一种基于关联性特征的宏基因组测序片段分装方法

Microorganisms are employed on a large scale in a variety of industrial production processes, such as production of food and ingredients, beverages, pharmaceutical compo- nents and fine- and bulk chemicals. Improving the behaviour of these microorganisms is of crucial importance in the design of efficient production processes. In this thesis we focus use directed evolution, which is a method to improve the phe- notype of a microorganism without knowing what to change in the DNA to obtain the desired effect. Improved strains are obtained through the natural evolution processes of variation and selection by enforcing a strong selective pressure in the laboratory. The mutations that occur during such short evolution can be determined by sequencing the DNA of the improved strains and comparing it to the starting strain. Finally, relating the relevant mutations to the enforced phenotype is called reverse engineering. We describe the reverse engineering of mutations in ADY2 enabling lactate transport across the cell wall and mutations in ACE2, which in combination with aneuploidy, could be related to the origin of multi-cellular growth in yeast. Next-generation sequencing was employed to reconstruct genomes and infer mutations. This technology has made tremendous progress since its introduction in 2008 and has become affordable for individual laboratories. Sequencing poses many challenges to the field of bioinformatics, since genomes are long and repetitive (e.g. yeasts have millions of base pairs and humans billions), but current sequencing technology can only read up to 100 to 1000 base pairs in sequence. The computational methods developed in this thesis enable the use of sequencing technology in industrial microbiology research. A starting strain in this field is the yeast Saccharomyces cerevisiae CEN.PK113-7D. To determine its genome sequence we devel- oped an algorithm that uses a Tabu-search that exploits previously sequenced genomes and heterogeneous sequencing data types. Additionally, two methods were developed to infer mutations between genomes in absence of a high-quality closely related reference genome. First, we describe an algorithm that estimates the number of times a DNA subsequence exists in the full genome using a Poisson mixture model. We used this method to infer copy number differences between aneuploid lager brewing yeasts. Second, we describe an algorithm that uses graph decomposition to find variation in metagenomes, which are a combination of many bacterial genomes within one sample and play an important role in industrial fermentation, such as in production of cheese and yoghurt.

Motivation: Modern sequencing technology now permits the sequencing of entire genomes, leading to thousands of new gene sequences in need of detailed annotation. It is too time consuming to predict the properties of each protein sequence manually and to organize the results of many prediction tools by hand. The prediction process must be automated so the predictions can be automatically organized, but the predictions must also be transparent. That is, the rationale for each prediction should be easily examinable by anyone that wishes to use the prediction. Results: Proteome Analyst (PA) is a web-based system for predicting the properties of each protein in a proteome. PA has three interesting features. First, it provides a single web-based tool that allows the user to select a wide range of analytic tools and automatically apply them to each protein in a proteome. In essence, PA provides one-stop automatic high-throughput analysis. Second, PA has the ability to explain its predictions to users. PA is based on established machine learning techniques, but makes every prediction transparent to its users. Third, PA allows users to easily create their own transparent custom predictors without programming. Availability: http://www.cs.ualberta.ca/~bioinfo/PA Supplementary Information: http://www.cs.ualberta.ca/~bioinfo/PA/Walkthrough http://www.cs.ualberta.ca/~bioinfo/PA/Experiments Contact: bioinfo@cs.ualberta.ca INTRODUCTION High-throughput sequencing technology has made it possible for even the smallest laboratory to sequence the genome of organelles, viruses and bacteria. Indeed there are now more than 1200 genome sequences deposited in public databases (http://www.ebi.ac.uk/genomes/) and this number is growing rapidly. Given the size and complexity of these data sets, most researchers are compelled to use automated annotation systems to identify or classify individual genes and proteins in their genomic data. A number of excellent systems have been developed over the past few years that permit automated genome-wide or proteome-wide annotation. These include GeneQuiz (Andrade et al., 1999), GeneAtlas (Kitson et al., 2002), EnsEMBL (Hubbard et al., 2002), PEDANT (Frishman et al., 2001), Genotator (Harris, 1997), Magpie (Gaasterland and Sensen, 1996) and GAIA (Overton et al., 1998). These use web-based tools to identify genes, parse data, translate sequences, search against public databases, identify domains or motifs and perform predictive analyses of protein sequences. Many of these packages provide user-customizable search schemas and graphical, hyperlinked output. The level of interpretation or inference offered by these annotation systems varies widely, with some offering only raw data (lists of homologues, calculated properties, etc.) in a consolidated format and others inferring function or ontology through detailed lexical analysis. Our PA system focuses on the task of prediction or classification. Our results show that classification/prediction can be used for many annotations. The most obvious is general-function, but we are also working on sub-cellular localization, specific function and protein-protein interaction are others we are working on. To use classification, we require an ontology or “dictionary” of class terms. Consequently, a number of controlled vocabularies or ontologies of protein function have started appearing. Among the first was the enzyme classification scheme (E.C. number) developed by the IUBMB and first employed in the ENZYME database (Bairoch, 1993). Later, Riley (1993) developed functional classification schemas for E. coli and other microbes. Variations on these functional vocabularies were added as other genomes and genome annotation systems were

