Proteome Analyst - Transparent High-throughput Protein Annotation: Function, Localization and Custom Predictors

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.

Human tuberculosis disease (TB) is caused by bacteria within the Mycobacterium tuberculosis complex, including M. tuberculosis (Mtb). Genetic variation within the pathogen can lead to drug resistance, affect virulence and transmissibility. I have analysed Mtb whole genome sequence data to improve the understanding of global genetic variation, and the resulting insights could ultimately assist the development of TB control measures. Whole genome sequencing platforms are being used to infer drug resistance profiles, and thereby could assist clinical management. I investigated the reproducibility of sequence data from two platforms (Illumina MiSeq, Ion Torrent PGM™) and two rapid analytic pipelines (TBProfiler, Mykrobe predictor). DNA replicates from the reference strain (H37Rv) and 10 drug-resistant strains were sequenced, and inferred drug resistance genotypes were compared to drug susceptibility testing phenotypes. Genome-wide association study (GWAS) can be used to detect mutations associated with Mtb drug resistance. A first GWAS (n=127) attempted to identify mutations associated with minimum inhibitory concentrations for first-line anti-tuberculosis drugs. A second GWAS was applied to a large global set (n>6400) to identify mutations associated with first- and secondline drug resistance. M. aurum is an environmental mycobacteria that has been proposed as a model for the development of anti-TB drugs. I have assembled and annotated its draft genome, and identified copy number variants in known drug resistance targets. Approximately 10% of the Mtb genome consists of two gene families (pe/ppe) that are poorly characterised, and are hypothesised to be important virulence factors. Using a de novo assembly approach, I characterised these genes and their diversity across a global collection of clinical isolates with high depth short-read sequence data (n=518). A follow-up study using a long-read sequence technology (n=18, diverse stain types) confirmed the findings. This work also generated new annotated reference genomes and characterised methylation sites, which may affect transmissibility, pathogenicity and virulence. A future direction of the TB genomics field is to identify genetic check points in host-pathogen interactions using both human and Mtb genotypes. I analysed the genomes of ~720 TB case–Mtb pairs and identified susceptibility markers, which are promising targets for future control measures.

Transcription factor binding sites (TFBS) in gene promoter regions are often predicted by using position specific scoring matrices (PSSMs), which summarize sequence patterns of experimentally determined TF binding sites. Although PSSMs are more reliable than simple consensus string matching in predicting a true binding site, they generally result in high numbers of false positive hits. This study attempts to reduce the number of false positive matches and generate new predictions by integrating various types of genomic data by two methods: a Bayesian allocation procedure, and support vector machine classification. Several methods will be explored to strengthen the prediction of a true TFBS in the Saccharomyces cerevisiae genome: binding site degeneracy, binding site conservation, phylogenetic profiling, TF binding site clustering, gene expression profiles, GO functional annotation, and k-mer counts in promoter regions. Binding site degeneracy (or redundancy) refers to the number of times a particular transcription factor's binding motif is discovered in the upstream region of a gene. Phylogenetic conservation takes into account the number of orthologous upstream regions in other genomes that contain a particular binding site. Phylogenetic profiling refers to the presence or absence of a gene across a large set of genomes. Binding site clusters are statistically significant clusters of TF binding sites detected by the algorithm ClusterBuster. Gene expression takes into account the idea that when the gene expression profiles of a transcription factor and a potential target gene are correlated, then it is more likely that the gene is a genuine target. Also, genes with highly correlated expression profiles are often regulated by the same TF(s). The GO annotation data takes advantage of the idea that common transcription targets often have related function. Finally, the distribution of the counts of all k-mers of length 4, 5, and 6 in gene's promoter region were examined as means to predict TF binding. In each case the data are compared to known true positives taken from ChIP-chip data, Transfac, and the Saccharomyces Genome Database. First, degeneracy, conservation, expression, and binding site clusters were examined independently and in combination via Bayesian allocation. Then, binding sites were predicted with a support vector machine (SVM) using all methods alone and in combination. The SVM works best when all genomic data are combined, but can also identify which methods contribute the most to accurate classification. On average, a support vector machine can classify binding sites with high sensitivity and an accuracy of almost 80%.

