Multi-curve fitting and tubulin-lattice signal removal for structure determination of large microtubule-based motors

Revealing high-resolution structures of microtubule-associated proteins (MAPs) is critical for understanding their fundamental roles in various cellular activities, such as cell motility and intracellular cargo transport. Nevertheless, large flexible molecular motors that dynamically bind and release microtubule networks are challenging for cryo-electron microscopy (cryo-EM). Traditional structure determination of MAPs bound to microtubules needs alignment information from the reconstruction of microtubules, which cannot be readily applied to large MAPs without a fixed binding pattern. Here, we developed a comprehensive approach to estimate the microtubule networks (multi-curve fitting), model the tubulin-lattice signals, and remove them (tubulin-lattice subtraction) from the raw cryo-EM micrographs. The approach does not require an ordered binding pattern of MAPs on microtubules, nor does it need a reconstruction of the microtubules. We demonstrated the capability of our approach using the reconstituted outer-arm dynein (OAD) bound to microtubule doublets. The tubulin-lattice subtraction improves the OAD alignment, thus leading to high-resolution reconstructions. In addition, the multi-curve fitting approach provides an accurate automatic alternative method to pick or segment filaments in 2D images and potentially in 3D tomograms. The accuracy of our approach has been demonstrated by using several other biological filaments. Our work provides a new tool to determine high-resolution structures of large MAPs bound to curved microtubule networks.     

Identification and localization of differences between Escherichia coli and Salmonella typhimurium genomes by suppressive subtractive hybridization

The availability of bacterial genome sequences raises an important new problem - how can one move from completely sequenced microorganisms as a reference to the hundreds and thousands of other strains or isolates of the same or related species that will not be sequenced in the near future? An efficient way to approach this task is the comparison of genomes by subtractive hybridization. Recently we developed a sensitive and reproducible subtraction procedure for comparison of bacterial genomes, based on the method of suppression subtractive hybridization (SSH). In this work we demonstrate the applicability of subtractive hybridization to the comparison of the related but markedly divergent bacterial species Escherichia coli and Salmonella typhimurium. Clone libraries representing sequence differences were obtained and, in the case of completely sequenced E. coli genome, the differences were directly placed in the genome map. About 60% of the differential clones identified by SSH were present in one of the genomes under comparison and absent from the other. Additional differences in most cases represent sequences that have diverged considerably in the course of evolution. Such an approach to comparative bacterial genomics can be applied both to studies of interspecies evolution - to elucidate the "strategies" that enable different genomes to fit their ecological niches - and to development of diagnostic probes for the rapid identification of pathogenic bacterial species.     

Intra-strain polymorphisms are detected but no genomic alteration is found in cloned mice

In-gel competitive reassociation (IGCR) is a method for differential subtraction of polymorphic (RFLP) DNA fragments between two DNA samples of interest without probes or specific sequence information. Here, we applied the IGCR procedure to two cloned mice derived from an F1 hybrid of the C57BL/6Cr and DBA/2 strains, in order to investigate the possibility of genomic alteration in the cloned mouse genomes. Each of the five of the genomic alterations we detected between the two cloned mice corresponded to the "intra-strain" polymorphisms in the C57BL/6Cr and DBA/2 mouse strains. Our result suggests that no severe aberration of genome sequences occurs due to somatic cell nuclear transfer.     

A combined computational and microarray-based approach identifies novel microRNAs encoded by human gamma-herpesviruses

We have developed an approach to identify microRNAs (miRNAs) that is based on bioinformatics and array-based technologies, without the use of cDNA cloning. The approach, designed for use on genomes of small size (<2 Mb), was tested on cells infected by either of two lymphotropic herpesviruses, KSHV and EBV. The viral genomes were scanned computationally for pre-miRNAs using an algorithm (VMir) we have developed. Candidate hairpins suggested by this analysis were then synthesized as oligonucleotides on microarrays, and the arrays were hybridized with small RNAs from infected cells. Candidate miRNAs that scored positive on the arrays were then subjected to confirmatory Northern blot analysis. Using this approach, 10 of the known KSHV pre-miRNAs were identified, as well as a novel pre-miRNA that had earlier escaped detection. This method also led to the identification of seven new EBV-encoded pre-miRNAs; by using additional computational approaches, we identified a total of 18 new EBV pre-miRNAs that produce 22 mature miRNA molecules, thereby more than quadrupling the total number of hitherto known EBV miRNAs. The advantages and limitations of the approach are discussed.     