Recent advances in High-Throughput Sequencing (HTS) technology have greatly facilitated the researches in bioinformatics field. With the ultra-high sequencing speed and improved base-calling accuracy, Illumina Genome Analyzer is currently the most widely used platform in the field. To use the raw reads generated from the sequencing machine, the 3’ adapter sequence attached to the real read in the process of ligation needs to be correctly trimmed. This is often done by some inhouse scripts or different packages with various parameters. They either use the Smith-Waterman algorithm or search for an exact match of the 3’ adapter sequence. In this report, I investigated methodologies as well as the strengths and weaknesses of five representative mainstream adapter trimming tools in order to suggest a direction for other researchers. Furthermore, four sets of detailed analysis were performed to evaluate the performances of these tools. I demonstrated that my adapter trimming method is flexible, accurate and efficient for Next Generation Sequencing (NGS) analysis. Type of Report: MS Project Report Department of Computer Science & Engineering Washington University in St. Louis Campus Box 1045 St. Louis, MO 63130 ph: (314) 935-6160 Master’s Project Report LCScanner: An Efficient and Accurate Trimming Tool for Illumina Next Generation Sequencing Reads by Xiang Zhou Master of Engineering in Computer Science Washington University in St. Louis, 2012 Research Advisor: Professor Weixiong Zhang

White-faced flying squirrels (Petaurista alborufus lena) inhabiting in subtropical forests of Taiwan, feed on leaves of diverse tree species. The predominant feature of their gastrointestinal tracts is an enlarged cecum that serves as an anaerobic container for microbial fermentation. Symbiotic gut microorganisms providing metabolic activities lacking in the host, are essential for energy extraction from the nutritionally poor diet. Although the importance of gut microbiota has been well demonstrated in humans and lab mice, there is a paucity of knowledge regarding gut microbial constituents and their functional capabilities in wild animals. Therefore, in this research, we applied culture-independent molecular tools and high-throughput sequencing techniques to provide the comprehensive understanding of the gut microbial communities in the wild-caught flying squirrels. Chapter 2 described the bacterial communities of various gut compartments based on 16S rRNA gene sequences. Chapter 3 provided the phylogenetic and functional profiles of the cecal microbiota based on fosmid end-sequences. Chapter 4 revealed the metabolic characteristics of the cecal microbiota based on the comparison of metagenomic and metatranscriptomic sequences. No matter using which approaches, Firmicutes was the predominant group of the gut microbiota. All results indicated that the microbial functions greatly contributed to nutrient cycling (including degradation of carbohydrates, metabolism of proteins, and synthesis of vitamins) in the gut environments. High gene expression for sugar degradation, detection and uptake revealed microbial adaptations for plant biomass usage in small folivorous mammals.

