Multiplexed Sequence Encoding: A Framework for DNA Communication

Synthetic DNA has great propensity for efficiently and stably storing non-biological information. With DNA writing and reading technologies rapidly advancing, new applications for synthetic DNA are emerging in data storage and communication. Traditionally, DNA communication has focused on the encoding and transfer of complete sets of information. Here, we explore the use of DNA for the communication of short messages that are fragmented across multiple distinct DNA molecules. We identified three pivotal points in a communication—data encoding, data transfer & data extraction—and developed novel tools to enable communication via molecules of DNA. To address data encoding, we designed DNA-based individualized keyboards (iKeys) to convert plaintext into DNA, while reducing the occurrence of DNA homopolymers to improve synthesis and sequencing processes. To address data transfer, we implemented a secret-sharing system—Multiplexed Sequence Encoding (MuSE)—that conceals messages between multiple distinct DNA molecules, requiring a combination key to reveal messages. To address data extraction, we achieved the first instance of chromatogram patterning through multiplexed sequencing, thereby enabling a new method for data extraction. We envision these approaches will enable more widespread communication of information via DNA.     

人类基因组DNA甲基化数据分析的研究现状

作为人类基因组最为典型的表观遗传现象,DNA甲基化在多种关键生理活动中扮演重要角色.系统分析基因组尺度的DNA甲基化概况意义重大.从Cp G岛等基本定义出发,阐述了高通量DNA甲基化的检测技术以及针对芯片技术与下一代测序技术的低水平数据处理方法;重点对比了基于机器学习理论对Cp G位点及Cp G岛甲基化水平的预测算法,以及所利用的特征对预测效果的影响与发展趋势;并对DNA差异甲基化在组织特异性、癌症等多种疾病中的计算分析进行了全面的综述.     

PathogenFinder - Distinguishing Friend from Foe Using Bacterial Whole Genome Sequence Data

Although the majority of bacteria are harmless or even beneficial to their host, others are highly virulent and can cause serious diseases, and even death. Due to the constantly decreasing cost of high-throughput sequencing there are now many completely sequenced genomes available from both human pathogenic and innocuous strains. The data can be used to identify gene families that correlate with pathogenicity and to develop tools to predict the pathogenicity of newly sequenced strains, investigations that previously were mainly done by means of more expensive and time consuming experimental approaches. We describe PathogenFinder (http://cge.cbs.dtu.dk/services/PathogenFinder/), a web-server for the prediction of bacterial pathogenicity by analysing the input proteome, genome, or raw reads provided by the user. The method relies on groups of proteins, created without regard to their annotated function or known involvement in pathogenicity. The method has been built to work with all taxonomic groups of bacteria and using the entire training-set, achieved an accuracy of 88.6% on an independent test-set, by correctly classifying 398 out of 449 completely sequenced bacteria. The approach here proposed is not biased on sets of genes known to be associated with pathogenicity, thus the approach could aid the discovery of novel pathogenicity factors. Furthermore the pathogenicity prediction web-server could be used to isolate the potential pathogenic features of both known and unknown strains.     

A Framework for Annotating Human Genome in Disease Context

Identification of gene-disease association is crucial to understanding disease mechanism. A rapid increase in biomedical literatures, led by advances of genome-scale technologies, poses challenge for manually-curated-based annotation databases to characterize gene-disease associations effectively and timely. We propose an automatic method-The Disease Ontology Annotation Framework (DOAF) to provide a comprehensive annotation of the human genome using the computable Disease Ontology (DO), the NCBO Annotator service and NCBI Gene Reference Into Function (GeneRIF). DOAF can keep the resulting knowledgebase current by periodically executing automatic pipeline to re-annotate the human genome using the latest DO and GeneRIF releases at any frequency such as daily or monthly. Further, DOAF provides a computable and programmable environment which enables large-scale and integrative analysis by working with external analytic software or online service platforms. A user-friendly web interface (doa.nubic.northwestern.edu) is implemented to allow users to efficiently query, download, and view disease annotations and the underlying evidences.     

