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We investigate the high spectral efficiency capabilities of a cellular data system that combines the following: 1) multiple transmit signals, each using a separately adaptive modulation; 2) adaptive array processing at the receiver; and 3) aggressive frequency reuse (reuse in every cell). We focus on the link capacity between one user and its serving base station, for both uncoded and ideally coded transmissions. System performance is measured in terms of average data throughput, where the average is over user location, shadow fading, and fast fading. We normalize this average by the total bandwidth, call it the mean spectral efficiency, and show why this metric is a useful representation of system capability. We then quantify it, using simulations, to characterize multiple-input multiple-output systems performance for a wide variety of channel conditions and system design options. Index Terms--Adaptive arrays, adaptive modulation, antenna diversity, cellular mobile communications.

Somatic mosaicism is an important cause of disease, but mosaic and somatic variants are often challenging to detect because they exist in only a fraction of cells. To address the need for benchmarking subclonal variants in normal cell populations, we developed a benchmark containing mosaic variants in the Genome in a Bottle Consortium (GIAB) HG002 reference material DNA from a large batch of a normal lymphoblastoid cell line. First, we used a somatic variant caller with high coverage (300x) Illumina whole genome sequencing data from the Ashkenazi Jewish trio to detect variants in HG002 not detected in at least 5% of cells from the combined parental data. These candidate mosaic variants were subsequently evaluated using >100x BGI, Element, and PacBio HiFi data. High confidence candidate SNVs with variant allele fractions above 5% were included in the HG002 draft mosaic variant benchmark, with 13/85 occurring in medically relevant gene regions. We also delineated a 2.45 Gbp subset of the previously defined germline autosomal benchmark regions for HG002 in which no additional mosaic variants >2% exist, enabling robust assessment of false positives. The variant allele fraction of some mosaic variants is different between batches of cells, so using data from the homogeneous batch of reference material DNA is critical for benchmarking these variants. External validation of this mosaic benchmark showed it can be used to reliably identify both false negatives and false positives for a variety of technologies and detection algorithms, demonstrating its utility for optimization and validation. By adding our characterization of mosaic variants in this widely-used cell line, we support extensive benchmarking efforts using it in simulation, spike-in, and mixture studies., Competing Interests: Competing Interests C.E.M. is a co-Founder of Onegevity. Y.W., M.R., A.V., L.M., W.C., S.C., J.H., R.M., and G.P. are Illumina employees and equity owners. A.C., P.C., K.S., D.C., A.K., and L.B. are employees of Google LLC and receive equity compensation. P.C.B. sits on the scientific advisory boards of Intersect Diagnostics Inc. and BioSymetrics Inc., and previously sat on that of Sage Bionetworks.

Research and medical genomics require comprehensive, scalable methods for the discovery of novel disease targets, evolutionary drivers and genetic markers with clinical significance. This necessitates a framework to identify all types of variants independent of their size or location. Here we present DRAGEN, which uses multigenome mapping with pangenome references, hardware acceleration and machine learning-based variant detection to provide insights into individual genomes, with ~30 min of computation time from raw reads to variant detection. DRAGEN outperforms current state-of-the-art methods in speed and accuracy across all variant types (single-nucleotide variations, insertions or deletions, short tandem repeats, structural variations and copy number variations) and incorporates specialized methods for analysis of medically relevant genes. We demonstrate the performance of DRAGEN across 3,202 whole-genome sequencing datasets by generating fully genotyped multisample variant call format files and demonstrate its scalability, accuracy and innovation to further advance the integration of comprehensive genomics. Overall, DRAGEN marks a major milestone in sequencing data analysis and will provide insights across various diseases, including Mendelian and rare diseases, with a highly comprehensive and scalable platform., Competing Interests: Competing interests: F.J.S. receives research support from Genentech, Illumina, PacBio and ONT. S.C., M.R., S.T., Z.H., M.R., A.V., G.P., C.R., A.F., V.O., S.M., J.H. and R.M. are employees of Illumina. The remaining authors declare no competing interests., (© 2024. The Author(s).)

The Genome in a Bottle Consortium (GIAB), hosted by the National Institute of Standards and Technology (NIST), is developing new matched tumor-normal samples, the first to be explicitly consented for public dissemination of genomic data and cell lines. Here, we describe a comprehensive genomic dataset from the first individual, HG008, including DNA from an adherent, epithelial-like pancreatic ductal adenocarcinoma (PDAC) tumor cell line and matched normal cells from duodenal and pancreatic tissues. Data for the tumor-normal matched samples comes from thirteen distinct state-of-the-art whole genome measurement technologies, including high depth short and long-read bulk whole genome sequencing (WGS), single cell WGS, and Hi-C, and karyotyping. These data will be used by the GIAB Consortium to develop matched tumor-normal benchmarks for somatic variant detection. We expect these data to facilitate innovation for whole genome measurement technologies, de novo assembly of tumor and normal genomes, and bioinformatic tools to identify small and structural somatic mutations. This first-of-its-kind broadly consented open-access resource will facilitate further understanding of sequencing methods used for cancer biology., Competing Interests: Competing interests A.S. and K.S. are employees of Arima Genomics. L.F.P. from BCM, was sponsored by Genentech Inc until September 2023. F.J.S from BCM, received research support from Illumina, ONT and Pacbio. A.R.H and H-C.Y. are employees of Bionano Genomics and own stock shares and options of Bionano Genomics, Inc. V.W., K.K., J.R., and I.G. are employees of BioSkryb Genomics. M.S., K.B., B.R.L. and S.L. are employees of Element Biosciences. S.B.K., C.L., P.B., A.M.W., I.J.M., A.A., C.K., M.W., and Y.K. are employees and shareholders of PacBio, Inc. D.L., H.B., N.I., and I.S. are employees and shareholders of Ultima Genomics. S.E. and M.W. are employees of Phase Genomics. E.C., G.H., S.G., and M.V. are employees of KromaTiD, Inc, E.C. is also a shareholder. F.B., E.S., K.S., S.T. and S.C. are employees of Illumina, Inc. All other authors have no competing interests.