Objective To investigate the value of gene sequencing and protein conformation in the diagnosis process by reporting a case of erythrocyte hexokinase deficiency and reviewing relevant literature. Methods One patient with erythrocyte hexokinase deficiency and his families in the pediatrics of Sun Yat-sen Memorial Hospital of Sun Yat-sen University on May 23, 2016 were selected as the research objects. Clinical data was collected from the initial records by retrospective analysis. Pathogenic gene was detected by target sequence capture and next generation high-throughput sequencing, and the effects of mutant genes on hexokinase function were studied by observing changes in the 3-dimensional structure of proteins. Results The patient exhibited chronic hemolytic anemia after birth. The high-throughput second-generation gene sequencing result of the DNA sample of the child showed that a de novo mutation in the HK1 gene located on chromosome 10, the substitution of cytidine at site 34 of exon1 by thymine : NM_033496: exon1: c. C34T: p. Arg12X, which was a nonsense mutation. Bioinformatics analysis and protein function simulation confirmed that the mutation of the gene can lead to the premature termination of the synthesis of the peptide chain. The short peptide synthesized after the mutation does not contain the binding group and catalytic group of the erythrocyte hexokinase, resulting in the loss of enzyme activity. The patient was eventually diagnosed with erythrocyte hexokinase-deficient hemolytic anemia caused by the HK1 gene mutation. Conclusions In clinical practice, patients with chronic non-spherical erythrocyte hemolytic anemia should be alert to erythrocyte hexosaminidase deficiency disease. Those with conditions should be diagnosed by gene sequencing and protein configuration analysis. Key words: Hexokinase deficiency; Hemolytic anemia; Sequence analysis; Protein structure, quaternary; HK1 gene

Objective To sequence transcriptomes of unfed female Haemaphysalis longicornis using IlluminaHiSeq highthroughput technology. Methods The data on sequences the transcriptomes were spliced and assembled, and the obtained sequences were analyzed with functional annotation, functional classification, metabolic pathway analysis and simple repeated sequence markers using bioinformatics methods. Results A total of 181 246 184 clean reads data were obtained and 107 428 unigene sequences were obtained after assembly, with an average length of 1 246.29. All unigene sequences were aligned with the Nr, Nt, Pfam, KOG, Swiss-prot, KEGG, GO databases using BLAST software. Compared with the Nr database, the long-horned blood scorpion gene sequence has a high homology (55.3%) with that of Ixodes scapularis . According to the annotation of the GO database, the functions of the all unigene sequences were divided into 3 categories (biological process, cellular component and molecular function) covering 56 functional groups; based on to the annotations of the KOG database, all the unigene sequences were assigned into 25 categories; while, according to the analysis of the KEGG database, there are 32 groups of genes involved in metabolic pathways and a major part of them (12.13%) are involved in signal transduction. A total of 45 863 simple sequence repeats (SSRs) were identified with SSR locus search. Single nucleotide polymorphism (SNP) analysis indicated that the number of SNPs for base transition was 195 369 and that for base transversion was 96 780. Conclusion The analysis on transcriptomes of unfed female Haemaphysalis longicornis lays a foundation for subsequent researches on gene expression and expression of the tick 【摘 要】 目的 利用 IlluminaHiSeq 高通量技术对长角血蜱饥饿雌成蜱进行转录组测序。 方法 将测序得到的数据进行拼接组装，并利用生物信息学方法对所得序列进行基因功能注释、功能分类、代谢途径分析和简单重复序列标记等分析。 结果 共获得 181 246 184 个 clean reads 数据，组装后得到 107 428 个 unigenes 序列，平均长度 1 246.29。以 BLAST 软件将所有的 unigenes 序列与 Nr，Nt，Pfam，KOG，Swiss-prot，KEGG，GO 数据库进行比对。与 Nr 数据库比对发现长角血蜱基因序列与同科肩突硬蜱（ Ixodes scapularis ）具有较高的同源性，为 55.3 %；根据 GO 数据库的注释，将其分为生物过程、细胞组分和分子功能 3 大类共 56 分支；通过与 KOG 数据库的注释划分 25 类；根据与 KEGG 数据库的分析发现含有代谢通路相关的基因有 32 类，其中较多的是信号转导（signal transduction）类，占 12.13 %。通过 SSR 位点查找共鉴定出 45 863 个 SSR；SNP 分析发现发生转换的 SNP 数为 195 369 个，发生颠换的 SNP 数为 96 780 个。 结论 通过对长角血蜱饥饿雌成蜱转录组水平分析，可为后续长角血蜱功能基因的挖掘及表达谱等方面的研究奠定基础。