Fast algorithms for large-scale genome alignment and comparison

We describe a suffix-tree algorithm that can align the entire genome sequences of eukaryotic and prokaryotic organisms with minimal use of computer time and memory. The new system, MUMmer 2, runs three times faster while using one-third as much memory as the original MUMmer system. It has been used successfully to align the entire human and mouse genomes to each other, and to align numerous smaller eukaryotic and prokaryotic genomes. A new module permits the alignment of multiple DNA sequence fragments, which has proven valuable in the comparison of incomplete genome sequences. We also describe a method to align more distantly related genomes by detecting protein sequence homology. This extension to MUMmer aligns two genomes after translating the sequence in all six reading frames, extracts all matching protein sequences and then clusters together matches. This method has been applied to both incomplete and complete genome sequences in order to detect regions of conserved synteny, in which multiple proteins from one organism are found in the same order and orientation in another. The system code is being made freely available by the authors.     

Alternative approaches to genome mapping and sequencing

The main methods used for large-scale mapping of the human and other genomes are reviewed. These methods comprise two procedures of random mapping/sequencing and an approach using linking and jumping libraries. Importantly, no method used up to now has proved efficient in comparative genome analysis. A new method is presented basing on slalom libraries. These libraries provide 10-100 times higher efficiency and may be used for mapping and sequencing whole genomes by small research groups.     

Hybridization monitor: a method for identifying differences between complex genomes

We have developed a method to identify and amplify differential fragments between two complex genomes. This technique, named hybridization-monitored genome differential analysis (HMDA), incorporates a monitor system into a PCR-based solid subtraction hybridization that tracks the entire hybridization process. This is achieved by monitoring the subtraction progress using PCR analysis of the conserved sequence of 18S rDNA in the tester sample after each round of subtraction. Homologous fragments can then be eliminated when bound to the driver DNA immobilized on a solid membrane. The hybridization continues until the conserved DNA sequence of 18S rDNA can no longer be detected, and most of the unbound DNA fragments left in the liquid were mainly the tester-specific fragments, thus greatly decreasing the complexity of DNA template of PCR amplification, increasing the amplification efficiency of differences accordingly, and ensuring high positive efficiency and coverage across the tester genome. We have applied the technique in a comparison between the genomes of Saccharomyces cerevisiae and Schizosaccharomyces pombe, which are two completely sequenced organisms. Results indicated that 95% of the subtracted clones have been confirmed to be different to the driver analyzed using the BLASTN homology alignment. With this technique, 240-fold enrichment of differences is obtained, and the coverage of the difference is up to 79%. These results indicate that HMDA can efficiently identify sequences that differ between two complex genomes.     

Alternative Approaches to Genome Mapping and Sequencing

The main methods used for large-scale mapping of the human and other genomes are reviewed. These methods comprise two procedures of random mapping/sequencing and an approach using linking and jumping libraries. Importantly, no method used up to now has proved efficient in comparative genome analysis. A new method is presented basing on slalom libraries. These libraries provide 10–100 times higher efficiency and may be used for mapping and sequencing whole genomes by small research groups.     

Characterization of human-specific DNA sequences obtained by genome subtraction

Recent studies on molecular evolution using nucleotide sequence data to clarify phylogenetic relationships among humans and the African great apes, have revealed that humans are more closely related to chimpanzees than to gorillas. However, the genetic basis of human uniqueness remains unclear. This is because phylogenetic studies have merely evaluated the degree of similarity by calculating the accumulation of nucleotide substitutions that have occurred in neutral DNA regions commonly present in all the species examined. In contrast, the genome subtraction method recently developed by us has revealed dissimilarity even among the genomes of the most closely related species. Here we describe the characteristics of the DNA sequences obtained by genome subtraction between humans and chimpanzees.     

Clustering of main orthologs for multiple genomes

The identification of orthologous genes shared by multiple genomes is critical for both functional and evolutionary studies in comparative genomics. While it is usually done by sequence similarity search and reconciled tree construction in practice, recently a new combinatorial approach and high-throughput system MSOAR for ortholog identification between closely related genomes based on genome rearrangement and gene duplication has been proposed in Fu et al. MSOAR assumes that orthologous genes correspond to each other in the most parsimonious evolutionary scenario, minimizing the number of genome rearrangement and (postspeciation) gene duplication events. However, the parsimony approach used by MSOAR limits it to pairwise genome comparisons. In this paper, we extend MSOAR to multiple (closely related) genomes and propose an ortholog clustering method, called MultiMSOAR, to infer main orthologs in multiple genomes. As a preliminary experiment, we apply MultiMSOAR to rat, mouse, and human genomes, and validate our results using gene annotations and gene function classifications in the public databases. We further compare our results to the ortholog clusters predicted by MultiParanoid, which is an extension of the well-known program InParanoid for pairwise genome comparisons. The comparison reveals that MultiMSOAR gives more detailed and accurate orthology information, since it can effectively distinguish main orthologs from inparalogs.     

