survival after AML diagnosis. These findings suggest that some genes associated with AML risk may also harbor somatic mutations that are clinically relevant. These results will guide a planned confirmatory prospective study to determine frequency and impact of both germline and somatic mutations of risk genes in AML patients, and may contribute to a better understanding leukemia risk assessment and potentially to prevention strategies. Disclosures Finn: Jazz Pharmaceuticals: Speakers Bureau; Celgene: Speakers Bureau; Seattle Genetics: Speakers Bureau. Cerhan:BMS/Celgene: Research Funding; NanoString: Research Funding. Foran:Revolution Medicine: Consultancy; Servier: Membership on an entity's Board of Directors or advisory committees; Abbvie: Research Funding; BMS: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Boehringer Ingelheim: Research Funding; H3Biosciences: Research Funding; Xencor: Research Funding; Trillium: Research Funding; Takeda: Research Funding; Kura Oncology: Research Funding; Aptose: Research Funding; Aprea: Research Funding; Actinium: Research Funding; Agios: Honoraria, Research Funding.

APOE4 is the strongest genetic risk factor associated with late-onset Alzheimer's disease (AD). To address the underlying mechanism, we develop cerebral organoid models using induced pluripotent stem cells (iPSCs) with APOE ε3/ε3 or ε4/ε4 genotype from individuals with either normal cognition or AD dementia. Cerebral organoids from AD patients carrying APOE ε4/ε4 show greater apoptosis and decreased synaptic integrity. While AD patient-derived cerebral organoids have increased levels of Aβ and phosphorylated tau compared to healthy subject-derived cerebral organoids, APOE4 exacerbates tau pathology in both healthy subject-derived and AD patient-derived organoids. Transcriptomics analysis by RNA-sequencing reveals that cerebral organoids from AD patients are associated with an enhancement of stress granules and disrupted RNA metabolism. Importantly, isogenic conversion of APOE4 to APOE3 attenuates the APOE4-related phenotypes in cerebral organoids from AD patients. Together, our study using human iPSC-organoids recapitulates APOE4-related phenotypes and suggests APOE4-related degenerative pathways contributing to AD pathogenesis.

IL-2 is a pleiotropic cytokine that promotes the differentiation of Th cell subsets, including Th1, Th2, and Th9 cells, but it impairs the development of Th17 and T follicular helper cells. Although IL-2 is produced by all polarized Th subsets to some level, how it impacts cytokine production when effector T cells are restimulated is unknown. We show in this article that Golgi transport inhibitors (GTIs) blocked IL-9 production. Mechanistically, GTIs blocked secretion of IL-2 that normally feeds back in a paracrine manner to promote STAT5 activation and IL-9 production. IL-2 feedback had no effect on Th1- or Th17-signature cytokine production, but it promoted Th2- and Th9-associated cytokine expression. These data suggest that the use of GTIs results in an underestimation of the presence of type 2 cytokine–secreting cells and highlight IL-2 as a critical component in optimal cytokine production by Th2 and Th9 cells in vitro and in vivo.

7021 Background: Case-control studies (CCS) have identified germline variants (GVs) & genes associated with AML risk and/or outcome. However, whether these genes also harbor somatic mutations (SMs) in AML and whether these SMs are associated with clinical phenotypes and outcome is unknown. We systemically interrogated PubMed (terms "AML genetic polymorphisms"), yielding 612 reports (1981-2018), from which we identified unique variants/single nucleotide polymorphisms (SNPs) in 100 genes associated with AML risk and/or outcome in CCS. To determine the prevalence and relevance of SMs in these genes in an unselected AML population, we performed an analysis using WES of cases in the highly-annotated MCAEC (Finn, Ca Epidemiol 2015). Methods: We performed WES using remnant cytogenetic pellets from AML diagnosis in 98 MCAEC patients with available samples, sequenced at a depth of ~100 million paired-end 100bp reads using Agilent SureSelectXT Human All Exon V5 + UTRs target enrichment kit. Sequencing reads were aligned to human reference genome, and SMs including non-synonymous (NS) and truncating single nucleotide variants (SNVs) and small INDELs were identified and filtered using Exome Sequencing Project, 1000 genome, HapMap, & Mayo Clinic (MC) internal Biobank GV database. Copy number aberrations (CNAs) were identified & filtered using public CN polymorphism databases. For this analysis we evaluated the prevalence of somatic SNVs, INDELs, & CNAs in the 100 nominated genetic risk genes in collaboration with MC Cancer Center to determine their association with clinical phenotype and outcome. Results: Of the 100 unique genes, there were 40 with SM in our dataset, with 126 total mutations. Most were mutated in a very small number of patients, allowing insufficient statistical power to perform association analyses. However, an NS mutation in CYP2B6 (c.785A > G, p.Lys262Arg) had a minor allele frequency of 12.0%; this variant has been reported in association with breast cancer risk, but not with AML. Conclusions: We studied SM’s in AML genetic risk genes using WES of tumor samples. While representing a smaller sample size, this suggests their contribution to AML development may be limited in a broader clinical population. These results & prevalence of the novel CYP2B6 rs2279343 variant in AML must be confirmed in larger cohorts.