Objective To predict the secretome and analyze the secreted proteins and signal peptides of Echinococcus multilocularis using genome-wide bioinformatics approaches thus to provide a platform for finding biomarkers for development of diagnosis and drugs.Methods Signal peptides of E.multilocularis were identified from the whole genome sequence using SignalP4.1 program,and the proteins containing signal sequences were analyzed with TMHMM v2.0,Phobius,Big-PI predictor and TargetPl.1 in a stepwise way to minimize the false-positive prediction.Subsequently,the sequence features of both the signal peptides and the secreted proteins were statistically analyzed by SPSS19.0 and Excel.Differences in the numbers of amino acid of each secreted and the non-secreted sequences were determined through kolmogorov-smirnov test (K-S test)and t test.Finally,the KAAS (KEGG Automatic Annotation Sever) functional annotation and clustering were performed for the secretome sequences.Results A total of 875 proteins encoding sequences containing signal peptides was found in 10 780 E.multilocularis genome sequences.Among them,307 sequences are membrane-binding proteins,38 proteins contain GPI anchor site and 12 proteins are located in mitochondria.Finally,a total of 518 proteins was recognized as secreted proteins.The signal sequences contain mostly 11-53amino acids,among them 61％ are hydrophobic.The secreted proteins identified possess 38-7 809 amino acids,which is significantly less than the amount possessed by non-secreted proteins(11-11 194 amino acids) (t=0.203,P＜0.01).KAAS analysis showed that these secreted proteins are mainly involved in human disease,metabolism,environmental information processing,organismal systems,cellular process and genetic information processing.Among them,6 sequences are related to parasitic infection.Conclusion The secretome of E.multilocularis containing 518 secreted protein sequences was predicted and analyzed thus to provide a database for further identification of diagnostics,vaccine and drug targets. Key words: Echinococcus multilocularis; Secretome; Signal peptide; Bioinformatics

In the last two decades, our understanding of the molecular mechanisms within the cell has witnessed a great leap forward. For the most part this is due to the fast innovation of the genomic measurements technologies and wide spread usage of computational methods which enables knowledge extraction from the massive datasets produced by these measurements. A notable example of a field that has substantially benefitted from this progress is cancer patient outcome prediction, in which the aim is to predict patient prognosis from common clinical variables such as tumor size, age or histological parameters. With the application of machine learning methods to gene expression profiles of the tumor a major improvement of the prediction accuracy could be realized. These models are later succeeded by Network based Outcome Predictors (NOP) that consider the cellular wiring diagram of cell in the model to identify stable and relevant markers that can accurately estimate outcome of patients. Problematically, after a decade of research in this area, NOPs did not find extensive application compared to the classical models due to contradicting reports regarding their performance, stability and relevance of markers in the literature. In this thesis, we introduce a new NOP - called FERAL - that alleviates several fundamental issues in state-of-the-art NOPs which prevented these models to reach the optimal prediction performance, stability and marker relevance. We furthermore demonstrate that generic biological networks do not contain sufficiently informative interactions to truly aid NOP. We therefore infer a phenotype-specific network called SyNet which connects pairs of genes that together achieve patient outcome prediction performance beyond what is attainable by individually genes. We show that a NOP that use identical gene expression datasets, yields superior performance merely by considering groups of genes suggested by SyNet. We, moreover, show that model performance is severely reduced if nodes in SyNet are shuffled, which confirms that also the links in SyNet are relevant to outcome prediction. An important limitation of current biological networks is that they are restricted to pairwise interactions. We show that higher order interactions between functional elements in the cell are relevant in outcome prediction. We later introduce a novel genomics method called Multi-Contact 4C (MC-4C) to measure and investigate multi-way interactions between functional elements. In contrast to existing methods, MC-4C exploits long-read 3rd generation sequencing technologies and detects higher order interactions that occur in a region of interest at the level of a single allele. We further devise a well-founded statistical model that is required for significance estimation of observed interactions. UsingMC-4C, we experimentally confirm a 26 years old hypothesis regarding the looping and co-localization of enhancers in the O -globin region in the mouse genome. Additionally, we provide the first experimental explanation for the “vermicelli” phenomenon that was observed through microscopic inspection of cells depleted of WAPL (the element responsible for unwinding of loops in mammalian cells). Therefore, targeted multi-way conformation analysis methods like MC-4C promise to uncover how the multitude of regulatory sequences and genes coordinate their activity in the spatial context of the genome.