ToPS: A Framework to Manipulate Probabilistic Models of Sequence Data

Discrete Markovian models can be used to characterize patterns in sequences of values and have many applications in biological sequence analysis, including gene prediction, CpG island detection, alignment, and protein profiling. We present ToPS, a computational framework that can be used to implement different applications in bioinformatics analysis by combining eight kinds of models: (i) independent and identically distributed process; (ii) variable-length Markov chain; (iii) inhomogeneous Markov chain; (iv) hidden Markov model; (v) profile hidden Markov model; (vi) pair hidden Markov model; (vii) generalized hidden Markov model; and (viii) similarity based sequence weighting. The framework includes functionality for training, simulation and decoding of the models. Additionally, it provides two methods to help parameter setting: Akaike and Bayesian information criteria (AIC and BIC). The models can be used stand-alone, combined in Bayesian classifiers, or included in more complex, multi-model, probabilistic architectures using GHMMs. In particular the framework provides a novel, flexible, implementation of decoding in GHMMs that detects when the architecture can be traversed efficiently.

This is a PLOS Computational Biology Software Article.

     

Whole Genome Sequence of a Turkish Individual

Although whole human genome sequencing can be done with readily available technical and financial resources, the need for detailed analyses of genomes of certain populations still exists. Here we present, for the first time, sequencing and analysis of a Turkish human genome. We have performed 35x coverage using paired-end sequencing, where over 95% of sequencing reads are mapped to the reference genome covering more than 99% of the bases. The assembly of unmapped reads rendered 11,654 contigs, 2,168 of which did not reveal any homology to known sequences, resulting in ∼1 Mbp of unmapped sequence. Single nucleotide polymorphism (SNP) discovery resulted in 3,537,794 SNP calls with 29,184 SNPs identified in coding regions, where 106 were nonsense and 259 were categorized as having a high-impact effect. The homo/hetero zygosity (1,415,123∶2,122,671 or 1∶1.5) and transition/transversion ratios (2,383,204∶1,154,590 or 2.06∶1) were within expected limits. Of the identified SNPs, 480,396 were potentially novel with 2,925 in coding regions, including 48 nonsense and 95 high-impact SNPs. Functional analysis of novel high-impact SNPs revealed various interaction networks, notably involving hereditary and neurological disorders or diseases. Assembly results indicated 713,640 indels (1∶1.09 insertion/deletion ratio), ranging from −52 bp to 34 bp in length and causing about 180 codon insertion/deletions and 246 frame shifts. Using paired-end- and read-depth-based methods, we discovered 9,109 structural variants and compared our variant findings with other populations. Our results suggest that whole genome sequencing is a valuable tool for understanding variations in the human genome across different populations. Detailed analyses of genomes of diverse origins greatly benefits research in genetics and medicine and should be conducted on a larger scale.     

TreeSeq,a Fast and Intuitive Tool for Analysis of Whole Genome and Metagenomic Sequence Data

Next-generation sequencing is not yet commonly used in clinical laboratories because of a lack of simple and intuitive tools. We developed a software tool (TreeSeq) with a quaternary tree search structure for the analysis of sequence data. This permits rapid searches for sequences of interest in large datasets. We used TreeSeq to screen a gut microbiota metagenomic dataset and a whole genome sequencing (WGS) dataset of a strain of Klebsiella pneumoniae for antibiotic resistance genes and compared the results with BLAST and phenotypic resistance determination. TreeSeq was more than thirty times faster than BLAST and accurately detected resistance gene sequences in complex metagenomic data and resistance genes corresponding with the phenotypic resistance pattern of the Klebsiella strain. Resistance genes found by TreeSeq were visualized as a gene coverage heat map, aiding in the interpretation of results. TreeSeq brings analysis of metagenomic and WGS data within reach of clinical diagnostics.     

Interpretation, Stratification and Evidence for Sequence Variants Affecting mRNA Splicing in Complete Human Genome Sequences

Information theory-based methods have been shown to be sensitive and specific for predicting and quantifying the effects of non-coding mutations in Mendelian diseases. We present the Shannon pipeline software for genome-scale mutation analysis and provide evidence that the software predicts variants affecting mRNA splicing. Individual information contents (in bits) of reference and variant splice sites are compared and significant differences are annotated and prioritized. The software has been implemented for CLC-Bio Genomics platform. Annotation indicates the context of novel mutations as well as common and rare SNPs with splicing effects. Potential natural and cryptic mRNA splicing variants are identified, and null mutations are distinguished from leaky mutations. Mutations and rare SNPs were predicted in genomes of three cancer cell lines (U2OS, U251 and A431), which were supported by expression analyses. After filtering, tractable numbers of potentially deleterious variants are predicted by the software, suitable for further laboratory investigation. In these cell lines, novel functional variants comprised 6-17 inactivating mutations, 1-5 leaky mutations and 6-13 cryptic splicing mutations. Predicted effects were validated by RNA-seq analysis of the three aforementioned cancer cell lines, and expression microarray analysis of SNPs in HapMap cell lines.     

