Mass spectrometry at the interface of proteomics and genomics

With the onset of modern DNA sequencing technologies, genomics is experiencing a revolution in terms of quantity and quality of sequencing data. Rapidly growing numbers of sequenced genomes and metagenomes present a tremendous challenge for bioinformatics tools that predict protein-coding regions. Experimental evidence of expressed genomic regions, both at the RNA and protein level, is becoming invaluable for genome annotation and training of gene prediction algorithms. Evidence of gene expression at the protein level using mass spectrometry-based proteomics is increasingly used in refinement of raw genome sequencing data. In a typical "proteogenomics" experiment, the whole proteome of an organism is extracted, digested into peptides and measured by a mass spectrometer. The peptide fragmentation spectra are identified by searching against a six-frame translation of the raw genomic assembly, thus enabling the identification of hitherto unpredicted protein-coding genomic regions. Application of mass spectrometry to genome annotation presents a range of challenges to the standard workflows in proteomics, especially in terms of proteome coverage and database search strategies. Here we provide an overview of the field and argue that the latest mass spectrometry technologies that enable high mass accuracy at high acquisition rates will prove to be especially well suited for proteogenomics applications.     

Applications of biotechnology to pharmacology and toxicology.

Strategies for the development of new more efficient drugs at a lower cost and for the evaluation of the effects of chemicals and metals on tissue and cell function are changing considerably. This is made possible by recent progress in various areas, particularly biotechnology and bioinformatics. The recent sequencing of the human genome and the design of more and more sophisticated technologies will largely influence the fields of pharmacology and toxicology. Thus, identification of new molecular targets, development of more powerful cell models, design of miniaturized and automated tests for high throughput screening of thousands of compounds synthesized by combinatorial chemistry and progress in genomic and proteomic technologies that permit simultaneous analysis of thousands of genes and their products, offer new investigative ways that will still widely be extended in the next future.     

Sweating the small stuff: microRNA discovery in plants

The class of small RNAs known as microRNAs (miRNAs) has a demonstrated role in the negative regulation of gene expression in both plants and animals. These small molecules have been shown to play a critical role in a wide range of developmental and physiological pathways. Although hundreds of different miRNAs have now been identified using cloning and computational approaches, characterization of their targets and biological roles has been more limited. New sequencing technologies promise to accelerate the sequencing of small RNAs and additional genetic and genomic strategies are being applied to assess their regulatory function on RNA targets. These technologies will enable the identification of large numbers of small RNAs from diverse species, and comparative genomics approaches based on these data are likely to identify additional miRNAs. Combined with bioinformatics and experimental approaches to separate miRNAs from short-interfering RNAs (siRNAs), the pace of miRNA discovery is likely to accelerate, leading to an improved understanding of miRNA function and biological significance.     

In silico analysis of Burkholderia pseudomallei genome sequence for potential drug targets

Recent advances in DNA sequencing technology have enabled elucidation of whole genome information from a plethora of organisms. In parallel with this technology, various bioinformatics tools have driven the comparative analysis of the genome sequences between species and within isolates. While drawing meaningful conclusions from a large amount of raw material, computer-aided identification of suitable targets for further experimental analysis and characterization, has also led to the prediction of non-human homologous essential genes in bacteria as promising candidates for novel drug discovery. Here, we present a comparative genomic analysis to identify essential genes in Burkholderia pseudomallei. Our in silico prediction has identified 312 essential genes which could also be potential drug candidates. These genes encode essential proteins to support the survival of B. pseudomallei including outer-inner membrane and surface structures, regulators, proteins involved in pathogenenicity, adaptation, chaperones as well as degradation of small and macromolecules, energy metabolism, information transfer, central/intermediate/miscellaneous metabolism pathways and some conserved hypothetical proteins of unknown function. Therefore, our in silico approach has enabled rapid screening and identification of potential drug targets for further characterization in the laboratory.     