ژن‌های کاذب نسخه‌هایی از ژن اجدادی می‌باشند که به مرور زمان فعالیت آنها نسبت به ژن اولیه تغییر کرده است و در ژنوم بر اثر فرآیندهایی مانند مضاعف شدگی ژنی و همچنین رونویسی واژگون ایجاد شده‌اند. ژن‌های کاذب تا مدت‌ها به‌عنوان توالی‌های غیر عملکردی ژنوم در نظر گرفته می‌شدند. با این وجود پژوهش‌های اخیر گزارشاتی مبنی بر فعالیت زیستی این ژن‌ها ارائه داده‌اند، در نتیجه عملکردی بودن این ژن‌ها موجب افزایش حاشیه نویسی صحیح‌تر این ژن‌ها در ژنوم موجودات شده است. در پژوهش حاضر به منظور بهبود حاشیه نویسی ژنوم گوسفند، برای نخستین بار با استفاده از روش‌های محاسباتی بر پایه بررسی تشابه با استفاده از نرم‌افزار PseudoPipe، ژن‌های کاذب مرتبط با ژن‌های کدکننده پروتئین در سطح ژنوم شناسایی شدند. همچنین گروه‌های کارکردی ژن‌های والدی که ژن‌های کاذب از آنها مشتق شده‌اند با استفاده از پایگاه اینترنتی DAVID بررسی شدند. در نهایت ویژگی‌های مختلف ژن‌های کاذب کاندید جدید شناسایی شده با ژن‌های کاذب شناخته‌شده در گونه‌های انسان، موش و گاو مقایسه شدند. به طور کلی 4098 ژن کاذب با سطح اطمینان بالا شامل 1102 ژن کاذب از نوع مضاعف شده و 2996 از نوع پردازش شده شناسایی شدند. نتایج نشان داد که ژن‌های کاذب شناسایی شده در فرآیندهای زیستی گوناگونی مانند splicing mRNA، پیدایش ریبوزوم، اتصال rRNA، انتقال الکترون میتوکندریایی، ترجمه و غیره نقش دارند. مقایسه ویژگی‌های مختلف ژن‌های شناسایی شده با دیگر گونه‌ها نشان داد که نتایج حاصل از این پژوهش در تطابق با پژوهش‌های گذشته می‌باشد. نتایج حاصل از این پژوهش به بهبود حاشیه نویسی ژنوم گوسفند کمک خواهد کرد.

Objective To study the effects of different pre-sequencing sample processing modes on the results of whole genome sequencing with high-throughput sequencing (HTS) by taking the largest RNA virus (human coronavirus, HCoV) as the representative. Methods Cell-cultured human coronavirus HCoV-OC43 strains were used as the representative samples and divided into different groups based on pre-sequencing processing modes as follows: untreated group, DNase and RNase treatment before nucleic acid extraction group, DNase treatment after nucleic acid extraction group, and DNase and RNase treatment before nucleic acid extraction and DNase treatment after nucleic acid extraction group. Nucleic acid samples of each group were analyzed by direct RNA sequencing (without amplification) and DNA sequencing after sequence independent single primer amplification (SISPA), respectively. Results No significant difference in viral genome coverage rates was observed between different groups. The highest genome coverage and sequencing accuracy were obtained in DNase treatment after nucleic acid extraction group by direct RNA sequencing, and the ratio of viral reads and the sequencing depth of each locus were effectively improved by SISPA amplification. Conclusions This study provided an optimized technical strategy for whole genome sequencing of RNA viruses such as coronavirus. Key words: Human coronavirus; Sequence independent single primer amplification; Whole genome sequencing; High-throughput sequencing

The 1000 genomes consortium, aimed at sequencing 2500 individuals from all across the world was the first project to sequence the genomes of a large number of people, to provide a comprehensive resource on human genetic variation ( ). The broader population spectrum of the 1000 genomes database and annotation of several SNPs with allele frequency < 1% provided the rationale in choosing it as our resource pool. Currently, the 1000 genomes database indexes 38.2 million SNPs, 3.9 million short indels and 14 thousand deletions for all the human chromosomes. The files are present in a specially encoded format known as Variant Call Format (VCF), split according to chromosomes with the average size of a chromosome file being approximately equal to a 50GB text file, necessitating the use of Linux platform. A simplified version of the annotation of data is presented in the figure below.