Bst DNA聚合酶大片段在全基因组扩增中的应用

Bst DNA聚合酶大片段作为一种常用的DNA聚合酶,因其独特的特点:能引发链置换反应、高保真、耐高温等,而成为一种重要的DNA多重置换扩增酶。目的:为减少成本,设计一种高产,方便且扩增活性高的Bst DNA聚合酶大片段表达体系;探究该酶应用于胃癌石蜡包埋组织基因组DNA的扩增条件。方法:采用p TWIN1质粒作为载体克隆表达Bst DNA聚合酶大片段,应用几丁质亲和层析柱纯化该酶,使用该酶对人类基因组DNA进行不同温度下扩增,探究其最适反应温度,并据此对胃癌石蜡包埋组织基因组DNA进行扩增。结果:由此得到的Bst DNA聚合酶大片段能运用于胃癌石蜡包埋组织基因组DNA的扩增,扩增效率可达200倍,并能应用于a CGH芯片。结论:扩增得到保真性高,覆盖基因组范围大的DNA扩增产物。该应用与a CGH结合,使得对少量的癌症石蜡包埋组织DNA样本进行全基因组扩增,并进行其基因拷贝数变异研究成为可能。     

Assessment of Whole Genome Amplification for Sequence Capture and Massively Parallel Sequencing

Exome sequence capture and massively parallel sequencing can be combined to achieve inexpensive and rapid global analyses of the functional sections of the genome. The difficulties of working with relatively small quantities of genetic material, as may be necessary when sharing tumor biopsies between collaborators for instance, can be overcome using whole genome amplification. However, the potential drawbacks of using a whole genome amplification technology based on random primers in combination with sequence capture followed by massively parallel sequencing have not yet been examined in detail, especially in the context of mutation discovery in tumor material. In this work, we compare mutations detected in sequence data for unamplified DNA, whole genome amplified DNA, and RNA originating from the same tumor tissue samples from 16 patients diagnosed with non-small cell lung cancer. The results obtained provide a comprehensive overview of the merits of these techniques for mutation analysis. We evaluated the identified genetic variants, and found that most (74%) of them were observed in both the amplified and the unamplified sequence data. Eighty-nine percent of the variations found by WGA were shared with unamplified DNA. We demonstrate a strategy for avoiding allelic bias by including RNA-sequencing information.     

Generation of Physical Map Contig-Specific Sequences Useful for Whole Genome Sequence Scaffolding

Along with the rapid advances of the nextgen sequencing technologies, more and more species are added to the list of organisms whose whole genomes are sequenced. However, the assembled draft genome of many organisms consists of numerous small contigs, due to the short length of the reads generated by nextgen sequencing platforms. In order to improve the assembly and bring the genome contigs together, more genome resources are needed. In this study, we developed a strategy to generate a valuable genome resource, physical map contig-specific sequences, which are randomly distributed genome sequences in each physical contig. Two-dimensional tagging method was used to create specific tags for 1,824 physical contigs, in which the cost was dramatically reduced. A total of 94,111,841 100-bp reads and 315,277 assembled contigs are identified containing physical map contig-specific tags. The physical map contig-specific sequences along with the currently available BAC end sequences were then used to anchor the catfish draft genome contigs. A total of 156,457 genome contigs (~79% of whole genome sequencing assembly) were anchored and grouped into 1,824 pools, in which 16,680 unique genes were annotated. The physical map contig-specific sequences are valuable resources to link physical map, genetic linkage map and draft whole genome sequences, consequently have the capability to improve the whole genome sequences assembly and scaffolding, and improve the genome-wide comparative analysis as well. The strategy developed in this study could also be adopted in other species whose whole genome assembly is still facing a challenge.     