Background: Copy number variation (CNV) is a key genetic characteristic for cancer diagnostics and can be used as a biomarker for the selection of therapeutic treatments. Using data sets established in our previous study, we benchmark the performance of cancer CNV calling by six most recent and commonly used software tools on their detection accuracy, sensitivity, and reproducibility. In comparison to other orthogonal methods, such as microarray and Bionano, we also explore the consistency of CNV calling across different technologies on a challenging genome., Results: While consistent results are observed for copy gain, loss, and loss of heterozygosity (LOH) calls across sequencing centers, CNV callers, and different technologies, variation of CNV calls are mostly affected by the determination of genome ploidy. Using consensus results from six CNV callers and confirmation from three orthogonal methods, we establish a high confident CNV call set for the reference cancer cell line (HCC1395)., Conclusions: NGS technologies and current bioinformatics tools can offer reliable results for detection of copy gain, loss, and LOH. However, when working with a hyper-diploid genome, some software tools can call excessive copy gain or loss due to inaccurate assessment of genome ploidy. With performance matrices on various experimental conditions, this study raises awareness within the cancer research community for the selection of sequencing platforms, sample preparation, sequencing coverage, and the choice of CNV detection tools., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Research and medical genomics require comprehensive and scalable solutions to drive the discovery of novel disease targets, evolutionary drivers, and genetic markers with clinical significance. This necessitates a framework to identify all types of variants independent of their size (e.g., SNV/SV) or location (e.g., repeats). Here we present DRAGEN that utilizes novel methods based on multigenomes, hardware acceleration, and machine learning based variant detection to provide novel insights into individual genomes with ~30min computation time (from raw reads to variant detection). DRAGEN outperforms all other state-of-the-art methods in speed and accuracy across all variant types (SNV, indel, STR, SV, CNV) and further incorporates specialized methods to obtain key insights in medically relevant genes (e.g., HLA, SMN, GBA). We showcase DRAGEN across 3,202 genomes and demonstrate its scalability, accuracy, and innovations to further advance the integration of comprehensive genomics for research and medical applications., Competing Interests: Competing interests FJS receives research support from Genentech, Illumina, PacBio and Oxford Nanopore. SC, MR, ST, ZH, MR, AV, GP, CR, VO, SM, JH and RM are employees of Illumina.

The precisionFDA Truth Challenge V2 aimed to assess the state of the art of variant calling in challenging genomic regions. Starting with FASTQs, 20 challenge participants applied their variant-calling pipelines and submitted 64 variant call sets for one or more sequencing technologies (Illumina, PacBio HiFi, and Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with updated Genome in a Bottle benchmark sets and genome stratifications. Challenge submissions included numerous innovative methods, with graph-based and machine learning methods scoring best for short-read and long-read datasets, respectively. With machine learning approaches, combining multiple sequencing technologies performed particularly well. Recent developments in sequencing and variant calling have enabled benchmarking variants in challenging genomic regions, paving the way for the identification of previously unknown clinically relevant variants., Competing Interests: DECLARATION OF INTERESTS C.B. is an employee and shareholder of SAGA Diagnostics AB. A.C., P.-C.C., A.K., M.N., G.B., S.G., and H.Y. are employees of Google, and A.C. is a shareholder. S.D.-B., D.K.-Z., D.T., Ö.K., G.B., K.N., E.A., R.B., I.J.J., A.D., V.S., A.J., and H.S.T. are employees of Seven Bridges Genomics. O.S. and S. T.W. are employees of DNAnexus. G.L., C.M., L.T.F., Y.D., and S.Z. are employees of Genetalks. V.J., M.R., B.L., C.R., S.C., and R.M. are employees of Illumina. S.M.E.S. and M.M. are employees of Roche. C.H. is an employee of Wasai Technology. H.F., Z.L., and L.C. are employees of Sentieon.

While the cost of whole genome sequencing (WGS) is approaching the realm of routine medical tests, it remains too tardy to help guide the management of many acute medical conditions. Rapid WGS is imperative in light of growing evidence of its utility in acute care, such as in diagnosis of genetic diseases in very ill infants, and genotype-guided choice of chemotherapy at cancer relapse. In such situations, delayed, empiric, or phenotype-based clinical decisions may meet with substantial morbidity or mortality. We previously described a rapid WGS method, STATseq, with a sensitivity of >96 % for nucleotide variants that allowed a provisional diagnosis of a genetic disease in 50 h. Here improvements in sequencing run time, read alignment, and variant calling are described that enable 26-h time to provisional molecular diagnosis with >99.5 % sensitivity and specificity of genotypes. STATseq appears to be an appropriate strategy for acutely ill patients with potentially actionable genetic diseases.