This PhD thesis research project aims to improve the current systems of management and control of food safety, focusing the attention on the study of two contaminating emerging pathogens in fish products: Lactococcus garvieae, the etiological agent of a hemorrhagic septicemia in aquaculture sector, and Morganella morganii, the most active histamine-producing species. In the first part of the research the molecular characterization and genetic polymorphism of L. garvieae strains coming from different food niches were carried out revealing an early separation of L. garvieae population into two independent genetic lineages. Subsequent phylogenetic analyses through a MLST approach confirms the significant genetic heterogeneity in this species, the separation into two principal evolution lineages, and the presence of a third lineage totally separated from the others, that could be the ancestor branch of the main lineages. Genome sequencing of representative selected strains and the comparison with other L. garvieae sequences already present in databases, confirmed the subdivision in the three different subgroups. Within each subgroup the nucleotide similarity was 99%, while comparing subgroups the similarity decreased (94-95%). Moreover, this preliminary comparison reveals that the core genome of L. garvieae contains, in additions to genes codifying for the main metabolic pathways, genes encoding putative virulence factors, such as hemolysins and adhesin. Genes related to carbohydrate fermentation (such as lactose and sucrose) seemed part of the dispensable genome, as well as the gene cluster related to the capsule formation. Experiments carried out to evaluate the expression of virulence-related genes in representative L garvieae strains, indicated an adaptive response to different environmental conditions, specific at the strain-level. Regarding the second part of the research, we started exploring the bacterial population of fresh tuna samples and evaluated the histamine content. Despite low levels of histamine were detected (<10 ppm), many samples showed high total viable counts after temperature abuse. The 10% of the 141 enteric bacteria isolated from samples were identified by 16S rDNA sequence analysis, as M. morganii, the most active histamine-producing species. Subsequently a new quantitative PCR assay for the detection of M. morganii in potentially contaminated fish products before histamine accumulates was developed, with a detection limit of 0.563 pg of pure DNA, corresponding to DNA extracted from approximately nine cells. To improve the knowledge at genome level of the histidine decarboxylase of M. morganii (hdc gene), a region of 7681 bp was sequenced. Results showed the presence of genes encoding two putative histidine/histamine antiporters upstream and downstream the hdc gene, followed by an histidyl-tRNA synthetase. Gene expression analysis indicates that in M. morganii hdc was inducible, showing the highest expression under acidic conditions, mainly in presence of histidine supplementation.…

Despite known risks of inappropriate disposal of biomedical solid waste; most cities in developing countries are still disposing unsorted and untreated solid biomedical waste in common dumpsites. While many studies reported the presence of pathogens in fresh biomedical waste from hospitals, none has reported on the abundance and diversity of bacterial community in aged solid biomedical waste from a common dumpsite. A qualitative survey was done to identify types of solid biomedical waste on the dumpsite. Soils, sludge or washings of biomedical wastes were sampled. Total DNA was extracted and v4 region of 16S rRNA amplicons were sequenced using an Illumina MiSeq platform. A total of 1,706,442 sequences from 15 samples passed quality control. The number of sequences per sample ranged from 70664 to 174456 (mean 121765, SD 35853). Diversity was high with an InvSimpson index of 63 (Range 5 – 496, SD 121). Thirty five phyla were identified, but only 9 accounted for 96% of all sequences. The dominant phyla were Proteobacteria 37.4%, Firmicutes 34.4%, Bacteroidetes 14.1 %, Actinobacteria 5.6% and Chloroflex 1.7%. Catchall analysis predicted a mean of 9399 species per sample. Overall, 31402 operational taxonomic units (OTUs) were detected, however, only 19.8% (6,202) OTUs were found more than ten times. The most predominant OTUs were Proteinclasticum (10.4%), Acinetobacter (6.9), Halomonas (3.9), Pseudomonas (1.7%), Escherichia/Shigella 1.5% and Planococcus (1.3%). Proteiniclasticum spp and Acinetobacter spp were found in 67% (10/15) of all samples at relative abundance of 1%. Taxonomic-to-phenotype mapping revealed the presence of 36.2% related to bacteria involved in dehalogenation, 11.6% degraders of aromatic hydrocarbons, 14.8% chitin degraders, 8.5% chlorophenol degradation and Atrazine metabolism 8.3%. Taxonomy-to human pathogen mapping found 34% related to human pathogens and 39.4% were unknown. Conclusions There’s rich and diverse bacterial community in aged solid biomedical waste. Some of the predominant OTUs are related to bacteria of industrial use. We found a good number of OTUs mapping to human pathogens. Most of OTUs mapped to unknown metabolism and also to group unknown whether they human pathogens or not. To our knowledge, this is the first reports on bacteria related to industrial use from solid biomedical waste. This finding will facilitate to design further research using functional metagenomics to better understand the potential of bacteria from aged solid biomedical waste.