Simultaneous genome sequencing of symbionts and their hosts

Second-generation sequencing has made possible the sequencing of genomes of interest for even small research groups. However, obtaining separate clean cultures and clonal or inbred samples of metazoan hosts and their bacterial symbionts is often difficult. We present a computational pipeline for separating metazoan and bacterial DNA in silico rather than at the bench. The method relies on the generation of deep coverage of all the genomes in a mixed sample using Illumina short-read sequencing technology, and using aggregate properties of the different genomes to identify read sets belonging to each. This inexpensive and rapid approach has been used to sequence several nematode genomes and their bacterial endosymbionts in the last year in our laboratory and can also be used to visualize and identify unexpected contaminants (or possible symbionts) in genomic DNA samples. We hope that this method will enable researchers studying symbiotic systems to move from gene-centric to genome-centric approaches.     

Clustering orthologous proteins across phylogenetically distant species

The quality of orthologous protein clusters (OPCs) is largely dependent on the results of the reciprocal BLAST (basic local alignment search tool) hits among genomes. The BLAST algorithm is very efficient and fast, but it is very difficult to get optimal solution among phylogenetically distant species because the genomes with large evolutionary distance typically have low similarity in their protein sequences. To reduce the false positives in the OPCs, thresholding is often employed on the BLAST scores. However, the thresholding also eliminates large numbers of true positives as the orthologs from distant species likely have low BLAST scores. To rectify this problem, we introduce a new hybrid method combining the Recursive and the Markov CLuster (MCL) algorithms without using the BLAST thresholding. In the first step, we use InParanoid to produce n(n-1)/2 ortholog tables from n genomes. After combining all the tables into one, our clustering algorithm clusters ortholog pairs recursively in the table. Then, our method employs MCL algorithm to compute the clusters and refines the clusters by adjusting the inflation factor. We tested our method using six different genomes and evaluated the results by comparing against Kegg Orthology (KO) OPCs, which are generated from manually curated pathways. To quantify the accuracy of the results, we introduced a new intuitive similarity measure based on our Least-move algorithm that computes the consistency between two OPCs. We compared the resulting OPCs with the KO OPCs using this measure. We also evaluated the performance of our method using InParanoid as the baseline approach. The experimental results show that, at the inflation factor 1.3, we produced 54% more orthologs than InParanoid sacrificing a little less accuracy (1.7% less) than InParanoid, and at the factor 1.4, produced not only 15% more orthologs than InParanoid but also a higher accuracy (1.4% more) than InParanoid.     

Assembling the SARS-CoV genome -- new method based on graph theoretical approach

Nowadays, scientists may learn a lot about the organisms studied just by analyzing their genetic material. This requires the development of methods of reading genomes with high accuracy. It has become clear that the knowledge of the changes occurring within a viral genome is indispensable for effective fighting of the pathogen. A good example is SARS-CoV, which was a cause of death of many people and frightened the entire world with its fast and hard to prevent propagation. Rapid development of sequencing methods, like shotgun sequencing or sequencing by hybridization (SBH), gives scientists a good tool for reading genomes. However, since sequencing methods can read fragments of up to 1000 bp only, methods for sequence assembling are required in order to read whole genomes. In this paper a new assembling method, based on graph theoretical approach, is presented. The method was tested on SARS-CoV and the results were compared to the outcome of other widely known methods.     

A novel method of characterizing genetic sequences: genome space with biological distance and applications

Background

Most existing methods for phylogenetic analysis involve developing an evolutionary model and then using some type of computational algorithm to perform multiple sequence alignment. There are two problems with this approach: (1) different evolutionary models can lead to different results, and (2) the computation time required for multiple alignments makes it impossible to analyse the phylogeny of a whole genome. This motivates us to create a new approach to characterize genetic sequences.

Methodology

To each DNA sequence, we associate a natural vector based on the distributions of nucleotides. This produces a one-to-one correspondence between the DNA sequence and its natural vector. We define the distance between two DNA sequences to be the distance between their associated natural vectors. This creates a genome space with a biological distance which makes global comparison of genomes with same topology possible. We use our proposed method to analyze the genomes of the new influenza A (H1N1) virus, human rhinoviruses (HRV) and mammalian mitochondrial. The result shows that a triple-reassortant swine virus circulating in North America and the Eurasian swine virus belong to the lineage of the influenza A (H1N1) virus. For the HRV and mammalian mitochondrial genomes, the results coincide with biologists'' analyses.

