BACKGROUND: New sequencing technologies provide options for the scientific community to design studies and build clinical workflows. These options expand user choice, and can enable more accurate, scalable, or affordable workflows depending on the fit between scientist needs and platform capability. However, it is essential to understand the performance of these new technologies for different tasks, especially for capabilities that were not possible or tractable in prior technologies. We investigate the new sequencing technology avidity from Element Biosciences. to help the scientific community understand the performance of the options to generate sequencing data. RESULTS: We show that Element whole genome sequencing achieves higher mapping and variant calling accuracy compared to Illumina sequencing at the same coverage, with larger differences at lower coverages (20-30x). We quantify base error rates of Element reads, finding lower error rates, especially in homopolymer and tandem repeat regions. We use Element's ability to generate paired end sequencing with longer insert sizes than typical short-read sequencing. We show that longer insert sizes result in even higher accuracy, with long insert Element sequencing giving more accurate genome analyses at all coverages. CONCLUSIONS: New options for sequencing technologies can analyze genomes comparably or better than prior standard methods.

Large-scale population variant data is often used to filter and aid interpretation of variant calls in a single sample. These approaches do not incorporate population information directly into the process of variant calling, and are often limited to filtering which trades recall for precision. In this study, we develop population-aware DeepVariant models with a new channel encoding allele frequencies from the 1000 Genomes Project. This model reduces variant calling errors, improving both precision and recall in single samples, and reduces rare homozygous and pathogenic clinvar calls cohort-wide. We assess the use of population-specific or diverse reference panels, finding the greatest accuracy with diverse panels, suggesting that large, diverse panels are preferable to individual populations, even when the population matches sample ancestry. Finally, we show that this benefit generalizes to samples with different ancestry from the training data even when the ancestry is also excluded from the reference panel.

MOTIVATION: Population-scale sequenced cohorts are foundational resources for genetic analyses, but processing raw reads into analysis-ready cohort-level variants remains challenging. RESULTS: We introduce an open-source cohort-calling method that uses the highly accurate caller DeepVariant and scalable merging tool GLnexus. Using callset quality metrics based on variant recall and precision in benchmark samples and Mendelian consistency in father-mother-child trios, we optimize the method across a range of cohort sizes, sequencing methods and sequencing depths. The resulting callsets show consistent quality improvements over those generated using existing best practices with reduced cost. We further evaluate our pipeline in the deeply sequenced 1000 Genomes Project (1KGP) samples and show superior callset quality metrics and imputation reference panel performance compared to an independently generated GATK Best Practices pipeline. AVAILABILITY AND IMPLEMENTATION: We publicly release the 1KGP individual-level variant calls and cohort callset (https://console.cloud.google.com/storage/browser/brain-genomics-public/research/cohort/1KGP) to foster additional development and evaluation of cohort merging methods as well as broad studies of genetic variation. Both DeepVariant (https://github.com/google/deepvariant) and GLnexus (https://github.com/dnanexus-rnd/GLnexus) are open-source, and the optimized GLnexus setup discovered in this study is also integrated into GLnexus public releases v1.2.2 and later. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In this paper an iterative Chinese new phrase extraction method based on the intra-phrase association and context variation statistics is proposed.A Chinese language model enhancement framework including lexicon expansion is then developed.Extensive experiments for Chinese broadcast news transcription were then performed to explore the achievable improvements with respect to the degree of temporal consistency for the adaptation corpora.Very encouraging results were obtained and detailed analysis discussed.