致病基因的定位候选克隆

基因组研究的迅猛发展,使我们有必要重新审视致病基因克隆的各种策略与技术,以及人类基因组研究在致病基因克隆中的作用。定位候选克隆基因策略强调充分利用已知的细胞遗传学、医学遗传学、分子遗传学、分子生物学和生物化学知识,特别是人类基因组研究的最新成果,综合功能克隆、定位克隆与传统候选基因研究的策略,分离鉴定致病基因。今天的定位克隆已几乎不再需要染色体步移,甚至有可能避开cDNA筛选。     

高通量DNA甲基化数据的处理和分析方法

DNA甲基化作为一种表观遗传学修饰,在调控基因表达、X染色体失活、印记基因等方面都发挥着重要的作用.不同的DNA甲基化的预处理方法结合二代测序产生了大量的高通量甲基化数据,这些数据的存储、处理和分析是当前亟需解决的问题.在本文中,总结了目前存在的三种高通量DNA甲基化检测技术（限制性内切酶法,亲和纯化法,重亚硫酸盐转换法）,以及针对这些技术产生的高通量数据开发的存储、处理和分析工具.另外,还注重介绍了单碱基水平的DNA甲基化检测技术,BS-Seq的测序原理、数据处理流程以及后续的分析工具.     

From biological databases to platforms for biomedical discovery

The use of high-throughput DNA sequencing and proteomic methods has led to an unprecedented increase in the amount of genomic and proteomic data. Application of computing technologies and development of computational tools to analyze and present these data has not kept pace with the accumulation of information. Here, we discuss the use of different database systems to store biological information and mention some of the key emerging computing technologies that are likely to have a key role in the future of bioinformatics.     

Impact of RNA structure on the prediction of donor and acceptor splice sites

Background  

gene identification in genomic DNA sequences by computational methods has become an important task in bioinformatics and computational gene prediction tools are now essential components of every genome sequencing project. Prediction of splice sites is a key step of all gene structural prediction algorithms.     

The (in)complete organelle genome: exploring the use and nonuse of available technologies for characterizing mitochondrial and plastid chromosomes

Not long ago, scientists paid dearly in time, money and skill for every nucleotide that they sequenced. Today, DNA sequencing technologies epitomize the slogan ‘faster, easier, cheaper and more’, and in many ways, sequencing an entire genome has become routine, even for the smallest laboratory groups. This is especially true for mitochondrial and plastid genomes. Given their relatively small sizes and high copy numbers per cell, organelle DNAs are currently among the most highly sequenced kind of chromosome. But accurately characterizing an organelle genome and the information it encodes can require much more than DNA sequencing and bioinformatics analyses. Organelle genomes can be surprisingly complex and can exhibit convoluted and unconventional modes of gene expression. Unravelling this complexity can demand a wide assortment of experiments, from pulsed‐field gel electrophoresis to Southern and Northern blots to RNA analyses. Here, we show that it is exactly these types of ‘complementary’ analyses that are often lacking from contemporary organelle genome papers, particularly short ‘genome announcement’ articles. Consequently, crucial and interesting features of organelle chromosomes are going undescribed, which could ultimately lead to a poor understanding and even a misrepresentation of these genomes and the genes they express. High‐throughput sequencing and bioinformatics have made it easy to sequence and assemble entire chromosomes, but they should not be used as a substitute for or at the expense of other types of genomic characterization methods.     

Whole Genome Sequencing of Elite Rice Cultivars as a Comprehensive Information Resource for Marker Assisted Selection

Current advances in sequencing technologies and bioinformatics revealed the genomic background of rice, a staple food for the poor people, and provided the basis to develop large genomic variation databases for thousands of cultivars. Proper analysis of this massive resource is expected to give novel insights into the structure, function, and evolution of the rice genome, and to aid the development of rice varieties through marker assisted selection or genomic selection. In this work we present sequencing and bioinformatics analyses of 104 rice varieties belonging to the major subspecies of Oryza sativa. We identified repetitive elements and recurrent copy number variation covering about 200 Mbp of the rice genome. Genotyping of over 18 million polymorphic locations within O. sativa allowed us to reconstruct the individual haplotype patterns shaping the genomic background of elite varieties used by farmers throughout the Americas. Based on a reconstruction of the alleles for the gene GBSSI, we could identify novel genetic markers for selection of varieties with high amylose content. We expect that both the analysis methods and the genomic information described here would be of great use for the rice research community and for other groups carrying on similar sequencing efforts in other crops.     