Whole Genome Sequence Analysis of Lactiplantibacillus Plantarum Bacteriophage P2

Phage P2 was isolated from failed fermentation broth carried out by Lactiplantibacillus plantarum IMAU10120. A previous study in our laboratory showed that this phage belonged to the Siphoviridae family. In this study, this phage’s genomic characteristics were analyzed using whole-genome sequencing. It was revealed that phage P2 was 77.9 kb in length and had 39.28% G + C content. Its genome included 96 coding sequences (CDS) and two tRNA genes involved in the function of the structure, DNA replication, packaging, and regulation. Phage P2 had higher host specificity; many tested strains were not infected. Cell wall adsorption experiments showed that the adsorption receptor component of phage P2 might be a part of the cell wall peptidoglycan. This research might enrich the knowledge about genomic information of lactobacillus phages and provide some primary data to establish phage control measures.     

Heat-Passing Framework for Robust Interpretation of Data in Networks

Researchers are regularly interested in interpreting the multipartite structure of data entities according to their functional relationships. Data is often heterogeneous with intricately hidden inner structure. With limited prior knowledge, researchers are likely to confront the problem of transforming this data into knowledge. We develop a new framework, called heat-passing, which exploits intrinsic similarity relationships within noisy and incomplete raw data, and constructs a meaningful map of the data. The proposed framework is able to rank, cluster, and visualize the data all at once. The novelty of this framework is derived from an analogy between the process of data interpretation and that of heat transfer, in which all data points contribute simultaneously and globally to reveal intrinsic similarities between regions of data, meaningful coordinates for embedding the data, and exemplar data points that lie at optimal positions for heat transfer. We demonstrate the effectiveness of the heat-passing framework for robustly partitioning the complex networks, analyzing the globin family of proteins and determining conformational states of macromolecules in the presence of high levels of noise. The results indicate that the methodology is able to reveal functionally consistent relationships in a robust fashion with no reference to prior knowledge. The heat-passing framework is very general and has the potential for applications to a broad range of research fields, for example, biological networks, social networks and semantic analysis of documents.     

A Whole Genome Amplification Method to Generate Long Fragments from Low Quantities of Genomic DNA

Several whole genome amplification strategies have been developed to preamplify the entire genome from minimal amounts of DNA for subsequent molecular genetic analysis. However, none of these techniques has proven to amplify long products from very low (nanogram or picogram) quantities of genomic DNA. Here we report a new whole genome amplification protocol using a degenerate primer (DOP-PCR) that generates products up to about 10 kb in length from less than 1 ng genomic template DNA. This new protocol (LL-DOP-PCR) allows in the subsequent PCR the specific amplification, with high fidelity, of DNA fragments that are more than 1 kb in length. LL-DOP-PCR provides significantly better coverage for microsatellites and unique sequences in comparison to a conventional DOP-PCR method.     

Examining Phylogenetic Relationships Among Gibbon Genera Using Whole Genome Sequence Data Using an Approximate Bayesian Computation Approach

Gibbons are believed to have diverged from the larger great apes ∼16.8 MYA and today reside in the rainforests of Southeast Asia. Based on their diploid chromosome number, the family Hylobatidae is divided into four genera, Nomascus, Symphalangus, Hoolock, and Hylobates. Genetic studies attempting to elucidate the phylogenetic relationships among gibbons using karyotypes, mitochondrial DNA (mtDNA), the Y chromosome, and short autosomal sequences have been inconclusive . To examine the relationships among gibbon genera in more depth, we performed second-generation whole genome sequencing (WGS) to a mean of ∼15× coverage in two individuals from each genus. We developed a coalescent-based approximate Bayesian computation (ABC) method incorporating a model of sequencing error generated by high coverage exome validation to infer the branching order, divergence times, and effective population sizes of gibbon taxa. Although Hoolock and Symphalangus are likely sister taxa, we could not confidently resolve a single bifurcating tree despite the large amount of data analyzed. Instead, our results support the hypothesis that all four gibbon genera diverged at approximately the same time. Assuming an autosomal mutation rate of 1 × 10⁻⁹/site/year this speciation process occurred ∼5 MYA during a period in the Early Pliocene characterized by climatic shifts and fragmentation of the Sunda shelf forests. Whole genome sequencing of additional individuals will be vital for inferring the extent of gene flow among species after the separation of the gibbon genera.