Chromosome 21 is the smallest human chromosome and represents a model for physical mapping of the entire human genome. Three copies of this autosome cause Down syndrome, the most frequent genetic disorder associated with significant mental retardation. Five years ago, in the framework of the international human genome project, and as an integrated part of the chromosome 21 community, a consortium, of academic groups from Japan and Germany was formed to map and sequence this chromosome. Several whole-genome and chromosome-specific DNA libraries were constructed for mapping purposes. Using a combination of nested-deletion [1] and shotgun sequencing approaches, our center determined and analyzed over 17 million bases of high-quality data. In total, over 33.5 million basepairs of DNA, distributed over four contigs, were sequenced with very high accuracy [2]. The largest of these contigs is nearly 25.5Mb long. Only three small clone gaps remain, which together comprise approximately 100kb. Thus, we achieved a coverage of 99.7% of 21q. In addition, 211,116 bp from the short arm were also sequenced. Analysis of the chromosome revealed far fewer genes than previously estimated. Instead of 800-1000 expected genes, we found only 225 genes, of which 127 are known genes and 98 are predicted genes. In addition, 59 pseudogenes were identified. The completion of the 21q sequence provides a unique resource for understanding the molecular pathophysiology of Down syndrome, as well as all other monogenic and complex disorders that map to this chromosome, including Alzheimer’s disease, leukemia, autoimmune disease, epilepsy and manic-depressive psychosis. It also stands as a structural framework from which the complete molecular architecture of the chromosome can be determined. With the announcement of the first “working draft” of the human genome by the international human genome sequencing consortium this past June, there has been a great interest in the total number of genes in the human genome[3]. Some of the current estimates range from as low as 34,000 genes to as high as 140,000 genes. The low gene-density of chromosome 21 was quite unexpected. Most striking is a 7-Mb region near the region near the centromere that contains only one gene. This region is much larger than the whole genome of some species, such as Escherichia coli, yet evolutionary processes permitted the existence of such a gene-poor SNA segment. This finding leads us to propose that there are additional large gene-less regions in other human/mammalian chromosomes. By combining the gene numbers of chromosomes 21 and 22 [4] (770genes; 2-3% of human genome) and assuming that together they represent the average gene content of the human genome, we estimate that the total number of human genes may be close to 40,000. As part of the international human genome sequencing project, we are also sequencing parts of chromosomes 11 and 18. Our high-throughput sequence production line generates about 10 Mb per day. In order to handle such a high volume of output, we have developed a system for automated data assembly, annotation and release. All the data is released through our website and through the DNA Database of Japan immediately according to the policies established by the international consortium. In addition to chromosomes 11, 18 and 21, we are also interested in the structure of the entire human genome. In collaboration with the University of Tokyo, we have developed a database called HGREP [5] (Human Genome Reconstruction Project) that contains working draft and finished sequences which cover more than 85% of the human genomic sequences. Sequence entries are aligned along the chromosomes based on sequence similarities to STS markers, BAC-end and other entry sequences. Further, biological features, such as genes, gene functions, repeats and CpG islands, are fully annotated on these entries. To identify evolutionally conserved and biologically important information in the genome, we are taking the strategy of comparative genomic sequence analysis between human and other related genomes such as those of rodents and primates. We are currently sequencing several regions of the mouse, including some regions that are counterpart to human chromosome 21, and are preparing genomic resources for sequencing of the chimpanzee. Through comparative methods, we are also trying to understand telomeric regions, structures at the end of chromosomes that play an important role in several biological functions and have been associated with human diseases like an oncogenesis and a cellular aging. The structure and function of all genes and their regulatory regions can only be fully understood by looking at how they interact under different circumstances and in several different backgrounds. Determining which genes and elements are human-specific will lead to a deeper understanding of who we are and will advance medical sciences drastically.