Rapid genotyping by MALDI-monitored nuclease selection from probe libraries

Data on five single-nucleotide polymorphisms (SNPs) per gene are estimated to allow association of disease risks or pharmacogenetic parameters with individual genes. Efficient technologies for rapidly detecting SNPs will therefore facilitate the mining of genomic information. Known methods for SNP analysis include restriction-fragment-length polymorphism polymerase chain reaction (PCR), allele-specific oligomer hybridization, oligomer-specific ligation assays, minisequencing, direct sequencing, fluorescence-detected 5'-exonuclease assays, and hybridization with PNA probes. Detection by mass spectrometry (MS) offers speed and high resolution. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI TOF MS) can detect primer extension products, mass-tagged oligonucleotides, DNA created by restriction endonuclease cleavage, and genomic DNA. We have previously reported MALDI-TOF-monitored nuclease selections of modified oligonucleotides with increased affinity for targets. Here we use nuclease selections for genotyping by treating DNA to be analyzed with oligonucleotide probes representing known genotypes and digesting probes that are not complementary to the DNA. With phosphodiesterase I, the target-bound, complementary probe is largely refractory to nuclease attack and its peak persists in mass spectra (Fig. 1A). In optimized assays, both alleles of a heterozygote were genotyped with six nonamer DNA probes (> or = 125 fmol each) and asymmetrically amplified DNA from exon 10 of the cystic fibrosis transmembrane regulatory gene (CFTR).     

Genomic DNA macroarrays as a tool for analysis of gene expression in Leishmania

Gene-array technologies have been applied in a wide number of organisms to study gene expression profiling under several physiological and experimental conditions. Gene-array implementations combined with the information arising from emerging genome sequencing projects are expected to be in the near future a major tool to characterize genes involved in different processes. So far, gene expression profile studies in trypanosomatids have been performed in microarrays that use a glass support to immobilize fragments of genomic DNA followed by fluorescent detection. Here, we wanted to test the potential of genomic DNA macroarrays of Leishmania infantum using nylon membranes and radioactive detection. Nylon macroarrays present a number of advantages since the processing of the membranes is based on standard Southern blotting protocols familiar to molecular biologists, and the data acquisition equipment is available to most research institutions. Nylon macroarrays were employed to search for genes showing increased mRNA abundance during an axenic differentiation of L. infantum promastigotes to amastigotes. Several clones were rescued and, after validation by Northern blot assays, these L. infantum sequences were used to screen the Leishmania major gene database. The L. major contigs with high homology to the L. infantum sequences allowed a consistent identification of the regulated genes.     

Predicting biological networks from genomic data

Continuing improvements in DNA sequencing technologies are providing us with vast amounts of genomic data from an ever-widening range of organisms. The resulting challenge for bioinformatics is to interpret this deluge of data and place it back into its biological context. Biological networks provide a conceptual framework with which we can describe part of this context, namely the different interactions that occur between the molecular components of a cell. Here, we review the computational methods available to predict biological networks from genomic sequence data and discuss how they relate to high-throughput experimental methods.     

Finding and decrypting of promoters contributes to the elucidation of gene function

The combination of full-scale genomic sequencing with high throughput expression analysis provides a new and largely unexploited basis for in silico functional genomics. Recent break through developments in locating and analyzing promoters now allow extending functional genomics in silico far beyond identification of protein sequences into the complex regulatory structures and mechanisms of the genome. However, only first examples of this new type of approach are emerging at present and intensive further developments of bioinformatics tools will be required before such analysis can become large-scale routine in genomic sequence analysis. Nevertheless, the door to a new dimension of functional analysis of the genomic sequence is open. Finally, only the tight integration of the enormous amount of knowledge gained from proteins sequence analysis with the complementary information about gene regulation will afford us with a more complete picture of the networks than constitute life.     