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

Hepatocellular carcinoma (HCC) is one of the most common cancers world-wide. Known risk factors and prognostic factors of the disease include virus infection of hepatitis B and hepatitis C, alcohol intake, carcinogenic substances such as aflatoxin B1. So far, multiple studies have identified the molecular genetic characteristics of the tumor utilizing next-generation sequencing technologies.The whole-genome and exome sequencing of HCC can help to find potential key oncogenes and tumor suppressor genes, as well as improving molecular diagnosis of HCC, which can help to find new therapeutic targets and determine prognosis. In 2011, The US researchers whole-genome sequencing (2 patients, with HBV and HCV infection, respectively), whole genome exome sequencing (47 patients, of which 33 with HCV infection, 7 with HBV infection, and 7 with non-viral infection), Sanger sequencing (94 patients, of which 52 with HBV infection, 36 with HCV infection, and 6 with non-viral infection), gene expression (4 patients, of which 2 with HBV infection and 2 with HCV infection), and Epigenetic inheritance (1 case, with HCV infection), as well as other research strategies in search of treatment approaches for liver cancer from genomics perspectives, in hope of improving long-term survival rate of patients with HCC[1]. This review summarizes the HCC-related predisposing genes from whole-genome and exome studies with next generation sequencing technologies. Key words: Hepatocellular carcinoma; Whole-genome sequencing; Exome sequencing

Characterization of the cytochrome [beta] gene in plant pathogenic, basidiomycetes and consequences for QoI resistance

Whole-genome exon sequencing technology is a genomic analysis method that uses target sequence capture technology to capture DNA from all exon regions of the genome for high-throughput sequencing, and has a high sensitivity for identifying disease-related low-frequency and rare mutations. Type 2 diabetes mellitus is characterized by progressive islet β-cell dysfunction and insulin resistance, and it is thought to be the result of a combination of genetic and environmental factors, in which genetic factors play an important role in the pathogenesis of type 2 diabetes. This article reviews the application of whole-genome exon sequencing in the screening of susceptibility genes and coding variants in type 2 diabetes mellitus. Key words: Exome sequencing; Diabetes mellitus, Type2; Gene mutation

Genomics is a field of study that is rapidly transforming many areas of biological and biomedical research which has enabled the transition from sequential studies of single genes to more ecological approach, involving the simultaneous study of many components and their interactions with the environment from pathways, through cell tissues to whole organisms and communities. Genomics application areas include clinical diagnostics, agro biotechnology, environmental biotechnology and pharmacogenomics. The focus of most genome research is on the nuclear genome, though mitochondrial genomes have been extremely useful for the identification of fish species and populations. Marine microbial assemblages are diverse and unique and the challenge is to discover what functions are played by these microorganisms. To provide adequate tools for marine biologists, therefore, one important aim will be to develop genomic approaches, such as whole genome sequencing and functional genomics, for key species across the evolutionary tree of marine organisms. Genomics is a highly dynamic research field. Hence, rapid developments in genomics can afford new opportunities for applications in marine environment, particularly in the areas of Fish genome resources conservation and genetic enhancement.

Vibrio alginolyticus is a Gram-negative bacillus that causes vibriosis to human and aquatic products, including fish, shrimp and shellfish. It poses a threat to public health and causes enormous economic losses to the aquaculture industry. However, research on genetic diversity and pathogenicity-related genetic elements based on whole genome is still lacking. In this study, sixty-eight strains of V. alginolyticus were collected from four provinces of China and the whole genome sequences were obtained. Combined with 113 publicly available genome sequences downloaded from NCBI, we inferred the population structure of V. alginolyticus by using fineSTRUCTURE software, and identified the virulence and antibiotic resistance factors using the VFDB, CARD and ResFinder database. The results indicated that V. alginolyticus included two main lineages, named Lineage 1 and Lineage 2. Both lineages distributed in America and Asia, but all the European genomes were classified into Lineage 1. A single cross-ocean transmission event was inferred from one of the 12 identified clonal groups in our dataset. V. alginolyticus genome contains a variety of virulence factors, such as tlh, OmpU, and IlpA, etc. The distribution of virulence factors revealed no lineage-specificity, but some of which revealed differences in their geographical distribution. A lower frequency of VP1611, vcrD, vopD, fleR/flrC and a higher frequency of IlpA were observed in genomes of Europe than other continents. In China, a lower frequency of fleR/flrC, and no IlpA were observed in genomes from Guangxi province. Among the identified antibiotic resistance genes, TxR and fos are significantly enriched in Lineage 2. In addition, TxR is more common in genomes from Asia, compared with the American and European genomes. But in China, the frequency of TxR in Sichuan genomes is much lower than in other provinces. We also found that large fragments of plasmids or ICEs that carried multiple drug resistance genes were present in five V. alginolyticus genomes (VA24, VA28, 2014V-1011, ZJ-T and Vb1833). Based on population genomics analysis, our study delineated the population structure, distribution of virulence and antibiotic resistance related factors of V. alginolyticus, which lays a foundation for future study of genetic characters and pathogenesis mechanism of this pathogen and will improve the works on monitoring, prevention and control of this pathogen.