With the development of new sequencing techniques, whole genomes of many species have become available. This huge amount of data gives rise to new opportunities and challenges. These new sequences provide valuable information on relationships among species, e.g. genome recombination and conservation. One of the principal ways to investigate such information is multiple sequence alignment (MSA). Currently, there is large amount of MSA data on the internet, such as the UCSC genome database, but how to effectively use this information to solve classical and new problems is still an area lacking of exploration. In this thesis, we explored how to use this information in four problems, i.e. sequence orthology search problem, multiple alignment improvement problem, short read mapping problem, and genome rearrangement inference problem. For the first problem, we developed a EM algorithm to iteratively align a query with a multiple alignment database with the information from a phylogeny relating the query species and the species in the multiple alignment. We also infer the query's location in the phylogeny. We showed that by doing alignment and phylogeny inference together, we can improve the accuracies for both problems. For the second problem, we developed an optimization algorithm to iteratively refine the multiple alignment quality. Experiment results showed our algorithm is very stable in term of resulting alignments. The results showed that our method is more accurate than existing methods, i.e. Mafft, Clustal-O, and Mavid, on test data from three sets of species from the UCSC genome database. For the third problem, we developed a model, PhyMap, to align a read to a multiple alignment allowing mismatches and indels. PhyMap computes local alignments of a query sequence against a fixed multiple-genome alignment of closely related species. PhyMap uses a known phylogenetic tree on the species in the multiple alignment to improve the quality of its computed alignments while also estimating the placement of the query on this tree. Both theoretical computation and experiment results show that our model can differentiate between orthologous and paralogous alignments better than other popular short read mapping tools (BWA, BOWTIE and BLAST). For the fourth problem, we gave a simple genome recombination model which can express insertions, deletions, inversions, translocations and inverted translocations on aligned genome segments. We also developed an MCMC algorithm to infer the order of the query segments. We proved that using any Euclidian metrics to measure distance between two sequence orders in the tree optimization goal function will lead to a degenerated solution where the inferred order will be the order of one of the leaf nodes. We also gave a graph-based formulation of the problem which can represent the probability distribution of the order of the query sequences.

Life finds a way: the recovery of frog populations from a chytridiomycosis outbreak

Identification of a possible role of thymine DNA glycosylase (TDG) in epigenome maintenance

Figure S1. Stacked bar-chart showing the relative abundance of microbial classes across the metagenomic sequencing data. Figure S2. Stacked bar-chart showing the relative abundance of bacterial classes across the 16S rRNA gene sequencing data. Figure S3. Barplots comparing random forest model accuracies based on non-rarefied-centered log-ratio transformed and rarefied 16S rRNA gene taxa abundances. Figure S4. Boxplots of genetic risk scores and the number of observed OTUs, between Crohn’s disease and control patients. Figure S5. Distribution of variable importance for 16S rRNA gene-identified genera disease classification. Figure S6. Boxplots of the relative abundance of the top 16S rRNA gene sequencing-identified genera for classifying disease state compared between sequencing technologies. Figure S7. Boxplots of genetic risk score and alpha-diversity between responders and non-responders to treatment. Figure S8. Pairwise Spearman correlation coefficients between features in the combined disease random forest. Figure S9. Pairwise Spearman correlation coefficients between features in the combined treatment response random forest. Figure S10. Features ranked by their relative importance for classifying disease state in the RISK validation cohort. Table S1. Table of demographic and phenotypic characteristics of pediatric patients. Table 2. Table of phenotypic characteristics and treatments of pediatric patients. (PDF 2350 kb)