A draft genome of field pennycress (Thlaspi arvense) provides tools for the domestication of a new winter biofuel crop

Field pennycress (Thlaspi arvense L.) is being domesticated as a new winter cover crop and biofuel species for the Midwestern United States that can be double-cropped between corn and soybeans. A genome sequence will enable the use of new technologies to make improvements in pennycress. To generate a draft genome, a hybrid sequencing approach was used to generate 47 Gb of DNA sequencing reads from both the Illumina and PacBio platforms. These reads were used to assemble 6,768 genomic scaffolds. The draft genome was annotated using the MAKER pipeline, which identified 27,390 predicted protein-coding genes, with almost all of these predicted peptides having significant sequence similarity to Arabidopsis proteins. A comprehensive analysis of pennycress gene homologues involved in glucosinolate biosynthesis, metabolism, and transport pathways revealed high sequence conservation compared with other Brassicaceae species, and helps validate the assembly of the pennycress gene space in this draft genome. Additional comparative genomic analyses indicate that the knowledge gained from years of basic Brassicaceae research will serve as a powerful tool for identifying gene targets whose manipulation can be predicted to result in improvements for pennycress.     

Critical role of bioinformatics in translating huge amounts of next-generation sequencing data into personalized medicine

Realizing personalized medicine requires integrating diverse data types with bioinformatics. The most vital data are genomic information for individuals that are from advanced next-generation sequencing (NGS) technologies at present. The technologies continue to advance in terms of both decreasing cost and sequencing speed with concomitant increase in the amount and complexity of the data. The prodigious data together with the requisite computational pipelines for data analysis and interpretation are stressors to IT infrastructure and the scientists conducting the work alike. Bioinformatics is increasingly becoming the rate-limiting step with numerous challenges to be overcome for translating NGS data for personalized medicine. We review some key bioinformatics tasks, issues, and challenges in contexts of IT requirements, data quality, analysis tools and pipelines, and validation of biomarkers.     

A prototype taxonomic microarray targeting the rpsA housekeeping gene permits species identification within the rhizobial genus Ensifer

To develop a reliable tool for the identification and classification of the different Ensifer species, without the need for sequencing, a prototype DNA microarray that targets the rpsA housekeeping gene was designed and tested. Internal segments of the rpsA gene from 34 reference strains, representing the different Ensifer species, were sequenced and the sequences were used to select 44 diagnostic oligonucleotides that served as probes for the identification microarray. Both, genomic DNA and specific rpsA PCR-products were tested as a target in hybridisation experiments. Experimental conditions were optimised and the diagnostic oligonucleotides were validated. Hybridisation results with the rpsA PCR-products showed reliable identification of the reference strains to species and genomovar level. Our data indicate that a microarray targeting housekeeping genes is a promising, accurate and relatively simple genotyping technique that would also be applicable for the identification and characterization of other bacterial groups of interest.     

Developing genomic resources in two Linum species via 454 pyrosequencing and genomic reduction

Recent advances in next-generation DNA sequencing (NGS) have enhanced the development of genomic resources such as contigs or single-nucleotide polymorphisms (SNPs) for evolutionary studies of a nonmodel species with a complex and unsequenced genome. This study presents an application of a NGS technique in combination with genomic reduction and advanced bioinformatics tools to identify contigs and SNPs from multiple samples of two Linum species. A full Roche 454 GS FLX run of 16 diverse Linum samples representing cultivated flax (Linum usitatissimum L.) and its wild progenitor (Linum bienne Mill.) generated approximately 1.6 million sequence reads with a total length of 498 Mbp. Application of the computational pipeline de novo identification of alleles identified 713 contigs and 1067 SNPs. A blast search revealed alignments of all 713 contigs with 491 existing Linum scaffolds and gene annotations associated with 512 contigs. Sanger sequencing confirmed 95% of 79 selected contigs and 94% of 272 SNPs and identified 211 new SNPs and 19 new indels. The scored 454 SNP data were highly imbalanced for assayed samples. These findings not only are useful for evolutionary studies of Linum species but also help to illustrate the utility of NGS technologies in SNP discovery for nonmodel organisms.     

Sequencing our way towards understanding global eukaryotic biodiversity

Microscopic eukaryotes are abundant, diverse and fill critical ecological roles across every ecosystem on Earth, yet there is a well-recognized gap in understanding of their global biodiversity. Fundamental advances in DNA sequencing and bioinformatics now allow accurate en masse biodiversity assessments of microscopic eukaryotes from environmental samples. Despite a promising outlook, the field of eukaryotic marker gene surveys faces significant challenges: how to generate data that are most useful to the community, especially in the face of evolving sequencing technologies and bioinformatics pipelines, and how to incorporate an expanding number of target genes.