Conclusions

Our approach provides a powerful new tool for analyzing and annotating genomes and their phylogenetic relationships. Whole or partial genomes can be handled more easily and more quickly than using multiple alignment methods. Once a genome space has been constructed, it can be stored in a database. There is no need to reconstruct the genome space for subsequent applications, whereas in multiple alignment methods, realignment is needed to add new sequences. Furthermore, one can make a global comparison of all genomes simultaneously, which no other existing method can achieve.     

Ab initio gene identification in metagenomic sequences

We describe an algorithm for gene identification in DNA sequences derived from shotgun sequencing of microbial communities. Accurate ab initio gene prediction in a short nucleotide sequence of anonymous origin is hampered by uncertainty in model parameters. While several machine learning approaches could be proposed to bypass this difficulty, one effective method is to estimate parameters from dependencies, formed in evolution, between frequencies of oligonucleotides in protein-coding regions and genome nucleotide composition. Original version of the method was proposed in 1999 and has been used since for (i) reconstructing codon frequency vector needed for gene finding in viral genomes and (ii) initializing parameters of self-training gene finding algorithms. With advent of new prokaryotic genomes en masse it became possible to enhance the original approach by using direct polynomial and logistic approximations of oligonucleotide frequencies, as well as by separating models for bacteria and archaea. These advances have increased the accuracy of model reconstruction and, subsequently, gene prediction. We describe the refined method and assess its accuracy on known prokaryotic genomes split into short sequences. Also, we show that as a result of application of the new method, several thousands of new genes could be added to existing annotations of several human and mouse gut metagenomes.     

A Novel Construction of Genome Space with Biological Geometry

A genome space is a moduli space of genomes. In this space, each point corresponds to a genome. The natural distance between two points in the genome space reflects the biological distance between these two genomes. Currently, there is no method to represent genomes by a point in a space without losing biological information. Here, we propose a new graphical representation for DNA sequences. The breakthrough of the subject is that we can construct the moment vectors from DNA sequences using this new graphical method and prove that the correspondence between moment vectors and DNA sequences is one-to-one. Using these moment vectors, we have constructed a novel genome space as a subspace in R^N. It allows us to show that the SARS-CoV is most closely related to a coronavirus from the palm civet not from a bird as initially suspected, and the newly discovered human coronavirus HCoV-HKU1 is more closely related to SARS than to any other known member of group 2 coronavirus. Furthermore, we reconstructed the phylogenetic tree for 34 lentiviruses (including human immunodeficiency virus) based on their whole genome sequences. Our genome space will provide a new powerful tool for analyzing the classification of genomes and their phylogenetic relationships.     

应用三元递减法筛选特异性心脏生长相关基因

心脏是由胚胎干细胞特异性分化而来的 ,但其分化的分子生物学机制尚不十分了解 .为建立一种新的筛选特异性心脏相关基因的方法 ,克隆特异性心脏生长相关基因 .从胚胎心、成年心和去胎心的胚胎中提取 m RNA,建立胎心 /成年心和胎心 /胎身两个递减性 c DNA文库 ,通过 DNA芯片和微阵列杂交筛选和克隆 ,建立了三元递减克隆的新方法 .获得了一个全长为 1 0 0 6 bp可编码1 94个氨基酸的新的与心脏生长相关的基因 ,它是 LIM家族的新成员 ,可特异性在心肌细胞表达 ,并可促进心肌细胞的生长 .结果表明 ,三元递减筛选法可以应用于寻找新的组织特异性表达的基因 .并且获得了一个新的与心脏生长相关的新基因 ,它可能在心肌生长和心肌肥厚的发生中发挥重要作用     

CpG_MI: a novel approach for identifying functional CpG islands in mammalian genomes

CpG islands (CGIs) are CpG-rich regions compared to CpG-depleted bulk DNA of mammalian genomes and are generally regarded as the epigenetic regulatory regions in association with unmethylation, promoter activity and histone modifications. Accurate identification of CpG islands with epigenetic regulatory function in bulk genomes is of wide interest. Here, the common features of functional CGIs are identified using an average mutual information method to differentiate functional CGIs from the remaining CGIs. A new approach (CpG mutual information, CpG_MI) was further explored to identify functional CGIs based on the cumulative mutual information of physical distances between two neighboring CpGs. Compared to current approaches, CpG_MI achieved the highest prediction accuracy. This approach also identified new functional CGIs overlapping with gene promoter regions which were missed by other algorithms. Nearly all CGIs identified by CpG_MI overlapped with histone modification marks. CpG_MI could also be used to identify potential functional CGIs in other mammalian genomes, as the CpG dinucleotide contents and cumulative mutual information distributions are almost the same among six mammalian genomes in our analysis. It is a reliable quantitative tool for the identification of functional CGIs from bulk genomes and helps in understanding the relationships between genomic functional elements and epigenomic modifications.