Objective To study the structural features and different types of the genomic molecular organization of human bocavirus in infected infants in Guangzhou. Methods Primers were designed on the basis of the genomic sequence of human bocavirus BJ3064 strain (DQ988933) in the GenBank, and amplify HBOV genome by PCR.The PCR-products were directly sequenced. The genomic nucleotide sequences were analyzed with the programs of CLUSTAL W/X and MEGA5.0.Results The strain GZ2010-1 genome is 5299bp in length. The genomic sequences of GZ2010-1 strain were compared with those sequences of HBOV1, HBOV2, HBOV3, and HBOV4 genotypes in GenBank, revealed that the homology with HBOV1 genotype ranged from 98% to 99%, homology with genotypes HBOV2, HBOV3, HBOV4 were 76%, 81% ,and 75% respectively. The F genes of GZ2010-1 strain was compared with F genes of HBOV1 genotype, showed that the homology was also between 98%-99%. Conclusions The complete nucleotide sequence of human bocavirus GZ2010-1 strain isolated from Guangzhou is 5299 bp in length,GenBank accession NO.is JN 128956. Sequence and phylogenetic analysis of the GZ2010-1 strain reveales that this isolate belongs to HBOV1. Key words: Human bocavirus; Genome; Sequence analysis; Guangzhou

Clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPRassociated (Cas) technology is an efficient means of creating targeted deletion or insertion variants (DIVs). Our lab has taken advantage of this to develop a genome engineering pipeline that remains efficient at scale, which puts saturation mutagenesis within reach. The pipeline involves inducing DIVs with CRISPR/ Cas9, amplifying the targeted regions via PCR, and subjecting the amplicons to high-throughput sequencing. A key component of this process is the ampliconDIVider software, which identifies and characterizes DIVs at the nucleotide level. The program recovers DIVs within user-defined regions, parses samples based on DNA barcodes, and uses the sam2pairwise program to rapidly visualize DIVs as pairwise alignments. We applied this approach in Danio rerio, and identified 678 unique DIV alleles induced by 114 CRISPR targets in 58 genes. Contrary to previous reports, we detected no evidence that the primary sequence of the CRISPR affected mutagenesis efficiency, other than that the rate was reduced by the addition of extra guanines to the 5’ end of the target. ampliconDIVider is flexible, and is not limited to the context of this pipeline, nor to DIVs induced specifically by CRISPRs. TGCCGTTTGTGA ... ATGGGCAGCTGGGCCGTGAGTTAGTGCCTGTGGACGAGGCTTTCATGAAGAGAGATGAAG ... AGCAGAAAGGTT ! Field Value CIGAR 27M6D27M Sequence ATGGGCAGCTGGGCCGTGAGTTAGTGCACGAGGCTTTCATGAAGAGAGATGAAG MD Tag MD:Z:27^CTGTGG27 sam2pairwise (https://github.com/mlafave/sam2pairwise) ATGGGCAGCTGGGCCGTGAGTTAGTGC------ACGAGGCTTTCATGAAGAGAGATGAAG ! ||||||||||||||||||||||||||| ||||||||||||||||||||||||||| ! ATGGGCAGCTGGGCCGTGAGTTAGTGCCTGTGGACGAGGCTTTCATGAAGAGAGATGAAG ! ampliconDIVider (https://github.com/mlafave/ampliconDIVider) samtools view file.bam | sam2pairwise ! Amplicon target DNA M13 PIG-tail Barcode Adapter Adapter Summary • ampliconDIVider facilitates high-throughput detection of deletion and insertion variants in defined regions • sam2pairwise turns SAM entries into pairwise alignments • The CRISPR pipeline is an efficient, scalable means for targeted mutagenesis • CRISPR sequence had no effect on mutagenesis rate in zebrafish, except when additional bases were added Amplicon target DNA M13 PIG-tail Barcode Trim non-genomic DNA, store barcode (cutadapt) Remove untrustworthy fragments Amplicon target A Barcode 1 Amplicon target A Barcode 2 Amplicon target B Barcode 1 Align reads to amplicon index (novoalign) Segregate by target and sample Amplicon target A Barcode 1 Amplicon target A Barcode 2 Amplicon target B Barcode 1 Target A Target B Barcode 1