Quality control (QC) and preprocessing are essential steps for sequencing data analysis to ensure the accuracy of results. However, existing tools cannot provide a satisfying solution with integrated comprehensive functions, proper architectures, and highly scalable acceleration. In this article, we demonstrate SOAPnuke as a tool with abundant functions for a "QC-Preprocess-QC" workflow and MapReduce acceleration framework. Four modules with different preprocessing functions are designed for processing datasets from genomic, small RNA, Digital Gene Expression, and metagenomic experiments, respectively. As a workflow-like tool, SOAPnuke centralizes processing functions into 1 executable and predefines their order to avoid the necessity of reformatting different files when switching tools. Furthermore, the MapReduce framework enables large scalability to distribute all the processing works to an entire compute cluster.We conducted a benchmarking where SOAPnuke and other tools are used to preprocess a ∼30× NA12878 dataset published by GIAB. The standalone operation of SOAPnuke struck a balance between resource occupancy and performance. When accelerated on 16 working nodes with MapReduce, SOAPnuke achieved ∼5.7 times the fastest speed of other tools.

SOAPnuke: a MapReduce acceleration-supported software for integrated quality control and preprocessing of high-throughput sequencing data.

Despite rapid advances in sequencing technologies, accurately calling genetic variants present in an individual genome from billions of short, errorful sequence reads remains challenging Here we show that a deep convolutional neural network can call genetic variation in aligned next-generation sequencing read data by learning statistical relationships between images of read pileups around putative variant and true genotype calls The approach, called DeepVariant, outperforms existing state-of-the-art tools The learned model generalizes across genome builds and mammalian species, allowing nonhuman sequencing projects to benefit from the wealth of human ground-truth data We further show that DeepVariant can learn to call variants in a variety of sequencing technologies and experimental designs, including deep whole genomes from 10X Genomics and Ion Ampliseq exomes, highlighting the benefits of using more automated and generalizable techniques for variant calling

/pdf/a-universal-snp-and-small-indel-variant-caller-using-deep-3cb12vccrv.pdf

A universal SNP and small-indel variant caller using deep neural networks.

Structural variants (SVs) rearrange large segments of DNA1 and can have profound consequences in evolution and human disease2,3. As national biobanks, disease-association studies, and clinical genetic testing have grown increasingly reliant on genome sequencing, population references such as the Genome Aggregation Database (gnomAD)4 have become integral in the interpretation of single-nucleotide variants (SNVs)5. However, there are no reference maps of SVs from high-coverage genome sequencing comparable to those for SNVs. Here we present a reference of sequence-resolved SVs constructed from 14,891 genomes across diverse global populations (54% non-European) in gnomAD. We discovered a rich and complex landscape of 433,371 SVs, from which we estimate that SVs are responsible for 25-29% of all rare protein-truncating events per genome. We found strong correlations between natural selection against damaging SNVs and rare SVs that disrupt or duplicate protein-coding sequence, which suggests that genes that are highly intolerant to loss-of-function are also sensitive to increased dosage6. We also uncovered modest selection against noncoding SVs in cis-regulatory elements, although selection against protein-truncating SVs was stronger than all noncoding effects. Finally, we identified very large (over one megabase), rare SVs in 3.9% of samples, and estimate that 0.13% of individuals may carry an SV that meets the existing criteria for clinically important incidental findings7. This SV resource is freely distributed via the gnomAD browser8 and will have broad utility in population genetics, disease-association studies, and diagnostic screening.

/pdf/a-structural-variation-reference-for-medical-and-population-2q18yekuvl.pdf

A structural variation reference for medical and population genetics

The incomplete identification of structural variants (SVs) from whole-genome sequencing data limits studies of human genetic diversity and disease association. Here, we apply a suite of long-read, short-read, strand-specific sequencing technologies, optical mapping, and variant discovery algorithms to comprehensively analyze three trios to define the full spectrum of human genetic variation in a haplotype-resolved manner. We identify 818,054 indel variants (<50 bp) and 27,622 SVs (≥50 bp) per genome. We also discover 156 inversions per genome and 58 of the inversions intersect with the critical regions of recurrent microdeletion and microduplication syndromes. Taken together, our SV callsets represent a three to sevenfold increase in SV detection compared to most standard high-throughput sequencing studies, including those from the 1000 Genomes Project. The methods and the dataset presented serve as a gold standard for the scientific community allowing us to make recommendations for maximizing structural variation sensitivity for future genome sequencing studies.