Each pair of plots show the accuracy of the assembly generated by various assembler algorithms for nanopore sequenced complement reads from E.coli (Panels A and C) and Yeast (Panels B and D) datasets. A&B: Line graphs plotted between % of complement reads and the % of genome covered, showing the extent of genome assembled by each assembler algorithm. C&D: Line graphs between the % of complement reads and % of alignment showing the confidence level of the contigs being assembled by various assembler algorithms. (PDF 798Â kb)

Overview of the running times for various assemblers. (PDF 236Â kb)

Overview of the influence of memory allocation on the running times for various assemblers. (PDF 155Â kb)

Each pair of plots give an overview of the computational requirements of each assembler for assembling nanopore sequenced complement reads from E. coli and Yeast datasets. A&B: Histogram with error bars plotted between % of complement reads and log values of wall time which represents the actual time consumed by each assembler to execute the task with respect to gradual increase in data size. C&D: Histograms with error bars plotted between % of complement reads and log values of CPU time which represents amount of time the CPU is actually executing instructions for each assembler with variation in data size. E&F: Histograms with error bars plotted between varying amount of allotted memory on X-axis and log values of the wall time, showing the influence of memory allocation on wall time consumption by various assembler algorithms. G&H: Histograms with error bars plotted between varying amount of memory and log values of the CPU time, illustrating the influence of memory allocation on the CPU time consumed by various assembler algorithms. In each set of these plots, left panel corresponds to E. coli dataset while the plots in the right panel correspond to the Yeast dataset. (PDF 838Â kb)

Each pair of plots give an overview of the comparisons of the quality of the assemblies across assemblers for nanopore sequenced template reads from E. coli and yeast datasets. A&B: Histograms with error bars plotted between % of template reads and N50_value of an assembly show the variation in N50 value of an assembly among different assembler algorithms and how it varies with respect to the data size. C&D: Histograms with error bars plotted between % of template reads and number of contigs generated from an assembly showing how the number of contigs generated vary for each respective assembler algorithm across various bins of respective datasets. E&F: Histograms showing the percentage of template reads employed on X-axis versus the average length of the contigs represented as mean of the contigs, obtained using each algorithm. Mean of the Contigs is the average value of the total sum of lengths of all the contigs. G&H: Histograms showing the sum of the lengths of all the contigs generated by an assembler as a function of the percentage of the total reads employed in the assembly. In each set of plots, left panel corresponds to E. coli dataset while the plots in the right panel correspond to the Yeast dataset. In all the plots labeled numeric values on histograms indicate corresponding values of the metric in respective color representing each tool. (PDF 670Â kb)

Each pair of plots show the accuracy of the assembly generated by various assembler algorithms for nanopore sequenced template reads from E.coli (Panels A and C) and Yeast (Panels B and D) datasets. A&B: Line graphs plotted between % of template reads and the % of genome covered, showing the extent of genome assembled by each assembler algorithm. C&D: Line graphs between the % of template reads and % of alignment showing the confidence level of the contigs being assembled by various assembler algorithms. (PDF 584Â kb)

Each pair of plots give an overview of the comparisons of the quality of the assemblies across assemblers for nanopore sequenced complement reads from E. coli and yeast datasets. A&B: Histograms with error bars plotted between % of complement reads and N50_value of an assembly show the variation in N50 value of an assembly among different assembler algorithms and how it varies with respect to the data size. C&D: Histograms with error bars plotted between % of complement reads and number of contigs generated from an assembly showing how the number of contigs generated vary for each respective assembler algorithm across various bins of respective datasets. E&F: Histograms showing the percentage of complement reads employed on X-axis versus the average length of the contigs represented as mean of the contigs, obtained using each algorithm. Mean of the Contigs is the average value of the total sum of lengths of all the contigs. G&H: Histograms showing the sum of the lengths of all the contigs generated by an assembler as a function of the percentage of the total reads employed in the assembly. In each set of plots, left panel corresponds to E. coli dataset while the plots in the right panel correspond to the Yeast dataset. In all the plots labeled numeric values on histograms indicate corresponding values of the metric in respective color representing each tool. (PDF 700Â kb)

Each pair of plots give an overview of the computational requirements of each assembler for assembling nanopore sequenced template reads from E. coli and Yeast datasets. A&B: Histogram with error bars plotted between % of template reads and log values of wall time which represents the actual time consumed by each assembler to execute the task with respect to gradual increase in data size. C&D: Histograms with error bars plotted between % of template reads and log values of CPU time which represents amount of time the CPU is actually executing instructions for each assembler with variation in data size. E&F: Histograms with error bars plotted between varying amount of allotted memory on X-axis and log values of the wall time, showing the influence of memory allocation on wall time consumption by various assembler algorithms. G&H: Histograms with error bars plotted between varying amount of memory and log values of the CPU time, illustrating the influence of memory allocation on the CPU time consumed by various assembler algorithms. In each set of these plots, left panel corresponds to E. coli dataset while the plots in the right panel correspond to the Yeast dataset. (PDF 813Â kb)