/pdf/multi-platform-discovery-of-haplotype-resolved-structural-z2y0o8bwmp.pdf

Multi-platform discovery of haplotype-resolved structural variation in human genomes

Several new genomics technologies have become available that offer long-read sequencing or long-range mapping with higher throughput and higher resolution analysis than ever before. These long-range technologies are rapidly advancing the field with improved reference genomes, more comprehensive variant identification and more complete views of transcriptomes and epigenomes. However, they also require new bioinformatics approaches to take full advantage of their unique characteristics while overcoming their complex errors and modalities. Here, we discuss several of the most important applications of the new technologies, focusing on both the currently available bioinformatics tools and opportunities for future research.

Piercing the dark matter: bioinformatics of long-range sequencing and mapping

National Natural Science Foundation of China [31420103919, 31672032]; Chinese Academy of Agricultural Sciences (CAAS-ASTIP-IVFCAAS) the China Agriculture Research System [CARS-26-10]; Beijing Training Project for the Leading Talents in S T [LJRC201412]; Beijing Key Laboratory for Pest Control and Sustainable Cultivation of Vegetables; Beijing Nova Program [Z171100001117039]

/pdf/genome-sequencing-of-the-sweetpotato-whitefly-bemisia-tabaci-1utaovz4w6.pdf

Genome sequencing of the sweetpotato whitefly Bemisia tabaci MED/Q

Nicotiana L. is a genus rich in polyploidy, which represents an ideal natural system for investigating speciation, biodiversity, and phytogeography. Despite a wealth of phylogenetic work on this genus, a robust evolutionary framework with a dated molecular phylogeny for the genus is still lacking. In this study, the 19 complete chloroplast genomes of Nicotiana species were assembled, and five published chloroplast genomes of Nicotiana were retrieved for comparative analyses. The results showed that the 24 chloroplast genomes of Nicotiana, ranging from 155,327 bp (N. paniculata) to 156,142 bp (N. heterantha) in size, exhibited typical quadripartite structure. The chloroplast genomes were rather conserved in genome structure, GC content, RNA editing sites, and gene content and order. The higher GC content observed in the IR regions could be a result of the presence of abundant rRNA and tRNA genes, which contained a relatively higher GC content. A total of seven hypervariable regions, as new molecular markers for phylogenetic analysis, were uncovered. Based on 78 protein-coding genes, we constructed a well-supported phylogenetic tree, which was largely in agreement with previous studies, except for a slight conflict in several sections. Chloroplast phylogenetic results indicated that the progenitors of diploid N. sylvestris, N. knightiana, and the common ancestor of N. sylvestris and N. glauca might have donated the maternal genomes of allopolyploid N. tabacum, N. rustica, and section Repandae, respectively. Meanwhile, the diploid section Noctiflorae lineages (N. glauca) acted as the most likely maternal progenitor of section Suaveolentes. Molecular dating results show that the polyploid events range considerably in ~0.12 million (section Nicotiana) to ~5.77 million (section Repandae) years ago. The younger polyploids (N. tabacum and N. rustica) were estimated to have arisen ~0.120 and ~0.186 Mya, respectively. The older polyploids (section Repandae and Suaveolentes) were considered to have originated from a single polyploid event at ~5.77 and ~4.49 Mya, respectively. In summary, the comparative analysis of chloroplast genomes of Nicotiana species has not only revealed a series of new insights into the genetic variation and phylogenetic relationships in Nicotiana but also provided rich genetic resources for speciation and biodiversity research in the future.

/pdf/comparative-chloroplast-genomes-of-nicotiana-species-2uhkhljo.pdf

Comparative Chloroplast Genomes of Nicotiana Species (Solanaceae): Insights Into the Genetic Variation, Phylogenetic Relationship, and Polyploid Speciation

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Genome sequencing of the sweetpotato whitefly Bemisia tabaci MED/Q

Comparative Chloroplast Genomes of Nicotiana Species (Solanaceae): Insights Into the Genetic Variation, Phylogenetic Relationship, and Polyploid Speciation